The ValidMind Library is a suite of developer tools and methods designed to automate the documentation and validation of your models.\n\nDesigned to be model agnostic, the ValidMind Library provides all the standard functionality without requiring you to rewrite any functions as long as your model is built in Python.
\n\nWith a rich array of documentation tools and test suites, from documenting descriptions of your datasets to testing your models for weak spots and overfit areas, the ValidMind Library helps you automate model documentation by feeding the ValidMind Platform with documentation artifacts and test results.
\n\nTo install the ValidMind Library:
\n\n\n
pip install validmind\n\n
To initialize the ValidMind Library, paste the code snippet with the model identifier credentials directly into your development source code, replacing this example with your own:
\n\n\n
import validmind as vm \n\nvm . init ( \n api_host = "https://api.dev.vm.validmind.ai/api/v1/tracking/tracking" , \n api_key = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" , \n api_secret = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" , \n project = "<project-identifier>" \n) \n\n
After you have pasted the code snippet into your development source code and executed the code, the Python Library API will register with ValidMind. You can now use the ValidMind Library to document and test your models, and to upload to the ValidMind Platform.
\n"}, "validmind.init": {"fullname": "validmind.init", "modulename": "validmind", "qualname": "init", "kind": "function", "doc": "Initializes the API client instances and calls the /ping endpoint to ensure\nthe provided credentials are valid and we can connect to the ValidMind API.
\n\nIf the API key and secret are not provided, the client will attempt to\nretrieve them from the environment variables VM_API_KEY and VM_API_SECRET.
\n\nArguments: \n\n\nproject (str, optional): The project CUID. Alias for model. Defaults to None. [DEPRECATED]model (str, optional): The model CUID. Defaults to None.api_key (str, optional): The API key. Defaults to None.api_secret (str, optional): The API secret. Defaults to None.api_host (str, optional): The API host. Defaults to None.monitoring (bool): The ongoing monitoring flag. Defaults to False.generate_descriptions (bool): Whether to use GenAI to generate test result descriptions. Defaults to True. \n\nRaises: \n\n\nValueError: If the API key and secret are not provided \n", "signature": "(\tproject : Optional [ str ] = None , \tapi_key : Optional [ str ] = None , \tapi_secret : Optional [ str ] = None , \tapi_host : Optional [ str ] = None , \tmodel : Optional [ str ] = None , \tmonitoring : bool = False , \tgenerate_descriptions : Optional [ bool ] = None ): ", "funcdef": "def"}, "validmind.reload": {"fullname": "validmind.reload", "modulename": "validmind", "qualname": "reload", "kind": "function", "doc": "Reconnect to the ValidMind API and reload the project configuration
\n", "signature": "(): ", "funcdef": "def"}, "validmind.init_dataset": {"fullname": "validmind.init_dataset", "modulename": "validmind", "qualname": "init_dataset", "kind": "function", "doc": "Initializes a VM Dataset, which can then be passed to other functions\nthat can perform additional analysis and tests on the data. This function\nalso ensures we are reading a valid dataset type.
\n\nThe following dataset types are supported:
\n\n\nPandas DataFrame \nPolars DataFrame \nNumpy ndarray \nTorch TensorDataset \n \n\nArguments: \n\n\ndataset : dataset from various python librariesmodel (VMModel): ValidMind model objecttargets (vm.vm.DatasetTargets): A list of target variablestarget_column (str): The name of the target column in the datasetfeature_columns (list): A list of names of feature columns in the datasetextra_columns (dictionary): A dictionary containing the names of theprediction_column and group_by_columns in the dataset \nclass_labels (dict): A list of class labels for classification problemstype (str): The type of dataset (one of DATASET_TYPES)input_id (str): The input ID for the dataset (e.g. \"my_dataset\"). By default,\nthis will be set to dataset but if you are passing this dataset as a\ntest input using some other key than dataset, then you should set\nthis to the same key. \n\nRaises: \n\n\nValueError: If the dataset type is not supported \n\nReturns: \n\n\n vm.vm.Dataset: A VM Dataset instance
\n \n", "signature": "(\tdataset , \tmodel = None , \tindex = None , \tindex_name : str = None , \tdate_time_index : bool = False , \tcolumns : list = None , \ttext_column : str = None , \ttarget_column : str = None , \tfeature_columns : list = None , \textra_columns : dict = None , \tclass_labels : dict = None , \ttype : str = None , \tinput_id : str = None , \t__log = True ) -> validmind . vm_models . dataset . dataset . VMDataset : ", "funcdef": "def"}, "validmind.init_model": {"fullname": "validmind.init_model", "modulename": "validmind", "qualname": "init_model", "kind": "function", "doc": "Initializes a VM Model, which can then be passed to other functions\nthat can perform additional analysis and tests on the data. This function\nalso ensures we are creating a model supported libraries.
\n\nArguments: \n\n\nmodel: A trained model or VMModel instanceinput_id (str): The input ID for the model (e.g. \"my_model\"). By default,\nthis will be set to model but if you are passing this model as a\ntest input using some other key than model, then you should set\nthis to the same key.attributes (dict): A dictionary of model attributespredict_fn (callable): A function that takes an input and returns a prediction**kwargs: Additional arguments to pass to the model \n\nRaises: \n\n\nValueError: If the model type is not supported \n\nReturns: \n\n\n vm.VMModel: A VM Model instance
\n \n", "signature": "(\tmodel : object = None , \tinput_id : str = 'model' , \tattributes : dict = None , \tpredict_fn : < built - in function callable > = None , \t__log = True , \t** kwargs ) -> validmind . vm_models . model . VMModel : ", "funcdef": "def"}, "validmind.init_r_model": {"fullname": "validmind.init_r_model", "modulename": "validmind", "qualname": "init_r_model", "kind": "function", "doc": "Initializes a VM Model for an R model
\n\nR models must be saved to disk and the filetype depends on the model type...\nCurrently we support the following model types:
\n\n\nLogisticRegression glm model in R: saved as an RDS file with saveRDS \nLinearRegression lm model in R: saved as an RDS file with saveRDS \nXGBClassifier: saved as a .json or .bin file with xgb.save \nXGBRegressor: saved as a .json or .bin file with xgb.save \n \n\nLogisticRegression and LinearRegression models are converted to sklearn models by extracting\nthe coefficients and intercept from the R model. XGB models are loaded using the xgboost\nsince xgb models saved in .json or .bin format can be loaded directly with either Python or R
\n\nArguments: \n\n\nmodel_path (str): The path to the R model saved as an RDS or XGB filemodel_type (str): The type of the model (one of R_MODEL_TYPES) \n\nReturns: \n\n\n vm.vm.Model: A VM Model instance
\n \n", "signature": "(\tmodel_path : str , \tinput_id : str = 'model' ) -> validmind . vm_models . model . VMModel : ", "funcdef": "def"}, "validmind.preview_template": {"fullname": "validmind.preview_template", "modulename": "validmind", "qualname": "preview_template", "kind": "function", "doc": "Preview the documentation template for the current project
\n\nThis function will display the documentation template for the current project. If\nthe project has not been initialized, then an error will be raised.
\n\nRaises: \n\n\nValueError: If the project has not been initialized \n", "signature": "(): ", "funcdef": "def"}, "validmind.run_documentation_tests": {"fullname": "validmind.run_documentation_tests", "modulename": "validmind", "qualname": "run_documentation_tests", "kind": "function", "doc": "Collect and run all the tests associated with a template
\n\nThis function will analyze the current project's documentation template and collect\nall the tests associated with it into a test suite. It will then run the test\nsuite, log the results to the ValidMind API, and display them to the user.
\n\nArguments: \n\n\nsection (str or list, optional): The section(s) to preview. Defaults to None.send (bool, optional): Whether to send the results to the ValidMind API. Defaults to True.fail_fast (bool, optional): Whether to stop running tests after the first failure. Defaults to False.inputs (dict, optional): A dictionary of test inputs to pass to the TestSuiteconfig: A dictionary of test parameters to override the defaults**kwargs: backwards compatibility for passing in test inputs using keyword arguments \n\nReturns: \n\n\n TestSuite or dict: The completed TestSuite instance or a dictionary of TestSuites if section is a list.
\n \n\nRaises: \n\n\nValueError: If the project has not been initialized \n", "signature": "(\tsection = None , \tsend = True , \tfail_fast = False , \tinputs = None , \tconfig = None , \t** kwargs ): ", "funcdef": "def"}, "validmind.log_metric": {"fullname": "validmind.log_metric", "modulename": "validmind", "qualname": "log_metric", "kind": "function", "doc": "Logs a unit metric
\n\nUnit metrics are key-value pairs where the key is the metric name and the value is\na scalar (int or float). These key-value pairs are associated with the currently\nselected model (inventory model in the ValidMind Platform) and keys can be logged\nto over time to create a history of the metric. On the ValidMind Platform, these metrics\nwill be used to create plots/visualizations for documentation and dashboards etc.
\n\nArguments: \n\n\nkey (str): The metric keyvalue (float): The metric valueinputs (list, optional): A list of input IDs that were used to compute the metric.params (dict, optional): Dictionary of parameters used to compute the metric.recorded_at (str, optional): The timestamp of the metric. Server will use\ncurrent time if not provided.thresholds (dict, optional): Dictionary of thresholds for the metric. \n", "signature": "(\tkey : str , \tvalue : float , \tinputs : Optional [ List [ str ]] = None , \tparams : Optional [ Dict [ str , Any ]] = None , \trecorded_at : Optional [ str ] = None , \tthresholds : Optional [ Dict [ str , Any ]] = None ): ", "funcdef": "def"}, "validmind.get_test_suite": {"fullname": "validmind.get_test_suite", "modulename": "validmind", "qualname": "get_test_suite", "kind": "function", "doc": "Gets a TestSuite object for the current project or a specific test suite
\n\nThis function provides an interface to retrieve the TestSuite instance for the\ncurrent project or a specific TestSuite instance identified by test_suite_id.\nThe project Test Suite will contain sections for every section in the project's\ndocumentation template and these Test Suite Sections will contain all the tests\nassociated with that template section.
\n\nArguments: \n\n\ntest_suite_id (str, optional): The test suite name. If not passed, then the\nproject's test suite will be returned. Defaults to None.section (str, optional): The section of the documentation template from which\nto retrieve the test suite. This only applies if test_suite_id is None.\nDefaults to None.args: Additional arguments to pass to the TestSuitekwargs: Additional keyword arguments to pass to the TestSuite \n", "signature": "(\ttest_suite_id : str = None , \tsection : str = None , \t* args , \t** kwargs ) -> validmind . vm_models . test_suite . test_suite . TestSuite : ", "funcdef": "def"}, "validmind.run_test_suite": {"fullname": "validmind.run_test_suite", "modulename": "validmind", "qualname": "run_test_suite", "kind": "function", "doc": "High Level function for running a test suite
\n\nThis function provides a high level interface for running a test suite. A test suite is\na collection of tests. This function will automatically find the correct test suite\nclass based on the test_suite_id, initialize each of the tests, and run them.
\n\nArguments: \n\n\ntest_suite_id (str): The test suite name (e.g. 'classifier_full_suite')config (dict, optional): A dictionary of parameters to pass to the tests in the\ntest suite. Defaults to None.send (bool, optional): Whether to post the test results to the API. send=False\nis useful for testing. Defaults to True.fail_fast (bool, optional): Whether to stop running tests after the first failure. Defaults to False.inputs (dict, optional): A dictionary of test inputs to pass to the TestSuite e.g. model, dataset\nmodels etc. These inputs will be accessible by any test in the test suite. See the test\ndocumentation or vm.describe_test() for more details on the inputs required for each.**kwargs: backwards compatibility for passing in test inputs using keyword arguments \n\nRaises: \n\n\nValueError: If the test suite name is not found or if there is an error initializing the test suite \n\nReturns: \n\n\n TestSuite: the TestSuite instance
\n \n", "signature": "(\ttest_suite_id , \tsend = True , \tfail_fast = False , \tconfig = None , \tinputs = None , \t** kwargs ): ", "funcdef": "def"}, "validmind.print_env": {"fullname": "validmind.print_env", "modulename": "validmind", "qualname": "print_env", "kind": "function", "doc": "Prints a log of the running environment for debugging.
\n\nOutput includes: ValidMind Library version, operating system details, installed dependencies, and the ISO 8601 timestamp at log creation.
\n", "signature": "(): ", "funcdef": "def"}, "validmind.tags": {"fullname": "validmind.tags", "modulename": "validmind", "qualname": "tags", "kind": "function", "doc": "Decorator for specifying tags for a test.
\n\nArguments: \n\n\n*tags: The tags to apply to the test. \n", "signature": "(* tags ): ", "funcdef": "def"}, "validmind.tasks": {"fullname": "validmind.tasks", "modulename": "validmind", "qualname": "tasks", "kind": "function", "doc": "Decorator for specifying the task types that a test is designed for.
\n\nArguments: \n\n\n*tasks: The task types that the test is designed for. \n", "signature": "(* tasks ): ", "funcdef": "def"}, "validmind.test": {"fullname": "validmind.test", "modulename": "validmind", "qualname": "test", "kind": "function", "doc": "Decorator for creating and registering custom tests
\n\nThis decorator registers the function it wraps as a test function within ValidMind\nunder the provided ID. Once decorated, the function can be run using the\nvalidmind.tests.run_test function.
\n\nThe function can take two different types of arguments:
\n\n\nInputs: ValidMind model or dataset (or list of models/datasets). These arguments\nmust use the following names: model, models, dataset, datasets. \nParameters: Any additional keyword arguments of any type (must have a default\nvalue) that can have any name. \n \n\nThe function should return one of the following types:
\n\n\nTable: Either a list of dictionaries or a pandas DataFrame \nPlot: Either a matplotlib figure or a plotly figure \nScalar: A single number (int or float) \nBoolean: A single boolean value indicating whether the test passed or failed \n \n\nThe function may also include a docstring. This docstring will be used and logged\nas the metric's description.
\n\nArguments: \n\n\nfunc: The function to decoratetest_id: The identifier for the metric. If not provided, the function name is used. \n\nReturns: \n\n\n The decorated function.
\n \n", "signature": "(func_or_id ): ", "funcdef": "def"}, "validmind.RawData": {"fullname": "validmind.RawData", "modulename": "validmind", "qualname": "RawData", "kind": "class", "doc": "Holds raw data for a test result
\n"}, "validmind.RawData.__init__": {"fullname": "validmind.RawData.__init__", "modulename": "validmind", "qualname": "RawData.__init__", "kind": "function", "doc": "Create a new RawData object
\n\nArguments: \n\n\nlog (bool): If True, log the raw data to ValidMind**kwargs: Keyword arguments to set as attributes e.g.\nRawData(log=True, dataset_duplicates=df_duplicates) \n", "signature": "(log : bool = False , ** kwargs "}, "validmind.RawData.inspect": {"fullname": "validmind.RawData.inspect", "modulename": "validmind", "qualname": "RawData.inspect", "kind": "function", "doc": "Inspect the raw data
\n", "signature": "(self , show : bool = True ): ", "funcdef": "def"}, "validmind.RawData.serialize": {"fullname": "validmind.RawData.serialize", "modulename": "validmind", "qualname": "RawData.serialize", "kind": "function", "doc": "
\n", "signature": "(self ): ", "funcdef": "def"}, "validmind.datasets": {"fullname": "validmind.datasets", "modulename": "validmind.datasets", "kind": "module", "doc": "Example datasets that can be used with the ValidMind Library.
\n"}, "validmind.datasets.classification": {"fullname": "validmind.datasets.classification", "modulename": "validmind.datasets.classification", "kind": "module", "doc": "Entrypoint for classification datasets.
\n"}, "validmind.datasets.classification.customer_churn": {"fullname": "validmind.datasets.classification.customer_churn", "modulename": "validmind.datasets.classification.customer_churn", "kind": "module", "doc": "
\n"}, "validmind.datasets.classification.customer_churn.load_data": {"fullname": "validmind.datasets.classification.customer_churn.load_data", "modulename": "validmind.datasets.classification.customer_churn", "qualname": "load_data", "kind": "function", "doc": "
\n", "signature": "(full_dataset = False ): ", "funcdef": "def"}, "validmind.datasets.classification.customer_churn.preprocess": {"fullname": "validmind.datasets.classification.customer_churn.preprocess", "modulename": "validmind.datasets.classification.customer_churn", "qualname": "preprocess", "kind": "function", "doc": "
\n", "signature": "(df ): ", "funcdef": "def"}, "validmind.datasets.classification.customer_churn.get_demo_test_config": {"fullname": "validmind.datasets.classification.customer_churn.get_demo_test_config", "modulename": "validmind.datasets.classification.customer_churn", "qualname": "get_demo_test_config", "kind": "function", "doc": "Returns input configuration for the default documentation\ntemplate assigned to this demo model
\n\nThe default documentation template uses the following inputs:
\n\n\nraw_dataset \ntrain_dataset \ntest_dataset \nmodel \n \n\nWe assign the following inputs depending on the input config expected\nby each test:
\n\n\nWhen a test expects a \"dataset\" we use the raw_dataset \nWhen a tets expects \"datasets\" we use the train_dataset and test_dataset \nWhen a test expects a \"model\" we use the model \nWhen a test expects \"model\" and \"dataset\" we use the model and test_dataset \nThe only exception is ClassifierPerformance since that runs twice: once\nwith the train_dataset (in sample) and once with the test_dataset (out of sample) \n \n", "signature": "(test_suite = None ): ", "funcdef": "def"}, "validmind.datasets.classification.taiwan_credit": {"fullname": "validmind.datasets.classification.taiwan_credit", "modulename": "validmind.datasets.classification.taiwan_credit", "kind": "module", "doc": "
\n"}, "validmind.datasets.classification.taiwan_credit.load_data": {"fullname": "validmind.datasets.classification.taiwan_credit.load_data", "modulename": "validmind.datasets.classification.taiwan_credit", "qualname": "load_data", "kind": "function", "doc": "
\n", "signature": "(): ", "funcdef": "def"}, "validmind.datasets.classification.taiwan_credit.preprocess": {"fullname": "validmind.datasets.classification.taiwan_credit.preprocess", "modulename": "validmind.datasets.classification.taiwan_credit", "qualname": "preprocess", "kind": "function", "doc": "
\n", "signature": "(df ): ", "funcdef": "def"}, "validmind.datasets.credit_risk": {"fullname": "validmind.datasets.credit_risk", "modulename": "validmind.datasets.credit_risk", "kind": "module", "doc": "Entrypoint for credit risk datasets.
\n"}, "validmind.datasets.credit_risk.lending_club": {"fullname": "validmind.datasets.credit_risk.lending_club", "modulename": "validmind.datasets.credit_risk.lending_club", "kind": "module", "doc": "
\n"}, "validmind.datasets.credit_risk.lending_club.load_data": {"fullname": "validmind.datasets.credit_risk.lending_club.load_data", "modulename": "validmind.datasets.credit_risk.lending_club", "qualname": "load_data", "kind": "function", "doc": "Load data from either an online source or offline files, automatically dropping specified columns for offline data.
\n\nParameters \n\n\nsource : 'online' for online data, 'offline' for offline files. Defaults to 'online'. \n\nReturns \n\n\n DataFrame containing the loaded data.
\n \n", "signature": "(source = 'online' , verbose = True ): ", "funcdef": "def"}, "validmind.datasets.credit_risk.lending_club.preprocess": {"fullname": "validmind.datasets.credit_risk.lending_club.preprocess", "modulename": "validmind.datasets.credit_risk.lending_club", "qualname": "preprocess", "kind": "function", "doc": "
\n", "signature": "(df , verbose = True ): ", "funcdef": "def"}, "validmind.datasets.credit_risk.lending_club.feature_engineering": {"fullname": "validmind.datasets.credit_risk.lending_club.feature_engineering", "modulename": "validmind.datasets.credit_risk.lending_club", "qualname": "feature_engineering", "kind": "function", "doc": "
\n", "signature": "(df , verbose = True ): ", "funcdef": "def"}, "validmind.datasets.credit_risk.lending_club.woe_encoding": {"fullname": "validmind.datasets.credit_risk.lending_club.woe_encoding", "modulename": "validmind.datasets.credit_risk.lending_club", "qualname": "woe_encoding", "kind": "function", "doc": "
\n", "signature": "(df , verbose = True ): ", "funcdef": "def"}, "validmind.datasets.credit_risk.lending_club.split": {"fullname": "validmind.datasets.credit_risk.lending_club.split", "modulename": "validmind.datasets.credit_risk.lending_club", "qualname": "split", "kind": "function", "doc": "Split dataset into train, validation (optional), and test sets.
\n\nArguments: \n\n\ndf: Input DataFramevalidation_split: If None, returns train/test split. If float, returns train/val/test splittest_size: Proportion of data for test set (default: 0.2)add_constant: Whether to add constant column for statsmodels (default: False) \n\nReturns: \n\n\n If validation_size is None:\n train_df, test_df\n If validation_size is float:\n train_df, validation_df, test_df
\n \n", "signature": "(\tdf , \tvalidation_size = None , \ttest_size = 0.2 , \tadd_constant = False , \tverbose = True ): ", "funcdef": "def"}, "validmind.datasets.credit_risk.lending_club.compute_scores": {"fullname": "validmind.datasets.credit_risk.lending_club.compute_scores", "modulename": "validmind.datasets.credit_risk.lending_club", "qualname": "compute_scores", "kind": "function", "doc": "
\n", "signature": "(probabilities ): ", "funcdef": "def"}, "validmind.datasets.credit_risk.lending_club.get_demo_test_config": {"fullname": "validmind.datasets.credit_risk.lending_club.get_demo_test_config", "modulename": "validmind.datasets.credit_risk.lending_club", "qualname": "get_demo_test_config", "kind": "function", "doc": "Get demo test configuration.
\n\nArguments: \n\n\nx_test: Test features DataFramey_test: Test target Series \n\nReturns: \n\n\n dict: Test configuration dictionary
\n \n", "signature": "(x_test = None , y_test = None ): ", "funcdef": "def"}, "validmind.datasets.credit_risk.lending_club.load_scorecard": {"fullname": "validmind.datasets.credit_risk.lending_club.load_scorecard", "modulename": "validmind.datasets.credit_risk.lending_club", "qualname": "load_scorecard", "kind": "function", "doc": "
\n", "signature": "(): ", "funcdef": "def"}, "validmind.datasets.credit_risk.lending_club.init_vm_objects": {"fullname": "validmind.datasets.credit_risk.lending_club.init_vm_objects", "modulename": "validmind.datasets.credit_risk.lending_club", "qualname": "init_vm_objects", "kind": "function", "doc": "
\n", "signature": "(scorecard ): ", "funcdef": "def"}, "validmind.datasets.credit_risk.lending_club.load_test_config": {"fullname": "validmind.datasets.credit_risk.lending_club.load_test_config", "modulename": "validmind.datasets.credit_risk.lending_club", "qualname": "load_test_config", "kind": "function", "doc": "
\n", "signature": "(scorecard ): ", "funcdef": "def"}, "validmind.datasets.credit_risk.lending_club_bias": {"fullname": "validmind.datasets.credit_risk.lending_club_bias", "modulename": "validmind.datasets.credit_risk.lending_club_bias", "kind": "module", "doc": "
\n"}, "validmind.datasets.credit_risk.lending_club_bias.load_data": {"fullname": "validmind.datasets.credit_risk.lending_club_bias.load_data", "modulename": "validmind.datasets.credit_risk.lending_club_bias", "qualname": "load_data", "kind": "function", "doc": "Load data from the specified CSV file.
\n\nReturns \n\n\n DataFrame containing the loaded data.
\n \n", "signature": "(): ", "funcdef": "def"}, "validmind.datasets.credit_risk.lending_club_bias.preprocess": {"fullname": "validmind.datasets.credit_risk.lending_club_bias.preprocess", "modulename": "validmind.datasets.credit_risk.lending_club_bias", "qualname": "preprocess", "kind": "function", "doc": "
\n", "signature": "(df ): ", "funcdef": "def"}, "validmind.datasets.credit_risk.lending_club_bias.split": {"fullname": "validmind.datasets.credit_risk.lending_club_bias.split", "modulename": "validmind.datasets.credit_risk.lending_club_bias", "qualname": "split", "kind": "function", "doc": "
\n", "signature": "(df , test_size = 0.3 ): ", "funcdef": "def"}, "validmind.datasets.credit_risk.lending_club_bias.compute_scores": {"fullname": "validmind.datasets.credit_risk.lending_club_bias.compute_scores", "modulename": "validmind.datasets.credit_risk.lending_club_bias", "qualname": "compute_scores", "kind": "function", "doc": "
\n", "signature": "(probabilities ): ", "funcdef": "def"}, "validmind.datasets.nlp": {"fullname": "validmind.datasets.nlp", "modulename": "validmind.datasets.nlp", "kind": "module", "doc": "Example datasets that can be used with the ValidMind Library.
\n"}, "validmind.datasets.nlp.cnn_dailymail": {"fullname": "validmind.datasets.nlp.cnn_dailymail", "modulename": "validmind.datasets.nlp.cnn_dailymail", "kind": "module", "doc": "
\n"}, "validmind.datasets.nlp.cnn_dailymail.load_data": {"fullname": "validmind.datasets.nlp.cnn_dailymail.load_data", "modulename": "validmind.datasets.nlp.cnn_dailymail", "qualname": "load_data", "kind": "function", "doc": "Load data from either online source or offline files.
\n\nParameters \n\n\nsource : 'online' for online data, 'offline' for offline data. Defaults to 'online'.dataset_size : Applicable if source is 'offline'. '300k' or '500k' for dataset size. Defaults to None. \n\nReturns \n\n\n DataFrame containing the loaded data.
\n \n", "signature": "(source = 'online' , dataset_size = None ): ", "funcdef": "def"}, "validmind.datasets.nlp.cnn_dailymail.display_nice": {"fullname": "validmind.datasets.nlp.cnn_dailymail.display_nice", "modulename": "validmind.datasets.nlp.cnn_dailymail", "qualname": "display_nice", "kind": "function", "doc": "Primary function to format and display a DataFrame.
\n", "signature": "(df , num_rows = None ): ", "funcdef": "def"}, "validmind.datasets.nlp.twitter_covid_19": {"fullname": "validmind.datasets.nlp.twitter_covid_19", "modulename": "validmind.datasets.nlp.twitter_covid_19", "kind": "module", "doc": "
\n"}, "validmind.datasets.nlp.twitter_covid_19.load_data": {"fullname": "validmind.datasets.nlp.twitter_covid_19.load_data", "modulename": "validmind.datasets.nlp.twitter_covid_19", "qualname": "load_data", "kind": "function", "doc": "
\n", "signature": "(full_dataset = False ): ", "funcdef": "def"}, "validmind.datasets.regression": {"fullname": "validmind.datasets.regression", "modulename": "validmind.datasets.regression", "kind": "module", "doc": "Entrypoint for regression datasets
\n"}, "validmind.datasets.regression.fred": {"fullname": "validmind.datasets.regression.fred", "modulename": "validmind.datasets.regression.fred", "kind": "module", "doc": "
\n"}, "validmind.datasets.regression.fred.load_all_data": {"fullname": "validmind.datasets.regression.fred.load_all_data", "modulename": "validmind.datasets.regression.fred", "qualname": "load_all_data", "kind": "function", "doc": "
\n", "signature": "(): ", "funcdef": "def"}, "validmind.datasets.regression.fred.load_data": {"fullname": "validmind.datasets.regression.fred.load_data", "modulename": "validmind.datasets.regression.fred", "qualname": "load_data", "kind": "function", "doc": "
\n", "signature": "(): ", "funcdef": "def"}, "validmind.datasets.regression.fred.load_processed_data": {"fullname": "validmind.datasets.regression.fred.load_processed_data", "modulename": "validmind.datasets.regression.fred", "qualname": "load_processed_data", "kind": "function", "doc": "
\n", "signature": "(): ", "funcdef": "def"}, "validmind.datasets.regression.fred.preprocess": {"fullname": "validmind.datasets.regression.fred.preprocess", "modulename": "validmind.datasets.regression.fred", "qualname": "preprocess", "kind": "function", "doc": "Split a time series DataFrame into train, validation, and test sets.
\n\nArguments: \n\n\ndf (pandas.DataFrame): The time series DataFrame to be split.split_option (str): The split option to choose from: 'train_test_val' (default) or 'train_test'.train_size (float): The proportion of the dataset to include in the training set. Default is 0.6.test_size (float): The proportion of the dataset to include in the test set. Default is 0.2. \n\nReturns: \n\n\n train_df (pandas.DataFrame): The training set.\n validation_df (pandas.DataFrame): The validation set (only returned if split_option is 'train_test_val').\n test_df (pandas.DataFrame): The test set.
\n \n", "signature": "(df , split_option = 'train_test_val' , train_size = 0.6 , test_size = 0.2 ): ", "funcdef": "def"}, "validmind.datasets.regression.fred.transform": {"fullname": "validmind.datasets.regression.fred.transform", "modulename": "validmind.datasets.regression.fred", "qualname": "transform", "kind": "function", "doc": "
\n", "signature": "(df , transform_func = 'diff' ): ", "funcdef": "def"}, "validmind.datasets.regression.fred.load_model": {"fullname": "validmind.datasets.regression.fred.load_model", "modulename": "validmind.datasets.regression.fred", "qualname": "load_model", "kind": "function", "doc": "
\n", "signature": "(model_name ): ", "funcdef": "def"}, "validmind.datasets.regression.fred.load_train_dataset": {"fullname": "validmind.datasets.regression.fred.load_train_dataset", "modulename": "validmind.datasets.regression.fred", "qualname": "load_train_dataset", "kind": "function", "doc": "
\n", "signature": "(model_path ): ", "funcdef": "def"}, "validmind.datasets.regression.fred.load_test_dataset": {"fullname": "validmind.datasets.regression.fred.load_test_dataset", "modulename": "validmind.datasets.regression.fred", "qualname": "load_test_dataset", "kind": "function", "doc": "
\n", "signature": "(model_name ): ", "funcdef": "def"}, "validmind.datasets.regression.lending_club": {"fullname": "validmind.datasets.regression.lending_club", "modulename": "validmind.datasets.regression.lending_club", "kind": "module", "doc": "
\n"}, "validmind.datasets.regression.lending_club.load_data": {"fullname": "validmind.datasets.regression.lending_club.load_data", "modulename": "validmind.datasets.regression.lending_club", "qualname": "load_data", "kind": "function", "doc": "
\n", "signature": "(): ", "funcdef": "def"}, "validmind.datasets.regression.lending_club.preprocess": {"fullname": "validmind.datasets.regression.lending_club.preprocess", "modulename": "validmind.datasets.regression.lending_club", "qualname": "preprocess", "kind": "function", "doc": "Split a time series DataFrame into train, validation, and test sets.
\n\nArguments: \n\n\ndf (pandas.DataFrame): The time series DataFrame to be split.split_option (str): The split option to choose from: 'train_test_val' (default) or 'train_test'.train_size (float): The proportion of the dataset to include in the training set. Default is 0.6.test_size (float): The proportion of the dataset to include in the test set. Default is 0.2. \n\nReturns: \n\n\n train_df (pandas.DataFrame): The training set.\n validation_df (pandas.DataFrame): The validation set (only returned if split_option is 'train_test_val').\n test_df (pandas.DataFrame): The test set.
\n \n", "signature": "(df , split_option = 'train_test_val' , train_size = 0.6 , test_size = 0.2 ): ", "funcdef": "def"}, "validmind.datasets.regression.lending_club.transform": {"fullname": "validmind.datasets.regression.lending_club.transform", "modulename": "validmind.datasets.regression.lending_club", "qualname": "transform", "kind": "function", "doc": "
\n", "signature": "(df , transform_func = 'diff' ): ", "funcdef": "def"}, "validmind.errors": {"fullname": "validmind.errors", "modulename": "validmind.errors", "kind": "module", "doc": "This module contains all the custom errors that are used in the ValidMind Library.
\n\nThe following base errors are defined for others:
\n\n\nBaseError \nAPIRequestError \n \n"}, "validmind.errors.BaseError": {"fullname": "validmind.errors.BaseError", "modulename": "validmind.errors", "qualname": "BaseError", "kind": "class", "doc": "Common base class for all non-exit exceptions.
\n", "bases": "builtins.Exception"}, "validmind.errors.BaseError.__init__": {"fullname": "validmind.errors.BaseError.__init__", "modulename": "validmind.errors", "qualname": "BaseError.__init__", "kind": "function", "doc": "
\n", "signature": "(message = '' "}, "validmind.errors.BaseError.description": {"fullname": "validmind.errors.BaseError.description", "modulename": "validmind.errors", "qualname": "BaseError.description", "kind": "function", "doc": "
\n", "signature": "(self , * args , ** kwargs ): ", "funcdef": "def"}, "validmind.errors.APIRequestError": {"fullname": "validmind.errors.APIRequestError", "modulename": "validmind.errors", "qualname": "APIRequestError", "kind": "class", "doc": "Generic error for API request errors that are not known.
\n", "bases": "BaseError"}, "validmind.errors.GetTestSuiteError": {"fullname": "validmind.errors.GetTestSuiteError", "modulename": "validmind.errors", "qualname": "GetTestSuiteError", "kind": "class", "doc": "When the test suite could not be found.
\n", "bases": "BaseError"}, "validmind.errors.MissingCacheResultsArgumentsError": {"fullname": "validmind.errors.MissingCacheResultsArgumentsError", "modulename": "validmind.errors", "qualname": "MissingCacheResultsArgumentsError", "kind": "class", "doc": "When the cache_results function is missing arguments.
\n", "bases": "BaseError"}, "validmind.errors.MissingOrInvalidModelPredictFnError": {"fullname": "validmind.errors.MissingOrInvalidModelPredictFnError", "modulename": "validmind.errors", "qualname": "MissingOrInvalidModelPredictFnError", "kind": "class", "doc": "When the pytorch model is missing a predict function or its predict\nmethod does not have the expected arguments.
\n", "bases": "BaseError"}, "validmind.errors.InitializeTestSuiteError": {"fullname": "validmind.errors.InitializeTestSuiteError", "modulename": "validmind.errors", "qualname": "InitializeTestSuiteError", "kind": "class", "doc": "When the test suite was found but could not be initialized.
\n", "bases": "BaseError"}, "validmind.errors.InvalidAPICredentialsError": {"fullname": "validmind.errors.InvalidAPICredentialsError", "modulename": "validmind.errors", "qualname": "InvalidAPICredentialsError", "kind": "class", "doc": "Generic error for API request errors that are not known.
\n", "bases": "APIRequestError"}, "validmind.errors.InvalidAPICredentialsError.description": {"fullname": "validmind.errors.InvalidAPICredentialsError.description", "modulename": "validmind.errors", "qualname": "InvalidAPICredentialsError.description", "kind": "function", "doc": "
\n", "signature": "(self , * args , ** kwargs ): ", "funcdef": "def"}, "validmind.errors.InvalidContentIdPrefixError": {"fullname": "validmind.errors.InvalidContentIdPrefixError", "modulename": "validmind.errors", "qualname": "InvalidContentIdPrefixError", "kind": "class", "doc": "When an invalid text content_id is sent to the API.
\n", "bases": "APIRequestError"}, "validmind.errors.InvalidMetricResultsError": {"fullname": "validmind.errors.InvalidMetricResultsError", "modulename": "validmind.errors", "qualname": "InvalidMetricResultsError", "kind": "class", "doc": "When an invalid metric results object is sent to the API.
\n", "bases": "APIRequestError"}, "validmind.errors.InvalidProjectError": {"fullname": "validmind.errors.InvalidProjectError", "modulename": "validmind.errors", "qualname": "InvalidProjectError", "kind": "class", "doc": "Generic error for API request errors that are not known.
\n", "bases": "APIRequestError"}, "validmind.errors.InvalidProjectError.description": {"fullname": "validmind.errors.InvalidProjectError.description", "modulename": "validmind.errors", "qualname": "InvalidProjectError.description", "kind": "function", "doc": "
\n", "signature": "(self , * args , ** kwargs ): ", "funcdef": "def"}, "validmind.errors.InvalidRequestBodyError": {"fullname": "validmind.errors.InvalidRequestBodyError", "modulename": "validmind.errors", "qualname": "InvalidRequestBodyError", "kind": "class", "doc": "When a POST/PUT request is made with an invalid request body.
\n", "bases": "APIRequestError"}, "validmind.errors.InvalidTestResultsError": {"fullname": "validmind.errors.InvalidTestResultsError", "modulename": "validmind.errors", "qualname": "InvalidTestResultsError", "kind": "class", "doc": "When an invalid test results object is sent to the API.
\n", "bases": "APIRequestError"}, "validmind.errors.InvalidTestParametersError": {"fullname": "validmind.errors.InvalidTestParametersError", "modulename": "validmind.errors", "qualname": "InvalidTestParametersError", "kind": "class", "doc": "When an invalid parameters for the test.
\n", "bases": "BaseError"}, "validmind.errors.InvalidInputError": {"fullname": "validmind.errors.InvalidInputError", "modulename": "validmind.errors", "qualname": "InvalidInputError", "kind": "class", "doc": "When an invalid input object.
\n", "bases": "BaseError"}, "validmind.errors.InvalidTextObjectError": {"fullname": "validmind.errors.InvalidTextObjectError", "modulename": "validmind.errors", "qualname": "InvalidTextObjectError", "kind": "class", "doc": "When an invalid Metadat (Text) object is sent to the API.
\n", "bases": "APIRequestError"}, "validmind.errors.InvalidValueFormatterError": {"fullname": "validmind.errors.InvalidValueFormatterError", "modulename": "validmind.errors", "qualname": "InvalidValueFormatterError", "kind": "class", "doc": "When an invalid value formatter is provided when serializing results.
\n", "bases": "BaseError"}, "validmind.errors.InvalidXGBoostTrainedModelError": {"fullname": "validmind.errors.InvalidXGBoostTrainedModelError", "modulename": "validmind.errors", "qualname": "InvalidXGBoostTrainedModelError", "kind": "class", "doc": "When an invalid XGBoost trained model is used when calling init_r_model.
\n", "bases": "BaseError"}, "validmind.errors.LoadTestError": {"fullname": "validmind.errors.LoadTestError", "modulename": "validmind.errors", "qualname": "LoadTestError", "kind": "class", "doc": "Exception raised when an error occurs while loading a test
\n", "bases": "BaseError"}, "validmind.errors.LoadTestError.__init__": {"fullname": "validmind.errors.LoadTestError.__init__", "modulename": "validmind.errors", "qualname": "LoadTestError.__init__", "kind": "function", "doc": "
\n", "signature": "(message : str , original_error : Optional [ Exception ] = None "}, "validmind.errors.MismatchingClassLabelsError": {"fullname": "validmind.errors.MismatchingClassLabelsError", "modulename": "validmind.errors", "qualname": "MismatchingClassLabelsError", "kind": "class", "doc": "When the class labels found in the dataset don't match the provided target labels.
\n", "bases": "BaseError"}, "validmind.errors.MissingAPICredentialsError": {"fullname": "validmind.errors.MissingAPICredentialsError", "modulename": "validmind.errors", "qualname": "MissingAPICredentialsError", "kind": "class", "doc": "Common base class for all non-exit exceptions.
\n", "bases": "BaseError"}, "validmind.errors.MissingAPICredentialsError.description": {"fullname": "validmind.errors.MissingAPICredentialsError.description", "modulename": "validmind.errors", "qualname": "MissingAPICredentialsError.description", "kind": "function", "doc": "
\n", "signature": "(self , * args , ** kwargs ): ", "funcdef": "def"}, "validmind.errors.MissingClassLabelError": {"fullname": "validmind.errors.MissingClassLabelError", "modulename": "validmind.errors", "qualname": "MissingClassLabelError", "kind": "class", "doc": "When the one or more class labels are missing from provided dataset targets.
\n", "bases": "BaseError"}, "validmind.errors.MissingDocumentationTemplate": {"fullname": "validmind.errors.MissingDocumentationTemplate", "modulename": "validmind.errors", "qualname": "MissingDocumentationTemplate", "kind": "class", "doc": "When the client config is missing the documentation template.
\n", "bases": "BaseError"}, "validmind.errors.MissingRequiredTestInputError": {"fullname": "validmind.errors.MissingRequiredTestInputError", "modulename": "validmind.errors", "qualname": "MissingRequiredTestInputError", "kind": "class", "doc": "When a required test context variable is missing.
\n", "bases": "BaseError"}, "validmind.errors.MissingDependencyError": {"fullname": "validmind.errors.MissingDependencyError", "modulename": "validmind.errors", "qualname": "MissingDependencyError", "kind": "class", "doc": "When a required dependency is missing.
\n", "bases": "BaseError"}, "validmind.errors.MissingDependencyError.__init__": {"fullname": "validmind.errors.MissingDependencyError.__init__", "modulename": "validmind.errors", "qualname": "MissingDependencyError.__init__", "kind": "function", "doc": "Arguments: \n\n\nmessage (str): The error message.required_dependencies (list): A list of required dependencies.extra (str): The particular validmind extra that will install the missing dependencies. \n", "signature": "(message = '' , required_dependencies = None , extra = None "}, "validmind.errors.MissingRExtrasError": {"fullname": "validmind.errors.MissingRExtrasError", "modulename": "validmind.errors", "qualname": "MissingRExtrasError", "kind": "class", "doc": "When the R extras have not been installed.
\n", "bases": "BaseError"}, "validmind.errors.MissingRExtrasError.description": {"fullname": "validmind.errors.MissingRExtrasError.description", "modulename": "validmind.errors", "qualname": "MissingRExtrasError.description", "kind": "function", "doc": "
\n", "signature": "(self , * args , ** kwargs ): ", "funcdef": "def"}, "validmind.errors.MissingTextContentIdError": {"fullname": "validmind.errors.MissingTextContentIdError", "modulename": "validmind.errors", "qualname": "MissingTextContentIdError", "kind": "class", "doc": "When a Text object is sent to the API without a content_id.
\n", "bases": "APIRequestError"}, "validmind.errors.MissingTextContentsError": {"fullname": "validmind.errors.MissingTextContentsError", "modulename": "validmind.errors", "qualname": "MissingTextContentsError", "kind": "class", "doc": "When a Text object is sent to the API without a \"text\" attribute.
\n", "bases": "APIRequestError"}, "validmind.errors.MissingModelIdError": {"fullname": "validmind.errors.MissingModelIdError", "modulename": "validmind.errors", "qualname": "MissingModelIdError", "kind": "class", "doc": "Common base class for all non-exit exceptions.
\n", "bases": "BaseError"}, "validmind.errors.MissingModelIdError.description": {"fullname": "validmind.errors.MissingModelIdError.description", "modulename": "validmind.errors", "qualname": "MissingModelIdError.description", "kind": "function", "doc": "
\n", "signature": "(self , * args , ** kwargs ): ", "funcdef": "def"}, "validmind.errors.TestInputInvalidDatasetError": {"fullname": "validmind.errors.TestInputInvalidDatasetError", "modulename": "validmind.errors", "qualname": "TestInputInvalidDatasetError", "kind": "class", "doc": "When an invalid dataset is used in a test context.
\n", "bases": "BaseError"}, "validmind.errors.UnsupportedColumnTypeError": {"fullname": "validmind.errors.UnsupportedColumnTypeError", "modulename": "validmind.errors", "qualname": "UnsupportedColumnTypeError", "kind": "class", "doc": "When an unsupported column type is found on a dataset.
\n", "bases": "BaseError"}, "validmind.errors.UnsupportedDatasetError": {"fullname": "validmind.errors.UnsupportedDatasetError", "modulename": "validmind.errors", "qualname": "UnsupportedDatasetError", "kind": "class", "doc": "When an unsupported dataset is used.
\n", "bases": "BaseError"}, "validmind.errors.UnsupportedFigureError": {"fullname": "validmind.errors.UnsupportedFigureError", "modulename": "validmind.errors", "qualname": "UnsupportedFigureError", "kind": "class", "doc": "When an unsupported figure object is constructed.
\n", "bases": "BaseError"}, "validmind.errors.UnsupportedRModelError": {"fullname": "validmind.errors.UnsupportedRModelError", "modulename": "validmind.errors", "qualname": "UnsupportedRModelError", "kind": "class", "doc": "When an unsupported R model is used.
\n", "bases": "BaseError"}, "validmind.errors.UnsupportedModelError": {"fullname": "validmind.errors.UnsupportedModelError", "modulename": "validmind.errors", "qualname": "UnsupportedModelError", "kind": "class", "doc": "When an unsupported model is used.
\n", "bases": "BaseError"}, "validmind.errors.UnsupportedModelForSHAPError": {"fullname": "validmind.errors.UnsupportedModelForSHAPError", "modulename": "validmind.errors", "qualname": "UnsupportedModelForSHAPError", "kind": "class", "doc": "When an unsupported model is used for SHAP importance.
\n", "bases": "BaseError"}, "validmind.errors.SkipTestError": {"fullname": "validmind.errors.SkipTestError", "modulename": "validmind.errors", "qualname": "SkipTestError", "kind": "class", "doc": "Useful error to throw when a test cannot be executed.
\n", "bases": "BaseError"}, "validmind.errors.raise_api_error": {"fullname": "validmind.errors.raise_api_error", "modulename": "validmind.errors", "qualname": "raise_api_error", "kind": "function", "doc": "Safely try to parse JSON from the response message in case the API\nreturns a non-JSON string or if the API returns a non-standard error
\n", "signature": "(error_string ): ", "funcdef": "def"}, "validmind.errors.should_raise_on_fail_fast": {"fullname": "validmind.errors.should_raise_on_fail_fast", "modulename": "validmind.errors", "qualname": "should_raise_on_fail_fast", "kind": "function", "doc": "Determine whether an error should be raised when fail_fast is True.
\n", "signature": "(error ) -> bool : ", "funcdef": "def"}, "validmind.test_suites": {"fullname": "validmind.test_suites", "modulename": "validmind.test_suites", "kind": "module", "doc": "Entrypoint for test suites.
\n"}, "validmind.test_suites.get_by_id": {"fullname": "validmind.test_suites.get_by_id", "modulename": "validmind.test_suites", "qualname": "get_by_id", "kind": "function", "doc": "Returns the test suite by ID
\n", "signature": "(test_suite_id : str ): ", "funcdef": "def"}, "validmind.test_suites.list_suites": {"fullname": "validmind.test_suites.list_suites", "modulename": "validmind.test_suites", "qualname": "list_suites", "kind": "function", "doc": "Returns a list of all available test suites
\n", "signature": "(pretty : bool = True ): ", "funcdef": "def"}, "validmind.test_suites.describe_suite": {"fullname": "validmind.test_suites.describe_suite", "modulename": "validmind.test_suites", "qualname": "describe_suite", "kind": "function", "doc": "Describes a Test Suite by ID
\n\nArguments: \n\n\ntest_suite_id: Test Suite IDverbose: If True, describe all plans and tests in the Test Suite \n\nReturns: \n\n\n pandas.DataFrame: A formatted table with the Test Suite description
\n \n", "signature": "(test_suite_id : str , verbose = False ): ", "funcdef": "def"}, "validmind.test_suites.describe_test_suite": {"fullname": "validmind.test_suites.describe_test_suite", "modulename": "validmind.test_suites", "qualname": "describe_test_suite", "kind": "function", "doc": "Describes a Test Suite by ID
\n\nArguments: \n\n\ntest_suite_id: Test Suite IDverbose: If True, describe all plans and tests in the Test Suite \n\nReturns: \n\n\n pandas.DataFrame: A formatted table with the Test Suite description
\n \n", "signature": "(test_suite_id : str , verbose = False ): ", "funcdef": "def"}, "validmind.test_suites.register_test_suite": {"fullname": "validmind.test_suites.register_test_suite", "modulename": "validmind.test_suites", "qualname": "register_test_suite", "kind": "function", "doc": "Registers a custom test suite
\n", "signature": "(\tsuite_id : str , \tsuite : validmind . vm_models . test_suite . test_suite . TestSuite ): ", "funcdef": "def"}, "validmind.test_suites.classifier": {"fullname": "validmind.test_suites.classifier", "modulename": "validmind.test_suites.classifier", "kind": "module", "doc": "Test suites for sklearn-compatible classifier models
\n\nIdeal setup is to have the API client to read a\ncustom test suite from the project's configuration
\n"}, "validmind.test_suites.classifier.ClassifierMetrics": {"fullname": "validmind.test_suites.classifier.ClassifierMetrics", "modulename": "validmind.test_suites.classifier", "qualname": "ClassifierMetrics", "kind": "class", "doc": "Test suite for sklearn classifier metrics
\n", "bases": "validmind.vm_models.test_suite.test_suite.TestSuite"}, "validmind.test_suites.classifier.ClassifierPerformance": {"fullname": "validmind.test_suites.classifier.ClassifierPerformance", "modulename": "validmind.test_suites.classifier", "qualname": "ClassifierPerformance", "kind": "class", "doc": "Test suite for sklearn classifier models
\n", "bases": "validmind.vm_models.test_suite.test_suite.TestSuite"}, "validmind.test_suites.classifier.ClassifierDiagnosis": {"fullname": "validmind.test_suites.classifier.ClassifierDiagnosis", "modulename": "validmind.test_suites.classifier", "qualname": "ClassifierDiagnosis", "kind": "class", "doc": "Test suite for sklearn classifier model diagnosis tests
\n", "bases": "validmind.vm_models.test_suite.test_suite.TestSuite"}, "validmind.test_suites.classifier.ClassifierModelValidation": {"fullname": "validmind.test_suites.classifier.ClassifierModelValidation", "modulename": "validmind.test_suites.classifier", "qualname": "ClassifierModelValidation", "kind": "class", "doc": "Test suite for binary classification models.
\n", "bases": "validmind.vm_models.test_suite.test_suite.TestSuite"}, "validmind.test_suites.classifier.ClassifierFullSuite": {"fullname": "validmind.test_suites.classifier.ClassifierFullSuite", "modulename": "validmind.test_suites.classifier", "qualname": "ClassifierFullSuite", "kind": "class", "doc": "Full test suite for binary classification models.
\n", "bases": "validmind.vm_models.test_suite.test_suite.TestSuite"}, "validmind.test_suites.cluster": {"fullname": "validmind.test_suites.cluster", "modulename": "validmind.test_suites.cluster", "kind": "module", "doc": "Test suites for sklearn-compatible clustering models
\n\nIdeal setup is to have the API client to read a\ncustom test suite from the project's configuration
\n"}, "validmind.test_suites.cluster.ClusterMetrics": {"fullname": "validmind.test_suites.cluster.ClusterMetrics", "modulename": "validmind.test_suites.cluster", "qualname": "ClusterMetrics", "kind": "class", "doc": "Test suite for sklearn clustering metrics
\n", "bases": "validmind.vm_models.test_suite.test_suite.TestSuite"}, "validmind.test_suites.cluster.ClusterPerformance": {"fullname": "validmind.test_suites.cluster.ClusterPerformance", "modulename": "validmind.test_suites.cluster", "qualname": "ClusterPerformance", "kind": "class", "doc": "Test suite for sklearn cluster performance
\n", "bases": "validmind.vm_models.test_suite.test_suite.TestSuite"}, "validmind.test_suites.cluster.ClusterFullSuite": {"fullname": "validmind.test_suites.cluster.ClusterFullSuite", "modulename": "validmind.test_suites.cluster", "qualname": "ClusterFullSuite", "kind": "class", "doc": "Full test suite for clustering models.
\n", "bases": "validmind.vm_models.test_suite.test_suite.TestSuite"}, "validmind.test_suites.embeddings": {"fullname": "validmind.test_suites.embeddings", "modulename": "validmind.test_suites.embeddings", "kind": "module", "doc": "Test suites for embeddings models
\n\nIdeal setup is to have the API client to read a\ncustom test suite from the project's configuration
\n"}, "validmind.test_suites.embeddings.EmbeddingsMetrics": {"fullname": "validmind.test_suites.embeddings.EmbeddingsMetrics", "modulename": "validmind.test_suites.embeddings", "qualname": "EmbeddingsMetrics", "kind": "class", "doc": "Test suite for embeddings metrics
\n", "bases": "validmind.vm_models.test_suite.test_suite.TestSuite"}, "validmind.test_suites.embeddings.EmbeddingsPerformance": {"fullname": "validmind.test_suites.embeddings.EmbeddingsPerformance", "modulename": "validmind.test_suites.embeddings", "qualname": "EmbeddingsPerformance", "kind": "class", "doc": "Test suite for embeddings model performance
\n", "bases": "validmind.vm_models.test_suite.test_suite.TestSuite"}, "validmind.test_suites.embeddings.EmbeddingsFullSuite": {"fullname": "validmind.test_suites.embeddings.EmbeddingsFullSuite", "modulename": "validmind.test_suites.embeddings", "qualname": "EmbeddingsFullSuite", "kind": "class", "doc": "Full test suite for embeddings models.
\n", "bases": "validmind.vm_models.test_suite.test_suite.TestSuite"}, "validmind.test_suites.llm": {"fullname": "validmind.test_suites.llm", "modulename": "validmind.test_suites.llm", "kind": "module", "doc": "Test suites for LLMs
\n"}, "validmind.test_suites.llm.PromptValidation": {"fullname": "validmind.test_suites.llm.PromptValidation", "modulename": "validmind.test_suites.llm", "qualname": "PromptValidation", "kind": "class", "doc": "Test suite for prompt validation
\n", "bases": "validmind.vm_models.test_suite.test_suite.TestSuite"}, "validmind.test_suites.llm.LLMClassifierFullSuite": {"fullname": "validmind.test_suites.llm.LLMClassifierFullSuite", "modulename": "validmind.test_suites.llm", "qualname": "LLMClassifierFullSuite", "kind": "class", "doc": "Full test suite for LLM classification models.
\n", "bases": "validmind.vm_models.test_suite.test_suite.TestSuite"}, "validmind.test_suites.nlp": {"fullname": "validmind.test_suites.nlp", "modulename": "validmind.test_suites.nlp", "kind": "module", "doc": "Test suites for NLP models
\n"}, "validmind.test_suites.nlp.NLPClassifierFullSuite": {"fullname": "validmind.test_suites.nlp.NLPClassifierFullSuite", "modulename": "validmind.test_suites.nlp", "qualname": "NLPClassifierFullSuite", "kind": "class", "doc": "Full test suite for NLP classification models.
\n", "bases": "validmind.vm_models.test_suite.test_suite.TestSuite"}, "validmind.test_suites.parameters_optimization": {"fullname": "validmind.test_suites.parameters_optimization", "modulename": "validmind.test_suites.parameters_optimization", "kind": "module", "doc": "Test suites for sklearn-compatible hyper parameters tunning
\n\nIdeal setup is to have the API client to read a\ncustom test suite from the project's configuration
\n"}, "validmind.test_suites.parameters_optimization.KmeansParametersOptimization": {"fullname": "validmind.test_suites.parameters_optimization.KmeansParametersOptimization", "modulename": "validmind.test_suites.parameters_optimization", "qualname": "KmeansParametersOptimization", "kind": "class", "doc": "Test suite for sklearn hyperparameters optimization
\n", "bases": "validmind.vm_models.test_suite.test_suite.TestSuite"}, "validmind.test_suites.regression": {"fullname": "validmind.test_suites.regression", "modulename": "validmind.test_suites.regression", "kind": "module", "doc": "
\n"}, "validmind.test_suites.regression.RegressionMetrics": {"fullname": "validmind.test_suites.regression.RegressionMetrics", "modulename": "validmind.test_suites.regression", "qualname": "RegressionMetrics", "kind": "class", "doc": "Test suite for performance metrics of regression metrics
\n", "bases": "validmind.vm_models.test_suite.test_suite.TestSuite"}, "validmind.test_suites.regression.RegressionPerformance": {"fullname": "validmind.test_suites.regression.RegressionPerformance", "modulename": "validmind.test_suites.regression", "qualname": "RegressionPerformance", "kind": "class", "doc": "Test suite for regression model performance
\n", "bases": "validmind.vm_models.test_suite.test_suite.TestSuite"}, "validmind.test_suites.regression.RegressionFullSuite": {"fullname": "validmind.test_suites.regression.RegressionFullSuite", "modulename": "validmind.test_suites.regression", "qualname": "RegressionFullSuite", "kind": "class", "doc": "Full test suite for regression models.
\n", "bases": "validmind.vm_models.test_suite.test_suite.TestSuite"}, "validmind.test_suites.statsmodels_timeseries": {"fullname": "validmind.test_suites.statsmodels_timeseries", "modulename": "validmind.test_suites.statsmodels_timeseries", "kind": "module", "doc": "Time Series Test Suites from statsmodels
\n"}, "validmind.test_suites.statsmodels_timeseries.RegressionModelDescription": {"fullname": "validmind.test_suites.statsmodels_timeseries.RegressionModelDescription", "modulename": "validmind.test_suites.statsmodels_timeseries", "qualname": "RegressionModelDescription", "kind": "class", "doc": "Test suite for performance metric of regression model of statsmodels library
\n", "bases": "validmind.vm_models.test_suite.test_suite.TestSuite"}, "validmind.test_suites.statsmodels_timeseries.RegressionModelsEvaluation": {"fullname": "validmind.test_suites.statsmodels_timeseries.RegressionModelsEvaluation", "modulename": "validmind.test_suites.statsmodels_timeseries", "qualname": "RegressionModelsEvaluation", "kind": "class", "doc": "Test suite for metrics comparison of regression model of statsmodels library
\n", "bases": "validmind.vm_models.test_suite.test_suite.TestSuite"}, "validmind.test_suites.summarization": {"fullname": "validmind.test_suites.summarization", "modulename": "validmind.test_suites.summarization", "kind": "module", "doc": "Test suites for llm summarization models
\n"}, "validmind.test_suites.summarization.SummarizationMetrics": {"fullname": "validmind.test_suites.summarization.SummarizationMetrics", "modulename": "validmind.test_suites.summarization", "qualname": "SummarizationMetrics", "kind": "class", "doc": "Test suite for Summarization metrics
\n", "bases": "validmind.vm_models.test_suite.test_suite.TestSuite"}, "validmind.test_suites.tabular_datasets": {"fullname": "validmind.test_suites.tabular_datasets", "modulename": "validmind.test_suites.tabular_datasets", "kind": "module", "doc": "Test suites for tabular datasets
\n"}, "validmind.test_suites.tabular_datasets.TabularDatasetDescription": {"fullname": "validmind.test_suites.tabular_datasets.TabularDatasetDescription", "modulename": "validmind.test_suites.tabular_datasets", "qualname": "TabularDatasetDescription", "kind": "class", "doc": "Test suite to extract metadata and descriptive\nstatistics from a tabular dataset
\n", "bases": "validmind.vm_models.test_suite.test_suite.TestSuite"}, "validmind.test_suites.tabular_datasets.TabularDataQuality": {"fullname": "validmind.test_suites.tabular_datasets.TabularDataQuality", "modulename": "validmind.test_suites.tabular_datasets", "qualname": "TabularDataQuality", "kind": "class", "doc": "Test suite for data quality on tabular datasets
\n", "bases": "validmind.vm_models.test_suite.test_suite.TestSuite"}, "validmind.test_suites.tabular_datasets.TabularDataset": {"fullname": "validmind.test_suites.tabular_datasets.TabularDataset", "modulename": "validmind.test_suites.tabular_datasets", "qualname": "TabularDataset", "kind": "class", "doc": "Test suite for tabular datasets.
\n", "bases": "validmind.vm_models.test_suite.test_suite.TestSuite"}, "validmind.test_suites.text_data": {"fullname": "validmind.test_suites.text_data", "modulename": "validmind.test_suites.text_data", "kind": "module", "doc": "Test suites for text datasets
\n"}, "validmind.test_suites.text_data.TextDataQuality": {"fullname": "validmind.test_suites.text_data.TextDataQuality", "modulename": "validmind.test_suites.text_data", "qualname": "TextDataQuality", "kind": "class", "doc": "Test suite for data quality on text data
\n", "bases": "validmind.vm_models.test_suite.test_suite.TestSuite"}, "validmind.test_suites.time_series": {"fullname": "validmind.test_suites.time_series", "modulename": "validmind.test_suites.time_series", "kind": "module", "doc": "Time Series Test Suites
\n"}, "validmind.test_suites.time_series.TimeSeriesDataQuality": {"fullname": "validmind.test_suites.time_series.TimeSeriesDataQuality", "modulename": "validmind.test_suites.time_series", "qualname": "TimeSeriesDataQuality", "kind": "class", "doc": "Test suite for data quality on time series datasets
\n", "bases": "validmind.vm_models.test_suite.test_suite.TestSuite"}, "validmind.test_suites.time_series.TimeSeriesUnivariate": {"fullname": "validmind.test_suites.time_series.TimeSeriesUnivariate", "modulename": "validmind.test_suites.time_series", "qualname": "TimeSeriesUnivariate", "kind": "class", "doc": "This test suite provides a preliminary understanding of the target variable(s)\nused in the time series dataset. It visualizations that present the raw time\nseries data and a histogram of the target variable(s).
\n\nThe raw time series data provides a visual inspection of the target variable's\nbehavior over time. This helps to identify any patterns or trends in the data,\nas well as any potential outliers or anomalies. The histogram of the target\nvariable displays the distribution of values, providing insight into the range\nand frequency of values observed in the data.
\n", "bases": "validmind.vm_models.test_suite.test_suite.TestSuite"}, "validmind.test_suites.time_series.TimeSeriesMultivariate": {"fullname": "validmind.test_suites.time_series.TimeSeriesMultivariate", "modulename": "validmind.test_suites.time_series", "qualname": "TimeSeriesMultivariate", "kind": "class", "doc": "This test suite provides a preliminary understanding of the features\nand relationship in multivariate dataset. It presents various\nmultivariate visualizations that can help identify patterns, trends,\nand relationships between pairs of variables. The visualizations are\ndesigned to explore the relationships between multiple features\nsimultaneously. They allow you to quickly identify any patterns or\ntrends in the data, as well as any potential outliers or anomalies.\nThe individual feature distribution can also be explored to provide\ninsight into the range and frequency of values observed in the data.\nThis multivariate analysis test suite aims to provide an overview of\nthe data structure and guide further exploration and modeling.
\n", "bases": "validmind.vm_models.test_suite.test_suite.TestSuite"}, "validmind.test_suites.time_series.TimeSeriesDataset": {"fullname": "validmind.test_suites.time_series.TimeSeriesDataset", "modulename": "validmind.test_suites.time_series", "qualname": "TimeSeriesDataset", "kind": "class", "doc": "Test suite for time series datasets.
\n", "bases": "validmind.vm_models.test_suite.test_suite.TestSuite"}, "validmind.test_suites.time_series.TimeSeriesModelValidation": {"fullname": "validmind.test_suites.time_series.TimeSeriesModelValidation", "modulename": "validmind.test_suites.time_series", "qualname": "TimeSeriesModelValidation", "kind": "class", "doc": "Test suite for time series model validation.
\n", "bases": "validmind.vm_models.test_suite.test_suite.TestSuite"}, "validmind.tests": {"fullname": "validmind.tests", "modulename": "validmind.tests", "kind": "module", "doc": "ValidMind Tests Module
\n"}, "validmind.tests.list_tests": {"fullname": "validmind.tests.list_tests", "modulename": "validmind.tests", "qualname": "list_tests", "kind": "function", "doc": "List all tests in the tests directory.
\n\nArguments: \n\n\nfilter (str, optional): Find tests where the ID, tasks or tags match the\nfilter string. Defaults to None.task (str, optional): Find tests that match the task. Can be used to\nnarrow down matches from the filter string. Defaults to None.tags (list, optional): Find tests that match list of tags. Can be used to\nnarrow down matches from the filter string. Defaults to None.pretty (bool, optional): If True, returns a pandas DataFrame with a\nformatted table. Defaults to True.truncate (bool, optional): If True, truncates the test description to the first\nline. Defaults to True. (only used if pretty=True) \n\nReturns: \n\n\n list or pandas.DataFrame: A list of all tests or a formatted table.
\n \n", "signature": "(filter = None , task = None , tags = None , pretty = True , truncate = True ): ", "funcdef": "def"}, "validmind.tests.load_test": {"fullname": "validmind.tests.load_test", "modulename": "validmind.tests", "qualname": "load_test", "kind": "function", "doc": "Load a test by test ID
\n\nTest IDs are in the format namespace.path_to_module.TestClassOrFuncName[:tag].\nThe tag is optional and is used to distinguish between multiple results from the\nsame test.
\n\nArguments: \n\n\ntest_id (str): The test ID in the format namespace.path_to_module.TestName[:tag]test_func (callable, optional): The test function to load. If not provided, the\ntest will be loaded from the test provider. Defaults to None. \n", "signature": "(\ttest_id : str , \ttest_func : < built - in function callable > = None , \treload : bool = False ): ", "funcdef": "def"}, "validmind.tests.describe_test": {"fullname": "validmind.tests.describe_test", "modulename": "validmind.tests", "qualname": "describe_test", "kind": "function", "doc": "Get or show details about the test
\n\nThis function can be used to see test details including the test name, description,\nrequired inputs and default params. It can also be used to get a dictionary of the\nabove information for programmatic use.
\n\nArguments: \n\n\ntest_id (str, optional): The test ID. Defaults to None.raw (bool, optional): If True, returns a dictionary with the test details.\nDefaults to False. \n", "signature": "(\ttest_id : Union [ Literal [ 'validmind.data_validation.ACFandPACFPlot' , 'validmind.data_validation.ADF' , 'validmind.data_validation.AutoAR' , 'validmind.data_validation.AutoMA' , 'validmind.data_validation.AutoStationarity' , 'validmind.data_validation.BivariateScatterPlots' , 'validmind.data_validation.BoxPierce' , 'validmind.data_validation.ChiSquaredFeaturesTable' , 'validmind.data_validation.ClassImbalance' , 'validmind.data_validation.DatasetDescription' , 'validmind.data_validation.DatasetSplit' , 'validmind.data_validation.DescriptiveStatistics' , 'validmind.data_validation.DickeyFullerGLS' , 'validmind.data_validation.Duplicates' , 'validmind.data_validation.EngleGrangerCoint' , 'validmind.data_validation.FeatureTargetCorrelationPlot' , 'validmind.data_validation.HighCardinality' , 'validmind.data_validation.HighPearsonCorrelation' , 'validmind.data_validation.IQROutliersBarPlot' , 'validmind.data_validation.IQROutliersTable' , 'validmind.data_validation.IsolationForestOutliers' , 'validmind.data_validation.JarqueBera' , 'validmind.data_validation.KPSS' , 'validmind.data_validation.LJungBox' , 'validmind.data_validation.LaggedCorrelationHeatmap' , 'validmind.data_validation.MissingValues' , 'validmind.data_validation.MissingValuesBarPlot' , 'validmind.data_validation.MutualInformation' , 'validmind.data_validation.PearsonCorrelationMatrix' , 'validmind.data_validation.PhillipsPerronArch' , 'validmind.data_validation.ProtectedClassesCombination' , 'validmind.data_validation.ProtectedClassesDescription' , 'validmind.data_validation.ProtectedClassesDisparity' , 'validmind.data_validation.ProtectedClassesThresholdOptimizer' , 'validmind.data_validation.RollingStatsPlot' , 'validmind.data_validation.RunsTest' , 'validmind.data_validation.ScatterPlot' , 'validmind.data_validation.ScoreBandDefaultRates' , 'validmind.data_validation.SeasonalDecompose' , 'validmind.data_validation.ShapiroWilk' , 'validmind.data_validation.Skewness' , 'validmind.data_validation.SpreadPlot' , 'validmind.data_validation.TabularCategoricalBarPlots' , 'validmind.data_validation.TabularDateTimeHistograms' , 'validmind.data_validation.TabularDescriptionTables' , 'validmind.data_validation.TabularNumericalHistograms' , 'validmind.data_validation.TargetRateBarPlots' , 'validmind.data_validation.TimeSeriesDescription' , 'validmind.data_validation.TimeSeriesDescriptiveStatistics' , 'validmind.data_validation.TimeSeriesFrequency' , 'validmind.data_validation.TimeSeriesHistogram' , 'validmind.data_validation.TimeSeriesLinePlot' , 'validmind.data_validation.TimeSeriesMissingValues' , 'validmind.data_validation.TimeSeriesOutliers' , 'validmind.data_validation.TooManyZeroValues' , 'validmind.data_validation.UniqueRows' , 'validmind.data_validation.WOEBinPlots' , 'validmind.data_validation.WOEBinTable' , 'validmind.data_validation.ZivotAndrewsArch' , 'validmind.data_validation.nlp.CommonWords' , 'validmind.data_validation.nlp.Hashtags' , 'validmind.data_validation.nlp.LanguageDetection' , 'validmind.data_validation.nlp.Mentions' , 'validmind.data_validation.nlp.PolarityAndSubjectivity' , 'validmind.data_validation.nlp.Punctuations' , 'validmind.data_validation.nlp.Sentiment' , 'validmind.data_validation.nlp.StopWords' , 'validmind.data_validation.nlp.TextDescription' , 'validmind.data_validation.nlp.Toxicity' , 'validmind.model_validation.BertScore' , 'validmind.model_validation.BleuScore' , 'validmind.model_validation.ClusterSizeDistribution' , 'validmind.model_validation.ContextualRecall' , 'validmind.model_validation.FeaturesAUC' , 'validmind.model_validation.MeteorScore' , 'validmind.model_validation.ModelMetadata' , 'validmind.model_validation.ModelPredictionResiduals' , 'validmind.model_validation.RegardScore' , 'validmind.model_validation.RegressionResidualsPlot' , 'validmind.model_validation.RougeScore' , 'validmind.model_validation.TimeSeriesPredictionWithCI' , 'validmind.model_validation.TimeSeriesPredictionsPlot' , 'validmind.model_validation.TimeSeriesR2SquareBySegments' , 'validmind.model_validation.TokenDisparity' , 'validmind.model_validation.ToxicityScore' , 'validmind.model_validation.embeddings.ClusterDistribution' , 'validmind.model_validation.embeddings.CosineSimilarityComparison' , 'validmind.model_validation.embeddings.CosineSimilarityDistribution' , 'validmind.model_validation.embeddings.CosineSimilarityHeatmap' , 'validmind.model_validation.embeddings.DescriptiveAnalytics' , 'validmind.model_validation.embeddings.EmbeddingsVisualization2D' , 'validmind.model_validation.embeddings.EuclideanDistanceComparison' , 'validmind.model_validation.embeddings.EuclideanDistanceHeatmap' , 'validmind.model_validation.embeddings.PCAComponentsPairwisePlots' , 'validmind.model_validation.embeddings.StabilityAnalysisKeyword' , 'validmind.model_validation.embeddings.StabilityAnalysisRandomNoise' , 'validmind.model_validation.embeddings.StabilityAnalysisSynonyms' , 'validmind.model_validation.embeddings.StabilityAnalysisTranslation' , 'validmind.model_validation.embeddings.TSNEComponentsPairwisePlots' , 'validmind.model_validation.ragas.AnswerCorrectness' , 'validmind.model_validation.ragas.AspectCritic' , 'validmind.model_validation.ragas.ContextEntityRecall' , 'validmind.model_validation.ragas.ContextPrecision' , 'validmind.model_validation.ragas.ContextPrecisionWithoutReference' , 'validmind.model_validation.ragas.ContextRecall' , 'validmind.model_validation.ragas.Faithfulness' , 'validmind.model_validation.ragas.NoiseSensitivity' , 'validmind.model_validation.ragas.ResponseRelevancy' , 'validmind.model_validation.ragas.SemanticSimilarity' , 'validmind.model_validation.sklearn.AdjustedMutualInformation' , 'validmind.model_validation.sklearn.AdjustedRandIndex' , 'validmind.model_validation.sklearn.CalibrationCurve' , 'validmind.model_validation.sklearn.ClassifierPerformance' , 'validmind.model_validation.sklearn.ClassifierThresholdOptimization' , 'validmind.model_validation.sklearn.ClusterCosineSimilarity' , 'validmind.model_validation.sklearn.ClusterPerformanceMetrics' , 'validmind.model_validation.sklearn.CompletenessScore' , 'validmind.model_validation.sklearn.ConfusionMatrix' , 'validmind.model_validation.sklearn.FeatureImportance' , 'validmind.model_validation.sklearn.FowlkesMallowsScore' , 'validmind.model_validation.sklearn.HomogeneityScore' , 'validmind.model_validation.sklearn.HyperParametersTuning' , 'validmind.model_validation.sklearn.KMeansClustersOptimization' , 'validmind.model_validation.sklearn.MinimumAccuracy' , 'validmind.model_validation.sklearn.MinimumF1Score' , 'validmind.model_validation.sklearn.MinimumROCAUCScore' , 'validmind.model_validation.sklearn.ModelParameters' , 'validmind.model_validation.sklearn.ModelsPerformanceComparison' , 'validmind.model_validation.sklearn.OverfitDiagnosis' , 'validmind.model_validation.sklearn.PermutationFeatureImportance' , 'validmind.model_validation.sklearn.PopulationStabilityIndex' , 'validmind.model_validation.sklearn.PrecisionRecallCurve' , 'validmind.model_validation.sklearn.ROCCurve' , 'validmind.model_validation.sklearn.RegressionErrors' , 'validmind.model_validation.sklearn.RegressionErrorsComparison' , 'validmind.model_validation.sklearn.RegressionPerformance' , 'validmind.model_validation.sklearn.RegressionR2Square' , 'validmind.model_validation.sklearn.RegressionR2SquareComparison' , 'validmind.model_validation.sklearn.RobustnessDiagnosis' , 'validmind.model_validation.sklearn.SHAPGlobalImportance' , 'validmind.model_validation.sklearn.ScoreProbabilityAlignment' , 'validmind.model_validation.sklearn.SilhouettePlot' , 'validmind.model_validation.sklearn.TrainingTestDegradation' , 'validmind.model_validation.sklearn.VMeasure' , 'validmind.model_validation.sklearn.WeakspotsDiagnosis' , 'validmind.model_validation.statsmodels.AutoARIMA' , 'validmind.model_validation.statsmodels.CumulativePredictionProbabilities' , 'validmind.model_validation.statsmodels.DurbinWatsonTest' , 'validmind.model_validation.statsmodels.GINITable' , 'validmind.model_validation.statsmodels.KolmogorovSmirnov' , 'validmind.model_validation.statsmodels.Lilliefors' , 'validmind.model_validation.statsmodels.PredictionProbabilitiesHistogram' , 'validmind.model_validation.statsmodels.RegressionCoeffs' , 'validmind.model_validation.statsmodels.RegressionFeatureSignificance' , 'validmind.model_validation.statsmodels.RegressionModelForecastPlot' , 'validmind.model_validation.statsmodels.RegressionModelForecastPlotLevels' , 'validmind.model_validation.statsmodels.RegressionModelSensitivityPlot' , 'validmind.model_validation.statsmodels.RegressionModelSummary' , 'validmind.model_validation.statsmodels.RegressionPermutationFeatureImportance' , 'validmind.model_validation.statsmodels.ScorecardHistogram' , 'validmind.ongoing_monitoring.CalibrationCurveDrift' , 'validmind.ongoing_monitoring.ClassDiscriminationDrift' , 'validmind.ongoing_monitoring.ClassImbalanceDrift' , 'validmind.ongoing_monitoring.ClassificationAccuracyDrift' , 'validmind.ongoing_monitoring.ConfusionMatrixDrift' , 'validmind.ongoing_monitoring.CumulativePredictionProbabilitiesDrift' , 'validmind.ongoing_monitoring.FeatureDrift' , 'validmind.ongoing_monitoring.PredictionAcrossEachFeature' , 'validmind.ongoing_monitoring.PredictionCorrelation' , 'validmind.ongoing_monitoring.PredictionProbabilitiesHistogramDrift' , 'validmind.ongoing_monitoring.PredictionQuantilesAcrossFeatures' , 'validmind.ongoing_monitoring.ROCCurveDrift' , 'validmind.ongoing_monitoring.ScoreBandsDrift' , 'validmind.ongoing_monitoring.ScorecardHistogramDrift' , 'validmind.ongoing_monitoring.TargetPredictionDistributionPlot' , 'validmind.prompt_validation.Bias' , 'validmind.prompt_validation.Clarity' , 'validmind.prompt_validation.Conciseness' , 'validmind.prompt_validation.Delimitation' , 'validmind.prompt_validation.NegativeInstruction' , 'validmind.prompt_validation.Robustness' , 'validmind.prompt_validation.Specificity' , 'validmind.unit_metrics.classification.Accuracy' , 'validmind.unit_metrics.classification.F1' , 'validmind.unit_metrics.classification.Precision' , 'validmind.unit_metrics.classification.ROC_AUC' , 'validmind.unit_metrics.classification.Recall' , 'validmind.unit_metrics.regression.AdjustedRSquaredScore' , 'validmind.unit_metrics.regression.GiniCoefficient' , 'validmind.unit_metrics.regression.HuberLoss' , 'validmind.unit_metrics.regression.KolmogorovSmirnovStatistic' , 'validmind.unit_metrics.regression.MeanAbsoluteError' , 'validmind.unit_metrics.regression.MeanAbsolutePercentageError' , 'validmind.unit_metrics.regression.MeanBiasDeviation' , 'validmind.unit_metrics.regression.MeanSquaredError' , 'validmind.unit_metrics.regression.QuantileLoss' , 'validmind.unit_metrics.regression.RSquaredScore' , 'validmind.unit_metrics.regression.RootMeanSquaredError' ], str ] = None , \traw : bool = False , \tshow : bool = True ): ", "funcdef": "def"}, "validmind.tests.run_test": {"fullname": "validmind.tests.run_test", "modulename": "validmind.tests", "qualname": "run_test", "kind": "function", "doc": "Run a ValidMind or custom test
\n\nThis function is the main entry point for running tests. It can run simple unit metrics,\nValidMind and custom tests, composite tests made up of multiple unit metrics and comparison\ntests made up of multiple tests.
\n\nArguments: \n\n\ntest_id (TestID, optional): Test ID to run. Not required if name and unit_metrics provided.params (dict, optional): Parameters to customize test behavior. See test details for available parameters.param_grid (Union[Dict[str, List[Any]], List[Dict[str, Any]]], optional): For comparison tests, either:\n\nDict mapping parameter names to lists of values (creates Cartesian product) \nList of parameter dictionaries to test \n inputs (Dict[str, Any], optional): Test inputs (models/datasets initialized with vm.init_model/dataset)input_grid (Union[Dict[str, List[Any]], List[Dict[str, Any]]], optional): For comparison tests, either:\n\nDict mapping input names to lists of values (creates Cartesian product) \nList of input dictionaries to test \n name (str, optional): Test name (required for composite metrics)unit_metrics (list, optional): Unit metric IDs to run as composite metricshow (bool, optional): Whether to display results. Defaults to True.generate_description (bool, optional): Whether to generate a description. Defaults to True.title (str, optional): Custom title for the test resultpost_process_fn (Callable[[TestResult], None], optional): Function to post-process the test result \n\nReturns: \n\n\n TestResult: A TestResult object containing the test results
\n \n\nRaises: \n\n\nValueError: If the test inputs are invalidLoadTestError: If the test class fails to load \n", "signature": "(\ttest_id : Union [ Literal [ 'validmind.data_validation.ACFandPACFPlot' , 'validmind.data_validation.ADF' , 'validmind.data_validation.AutoAR' , 'validmind.data_validation.AutoMA' , 'validmind.data_validation.AutoStationarity' , 'validmind.data_validation.BivariateScatterPlots' , 'validmind.data_validation.BoxPierce' , 'validmind.data_validation.ChiSquaredFeaturesTable' , 'validmind.data_validation.ClassImbalance' , 'validmind.data_validation.DatasetDescription' , 'validmind.data_validation.DatasetSplit' , 'validmind.data_validation.DescriptiveStatistics' , 'validmind.data_validation.DickeyFullerGLS' , 'validmind.data_validation.Duplicates' , 'validmind.data_validation.EngleGrangerCoint' , 'validmind.data_validation.FeatureTargetCorrelationPlot' , 'validmind.data_validation.HighCardinality' , 'validmind.data_validation.HighPearsonCorrelation' , 'validmind.data_validation.IQROutliersBarPlot' , 'validmind.data_validation.IQROutliersTable' , 'validmind.data_validation.IsolationForestOutliers' , 'validmind.data_validation.JarqueBera' , 'validmind.data_validation.KPSS' , 'validmind.data_validation.LJungBox' , 'validmind.data_validation.LaggedCorrelationHeatmap' , 'validmind.data_validation.MissingValues' , 'validmind.data_validation.MissingValuesBarPlot' , 'validmind.data_validation.MutualInformation' , 'validmind.data_validation.PearsonCorrelationMatrix' , 'validmind.data_validation.PhillipsPerronArch' , 'validmind.data_validation.ProtectedClassesCombination' , 'validmind.data_validation.ProtectedClassesDescription' , 'validmind.data_validation.ProtectedClassesDisparity' , 'validmind.data_validation.ProtectedClassesThresholdOptimizer' , 'validmind.data_validation.RollingStatsPlot' , 'validmind.data_validation.RunsTest' , 'validmind.data_validation.ScatterPlot' , 'validmind.data_validation.ScoreBandDefaultRates' , 'validmind.data_validation.SeasonalDecompose' , 'validmind.data_validation.ShapiroWilk' , 'validmind.data_validation.Skewness' , 'validmind.data_validation.SpreadPlot' , 'validmind.data_validation.TabularCategoricalBarPlots' , 'validmind.data_validation.TabularDateTimeHistograms' , 'validmind.data_validation.TabularDescriptionTables' , 'validmind.data_validation.TabularNumericalHistograms' , 'validmind.data_validation.TargetRateBarPlots' , 'validmind.data_validation.TimeSeriesDescription' , 'validmind.data_validation.TimeSeriesDescriptiveStatistics' , 'validmind.data_validation.TimeSeriesFrequency' , 'validmind.data_validation.TimeSeriesHistogram' , 'validmind.data_validation.TimeSeriesLinePlot' , 'validmind.data_validation.TimeSeriesMissingValues' , 'validmind.data_validation.TimeSeriesOutliers' , 'validmind.data_validation.TooManyZeroValues' , 'validmind.data_validation.UniqueRows' , 'validmind.data_validation.WOEBinPlots' , 'validmind.data_validation.WOEBinTable' , 'validmind.data_validation.ZivotAndrewsArch' , 'validmind.data_validation.nlp.CommonWords' , 'validmind.data_validation.nlp.Hashtags' , 'validmind.data_validation.nlp.LanguageDetection' , 'validmind.data_validation.nlp.Mentions' , 'validmind.data_validation.nlp.PolarityAndSubjectivity' , 'validmind.data_validation.nlp.Punctuations' , 'validmind.data_validation.nlp.Sentiment' , 'validmind.data_validation.nlp.StopWords' , 'validmind.data_validation.nlp.TextDescription' , 'validmind.data_validation.nlp.Toxicity' , 'validmind.model_validation.BertScore' , 'validmind.model_validation.BleuScore' , 'validmind.model_validation.ClusterSizeDistribution' , 'validmind.model_validation.ContextualRecall' , 'validmind.model_validation.FeaturesAUC' , 'validmind.model_validation.MeteorScore' , 'validmind.model_validation.ModelMetadata' , 'validmind.model_validation.ModelPredictionResiduals' , 'validmind.model_validation.RegardScore' , 'validmind.model_validation.RegressionResidualsPlot' , 'validmind.model_validation.RougeScore' , 'validmind.model_validation.TimeSeriesPredictionWithCI' , 'validmind.model_validation.TimeSeriesPredictionsPlot' , 'validmind.model_validation.TimeSeriesR2SquareBySegments' , 'validmind.model_validation.TokenDisparity' , 'validmind.model_validation.ToxicityScore' , 'validmind.model_validation.embeddings.ClusterDistribution' , 'validmind.model_validation.embeddings.CosineSimilarityComparison' , 'validmind.model_validation.embeddings.CosineSimilarityDistribution' , 'validmind.model_validation.embeddings.CosineSimilarityHeatmap' , 'validmind.model_validation.embeddings.DescriptiveAnalytics' , 'validmind.model_validation.embeddings.EmbeddingsVisualization2D' , 'validmind.model_validation.embeddings.EuclideanDistanceComparison' , 'validmind.model_validation.embeddings.EuclideanDistanceHeatmap' , 'validmind.model_validation.embeddings.PCAComponentsPairwisePlots' , 'validmind.model_validation.embeddings.StabilityAnalysisKeyword' , 'validmind.model_validation.embeddings.StabilityAnalysisRandomNoise' , 'validmind.model_validation.embeddings.StabilityAnalysisSynonyms' , 'validmind.model_validation.embeddings.StabilityAnalysisTranslation' , 'validmind.model_validation.embeddings.TSNEComponentsPairwisePlots' , 'validmind.model_validation.ragas.AnswerCorrectness' , 'validmind.model_validation.ragas.AspectCritic' , 'validmind.model_validation.ragas.ContextEntityRecall' , 'validmind.model_validation.ragas.ContextPrecision' , 'validmind.model_validation.ragas.ContextPrecisionWithoutReference' , 'validmind.model_validation.ragas.ContextRecall' , 'validmind.model_validation.ragas.Faithfulness' , 'validmind.model_validation.ragas.NoiseSensitivity' , 'validmind.model_validation.ragas.ResponseRelevancy' , 'validmind.model_validation.ragas.SemanticSimilarity' , 'validmind.model_validation.sklearn.AdjustedMutualInformation' , 'validmind.model_validation.sklearn.AdjustedRandIndex' , 'validmind.model_validation.sklearn.CalibrationCurve' , 'validmind.model_validation.sklearn.ClassifierPerformance' , 'validmind.model_validation.sklearn.ClassifierThresholdOptimization' , 'validmind.model_validation.sklearn.ClusterCosineSimilarity' , 'validmind.model_validation.sklearn.ClusterPerformanceMetrics' , 'validmind.model_validation.sklearn.CompletenessScore' , 'validmind.model_validation.sklearn.ConfusionMatrix' , 'validmind.model_validation.sklearn.FeatureImportance' , 'validmind.model_validation.sklearn.FowlkesMallowsScore' , 'validmind.model_validation.sklearn.HomogeneityScore' , 'validmind.model_validation.sklearn.HyperParametersTuning' , 'validmind.model_validation.sklearn.KMeansClustersOptimization' , 'validmind.model_validation.sklearn.MinimumAccuracy' , 'validmind.model_validation.sklearn.MinimumF1Score' , 'validmind.model_validation.sklearn.MinimumROCAUCScore' , 'validmind.model_validation.sklearn.ModelParameters' , 'validmind.model_validation.sklearn.ModelsPerformanceComparison' , 'validmind.model_validation.sklearn.OverfitDiagnosis' , 'validmind.model_validation.sklearn.PermutationFeatureImportance' , 'validmind.model_validation.sklearn.PopulationStabilityIndex' , 'validmind.model_validation.sklearn.PrecisionRecallCurve' , 'validmind.model_validation.sklearn.ROCCurve' , 'validmind.model_validation.sklearn.RegressionErrors' , 'validmind.model_validation.sklearn.RegressionErrorsComparison' , 'validmind.model_validation.sklearn.RegressionPerformance' , 'validmind.model_validation.sklearn.RegressionR2Square' , 'validmind.model_validation.sklearn.RegressionR2SquareComparison' , 'validmind.model_validation.sklearn.RobustnessDiagnosis' , 'validmind.model_validation.sklearn.SHAPGlobalImportance' , 'validmind.model_validation.sklearn.ScoreProbabilityAlignment' , 'validmind.model_validation.sklearn.SilhouettePlot' , 'validmind.model_validation.sklearn.TrainingTestDegradation' , 'validmind.model_validation.sklearn.VMeasure' , 'validmind.model_validation.sklearn.WeakspotsDiagnosis' , 'validmind.model_validation.statsmodels.AutoARIMA' , 'validmind.model_validation.statsmodels.CumulativePredictionProbabilities' , 'validmind.model_validation.statsmodels.DurbinWatsonTest' , 'validmind.model_validation.statsmodels.GINITable' , 'validmind.model_validation.statsmodels.KolmogorovSmirnov' , 'validmind.model_validation.statsmodels.Lilliefors' , 'validmind.model_validation.statsmodels.PredictionProbabilitiesHistogram' , 'validmind.model_validation.statsmodels.RegressionCoeffs' , 'validmind.model_validation.statsmodels.RegressionFeatureSignificance' , 'validmind.model_validation.statsmodels.RegressionModelForecastPlot' , 'validmind.model_validation.statsmodels.RegressionModelForecastPlotLevels' , 'validmind.model_validation.statsmodels.RegressionModelSensitivityPlot' , 'validmind.model_validation.statsmodels.RegressionModelSummary' , 'validmind.model_validation.statsmodels.RegressionPermutationFeatureImportance' , 'validmind.model_validation.statsmodels.ScorecardHistogram' , 'validmind.ongoing_monitoring.CalibrationCurveDrift' , 'validmind.ongoing_monitoring.ClassDiscriminationDrift' , 'validmind.ongoing_monitoring.ClassImbalanceDrift' , 'validmind.ongoing_monitoring.ClassificationAccuracyDrift' , 'validmind.ongoing_monitoring.ConfusionMatrixDrift' , 'validmind.ongoing_monitoring.CumulativePredictionProbabilitiesDrift' , 'validmind.ongoing_monitoring.FeatureDrift' , 'validmind.ongoing_monitoring.PredictionAcrossEachFeature' , 'validmind.ongoing_monitoring.PredictionCorrelation' , 'validmind.ongoing_monitoring.PredictionProbabilitiesHistogramDrift' , 'validmind.ongoing_monitoring.PredictionQuantilesAcrossFeatures' , 'validmind.ongoing_monitoring.ROCCurveDrift' , 'validmind.ongoing_monitoring.ScoreBandsDrift' , 'validmind.ongoing_monitoring.ScorecardHistogramDrift' , 'validmind.ongoing_monitoring.TargetPredictionDistributionPlot' , 'validmind.prompt_validation.Bias' , 'validmind.prompt_validation.Clarity' , 'validmind.prompt_validation.Conciseness' , 'validmind.prompt_validation.Delimitation' , 'validmind.prompt_validation.NegativeInstruction' , 'validmind.prompt_validation.Robustness' , 'validmind.prompt_validation.Specificity' , 'validmind.unit_metrics.classification.Accuracy' , 'validmind.unit_metrics.classification.F1' , 'validmind.unit_metrics.classification.Precision' , 'validmind.unit_metrics.classification.ROC_AUC' , 'validmind.unit_metrics.classification.Recall' , 'validmind.unit_metrics.regression.AdjustedRSquaredScore' , 'validmind.unit_metrics.regression.GiniCoefficient' , 'validmind.unit_metrics.regression.HuberLoss' , 'validmind.unit_metrics.regression.KolmogorovSmirnovStatistic' , 'validmind.unit_metrics.regression.MeanAbsoluteError' , 'validmind.unit_metrics.regression.MeanAbsolutePercentageError' , 'validmind.unit_metrics.regression.MeanBiasDeviation' , 'validmind.unit_metrics.regression.MeanSquaredError' , 'validmind.unit_metrics.regression.QuantileLoss' , 'validmind.unit_metrics.regression.RSquaredScore' , 'validmind.unit_metrics.regression.RootMeanSquaredError' ], str , NoneType ] = None , \tname : Optional [ str ] = None , \tunit_metrics : Optional [ List [ Union [ Literal [ 'validmind.data_validation.ACFandPACFPlot' , 'validmind.data_validation.ADF' , 'validmind.data_validation.AutoAR' , 'validmind.data_validation.AutoMA' , 'validmind.data_validation.AutoStationarity' , 'validmind.data_validation.BivariateScatterPlots' , 'validmind.data_validation.BoxPierce' , 'validmind.data_validation.ChiSquaredFeaturesTable' , 'validmind.data_validation.ClassImbalance' , 'validmind.data_validation.DatasetDescription' , 'validmind.data_validation.DatasetSplit' , 'validmind.data_validation.DescriptiveStatistics' , 'validmind.data_validation.DickeyFullerGLS' , 'validmind.data_validation.Duplicates' , 'validmind.data_validation.EngleGrangerCoint' , 'validmind.data_validation.FeatureTargetCorrelationPlot' , 'validmind.data_validation.HighCardinality' , 'validmind.data_validation.HighPearsonCorrelation' , 'validmind.data_validation.IQROutliersBarPlot' , 'validmind.data_validation.IQROutliersTable' , 'validmind.data_validation.IsolationForestOutliers' , 'validmind.data_validation.JarqueBera' , 'validmind.data_validation.KPSS' , 'validmind.data_validation.LJungBox' , 'validmind.data_validation.LaggedCorrelationHeatmap' , 'validmind.data_validation.MissingValues' , 'validmind.data_validation.MissingValuesBarPlot' , 'validmind.data_validation.MutualInformation' , 'validmind.data_validation.PearsonCorrelationMatrix' , 'validmind.data_validation.PhillipsPerronArch' , 'validmind.data_validation.ProtectedClassesCombination' , 'validmind.data_validation.ProtectedClassesDescription' , 'validmind.data_validation.ProtectedClassesDisparity' , 'validmind.data_validation.ProtectedClassesThresholdOptimizer' , 'validmind.data_validation.RollingStatsPlot' , 'validmind.data_validation.RunsTest' , 'validmind.data_validation.ScatterPlot' , 'validmind.data_validation.ScoreBandDefaultRates' , 'validmind.data_validation.SeasonalDecompose' , 'validmind.data_validation.ShapiroWilk' , 'validmind.data_validation.Skewness' , 'validmind.data_validation.SpreadPlot' , 'validmind.data_validation.TabularCategoricalBarPlots' , 'validmind.data_validation.TabularDateTimeHistograms' , 'validmind.data_validation.TabularDescriptionTables' , 'validmind.data_validation.TabularNumericalHistograms' , 'validmind.data_validation.TargetRateBarPlots' , 'validmind.data_validation.TimeSeriesDescription' , 'validmind.data_validation.TimeSeriesDescriptiveStatistics' , 'validmind.data_validation.TimeSeriesFrequency' , 'validmind.data_validation.TimeSeriesHistogram' , 'validmind.data_validation.TimeSeriesLinePlot' , 'validmind.data_validation.TimeSeriesMissingValues' , 'validmind.data_validation.TimeSeriesOutliers' , 'validmind.data_validation.TooManyZeroValues' , 'validmind.data_validation.UniqueRows' , 'validmind.data_validation.WOEBinPlots' , 'validmind.data_validation.WOEBinTable' , 'validmind.data_validation.ZivotAndrewsArch' , 'validmind.data_validation.nlp.CommonWords' , 'validmind.data_validation.nlp.Hashtags' , 'validmind.data_validation.nlp.LanguageDetection' , 'validmind.data_validation.nlp.Mentions' , 'validmind.data_validation.nlp.PolarityAndSubjectivity' , 'validmind.data_validation.nlp.Punctuations' , 'validmind.data_validation.nlp.Sentiment' , 'validmind.data_validation.nlp.StopWords' , 'validmind.data_validation.nlp.TextDescription' , 'validmind.data_validation.nlp.Toxicity' , 'validmind.model_validation.BertScore' , 'validmind.model_validation.BleuScore' , 'validmind.model_validation.ClusterSizeDistribution' , 'validmind.model_validation.ContextualRecall' , 'validmind.model_validation.FeaturesAUC' , 'validmind.model_validation.MeteorScore' , 'validmind.model_validation.ModelMetadata' , 'validmind.model_validation.ModelPredictionResiduals' , 'validmind.model_validation.RegardScore' , 'validmind.model_validation.RegressionResidualsPlot' , 'validmind.model_validation.RougeScore' , 'validmind.model_validation.TimeSeriesPredictionWithCI' , 'validmind.model_validation.TimeSeriesPredictionsPlot' , 'validmind.model_validation.TimeSeriesR2SquareBySegments' , 'validmind.model_validation.TokenDisparity' , 'validmind.model_validation.ToxicityScore' , 'validmind.model_validation.embeddings.ClusterDistribution' , 'validmind.model_validation.embeddings.CosineSimilarityComparison' , 'validmind.model_validation.embeddings.CosineSimilarityDistribution' , 'validmind.model_validation.embeddings.CosineSimilarityHeatmap' , 'validmind.model_validation.embeddings.DescriptiveAnalytics' , 'validmind.model_validation.embeddings.EmbeddingsVisualization2D' , 'validmind.model_validation.embeddings.EuclideanDistanceComparison' , 'validmind.model_validation.embeddings.EuclideanDistanceHeatmap' , 'validmind.model_validation.embeddings.PCAComponentsPairwisePlots' , 'validmind.model_validation.embeddings.StabilityAnalysisKeyword' , 'validmind.model_validation.embeddings.StabilityAnalysisRandomNoise' , 'validmind.model_validation.embeddings.StabilityAnalysisSynonyms' , 'validmind.model_validation.embeddings.StabilityAnalysisTranslation' , 'validmind.model_validation.embeddings.TSNEComponentsPairwisePlots' , 'validmind.model_validation.ragas.AnswerCorrectness' , 'validmind.model_validation.ragas.AspectCritic' , 'validmind.model_validation.ragas.ContextEntityRecall' , 'validmind.model_validation.ragas.ContextPrecision' , 'validmind.model_validation.ragas.ContextPrecisionWithoutReference' , 'validmind.model_validation.ragas.ContextRecall' , 'validmind.model_validation.ragas.Faithfulness' , 'validmind.model_validation.ragas.NoiseSensitivity' , 'validmind.model_validation.ragas.ResponseRelevancy' , 'validmind.model_validation.ragas.SemanticSimilarity' , 'validmind.model_validation.sklearn.AdjustedMutualInformation' , 'validmind.model_validation.sklearn.AdjustedRandIndex' , 'validmind.model_validation.sklearn.CalibrationCurve' , 'validmind.model_validation.sklearn.ClassifierPerformance' , 'validmind.model_validation.sklearn.ClassifierThresholdOptimization' , 'validmind.model_validation.sklearn.ClusterCosineSimilarity' , 'validmind.model_validation.sklearn.ClusterPerformanceMetrics' , 'validmind.model_validation.sklearn.CompletenessScore' , 'validmind.model_validation.sklearn.ConfusionMatrix' , 'validmind.model_validation.sklearn.FeatureImportance' , 'validmind.model_validation.sklearn.FowlkesMallowsScore' , 'validmind.model_validation.sklearn.HomogeneityScore' , 'validmind.model_validation.sklearn.HyperParametersTuning' , 'validmind.model_validation.sklearn.KMeansClustersOptimization' , 'validmind.model_validation.sklearn.MinimumAccuracy' , 'validmind.model_validation.sklearn.MinimumF1Score' , 'validmind.model_validation.sklearn.MinimumROCAUCScore' , 'validmind.model_validation.sklearn.ModelParameters' , 'validmind.model_validation.sklearn.ModelsPerformanceComparison' , 'validmind.model_validation.sklearn.OverfitDiagnosis' , 'validmind.model_validation.sklearn.PermutationFeatureImportance' , 'validmind.model_validation.sklearn.PopulationStabilityIndex' , 'validmind.model_validation.sklearn.PrecisionRecallCurve' , 'validmind.model_validation.sklearn.ROCCurve' , 'validmind.model_validation.sklearn.RegressionErrors' , 'validmind.model_validation.sklearn.RegressionErrorsComparison' , 'validmind.model_validation.sklearn.RegressionPerformance' , 'validmind.model_validation.sklearn.RegressionR2Square' , 'validmind.model_validation.sklearn.RegressionR2SquareComparison' , 'validmind.model_validation.sklearn.RobustnessDiagnosis' , 'validmind.model_validation.sklearn.SHAPGlobalImportance' , 'validmind.model_validation.sklearn.ScoreProbabilityAlignment' , 'validmind.model_validation.sklearn.SilhouettePlot' , 'validmind.model_validation.sklearn.TrainingTestDegradation' , 'validmind.model_validation.sklearn.VMeasure' , 'validmind.model_validation.sklearn.WeakspotsDiagnosis' , 'validmind.model_validation.statsmodels.AutoARIMA' , 'validmind.model_validation.statsmodels.CumulativePredictionProbabilities' , 'validmind.model_validation.statsmodels.DurbinWatsonTest' , 'validmind.model_validation.statsmodels.GINITable' , 'validmind.model_validation.statsmodels.KolmogorovSmirnov' , 'validmind.model_validation.statsmodels.Lilliefors' , 'validmind.model_validation.statsmodels.PredictionProbabilitiesHistogram' , 'validmind.model_validation.statsmodels.RegressionCoeffs' , 'validmind.model_validation.statsmodels.RegressionFeatureSignificance' , 'validmind.model_validation.statsmodels.RegressionModelForecastPlot' , 'validmind.model_validation.statsmodels.RegressionModelForecastPlotLevels' , 'validmind.model_validation.statsmodels.RegressionModelSensitivityPlot' , 'validmind.model_validation.statsmodels.RegressionModelSummary' , 'validmind.model_validation.statsmodels.RegressionPermutationFeatureImportance' , 'validmind.model_validation.statsmodels.ScorecardHistogram' , 'validmind.ongoing_monitoring.CalibrationCurveDrift' , 'validmind.ongoing_monitoring.ClassDiscriminationDrift' , 'validmind.ongoing_monitoring.ClassImbalanceDrift' , 'validmind.ongoing_monitoring.ClassificationAccuracyDrift' , 'validmind.ongoing_monitoring.ConfusionMatrixDrift' , 'validmind.ongoing_monitoring.CumulativePredictionProbabilitiesDrift' , 'validmind.ongoing_monitoring.FeatureDrift' , 'validmind.ongoing_monitoring.PredictionAcrossEachFeature' , 'validmind.ongoing_monitoring.PredictionCorrelation' , 'validmind.ongoing_monitoring.PredictionProbabilitiesHistogramDrift' , 'validmind.ongoing_monitoring.PredictionQuantilesAcrossFeatures' , 'validmind.ongoing_monitoring.ROCCurveDrift' , 'validmind.ongoing_monitoring.ScoreBandsDrift' , 'validmind.ongoing_monitoring.ScorecardHistogramDrift' , 'validmind.ongoing_monitoring.TargetPredictionDistributionPlot' , 'validmind.prompt_validation.Bias' , 'validmind.prompt_validation.Clarity' , 'validmind.prompt_validation.Conciseness' , 'validmind.prompt_validation.Delimitation' , 'validmind.prompt_validation.NegativeInstruction' , 'validmind.prompt_validation.Robustness' , 'validmind.prompt_validation.Specificity' , 'validmind.unit_metrics.classification.Accuracy' , 'validmind.unit_metrics.classification.F1' , 'validmind.unit_metrics.classification.Precision' , 'validmind.unit_metrics.classification.ROC_AUC' , 'validmind.unit_metrics.classification.Recall' , 'validmind.unit_metrics.regression.AdjustedRSquaredScore' , 'validmind.unit_metrics.regression.GiniCoefficient' , 'validmind.unit_metrics.regression.HuberLoss' , 'validmind.unit_metrics.regression.KolmogorovSmirnovStatistic' , 'validmind.unit_metrics.regression.MeanAbsoluteError' , 'validmind.unit_metrics.regression.MeanAbsolutePercentageError' , 'validmind.unit_metrics.regression.MeanBiasDeviation' , 'validmind.unit_metrics.regression.MeanSquaredError' , 'validmind.unit_metrics.regression.QuantileLoss' , 'validmind.unit_metrics.regression.RSquaredScore' , 'validmind.unit_metrics.regression.RootMeanSquaredError' ], str ]]] = None , \tinputs : Optional [ Dict [ str , Any ]] = None , \tinput_grid : Union [ Dict [ str , List [ Any ]], List [ Dict [ str , Any ]], NoneType ] = None , \tparams : Optional [ Dict [ str , Any ]] = None , \tparam_grid : Union [ Dict [ str , List [ Any ]], List [ Dict [ str , Any ]], NoneType ] = None , \tshow : bool = True , \tgenerate_description : bool = True , \ttitle : Optional [ str ] = None , \tpost_process_fn : Optional [ Callable [[ validmind . vm_models . result . result . TestResult ], NoneType ]] = None , \t** kwargs ) -> validmind . vm_models . result . result . TestResult : ", "funcdef": "def"}, "validmind.tests.register_test_provider": {"fullname": "validmind.tests.register_test_provider", "modulename": "validmind.tests", "qualname": "register_test_provider", "kind": "function", "doc": "Register an external test provider
\n\nArguments: \n\n\nnamespace (str): The namespace of the test providertest_provider (TestProvider): The test provider \n", "signature": "(\tnamespace : str , \ttest_provider : validmind . tests . test_providers . TestProvider ) -> None : ", "funcdef": "def"}, "validmind.tests.LoadTestError": {"fullname": "validmind.tests.LoadTestError", "modulename": "validmind.tests", "qualname": "LoadTestError", "kind": "class", "doc": "Exception raised when an error occurs while loading a test
\n", "bases": "validmind.errors.BaseError"}, "validmind.tests.LoadTestError.__init__": {"fullname": "validmind.tests.LoadTestError.__init__", "modulename": "validmind.tests", "qualname": "LoadTestError.__init__", "kind": "function", "doc": "
\n", "signature": "(message : str , original_error : Optional [ Exception ] = None "}, "validmind.tests.LocalTestProvider": {"fullname": "validmind.tests.LocalTestProvider", "modulename": "validmind.tests", "qualname": "LocalTestProvider", "kind": "class", "doc": "Test providers in ValidMind are responsible for loading tests from different sources,\nsuch as local files, databases, or remote services. The LocalTestProvider specifically\nloads tests from the local file system.
\n\nTo use the LocalTestProvider, you need to provide the root_folder, which is the\nroot directory for local tests. The test_id is a combination of the namespace (set\nwhen registering the test provider) and the path to the test class module, where\nslashes are replaced by dots and the .py extension is left out.
\n\nExample usage:
\n\n# Create an instance of LocalTestProvider with the root folder\ntest_provider = LocalTestProvider(\"/path/to/tests/folder\")\n\n# Register the test provider with a namespace\nregister_test_provider(\"my_namespace\", test_provider)\n\n# List all tests in the namespace (returns a list of test IDs)\ntest_provider.list_tests()\n# this is used by the validmind.tests.list_tests() function to aggregate all tests\n# from all test providers\n\n# Load a test using the test_id (namespace + path to test class module)\ntest = test_provider.load_test(\"my_namespace.my_test_class\")\n# full path to the test class module is /path/to/tests/folder/my_test_class.py\nAttributes: \n\n\nroot_folder (str): The root directory for local tests. \n"}, "validmind.tests.LocalTestProvider.__init__": {"fullname": "validmind.tests.LocalTestProvider.__init__", "modulename": "validmind.tests", "qualname": "LocalTestProvider.__init__", "kind": "function", "doc": "Initialize the LocalTestProvider with the given root_folder\n(see class docstring for details)
\n\nArguments: \n\n\nroot_folder (str): The root directory for local tests. \n", "signature": "(root_folder : str "}, "validmind.tests.LocalTestProvider.list_tests": {"fullname": "validmind.tests.LocalTestProvider.list_tests", "modulename": "validmind.tests", "qualname": "LocalTestProvider.list_tests", "kind": "function", "doc": "List all tests in the given namespace
\n\nReturns: \n\n\n list: A list of test IDs
\n \n", "signature": "(self ): ", "funcdef": "def"}, "validmind.tests.LocalTestProvider.load_test": {"fullname": "validmind.tests.LocalTestProvider.load_test", "modulename": "validmind.tests", "qualname": "LocalTestProvider.load_test", "kind": "function", "doc": "Load the test identified by the given test_id.
\n\nArguments: \n\n\ntest_id (str): The identifier of the test. This corresponds to the relativepath of the python file from the root folder, with slashes replaced by dots \n \n\nReturns: \n\n\n The test class that matches the last part of the test_id.
\n \n\nRaises: \n\n\nLocalTestProviderLoadModuleError: If the test module cannot be importedLocalTestProviderLoadTestError: If the test class cannot be found in the module \n", "signature": "(self , test_id : str ): ", "funcdef": "def"}, "validmind.tests.TestProvider": {"fullname": "validmind.tests.TestProvider", "modulename": "validmind.tests", "qualname": "TestProvider", "kind": "class", "doc": "Protocol for user-defined test providers
\n", "bases": "typing.Protocol"}, "validmind.tests.TestProvider.__init__": {"fullname": "validmind.tests.TestProvider.__init__", "modulename": "validmind.tests", "qualname": "TestProvider.__init__", "kind": "function", "doc": "
\n", "signature": "(* args , ** kwargs "}, "validmind.tests.TestProvider.list_tests": {"fullname": "validmind.tests.TestProvider.list_tests", "modulename": "validmind.tests", "qualname": "TestProvider.list_tests", "kind": "function", "doc": "List all tests in the given namespace
\n\nReturns: \n\n\n list: A list of test IDs
\n \n", "signature": "(self ) -> List [ str ] : ", "funcdef": "def"}, "validmind.tests.TestProvider.load_test": {"fullname": "validmind.tests.TestProvider.load_test", "modulename": "validmind.tests", "qualname": "TestProvider.load_test", "kind": "function", "doc": "Load the test function identified by the given test_id
\n\nArguments: \n\n\ntest_id (str): The test ID (does not contain the namespace under which\nthe test is registered) \n\nReturns: \n\n\n callable: The test function
\n \n\nRaises: \n\n\nFileNotFoundError: If the test is not found \n", "signature": "(self , test_id : str ) -> < built - in function callable > : ", "funcdef": "def"}, "validmind.tests.list_tags": {"fullname": "validmind.tests.list_tags", "modulename": "validmind.tests", "qualname": "list_tags", "kind": "function", "doc": "List unique tags from all test classes.
\n", "signature": "(): ", "funcdef": "def"}, "validmind.tests.list_tasks": {"fullname": "validmind.tests.list_tasks", "modulename": "validmind.tests", "qualname": "list_tasks", "kind": "function", "doc": "List unique tasks from all test classes.
\n", "signature": "(): ", "funcdef": "def"}, "validmind.tests.list_tasks_and_tags": {"fullname": "validmind.tests.list_tasks_and_tags", "modulename": "validmind.tests", "qualname": "list_tasks_and_tags", "kind": "function", "doc": "List all task types and their associated tags, with one row per task type and\nall tags for a task type in one row.
\n\nReturns: \n\n\n pandas.DataFrame: A DataFrame with 'Task Type' and concatenated 'Tags'.
\n \n", "signature": "(as_json = False ): ", "funcdef": "def"}, "validmind.tests.test": {"fullname": "validmind.tests.test", "modulename": "validmind.tests", "qualname": "test", "kind": "function", "doc": "Decorator for creating and registering custom tests
\n\nThis decorator registers the function it wraps as a test function within ValidMind\nunder the provided ID. Once decorated, the function can be run using the\nvalidmind.tests.run_test function.
\n\nThe function can take two different types of arguments:
\n\n\nInputs: ValidMind model or dataset (or list of models/datasets). These arguments\nmust use the following names: model, models, dataset, datasets. \nParameters: Any additional keyword arguments of any type (must have a default\nvalue) that can have any name. \n \n\nThe function should return one of the following types:
\n\n\nTable: Either a list of dictionaries or a pandas DataFrame \nPlot: Either a matplotlib figure or a plotly figure \nScalar: A single number (int or float) \nBoolean: A single boolean value indicating whether the test passed or failed \n \n\nThe function may also include a docstring. This docstring will be used and logged\nas the metric's description.
\n\nArguments: \n\n\nfunc: The function to decoratetest_id: The identifier for the metric. If not provided, the function name is used. \n\nReturns: \n\n\n The decorated function.
\n \n", "signature": "(func_or_id ): ", "funcdef": "def"}, "validmind.tests.tags": {"fullname": "validmind.tests.tags", "modulename": "validmind.tests", "qualname": "tags", "kind": "function", "doc": "Decorator for specifying tags for a test.
\n\nArguments: \n\n\n*tags: The tags to apply to the test. \n", "signature": "(* tags ): ", "funcdef": "def"}, "validmind.tests.tasks": {"fullname": "validmind.tests.tasks", "modulename": "validmind.tests", "qualname": "tasks", "kind": "function", "doc": "Decorator for specifying the task types that a test is designed for.
\n\nArguments: \n\n\n*tasks: The task types that the test is designed for. \n", "signature": "(* tasks ): ", "funcdef": "def"}, "validmind.tests.data_validation": {"fullname": "validmind.tests.data_validation", "modulename": "validmind.tests.data_validation", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.ACFandPACFPlot": {"fullname": "validmind.tests.data_validation.ACFandPACFPlot", "modulename": "validmind.tests.data_validation.ACFandPACFPlot", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.ACFandPACFPlot.ACFandPACFPlot": {"fullname": "validmind.tests.data_validation.ACFandPACFPlot.ACFandPACFPlot", "modulename": "validmind.tests.data_validation.ACFandPACFPlot", "qualname": "ACFandPACFPlot", "kind": "function", "doc": "Analyzes time series data using Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots to\nreveal trends and correlations.
\n\nPurpose \n\nThe ACF (Autocorrelation Function) and PACF (Partial Autocorrelation Function) plot test is employed to analyze\ntime series data in machine learning models. It illuminates the correlation of the data over time by plotting the\ncorrelation of the series with its own lags (ACF), and the correlations after removing effects already accounted\nfor by earlier lags (PACF). This information can identify trends, such as seasonality, degrees of autocorrelation,\nand inform the selection of order parameters for AutoRegressive Integrated Moving Average (ARIMA) models.
\n\nTest Mechanism \n\nThe ACFandPACFPlot test accepts a dataset with a time-based index. It first confirms the index is of a datetime\ntype, then handles any NaN values. The test subsequently generates ACF and PACF plots for each column in the\ndataset, producing a subplot for each. If the dataset doesn't include key columns, an error is returned.
\n\nSigns of High Risk \n\n\nSudden drops in the correlation at a specific lag might signal a model at high risk. \nConsistent high correlation across multiple lags could also indicate non-stationarity in the data, which may\nsuggest that a model estimated on this data won't generalize well to future, unknown data. \n \n\nStrengths \n\n\nACF and PACF plots offer clear graphical representations of the correlations in time series data. \nThese plots are effective at revealing important data characteristics such as seasonality, trends, and\ncorrelation patterns. \nThe insights from these plots aid in better model configuration, particularly in the selection of ARIMA model\nparameters. \n \n\nLimitations \n\n\nACF and PACF plots are exclusively for time series data and hence, can't be applied to all ML models. \nThese plots require large, consistent datasets as gaps could lead to misleading results. \nThe plots can only represent linear correlations and fail to capture any non-linear relationships within the data. \nThe plots might be difficult for non-experts to interpret and should not replace more advanced analyses. \n \n", "signature": "(dataset : validmind . vm_models . dataset . dataset . VMDataset ): ", "funcdef": "def"}, "validmind.tests.data_validation.ADF": {"fullname": "validmind.tests.data_validation.ADF", "modulename": "validmind.tests.data_validation.ADF", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.ADF.ADF": {"fullname": "validmind.tests.data_validation.ADF.ADF", "modulename": "validmind.tests.data_validation.ADF", "qualname": "ADF", "kind": "function", "doc": "Assesses the stationarity of a time series dataset using the Augmented Dickey-Fuller (ADF) test.
\n\nPurpose \n\nThe Augmented Dickey-Fuller (ADF) test metric is used to determine the order of integration, i.e., the stationarity\nof a given time series dataset. The stationary property of data is pivotal in many machine learning models as it\nimpacts the reliability and effectiveness of predictions and forecasts.
\n\nTest Mechanism \n\nThe ADF test is executed using the adfuller function from the statsmodels library on each feature of the\ndataset. Multiple outputs are generated for each run, including the ADF test statistic and p-value, count of lags\nused, the number of observations considered in the test, critical values at various confidence levels, and the\ninformation criterion. These results are stored for each feature for subsequent analysis.
\n\nSigns of High Risk \n\n\nAn inflated ADF statistic and high p-value (generally above 0.05) indicate a high risk to the model's performance\ndue to the presence of a unit root indicating non-stationarity. \nNon-stationarity might result in untrustworthy or insufficient forecasts. \n \n\nStrengths \n\n\nThe ADF test is robust to sophisticated correlations within the data, making it suitable for settings where data\ndisplays complex stochastic behavior. \nIt provides explicit outputs like test statistics, critical values, and information criterion, enhancing\nunderstanding and transparency in the model validation process. \n \n\nLimitations \n\n\nThe ADF test might demonstrate low statistical power, making it challenging to differentiate between a unit root\nand near-unit-root processes, potentially causing false negatives. \nIt assumes the data follows an autoregressive process, which might not always be the case. \nThe test struggles with time series data that have structural breaks. \n \n", "signature": "(dataset : validmind . vm_models . dataset . dataset . VMDataset ): ", "funcdef": "def"}, "validmind.tests.data_validation.AutoAR": {"fullname": "validmind.tests.data_validation.AutoAR", "modulename": "validmind.tests.data_validation.AutoAR", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.AutoAR.AutoAR": {"fullname": "validmind.tests.data_validation.AutoAR.AutoAR", "modulename": "validmind.tests.data_validation.AutoAR", "qualname": "AutoAR", "kind": "function", "doc": "Automatically identifies the optimal Autoregressive (AR) order for a time series using BIC and AIC criteria.
\n\nPurpose \n\nThe AutoAR test is intended to automatically identify the Autoregressive (AR) order of a time series by utilizing\nthe Bayesian Information Criterion (BIC) and Akaike Information Criterion (AIC). AR order is crucial in forecasting\ntasks as it dictates the quantity of prior terms in the sequence to use for predicting the current term. The\nobjective is to select the most fitting AR model that encapsulates the trend and seasonality in the time series\ndata.
\n\nTest Mechanism \n\nThe test mechanism operates by iterating through a possible range of AR orders up to a defined maximum. An AR model\nis fitted for each order, and the corresponding BIC and AIC are computed. BIC and AIC statistical measures are\ndesigned to penalize models for complexity, preferring simpler models that fit the data proficiently. To verify the\nstationarity of the time series, the Augmented Dickey-Fuller test is executed. The AR order, BIC, and AIC findings\nare compiled into a dataframe for effortless comparison. Then, the AR order with the smallest BIC is established as\nthe desirable order for each variable.
\n\nSigns of High Risk \n\n\nAn augmented Dickey Fuller test p-value > 0.05, indicating the time series isn't stationary, may lead to\ninaccurate results. \nProblems with the model fitting procedure, such as computational or convergence issues. \nContinuous selection of the maximum specified AR order may suggest an insufficient set limit. \n \n\nStrengths \n\n\nThe test independently pinpoints the optimal AR order, thereby reducing potential human bias. \nIt strikes a balance between model simplicity and goodness-of-fit to avoid overfitting. \nHas the capability to account for stationarity in a time series, an essential aspect for dependable AR modeling. \nThe results are aggregated into a comprehensive table, enabling an easy interpretation. \n \n\nLimitations \n\n\nThe tests need a stationary time series input. \nThey presume a linear relationship between the series and its lags. \nThe search for the best model is constrained by the maximum AR order supplied in the parameters. Therefore, a low\nmax_ar_order could result in subpar outcomes. \nAIC and BIC may not always agree on the selection of the best model. This potentially requires the user to juggle\ninterpretational choices. \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tmax_ar_order : int = 3 ): ", "funcdef": "def"}, "validmind.tests.data_validation.AutoMA": {"fullname": "validmind.tests.data_validation.AutoMA", "modulename": "validmind.tests.data_validation.AutoMA", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.AutoMA.AutoMA": {"fullname": "validmind.tests.data_validation.AutoMA.AutoMA", "modulename": "validmind.tests.data_validation.AutoMA", "qualname": "AutoMA", "kind": "function", "doc": "Automatically selects the optimal Moving Average (MA) order for each variable in a time series dataset based on\nminimal BIC and AIC values.
\n\nPurpose \n\nThe AutoMA metric serves an essential role of automated decision-making for selecting the optimal Moving Average\n(MA) order for every variable in a given time series dataset. The selection is dependent on the minimalization of\nBIC (Bayesian Information Criterion) and AIC (Akaike Information Criterion); these are established statistical\ntools used for model selection. Furthermore, prior to the commencement of the model fitting process, the algorithm\nconducts a stationarity test (Augmented Dickey-Fuller test) on each series.
\n\nTest Mechanism \n\nStarting off, the AutoMA algorithm checks whether the max_ma_order parameter has been provided. It consequently\nloops through all variables in the dataset, carrying out the Dickey-Fuller test for stationarity. For each\nstationary variable, it fits an ARIMA model for orders running from 0 to max_ma_order. The result is a list\nshowcasing the BIC and AIC values of the ARIMA models based on different orders. The MA order, which yields the\nsmallest BIC, is chosen as the 'best MA order' for every single variable. The final results include a table\nsummarizing the auto MA analysis and another table listing the best MA order for each variable.
\n\nSigns of High Risk \n\n\nWhen a series is non-stationary (p-value>0.05 in the Dickey-Fuller test), the produced result could be inaccurate. \nAny error that arises in the process of fitting the ARIMA models, especially with a higher MA order, can\npotentially indicate risks and might need further investigation. \n \n\nStrengths \n\n\nThe metric facilitates automation in the process of selecting the MA order for time series forecasting. This\nsignificantly saves time and reduces efforts conventionally necessary for manual hyperparameter tuning. \nThe use of both BIC and AIC enhances the likelihood of selecting the most suitable model. \nThe metric ascertains the stationarity of the series prior to model fitting, thus ensuring that the underlying\nassumptions of the MA model are fulfilled. \n \n\nLimitations \n\n\nIf the time series fails to be stationary, the metric may yield inaccurate results. Consequently, it necessitates\npre-processing steps to stabilize the series before fitting the ARIMA model. \nThe metric adopts a rudimentary model selection process based on BIC and doesn't consider other potential model\nselection strategies. Depending on the specific dataset, other strategies could be more appropriate. \nThe 'max_ma_order' parameter must be manually input which doesn't always guarantee optimal performance,\nespecially when configured too low. \nThe computation time increases with the rise in max_ma_order, hence, the metric may become computationally\ncostly for larger values. \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tmax_ma_order : int = 3 ): ", "funcdef": "def"}, "validmind.tests.data_validation.AutoStationarity": {"fullname": "validmind.tests.data_validation.AutoStationarity", "modulename": "validmind.tests.data_validation.AutoStationarity", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.AutoStationarity.AutoStationarity": {"fullname": "validmind.tests.data_validation.AutoStationarity.AutoStationarity", "modulename": "validmind.tests.data_validation.AutoStationarity", "qualname": "AutoStationarity", "kind": "function", "doc": "Automates Augmented Dickey-Fuller test to assess stationarity across multiple time series in a DataFrame.
\n\nPurpose \n\nThe AutoStationarity metric is intended to automatically detect and evaluate the stationary nature of each time\nseries in a DataFrame. It incorporates the Augmented Dickey-Fuller (ADF) test, a statistical approach used to\nassess stationarity. Stationarity is a fundamental property suggesting that statistic features like mean and\nvariance remain unchanged over time. This is necessary for many time-series models.
\n\nTest Mechanism \n\nThe mechanism for the AutoStationarity test involves applying the Augmented Dicky-Fuller test to each time series\nwithin the given dataframe to assess if they are stationary. Every series in the dataframe is looped, using the ADF\ntest up to a defined maximum order (configurable and by default set to 5). The p-value resulting from the ADF test\nis compared against a predetermined threshold (also configurable and by default set to 0.05). The time series is\ndeemed stationary at its current differencing order if the p-value is less than the threshold.
\n\nSigns of High Risk \n\n\nA significant number of series not achieving stationarity even at the maximum order of differencing can indicate\nhigh risk or potential failure in the model. \nThis could suggest the series may not be appropriately modeled by a stationary process, hence other modeling\napproaches might be required. \n \n\nStrengths \n\n\nThe key strength in this metric lies in the automation of the ADF test, enabling mass stationarity analysis\nacross various time series and boosting the efficiency and credibility of the analysis. \nThe utilization of the ADF test, a widely accepted method for testing stationarity, lends authenticity to the\nresults derived. \nThe introduction of the max order and threshold parameters give users the autonomy to determine their preferred\nlevels of stringency in the tests. \n \n\nLimitations \n\n\nThe Augmented Dickey-Fuller test and the stationarity test are not without their limitations. These tests are\npremised on the assumption that the series can be modeled by an autoregressive process, which may not always hold\ntrue. \nThe stationarity check is highly sensitive to the choice of threshold for the significance level; an extremely\nhigh or low threshold could lead to incorrect results regarding the stationarity properties. \nThere's also a risk of over-differencing if the maximum order is set too high, which could induce unnecessary\ncycles. \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tmax_order : int = 5 , \tthreshold : float = 0.05 ): ", "funcdef": "def"}, "validmind.tests.data_validation.BivariateScatterPlots": {"fullname": "validmind.tests.data_validation.BivariateScatterPlots", "modulename": "validmind.tests.data_validation.BivariateScatterPlots", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.BivariateScatterPlots.BivariateScatterPlots": {"fullname": "validmind.tests.data_validation.BivariateScatterPlots.BivariateScatterPlots", "modulename": "validmind.tests.data_validation.BivariateScatterPlots", "qualname": "BivariateScatterPlots", "kind": "function", "doc": "Generates bivariate scatterplots to visually inspect relationships between pairs of numerical predictor variables\nin machine learning classification tasks.
\n\nPurpose \n\nThis function is intended for visual inspection and monitoring of relationships between pairs of numerical\nvariables in a machine learning model targeting classification tasks. It helps in understanding how predictor\nvariables (features) interact with each other, which can inform feature selection, model-building strategies, and\nidentify potential biases or irregularities in the data.
\n\nTest Mechanism \n\nThe function creates scatter plots for each pair of numerical features in the dataset. It first filters out\nnon-numerical and binary features, ensuring the plots focus on meaningful numerical relationships. The resulting\nscatterplots are color-coded uniformly to avoid visual distraction, and the function returns a tuple of Plotly\nfigure objects, each representing a scatter plot for a pair of features.
\n\nSigns of High Risk \n\n\nVisual patterns suggesting non-linear relationships, multicollinearity, clustering, or outlier points in the\nscatter plots. \nSuch issues could affect the assumptions and performance of certain models, especially those assuming linearity,\nlike logistic regression. \n \n\nStrengths \n\n\nScatterplots provide an intuitive and visual tool to explore relationships between two variables. \nThey are useful for identifying outliers, variable associations, and trends, including non-linear patterns. \nSupports visualization of binary or multi-class classification datasets, focusing on numerical features. \n \n\nLimitations \n\n\nScatterplots are limited to bivariate analysis, showing relationships between only two variables at a time. \nNot ideal for very large datasets where overlapping points can reduce the clarity of the visualization. \nScatterplots are exploratory tools and do not provide quantitative measures of model quality or performance. \nInterpretation is subjective and relies on the domain knowledge and judgment of the viewer. \n \n", "signature": "(dataset ): ", "funcdef": "def"}, "validmind.tests.data_validation.BoxPierce": {"fullname": "validmind.tests.data_validation.BoxPierce", "modulename": "validmind.tests.data_validation.BoxPierce", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.BoxPierce.BoxPierce": {"fullname": "validmind.tests.data_validation.BoxPierce.BoxPierce", "modulename": "validmind.tests.data_validation.BoxPierce", "qualname": "BoxPierce", "kind": "function", "doc": "Detects autocorrelation in time-series data through the Box-Pierce test to validate model performance.
\n\nPurpose \n\nThe Box-Pierce test is utilized to detect the presence of autocorrelation in a time-series dataset.\nAutocorrelation, or serial correlation, refers to the degree of similarity between observations based on the\ntemporal spacing between them. This test is essential for affirming the quality of a time-series model by ensuring\nthat the error terms in the model are random and do not adhere to a specific pattern.
\n\nTest Mechanism \n\nThe implementation of the Box-Pierce test involves calculating a test statistic along with a corresponding p-value\nderived from the dataset features. These quantities are used to test the null hypothesis that posits the data to be\nindependently distributed. This is achieved by iterating over every feature column in the time-series data and\napplying the acorr_ljungbox function of the statsmodels library. The function yields the Box-Pierce test\nstatistic as well as the respective p-value, all of which are cached as test results.
\n\nSigns of High Risk \n\n\nA low p-value, typically under 0.05 as per statistical convention, throws the null hypothesis of independence\ninto question. This implies that the dataset potentially houses autocorrelations, thus indicating a high-risk\nscenario concerning model performance. \nLarge Box-Pierce test statistic values may indicate the presence of autocorrelation. \n \n\nStrengths \n\n\nDetects patterns in data that are supposed to be random, thereby ensuring no underlying autocorrelation. \nCan be computed efficiently given its low computational complexity. \nCan be widely applied to most regression problems, making it very versatile. \n \n\nLimitations \n\n\nAssumes homoscedasticity (constant variance) and normality of residuals, which may not always be the case in\nreal-world datasets. \nMay exhibit reduced power for detecting complex autocorrelation schemes such as higher-order or negative\ncorrelations. \nIt only provides a general indication of the existence of autocorrelation, without providing specific insights\ninto the nature or patterns of the detected autocorrelation. \nIn the presence of trends or seasonal patterns, the Box-Pierce test may yield misleading results. \nApplicability is limited to time-series data, which limits its overall utility. \n \n", "signature": "(dataset ): ", "funcdef": "def"}, "validmind.tests.data_validation.ChiSquaredFeaturesTable": {"fullname": "validmind.tests.data_validation.ChiSquaredFeaturesTable", "modulename": "validmind.tests.data_validation.ChiSquaredFeaturesTable", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.ChiSquaredFeaturesTable.ChiSquaredFeaturesTable": {"fullname": "validmind.tests.data_validation.ChiSquaredFeaturesTable.ChiSquaredFeaturesTable", "modulename": "validmind.tests.data_validation.ChiSquaredFeaturesTable", "qualname": "ChiSquaredFeaturesTable", "kind": "function", "doc": "Assesses the statistical association between categorical features and a target variable using the Chi-Squared test.
\n\nPurpose \n\nThe ChiSquaredFeaturesTable function is designed to evaluate the relationship between categorical features and a\ntarget variable in a dataset. It performs a Chi-Squared test of independence for each categorical feature to\ndetermine whether a statistically significant association exists with the target variable. This is particularly\nuseful in Model Risk Management for understanding the relevance of features and identifying potential biases in a\nclassification model.
\n\nTest Mechanism \n\nThe function creates a contingency table for each categorical feature and the target variable, then applies the\nChi-Squared test to compute the Chi-squared statistic and the p-value. The results for each feature include the\nvariable name, Chi-squared statistic, p-value, p-value threshold, and a pass/fail status based on whether the\np-value is below the specified threshold. The output is a DataFrame summarizing these results, sorted by p-value to\nhighlight the most statistically significant associations.
\n\nSigns of High Risk \n\n\nHigh p-values (greater than the set threshold) indicate a lack of significant association between a feature and\nthe target variable, resulting in a 'Fail' status. \nFeatures with a 'Fail' status might not be relevant for the model, which could negatively impact model\nperformance. \n \n\nStrengths \n\n\nProvides a clear, statistical assessment of the relationship between categorical features and the target variable. \nProduces an easily interpretable summary with a 'Pass/Fail' outcome for each feature, helping in feature\nselection. \nThe p-value threshold is adjustable, allowing for flexibility in statistical rigor. \n \n\nLimitations \n\n\nAssumes the dataset is tabular and consists of categorical variables, which may not be suitable for all datasets. \nThe test is designed for classification tasks and is not applicable to regression problems. \nAs with all hypothesis tests, the Chi-Squared test can only detect associations, not causal relationships. \nThe choice of p-value threshold can affect the interpretation of feature relevance, and different thresholds may\nlead to different conclusions. \n \n", "signature": "(dataset , p_threshold = 0.05 ): ", "funcdef": "def"}, "validmind.tests.data_validation.ClassImbalance": {"fullname": "validmind.tests.data_validation.ClassImbalance", "modulename": "validmind.tests.data_validation.ClassImbalance", "kind": "module", "doc": "Threshold based tests
\n"}, "validmind.tests.data_validation.ClassImbalance.ClassImbalance": {"fullname": "validmind.tests.data_validation.ClassImbalance.ClassImbalance", "modulename": "validmind.tests.data_validation.ClassImbalance", "qualname": "ClassImbalance", "kind": "function", "doc": "Evaluates and quantifies class distribution imbalance in a dataset used by a machine learning model.
\n\nPurpose \n\nThe Class Imbalance test is designed to evaluate the distribution of target classes in a dataset that's utilized by\na machine learning model. Specifically, it aims to ensure that the classes aren't overly skewed, which could lead\nto bias in the model's predictions. It's crucial to have a balanced training dataset to avoid creating a model\nthat's biased with high accuracy for the majority class and low accuracy for the minority class.
\n\nTest Mechanism \n\nThis Class Imbalance test operates by calculating the frequency (expressed as a percentage) of each class in the\ntarget column of the dataset. It then checks whether each class appears in at least a set minimum percentage of the\ntotal records. This minimum percentage is a modifiable parameter, but the default value is set to 10%.
\n\nSigns of High Risk \n\n\nAny class that represents less than the pre-set minimum percentage threshold is marked as high risk, implying a\npotential class imbalance. \nThe function provides a pass/fail outcome for each class based on this criterion. \nFundamentally, if any class fails this test, it's highly likely that the dataset possesses imbalanced class\ndistribution. \n \n\nStrengths \n\n\nThe test can spot under-represented classes that could affect the efficiency of a machine learning model. \nThe calculation is straightforward and swift. \nThe test is highly informative because it not only spots imbalance, but it also quantifies the degree of\nimbalance. \nThe adjustable threshold enables flexibility and adaptation to differing use-cases or domain-specific needs. \nThe test creates a visually insightful plot showing the classes and their corresponding proportions, enhancing\ninterpretability and comprehension of the data. \n \n\nLimitations \n\n\nThe test might struggle to perform well or provide vital insights for datasets with a high number of classes. In\nsuch cases, the imbalance could be inevitable due to the inherent class distribution. \nSensitivity to the threshold value might result in faulty detection of imbalance if the threshold is set\nexcessively high. \nRegardless of the percentage threshold, it doesn't account for varying costs or impacts of misclassifying\ndifferent classes, which might fluctuate based on specific applications or domains. \nWhile it can identify imbalances in class distribution, it doesn't provide direct methods to address or correct\nthese imbalances. \nThe test is only applicable for classification operations and unsuitable for regression or clustering tasks. \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tmin_percent_threshold : int = 10 ) -> Tuple [ Dict [ str , Any ], plotly . graph_objs . _figure . Figure , bool ] : ", "funcdef": "def"}, "validmind.tests.data_validation.DatasetDescription": {"fullname": "validmind.tests.data_validation.DatasetDescription", "modulename": "validmind.tests.data_validation.DatasetDescription", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.DatasetDescription.infer_datatypes": {"fullname": "validmind.tests.data_validation.DatasetDescription.infer_datatypes", "modulename": "validmind.tests.data_validation.DatasetDescription", "qualname": "infer_datatypes", "kind": "function", "doc": "
\n", "signature": "(df ): ", "funcdef": "def"}, "validmind.tests.data_validation.DatasetDescription.get_numerical_histograms": {"fullname": "validmind.tests.data_validation.DatasetDescription.get_numerical_histograms", "modulename": "validmind.tests.data_validation.DatasetDescription", "qualname": "get_numerical_histograms", "kind": "function", "doc": "Returns a collection of histograms for a numerical column, each one\nwith a different bin size
\n", "signature": "(df , column ): ", "funcdef": "def"}, "validmind.tests.data_validation.DatasetDescription.get_column_histograms": {"fullname": "validmind.tests.data_validation.DatasetDescription.get_column_histograms", "modulename": "validmind.tests.data_validation.DatasetDescription", "qualname": "get_column_histograms", "kind": "function", "doc": "Returns a collection of histograms for a numerical or categorical column.\nWe store different combinations of bin sizes to allow analyzing the data better
\n\nWill be used in favor of _get_histogram in the future
\n", "signature": "(df , column , type_ ): ", "funcdef": "def"}, "validmind.tests.data_validation.DatasetDescription.describe_column": {"fullname": "validmind.tests.data_validation.DatasetDescription.describe_column", "modulename": "validmind.tests.data_validation.DatasetDescription", "qualname": "describe_column", "kind": "function", "doc": "Gets descriptive statistics for a single column in a Pandas DataFrame.
\n", "signature": "(df , column ): ", "funcdef": "def"}, "validmind.tests.data_validation.DatasetDescription.DatasetDescription": {"fullname": "validmind.tests.data_validation.DatasetDescription.DatasetDescription", "modulename": "validmind.tests.data_validation.DatasetDescription", "qualname": "DatasetDescription", "kind": "function", "doc": "Provides comprehensive analysis and statistical summaries of each column in a machine learning model's dataset.
\n\nPurpose \n\nThe test depicted in the script is meant to run a comprehensive analysis on a Machine Learning model's datasets.\nThe test or metric is implemented to obtain a complete summary of the columns in the dataset, including vital\nstatistics of each column such as count, distinct values, missing values, histograms for numerical, categorical,\nboolean, and text columns. This summary gives a comprehensive overview of the dataset to better understand the\ncharacteristics of the data that the model is trained on or evaluates.
\n\nTest Mechanism \n\nThe DatasetDescription class accomplishes the purpose as follows: firstly, the test method \"run\" infers the data\ntype of each column in the dataset and stores the details (id, column type). For each column, the\n\"describe_column\" method is invoked to collect statistical information about the column, including count,\nmissing value count and its proportion to the total, unique value count, and its proportion to the total. Depending\non the data type of a column, histograms are generated that reflect the distribution of data within the column.\nNumerical columns use the \"get_numerical_histograms\" method to calculate histogram distribution, whereas for\ncategorical, boolean and text columns, a histogram is computed with frequencies of each unique value in the\ndatasets. For unsupported types, an error is raised. Lastly, a summary table is built to aggregate all the\nstatistical insights and histograms of the columns in a dataset.
\n\nSigns of High Risk \n\n\nHigh ratio of missing values to total values in one or more columns which may impact the quality of the\npredictions. \nUnsupported data types in dataset columns. \nLarge number of unique values in the dataset's columns which might make it harder for the model to establish\npatterns. \nExtreme skewness or irregular distribution of data as reflected in the histograms. \n \n\nStrengths \n\n\nProvides a detailed analysis of the dataset with versatile summaries like count, unique values, histograms, etc. \nFlexibility in handling different types of data: numerical, categorical, boolean, and text. \nUseful in detecting problems in the dataset like missing values, unsupported data types, irregular data\ndistribution, etc. \nThe summary gives a comprehensive understanding of dataset features allowing developers to make informed\ndecisions. \n \n\nLimitations \n\n\nThe computation can be expensive from a resource standpoint, particularly for large datasets with numerous columns. \nThe histograms use an arbitrary number of bins which may not be the optimal number of bins for specific data\ndistribution. \nUnsupported data types for columns will raise an error which may limit evaluating the dataset. \nColumns with all null or missing values are not included in histogram computation. \nThis test only validates the quality of the dataset but doesn't address the model's performance directly. \n \n", "signature": "(dataset : validmind . vm_models . dataset . dataset . VMDataset ): ", "funcdef": "def"}, "validmind.tests.data_validation.DatasetSplit": {"fullname": "validmind.tests.data_validation.DatasetSplit", "modulename": "validmind.tests.data_validation.DatasetSplit", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.DatasetSplit.DatasetSplit": {"fullname": "validmind.tests.data_validation.DatasetSplit.DatasetSplit", "modulename": "validmind.tests.data_validation.DatasetSplit", "qualname": "DatasetSplit", "kind": "function", "doc": "Evaluates and visualizes the distribution proportions among training, testing, and validation datasets of an ML\nmodel.
\n\nPurpose \n\nThe DatasetSplit test is designed to evaluate and visualize the distribution of data among training, testing, and\nvalidation datasets, if available, within a given machine learning model. The main purpose is to assess whether the\nmodel's datasets are split appropriately, as an imbalanced split might affect the model's ability to learn from the\ndata and generalize to unseen data.
\n\nTest Mechanism \n\nThe DatasetSplit test first calculates the total size of all available datasets in the model. Then, for each\nindividual dataset, the methodology involves determining the size of the dataset and its proportion relative to the\ntotal size. The results are then conveniently summarized in a table that shows dataset names, sizes, and\nproportions. Absolute size and proportion of the total dataset size are displayed for each individual dataset.
\n\nSigns of High Risk \n\n\nA very small training dataset, which may result in the model not learning enough from the data. \nA very large training dataset and a small test dataset, which may lead to model overfitting and poor\ngeneralization to unseen data. \nA small or non-existent validation dataset, which might complicate the model's performance assessment. \n \n\nStrengths \n\n\nThe DatasetSplit test provides a clear, understandable visualization of dataset split proportions, which can\nhighlight any potential imbalance in dataset splits quickly. \nIt covers a wide range of task types including classification, regression, and text-related tasks. \nThe metric is not tied to any specific data type and is applicable to tabular data, time series data, or text\ndata. \n \n\nLimitations \n\n\nThe DatasetSplit test does not provide any insight into the quality or diversity of the data within each split,\njust the size and proportion. \nThe test does not give any recommendations or adjustments for imbalanced datasets. \nPotential lack of compatibility with more complex modes of data splitting (for example, stratified or time-based\nsplits) could limit the applicability of this test. \n \n", "signature": "(datasets : List [ validmind . vm_models . dataset . dataset . VMDataset ] ): ", "funcdef": "def"}, "validmind.tests.data_validation.DescriptiveStatistics": {"fullname": "validmind.tests.data_validation.DescriptiveStatistics", "modulename": "validmind.tests.data_validation.DescriptiveStatistics", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.DescriptiveStatistics.get_summary_statistics_numerical": {"fullname": "validmind.tests.data_validation.DescriptiveStatistics.get_summary_statistics_numerical", "modulename": "validmind.tests.data_validation.DescriptiveStatistics", "qualname": "get_summary_statistics_numerical", "kind": "function", "doc": "
\n", "signature": "(df , numerical_fields ): ", "funcdef": "def"}, "validmind.tests.data_validation.DescriptiveStatistics.get_summary_statistics_categorical": {"fullname": "validmind.tests.data_validation.DescriptiveStatistics.get_summary_statistics_categorical", "modulename": "validmind.tests.data_validation.DescriptiveStatistics", "qualname": "get_summary_statistics_categorical", "kind": "function", "doc": "
\n", "signature": "(df , categorical_fields ): ", "funcdef": "def"}, "validmind.tests.data_validation.DescriptiveStatistics.DescriptiveStatistics": {"fullname": "validmind.tests.data_validation.DescriptiveStatistics.DescriptiveStatistics", "modulename": "validmind.tests.data_validation.DescriptiveStatistics", "qualname": "DescriptiveStatistics", "kind": "function", "doc": "Performs a detailed descriptive statistical analysis of both numerical and categorical data within a model's\ndataset.
\n\nPurpose \n\nThe purpose of the Descriptive Statistics metric is to provide a comprehensive summary of both numerical and\ncategorical data within a dataset. This involves statistics such as count, mean, standard deviation, minimum and\nmaximum values for numerical data. For categorical data, it calculates the count, number of unique values, most\ncommon value and its frequency, and the proportion of the most frequent value relative to the total. The goal is to\nvisualize the overall distribution of the variables in the dataset, aiding in understanding the model's behavior\nand predicting its performance.
\n\nTest Mechanism \n\nThe testing mechanism utilizes two in-built functions of pandas dataframes: describe() for numerical fields and\nvalue_counts() for categorical fields. The describe() function pulls out several summary statistics, while\nvalue_counts() accounts for unique values. The resulting data is formatted into two distinct tables, one for\nnumerical and another for categorical variable summaries. These tables provide a clear summary of the main\ncharacteristics of the variables, which can be instrumental in assessing the model's performance.
\n\nSigns of High Risk \n\n\nSkewed data or significant outliers can represent high risk. For numerical data, this may be reflected via a\nsignificant difference between the mean and median (50% percentile). \nFor categorical data, a lack of diversity (low count of unique values), or overdominance of a single category\n(high frequency of the top value) can indicate high risk. \n \n\nStrengths \n\n\nProvides a comprehensive summary of the dataset, shedding light on the distribution and characteristics of the\nvariables under consideration. \nIt is a versatile and robust method, applicable to both numerical and categorical data. \nHelps highlight crucial anomalies such as outliers, extreme skewness, or lack of diversity, which are vital in\nunderstanding model behavior during testing and validation. \n \n\nLimitations \n\n\nWhile this metric offers a high-level overview of the data, it may fail to detect subtle correlations or complex\npatterns. \nDoes not offer any insights on the relationship between variables. \nAlone, descriptive statistics cannot be used to infer properties about future unseen data. \nShould be used in conjunction with other statistical tests to provide a comprehensive understanding of the\nmodel's data. \n \n", "signature": "(dataset : validmind . vm_models . dataset . dataset . VMDataset ): ", "funcdef": "def"}, "validmind.tests.data_validation.DickeyFullerGLS": {"fullname": "validmind.tests.data_validation.DickeyFullerGLS", "modulename": "validmind.tests.data_validation.DickeyFullerGLS", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.DickeyFullerGLS.DickeyFullerGLS": {"fullname": "validmind.tests.data_validation.DickeyFullerGLS.DickeyFullerGLS", "modulename": "validmind.tests.data_validation.DickeyFullerGLS", "qualname": "DickeyFullerGLS", "kind": "function", "doc": "Assesses stationarity in time series data using the Dickey-Fuller GLS test to determine the order of integration.
\n\nPurpose \n\nThe Dickey-Fuller GLS (DFGLS) test is utilized to determine the order of integration in time series data. For\nmachine learning models dealing with time series and forecasting, this metric evaluates the existence of a unit\nroot, thereby checking whether a time series is non-stationary. This analysis is a crucial initial step when\ndealing with time series data.
\n\nTest Mechanism \n\nThis code implements the Dickey-Fuller GLS unit root test on each attribute of the dataset. This process involves\niterating through every column of the dataset and applying the DFGLS test to assess the presence of a unit root.\nThe resulting information, including the test statistic ('stat'), the p-value ('pvalue'), the quantity of lagged\ndifferences utilized in the regression ('usedlag'), and the number of observations ('nobs'), is subsequently stored.
\n\nSigns of High Risk \n\n\nA high p-value for the DFGLS test represents a high risk. Specifically, a p-value above a typical threshold of\n0.05 suggests that the time series data is quite likely to be non-stationary, thus presenting a high risk for\ngenerating unreliable forecasts. \n \n\nStrengths \n\n\nThe Dickey-Fuller GLS test is a potent tool for checking the stationarity of time series data. \nIt helps to verify the assumptions of the models before the actual construction of the machine learning models\nproceeds. \nThe results produced by this metric offer a clear insight into whether the data is appropriate for specific\nmachine learning models, especially those demanding the stationarity of time series data. \n \n\nLimitations \n\n\nDespite its benefits, the DFGLS test does present some drawbacks. It can potentially lead to inaccurate\nconclusions if the time series data incorporates a structural break. \nIf the time series tends to follow a trend while still being stationary, the test might misinterpret it,\nnecessitating further detrending. \nThe test also presents challenges when dealing with shorter time series data or volatile data, not producing\nreliable results in these cases. \n \n", "signature": "(dataset : validmind . vm_models . dataset . dataset . VMDataset ): ", "funcdef": "def"}, "validmind.tests.data_validation.Duplicates": {"fullname": "validmind.tests.data_validation.Duplicates", "modulename": "validmind.tests.data_validation.Duplicates", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.Duplicates.Duplicates": {"fullname": "validmind.tests.data_validation.Duplicates.Duplicates", "modulename": "validmind.tests.data_validation.Duplicates", "qualname": "Duplicates", "kind": "function", "doc": "Tests dataset for duplicate entries, ensuring model reliability via data quality verification.
\n\nPurpose \n\nThe 'Duplicates' test is designed to check for duplicate rows within the dataset provided to the model. It serves\nas a measure of data quality, ensuring that the model isn't merely memorizing duplicate entries or being swayed by\nredundant information. This is an important step in the pre-processing of data for both classification and\nregression tasks.
\n\nTest Mechanism \n\nThis test operates by checking each row for duplicates in the dataset. If a text column is specified in the\ndataset, the test is conducted on this column; if not, the test is run on all feature columns. The number and\npercentage of duplicates are calculated and returned in a DataFrame. Additionally, a test is passed if the total\ncount of duplicates falls below a specified minimum threshold.
\n\nSigns of High Risk \n\n\nA high number of duplicate rows in the dataset, which can lead to overfitting where the model performs well on\nthe training data but poorly on unseen data. \nA high percentage of duplicate rows in the dataset, indicating potential problems with data collection or\nprocessing. \n \n\nStrengths \n\n\nAssists in improving the reliability of the model's training process by ensuring the training data is not\ncontaminated with duplicate entries, which can distort statistical analyses. \nProvides both absolute numbers and percentage values of duplicate rows, giving a thorough overview of data\nquality. \nHighly customizable as it allows for setting a user-defined minimum threshold to determine if the test has been\npassed. \n \n\nLimitations \n\n\nDoes not distinguish between benign duplicates (i.e., coincidental identical entries in different rows) and\nproblematic duplicates originating from data collection or processing errors. \nThe test becomes more computationally intensive as the size of the dataset increases, which might not be suitable\nfor very large datasets. \nCan only check for exact duplicates and may miss semantically similar information packaged differently. \n \n", "signature": "(dataset , min_threshold = 1 ): ", "funcdef": "def"}, "validmind.tests.data_validation.EngleGrangerCoint": {"fullname": "validmind.tests.data_validation.EngleGrangerCoint", "modulename": "validmind.tests.data_validation.EngleGrangerCoint", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.EngleGrangerCoint.EngleGrangerCoint": {"fullname": "validmind.tests.data_validation.EngleGrangerCoint.EngleGrangerCoint", "modulename": "validmind.tests.data_validation.EngleGrangerCoint", "qualname": "EngleGrangerCoint", "kind": "function", "doc": "Assesses the degree of co-movement between pairs of time series data using the Engle-Granger cointegration test.
\n\nPurpose \n\nThe intent of this Engle-Granger cointegration test is to explore and quantify the degree of co-movement between\npairs of time series variables in a dataset. This is particularly useful in enhancing the accuracy of predictive\nregressions whenever the underlying variables are co-integrated, i.e., they move together over time.
\n\nTest Mechanism \n\nThe test first drops any non-applicable values from the input dataset and then iterates over each pair of variables\nto apply the Engle-Granger cointegration test. The test generates a 'p' value, which is then compared against a\npre-specified threshold (0.05 by default). The pair is labeled as 'Cointegrated' if the 'p' value is less than or\nequal to the threshold or 'Not cointegrated' otherwise. A summary table is returned by the metric showing\ncointegration results for each variable pair.
\n\nSigns of High Risk \n\n\nA significant number of hypothesized cointegrated variables do not pass the test. \nA considerable number of 'p' values are close to the threshold, indicating minor data fluctuations can switch the\ndecision between 'Cointegrated' and 'Not cointegrated'. \n \n\nStrengths \n\n\nProvides an effective way to analyze relationships between time series, particularly in contexts where it's\nessential to check if variables move together in a statistically significant manner. \nUseful in various domains, especially finance or economics, where predictive models often hinge on understanding\nhow different variables move together over time. \n \n\nLimitations \n\n\nAssumes that the time series are integrated of the same order, which isn't always true in multivariate time\nseries datasets. \nThe presence of non-stationary characteristics in the series or structural breaks can result in falsely positive\nor negative cointegration results. \nMay not perform well for small sample sizes due to lack of statistical power and should be supplemented with\nother predictive indicators for a more robust model evaluation. \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tthreshold : float = 0.05 ): ", "funcdef": "def"}, "validmind.tests.data_validation.FeatureTargetCorrelationPlot": {"fullname": "validmind.tests.data_validation.FeatureTargetCorrelationPlot", "modulename": "validmind.tests.data_validation.FeatureTargetCorrelationPlot", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.FeatureTargetCorrelationPlot.FeatureTargetCorrelationPlot": {"fullname": "validmind.tests.data_validation.FeatureTargetCorrelationPlot.FeatureTargetCorrelationPlot", "modulename": "validmind.tests.data_validation.FeatureTargetCorrelationPlot", "qualname": "FeatureTargetCorrelationPlot", "kind": "function", "doc": "Visualizes the correlation between input features and the model's target output in a color-coded horizontal bar\nplot.
\n\nPurpose \n\nThis test is designed to graphically illustrate the correlations between distinct input features and the target\noutput of a Machine Learning model. Understanding how each feature influences the model's predictions is crucial\u2014a\nhigher correlation indicates a stronger influence of the feature on the target variable. This correlation study is\nespecially advantageous during feature selection and for comprehending the model's operation.
\n\nTest Mechanism \n\nThis FeatureTargetCorrelationPlot test computes and presents the correlations between the features and the target\nvariable using a specific dataset. These correlations are calculated and are then graphically represented in a\nhorizontal bar plot, color-coded based on the strength of the correlation. A hovering template can also be utilized\nfor informative tooltips. It is possible to specify the features to be analyzed and adjust the graph's height\naccording to need.
\n\nSigns of High Risk \n\n\nThere are no strong correlations (either positive or negative) between features and the target variable. This\ncould suggest high risk as the supplied features do not appear to significantly impact the prediction output. \nThe presence of duplicated correlation values might hint at redundancy in the feature set. \n \n\nStrengths \n\n\nProvides visual assistance to interpreting correlations more effectively. \nGives a clear and simple tour of how each feature affects the model's target variable. \nBeneficial for feature selection and grasping the model's prediction nature. \nPrecise correlation values for each feature are offered by the hover template, contributing to a granular-level\ncomprehension. \n \n\nLimitations \n\n\nThe test only accepts numerical data, meaning variables of other types need to be prepared beforehand. \nThe plot assumes all correlations to be linear, thus non-linear relationships might not be captured effectively. \nNot apt for models that employ complex feature interactions, like Decision Trees or Neural Networks, as the test\nmay not accurately reflect their importance. \n \n", "signature": "(dataset , fig_height = 600 ): ", "funcdef": "def"}, "validmind.tests.data_validation.HighCardinality": {"fullname": "validmind.tests.data_validation.HighCardinality", "modulename": "validmind.tests.data_validation.HighCardinality", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.HighCardinality.HighCardinality": {"fullname": "validmind.tests.data_validation.HighCardinality.HighCardinality", "modulename": "validmind.tests.data_validation.HighCardinality", "qualname": "HighCardinality", "kind": "function", "doc": "Assesses the number of unique values in categorical columns to detect high cardinality and potential overfitting.
\n\nPurpose \n\nThe \u201cHigh Cardinality\u201d test is used to evaluate the number of unique values present in the categorical columns of a\ndataset. In this context, high cardinality implies the presence of a large number of unique, non-repetitive values\nin the dataset.
\n\nTest Mechanism \n\nThe test first infers the dataset's type and then calculates an initial numeric threshold based on the test\nparameters. It only considers columns classified as \"Categorical\". For each of these columns, the number of\ndistinct values (n_distinct) and the percentage of distinct values (p_distinct) are calculated. The test will pass\nif n_distinct is less than the calculated numeric threshold. Lastly, the results, which include details such as\ncolumn name, number of distinct values, and pass/fail status, are compiled into a table.
\n\nSigns of High Risk \n\n\nA large number of distinct values (high cardinality) in one or more categorical columns implies a high risk. \nA column failing the test (n_distinct >= num_threshold) is another indicator of high risk. \n \n\nStrengths \n\n\nThe High Cardinality test is effective in early detection of potential overfitting and unwanted noise. \nIt aids in identifying potential outliers and inconsistencies, thereby improving data quality. \nThe test can be applied to both classification and regression task types, demonstrating its versatility. \n \n\nLimitations \n\n\nThe test is restricted to only \"Categorical\" data types and is thus not suitable for numerical or continuous\nfeatures, limiting its scope. \nThe test does not consider the relevance or importance of unique values in categorical features, potentially\ncausing it to overlook critical data points. \nThe threshold (both number and percent) used for the test is static and may not be optimal for diverse datasets\nand varied applications. Further mechanisms to adjust and refine this threshold could enhance its effectiveness. \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tnum_threshold : int = 100 , \tpercent_threshold : float = 0.1 , \tthreshold_type : str = 'percent' ): ", "funcdef": "def"}, "validmind.tests.data_validation.HighPearsonCorrelation": {"fullname": "validmind.tests.data_validation.HighPearsonCorrelation", "modulename": "validmind.tests.data_validation.HighPearsonCorrelation", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.HighPearsonCorrelation.HighPearsonCorrelation": {"fullname": "validmind.tests.data_validation.HighPearsonCorrelation.HighPearsonCorrelation", "modulename": "validmind.tests.data_validation.HighPearsonCorrelation", "qualname": "HighPearsonCorrelation", "kind": "function", "doc": "Identifies highly correlated feature pairs in a dataset suggesting feature redundancy or multicollinearity.
\n\nPurpose \n\nThe High Pearson Correlation test measures the linear relationship between features in a dataset, with the main\ngoal of identifying high correlations that might indicate feature redundancy or multicollinearity. Identification\nof such issues allows developers and risk management teams to properly deal with potential impacts on the machine\nlearning model's performance and interpretability.
\n\nTest Mechanism \n\nThe test works by generating pairwise Pearson correlations for all features in the dataset, then sorting and\neliminating duplicate and self-correlations. It assigns a Pass or Fail based on whether the absolute value of the\ncorrelation coefficient surpasses a pre-set threshold (defaulted at 0.3). It lastly returns the top n strongest\ncorrelations regardless of passing or failing status (where n is 10 by default but can be configured by passing the\ntop_n_correlations parameter).
\n\nSigns of High Risk \n\n\nA high risk indication would be the presence of correlation coefficients exceeding the threshold. \nIf the features share a strong linear relationship, this could lead to potential multicollinearity and model\noverfitting. \nRedundancy of variables can undermine the interpretability of the model due to uncertainty over the authenticity\nof individual variable's predictive power. \n \n\nStrengths \n\n\nProvides a quick and simple means of identifying relationships between feature pairs. \nGenerates a transparent output that displays pairs of correlated variables, the Pearson correlation coefficient,\nand a Pass or Fail status for each. \nAids in early identification of potential multicollinearity issues that may disrupt model training. \n \n\nLimitations \n\n\nCan only delineate linear relationships, failing to shed light on nonlinear relationships or dependencies. \nSensitive to outliers where a few outliers could notably affect the correlation coefficient. \nLimited to identifying redundancy only within feature pairs; may fail to spot more complex relationships among\nthree or more variables. \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tmax_threshold : float = 0.3 , \ttop_n_correlations : int = 10 , \tfeature_columns : list = None ): ", "funcdef": "def"}, "validmind.tests.data_validation.IQROutliersBarPlot": {"fullname": "validmind.tests.data_validation.IQROutliersBarPlot", "modulename": "validmind.tests.data_validation.IQROutliersBarPlot", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.IQROutliersBarPlot.compute_outliers": {"fullname": "validmind.tests.data_validation.IQROutliersBarPlot.compute_outliers", "modulename": "validmind.tests.data_validation.IQROutliersBarPlot", "qualname": "compute_outliers", "kind": "function", "doc": "
\n", "signature": "(series , threshold ): ", "funcdef": "def"}, "validmind.tests.data_validation.IQROutliersBarPlot.IQROutliersBarPlot": {"fullname": "validmind.tests.data_validation.IQROutliersBarPlot.IQROutliersBarPlot", "modulename": "validmind.tests.data_validation.IQROutliersBarPlot", "qualname": "IQROutliersBarPlot", "kind": "function", "doc": "Visualizes outlier distribution across percentiles in numerical data using the Interquartile Range (IQR) method.
\n\nPurpose \n\nThe InterQuartile Range Outliers Bar Plot (IQROutliersBarPlot) metric aims to visually analyze and evaluate the\nextent of outliers in numeric variables based on percentiles. Its primary purpose is to clarify the dataset's\ndistribution, flag possible abnormalities in it, and gauge potential risks associated with processing potentially\nskewed data, which can affect the machine learning model's predictive prowess.
\n\nTest Mechanism \n\nThe examination invokes a series of steps:
\n\n\nFor every numeric feature in the dataset, the 25th percentile (Q1) and 75th percentile (Q3) are calculated\nbefore deriving the Interquartile Range (IQR), the difference between Q1 and Q3. \nSubsequently, the metric calculates the lower and upper thresholds by subtracting Q1 from the threshold times\nIQR and adding Q3 to threshold times IQR, respectively. The default threshold is set at 1.5. \nAny value in the feature that falls below the lower threshold or exceeds the upper threshold is labeled as an\noutlier. \nThe number of outliers are tallied for different percentiles, such as [0-25], [25-50], [50-75], and [75-100]. \nThese counts are employed to construct a bar plot for the feature, showcasing the distribution of outliers\nacross different percentiles. \n \n\nSigns of High Risk \n\n\nA prevalence of outliers in the data, potentially skewing its distribution. \nOutliers dominating higher percentiles (75-100) which implies the presence of extreme values, capable of severely\ninfluencing the model's performance. \nCertain features harboring most of their values as outliers, which signifies that these features might not\ncontribute positively to the model's forecasting ability. \n \n\nStrengths \n\n\nEffectively identifies outliers in the data through visual means, facilitating easier comprehension and offering\ninsights into the outliers' possible impact on the model. \nProvides flexibility by accommodating all numeric features or a chosen subset. \nTask-agnostic in nature; it is viable for both classification and regression tasks. \nCan handle large datasets as its operation does not hinge on computationally heavy operations. \n \n\nLimitations \n\n\nIts application is limited to numerical variables and does not extend to categorical ones. \nOnly reveals the presence and distribution of outliers and does not provide insights into how these outliers\nmight affect the model's predictive performance. \nThe assumption that data is unimodal and symmetric may not always hold true. In cases with non-normal\ndistributions, the results can be misleading. \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tthreshold : float = 1.5 , \tfig_width : int = 800 ): ", "funcdef": "def"}, "validmind.tests.data_validation.IQROutliersTable": {"fullname": "validmind.tests.data_validation.IQROutliersTable", "modulename": "validmind.tests.data_validation.IQROutliersTable", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.IQROutliersTable.compute_outliers": {"fullname": "validmind.tests.data_validation.IQROutliersTable.compute_outliers", "modulename": "validmind.tests.data_validation.IQROutliersTable", "qualname": "compute_outliers", "kind": "function", "doc": "
\n", "signature": "(series , threshold = 1.5 ): ", "funcdef": "def"}, "validmind.tests.data_validation.IQROutliersTable.IQROutliersTable": {"fullname": "validmind.tests.data_validation.IQROutliersTable.IQROutliersTable", "modulename": "validmind.tests.data_validation.IQROutliersTable", "qualname": "IQROutliersTable", "kind": "function", "doc": "Determines and summarizes outliers in numerical features using the Interquartile Range method.
\n\nPurpose \n\nThe \"Interquartile Range Outliers Table\" (IQROutliersTable) metric is designed to identify and summarize outliers\nwithin numerical features of a dataset using the Interquartile Range (IQR) method. This exercise is crucial in the\npre-processing of data because outliers can substantially distort statistical analysis and impact the performance\nof machine learning models.
\n\nTest Mechanism \n\nThe IQR, which is the range separating the first quartile (25th percentile) from the third quartile (75th\npercentile), is calculated for each numerical feature within the dataset. An outlier is defined as a data point\nfalling below the \"Q1 - 1.5 * IQR\" or above \"Q3 + 1.5 * IQR\" range. The test computes the number of outliers and\ntheir summary statistics (minimum, 25th percentile, median, 75th percentile, and maximum values) for each numerical\nfeature. If no specific features are chosen, the test applies to all numerical features in the dataset. The default\noutlier threshold is set to 1.5 but can be customized by the user.
\n\nSigns of High Risk \n\n\nA large number of outliers in multiple features. \nOutliers significantly distanced from the mean value of variables. \nExtremely high or low outlier values indicative of data entry errors or other data quality issues. \n \n\nStrengths \n\n\nProvides a comprehensive summary of outliers for each numerical feature, helping pinpoint features with potential\nquality issues. \nThe IQR method is robust to extremely high or low outlier values as it is based on quartile calculations. \nCan be customized to work on selected features and set thresholds for outliers. \n \n\nLimitations \n\n\nMight cause false positives if the variable deviates from a normal or near-normal distribution, especially for\nskewed distributions. \nDoes not provide interpretation or recommendations for addressing outliers, relying on further analysis by users\nor data scientists. \nOnly applicable to numerical features, not categorical data. \nDefault thresholds may not be optimal for data with heavy pre-processing, manipulation, or inherently high\nkurtosis (heavy tails). \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tthreshold : float = 1.5 ): ", "funcdef": "def"}, "validmind.tests.data_validation.IsolationForestOutliers": {"fullname": "validmind.tests.data_validation.IsolationForestOutliers", "modulename": "validmind.tests.data_validation.IsolationForestOutliers", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.IsolationForestOutliers.IsolationForestOutliers": {"fullname": "validmind.tests.data_validation.IsolationForestOutliers.IsolationForestOutliers", "modulename": "validmind.tests.data_validation.IsolationForestOutliers", "qualname": "IsolationForestOutliers", "kind": "function", "doc": "Detects outliers in a dataset using the Isolation Forest algorithm and visualizes results through scatter plots.
\n\nPurpose \n\nThe IsolationForestOutliers test is designed to identify anomalies or outliers in the model's dataset using the\nisolation forest algorithm. This algorithm assumes that anomalous data points can be isolated more quickly due to\ntheir distinctive properties. By creating isolation trees and identifying instances with shorter average path\nlengths, the test is able to pick out data points that differ from the majority.
\n\nTest Mechanism \n\nThe test uses the isolation forest algorithm, which builds an ensemble of isolation trees by randomly selecting\nfeatures and splitting the data based on random thresholds. It isolates anomalies rather than focusing on normal\ndata points. For each pair of variables, a scatter plot is generated which distinguishes the identified outliers\nfrom the inliers. The results of the test can be visualized using these scatter plots, illustrating the distinction\nbetween outliers and inliers.
\n\nSigns of High Risk \n\n\nThe presence of high contamination, indicating a large number of anomalies \nInability to detect clusters of anomalies that are close in the feature space \nMisclassifying normal instances as anomalies \nFailure to detect actual anomalies \n \n\nStrengths \n\n\nAbility to handle large, high-dimensional datasets \nEfficiency in isolating anomalies instead of normal instances \nInsensitivity to the underlying distribution of data \nAbility to recognize anomalies even when they are not separated from the main data cloud through identifying\ndistinctive properties \nVisually presents the test results for better understanding and interpretability \n \n\nLimitations \n\n\nDifficult to detect anomalies that are close to each other or prevalent in datasets \nDependency on the contamination parameter which may need fine-tuning to be effective \nPotential failure in detecting collective anomalies if they behave similarly to normal data \nPotential lack of precision in identifying which features contribute most to the anomalous behavior \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \trandom_state : int = 0 , \tcontamination : float = 0.1 , \tfeature_columns : list = None ): ", "funcdef": "def"}, "validmind.tests.data_validation.JarqueBera": {"fullname": "validmind.tests.data_validation.JarqueBera", "modulename": "validmind.tests.data_validation.JarqueBera", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.JarqueBera.JarqueBera": {"fullname": "validmind.tests.data_validation.JarqueBera.JarqueBera", "modulename": "validmind.tests.data_validation.JarqueBera", "qualname": "JarqueBera", "kind": "function", "doc": "Assesses normality of dataset features in an ML model using the Jarque-Bera test.
\n\nPurpose \n\nThe purpose of the Jarque-Bera test as implemented in this metric is to determine if the features in the dataset of\na given Machine Learning model follow a normal distribution. This is crucial for understanding the distribution and\nbehavior of the model's features, as numerous statistical methods assume normal distribution of the data.
\n\nTest Mechanism \n\nThe test mechanism involves computing the Jarque-Bera statistic, p-value, skew, and kurtosis for each feature in\nthe dataset. It utilizes the 'jarque_bera' function from the 'statsmodels' library in Python, storing the results\nin a dictionary. The test evaluates the skewness and kurtosis to ascertain whether the dataset follows a normal\ndistribution. A significant p-value (typically less than 0.05) implies that the data does not possess normal\ndistribution.
\n\nSigns of High Risk \n\n\nA high Jarque-Bera statistic and a low p-value (usually less than 0.05) indicate high-risk conditions. \nSuch results suggest the data significantly deviates from a normal distribution. If a machine learning model\nexpects feature data to be normally distributed, these findings imply that it may not function as intended. \n \n\nStrengths \n\n\nProvides insights into the shape of the data distribution, helping determine whether a given set of data follows\na normal distribution. \nParticularly useful for risk assessment for models that assume a normal distribution of data. \nBy measuring skewness and kurtosis, it provides additional insights into the nature and magnitude of a\ndistribution's deviation. \n \n\nLimitations \n\n\nOnly checks for normality in the data distribution. It cannot provide insights into other types of distributions. \nDatasets that aren't normally distributed but follow some other distribution might lead to inaccurate risk\nassessments. \nHighly sensitive to large sample sizes, often rejecting the null hypothesis (that data is normally distributed)\neven for minor deviations in larger datasets. \n \n", "signature": "(dataset ): ", "funcdef": "def"}, "validmind.tests.data_validation.KPSS": {"fullname": "validmind.tests.data_validation.KPSS", "modulename": "validmind.tests.data_validation.KPSS", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.KPSS.KPSS": {"fullname": "validmind.tests.data_validation.KPSS.KPSS", "modulename": "validmind.tests.data_validation.KPSS", "qualname": "KPSS", "kind": "function", "doc": "Assesses the stationarity of time-series data in a machine learning model using the KPSS unit root test.
\n\nPurpose \n\nThe KPSS (Kwiatkowski-Phillips-Schmidt-Shin) unit root test is utilized to ensure the stationarity of data within a\nmachine learning model. It specifically works on time-series data to establish the order of integration, which is\nessential for accurate forecasting. A fundamental requirement for any time series model is that the series should\nbe stationary.
\n\nTest Mechanism \n\nThis test calculates the KPSS score for each feature in the dataset. The KPSS score includes a statistic, a\np-value, a used lag, and critical values. The core principle behind the KPSS test is to evaluate the hypothesis\nthat an observable time series is stationary around a deterministic trend. If the computed statistic exceeds the\ncritical value, the null hypothesis (that the series is stationary) is rejected, indicating that the series is\nnon-stationary.
\n\nSigns of High Risk \n\n\nHigh KPSS score, particularly if the calculated statistic is higher than the critical value. \nRejection of the null hypothesis, indicating that the series is recognized as non-stationary, can severely affect\nthe model's forecasting capability. \n \n\nStrengths \n\n\nDirectly measures the stationarity of a series, fulfilling a key prerequisite for many time-series models. \nThe underlying logic of the test is intuitive and simple, making it easy to understand and accessible for both\ndevelopers and risk management teams. \n \n\nLimitations \n\n\nAssumes the absence of a unit root in the series and doesn't differentiate between series that are stationary and\nthose border-lining stationarity. \nThe test may have restricted power against certain alternatives. \nThe reliability of the test is contingent on the number of lags selected, which introduces potential bias in the\nmeasurement. \n \n", "signature": "(dataset : validmind . vm_models . dataset . dataset . VMDataset ): ", "funcdef": "def"}, "validmind.tests.data_validation.LJungBox": {"fullname": "validmind.tests.data_validation.LJungBox", "modulename": "validmind.tests.data_validation.LJungBox", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.LJungBox.LJungBox": {"fullname": "validmind.tests.data_validation.LJungBox.LJungBox", "modulename": "validmind.tests.data_validation.LJungBox", "qualname": "LJungBox", "kind": "function", "doc": "Assesses autocorrelations in dataset features by performing a Ljung-Box test on each feature.
\n\nPurpose \n\nThe Ljung-Box test is a type of statistical test utilized to ascertain whether there are autocorrelations within a\ngiven dataset that differ significantly from zero. In the context of a machine learning model, this test is\nprimarily used to evaluate data utilized in regression tasks, especially those involving time series and\nforecasting.
\n\nTest Mechanism \n\nThe test operates by iterating over each feature within the dataset and applying the acorr_ljungbox\nfunction from the statsmodels.stats.diagnostic library. This function calculates the Ljung-Box statistic and\np-value for each feature. These results are then stored in a pandas DataFrame where the columns are the feature names,\nstatistic, and p-value respectively. Generally, a lower p-value indicates a higher likelihood of significant\nautocorrelations within the feature.
\n\nSigns of High Risk \n\n\nHigh Ljung-Box statistic values or low p-values. \nPresence of significant autocorrelations in the respective features. \nPotential for negative impact on model performance or bias if autocorrelations are not properly handled. \n \n\nStrengths \n\n\nPowerful tool for detecting autocorrelations within datasets, especially in time series data. \nProvides quantitative measures (statistic and p-value) for precise evaluation. \nHelps avoid issues related to autoregressive residuals and other challenges in regression models. \n \n\nLimitations \n\n\nCannot detect all types of non-linearity or complex interrelationships among variables. \nTesting individual features may not fully encapsulate the dynamics of the data if features interact with each other. \nDesigned more for traditional statistical models and may not be fully compatible with certain types of complex\nmachine learning models. \n \n", "signature": "(dataset ): ", "funcdef": "def"}, "validmind.tests.data_validation.LaggedCorrelationHeatmap": {"fullname": "validmind.tests.data_validation.LaggedCorrelationHeatmap", "modulename": "validmind.tests.data_validation.LaggedCorrelationHeatmap", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.LaggedCorrelationHeatmap.LaggedCorrelationHeatmap": {"fullname": "validmind.tests.data_validation.LaggedCorrelationHeatmap.LaggedCorrelationHeatmap", "modulename": "validmind.tests.data_validation.LaggedCorrelationHeatmap", "qualname": "LaggedCorrelationHeatmap", "kind": "function", "doc": "Assesses and visualizes correlation between target variable and lagged independent variables in a time-series\ndataset.
\n\nPurpose \n\nThe LaggedCorrelationHeatmap metric is utilized to appraise and illustrate the correlation between the target\nvariable and delayed copies (lags) of independent variables in a time-series dataset. It assists in revealing\nrelationships in time-series data where the influence of an independent variable on the dependent variable is not\nimmediate but occurs after a period (lags).
\n\nTest Mechanism \n\nTo execute this test, Python's Pandas library pairs with Plotly to perform computations and present the\nvisualization in the form of a heatmap. The test begins by extracting the target variable and corresponding\nindependent variables from the dataset. Then, generation of lags of independent variables takes place, followed by\nthe calculation of correlation between these lagged variables and the target variable. The outcome is a correlation\nmatrix that gets recorded and illustrated as a heatmap, where different color intensities represent the strength of\nthe correlation, making patterns easier to identify.
\n\nSigns of High Risk \n\n\nInsignificant correlations across the heatmap, indicating a lack of noteworthy relationships between variables. \nCorrelations that break intuition or previous understanding, suggesting potential issues with the dataset or the\nmodel. \n \n\nStrengths \n\n\nThis metric serves as an exceptional tool for exploring and visualizing time-dependent relationships between\nfeatures and the target variable in a time-series dataset. \nIt aids in identifying delayed effects that might go unnoticed with other correlation measures. \nThe heatmap offers an intuitive visual representation of time-dependent correlations and influences. \n \n\nLimitations \n\n\nThe metric presumes linear relationships between variables, potentially ignoring non-linear relationships. \nThe correlation considered is linear; therefore, intricate non-linear interactions might be overlooked. \nThe metric is only applicable for time-series data, limiting its utility outside of this context. \nThe number of lags chosen can significantly influence the results; too many lags can render the heatmap difficult\nto interpret, while too few might overlook delayed effects. \nThis metric does not take into account any causal relationships, but merely demonstrates correlation. \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tnum_lags : int = 10 ): ", "funcdef": "def"}, "validmind.tests.data_validation.MissingValues": {"fullname": "validmind.tests.data_validation.MissingValues", "modulename": "validmind.tests.data_validation.MissingValues", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.MissingValues.MissingValues": {"fullname": "validmind.tests.data_validation.MissingValues.MissingValues", "modulename": "validmind.tests.data_validation.MissingValues", "qualname": "MissingValues", "kind": "function", "doc": "Evaluates dataset quality by ensuring missing value ratio across all features does not exceed a set threshold.
\n\nPurpose \n\nThe Missing Values test is designed to evaluate the quality of a dataset by measuring the number of missing values\nacross all features. The objective is to ensure that the ratio of missing data to total data is less than a\npredefined threshold, defaulting to 1, in order to maintain the data quality necessary for reliable predictive\nstrength in a machine learning model.
\n\nTest Mechanism \n\nThe mechanism for this test involves iterating through each column of the dataset, counting missing values\n(represented as NaNs), and calculating the percentage they represent against the total number of rows. The test\nthen checks if these missing value counts are less than the predefined min_threshold. The results are shown in a\ntable summarizing each column, the number of missing values, the percentage of missing values in each column, and a\nPass/Fail status based on the threshold comparison.
\n\nSigns of High Risk \n\n\nWhen the number of missing values in any column exceeds the min_threshold value. \nPresence of missing values across many columns, leading to multiple instances of failing the threshold. \n \n\nStrengths \n\n\nQuick and granular identification of missing data across each feature in the dataset. \nProvides an effective and straightforward means of maintaining data quality, essential for constructing efficient\nmachine learning models. \n \n\nLimitations \n\n\nDoes not suggest the root causes of the missing values or recommend ways to impute or handle them. \nMay overlook features with significant missing data but still less than the min_threshold, potentially\nimpacting the model. \nDoes not account for data encoded as values like \"-999\" or \"None,\" which might not technically classify as\nmissing but could bear similar implications. \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tmin_threshold : int = 1 ): ", "funcdef": "def"}, "validmind.tests.data_validation.MissingValuesBarPlot": {"fullname": "validmind.tests.data_validation.MissingValuesBarPlot", "modulename": "validmind.tests.data_validation.MissingValuesBarPlot", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.MissingValuesBarPlot.MissingValuesBarPlot": {"fullname": "validmind.tests.data_validation.MissingValuesBarPlot.MissingValuesBarPlot", "modulename": "validmind.tests.data_validation.MissingValuesBarPlot", "qualname": "MissingValuesBarPlot", "kind": "function", "doc": "Assesses the percentage and distribution of missing values in the dataset via a bar plot, with emphasis on\nidentifying high-risk columns based on a user-defined threshold.
\n\nPurpose \n\nThe 'MissingValuesBarPlot' metric provides a color-coded visual representation of the percentage of missing values\nfor each column in an ML model's dataset. The primary purpose of this metric is to easily identify and quantify\nmissing data, which are essential steps in data preprocessing. The presence of missing data can potentially skew\nthe model's predictions and decrease its accuracy. Additionally, this metric uses a pre-set threshold to categorize\nvarious columns into ones that contain missing data above the threshold (high risk) and below the threshold (less\nrisky).
\n\nTest Mechanism \n\nThe test mechanism involves scanning each column in the input dataset and calculating the percentage of missing\nvalues. It then compares each column's missing data percentage with the predefined threshold, categorizing columns\nwith missing data above the threshold as high-risk. The test generates a bar plot in which columns with missing\ndata are represented on the y-axis and their corresponding missing data percentages are displayed on the x-axis.\nThe color of each bar reflects the missing data percentage in relation to the threshold: grey for values below the\nthreshold and light coral for those exceeding it. The user-defined threshold is represented by a red dashed line on\nthe plot.
\n\nSigns of High Risk \n\n\nColumns with higher percentages of missing values beyond the threshold are high-risk. These are visually\nrepresented by light coral bars on the bar plot. \n \n\nStrengths \n\n\nHelps in quickly identifying and quantifying missing data across all columns of the dataset. \nFacilitates pattern recognition through visual representation. \nEnables customization of the level of risk tolerance via a user-defined threshold. \nSupports both classification and regression tasks, sharing its versatility. \n \n\nLimitations \n\n\nIt only considers the quantity of missing values, not differentiating between different types of missingness\n(Missing completely at random - MCAR, Missing at random - MAR, Not Missing at random - NMAR). \nIt doesn't offer insights into potential approaches for handling missing entries, such as various imputation\nstrategies. \nThe metric does not consider possible impacts of the missing data on the model's accuracy or precision. \nInterpretation of the findings and the next steps might require an expert understanding of the field. \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tthreshold : int = 80 , \tfig_height : int = 600 ): ", "funcdef": "def"}, "validmind.tests.data_validation.MutualInformation": {"fullname": "validmind.tests.data_validation.MutualInformation", "modulename": "validmind.tests.data_validation.MutualInformation", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.MutualInformation.MutualInformation": {"fullname": "validmind.tests.data_validation.MutualInformation.MutualInformation", "modulename": "validmind.tests.data_validation.MutualInformation", "qualname": "MutualInformation", "kind": "function", "doc": "Calculates mutual information scores between features and target variable to evaluate feature relevance.
\n\nPurpose \n\nThe Mutual Information test quantifies the predictive power of each feature by measuring its statistical\ndependency with the target variable. This helps identify relevant features for model training and\ndetect potential redundant or irrelevant variables, supporting feature selection decisions and model\ninterpretability.
\n\nTest Mechanism \n\nThe test employs sklearn's mutual_info_classif/mutual_info_regression functions to compute mutual\ninformation between each feature and the target. It produces a normalized score (0 to 1) for each\nfeature, where higher scores indicate stronger relationships. Results are presented in both tabular\nformat and visualized through a bar plot with a configurable threshold line.
\n\nSigns of High Risk \n\n\nMany features showing very low mutual information scores \nKey business features exhibiting unexpectedly low scores \nAll features showing similar, low information content \nLarge discrepancy between business importance and MI scores \nHighly skewed distribution of MI scores \nCritical features below the minimum threshold \nUnexpected zero or near-zero scores for known important features \nInconsistent scores across different data samples \n \n\nStrengths \n\n\nCaptures non-linear relationships between features and target \nScale-invariant measurement of feature relevance \nWorks for both classification and regression tasks \nProvides interpretable scores (0 to 1 scale) \nSupports automated feature selection \nNo assumptions about data distribution \nHandles numerical and categorical features \nComputationally efficient for most datasets \n \n\nLimitations \n\n\nRequires sufficient data for reliable estimates \nMay be computationally intensive for very large datasets \nCannot detect redundant features (pairwise relationships) \nSensitive to feature discretization for continuous variables \nDoes not account for feature interactions \nMay underestimate importance of rare but crucial events \nCannot handle missing values directly \nMay be affected by extreme class imbalance \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tmin_threshold : float = 0.01 , \ttask : str = 'classification' ): ", "funcdef": "def"}, "validmind.tests.data_validation.PearsonCorrelationMatrix": {"fullname": "validmind.tests.data_validation.PearsonCorrelationMatrix", "modulename": "validmind.tests.data_validation.PearsonCorrelationMatrix", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.PearsonCorrelationMatrix.PearsonCorrelationMatrix": {"fullname": "validmind.tests.data_validation.PearsonCorrelationMatrix.PearsonCorrelationMatrix", "modulename": "validmind.tests.data_validation.PearsonCorrelationMatrix", "qualname": "PearsonCorrelationMatrix", "kind": "function", "doc": "Evaluates linear dependency between numerical variables in a dataset via a Pearson Correlation coefficient heat map.
\n\nPurpose \n\nThis test is intended to evaluate the extent of linear dependency between all pairs of numerical variables in the\ngiven dataset. It provides the Pearson Correlation coefficient, which reveals any high correlations present. The\npurpose of doing this is to identify potential redundancy, as variables that are highly correlated can often be\nremoved to reduce the dimensionality of the dataset without significantly impacting the model's performance.
\n\nTest Mechanism \n\nThis metric test generates a correlation matrix for all numerical variables in the dataset using the Pearson\ncorrelation formula. A heat map is subsequently created to visualize this matrix effectively. The color of each\npoint on the heat map corresponds to the magnitude and direction (positive or negative) of the correlation, with a\nrange from -1 (perfect negative correlation) to 1 (perfect positive correlation). Any correlation coefficients\nhigher than 0.7 (in absolute terms) are indicated in white in the heat map, suggesting a high degree of correlation.
\n\nSigns of High Risk \n\n\nA large number of variables in the dataset showing a high degree of correlation (coefficients approaching \u00b11).\nThis indicates redundancy within the dataset, suggesting that some variables may not be contributing new\ninformation to the model. \nPotential risk of overfitting. \n \n\nStrengths \n\n\nDetects and quantifies the linearity of relationships between variables, aiding in identifying redundant\nvariables to simplify models and potentially improve performance. \nThe heatmap visualization provides an easy-to-understand overview of correlations, beneficial for users not\ncomfortable with numerical matrices. \n \n\nLimitations \n\n\nLimited to detecting linear relationships, potentially missing non-linear relationships which impede\nopportunities for dimensionality reduction. \nMeasures only the degree of linear relationship, not the strength of one variable's effect on another. \nThe 0.7 correlation threshold is arbitrary and might exclude valid dependencies with lower coefficients. \n \n", "signature": "(dataset ): ", "funcdef": "def"}, "validmind.tests.data_validation.PhillipsPerronArch": {"fullname": "validmind.tests.data_validation.PhillipsPerronArch", "modulename": "validmind.tests.data_validation.PhillipsPerronArch", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.PhillipsPerronArch.PhillipsPerronArch": {"fullname": "validmind.tests.data_validation.PhillipsPerronArch.PhillipsPerronArch", "modulename": "validmind.tests.data_validation.PhillipsPerronArch", "qualname": "PhillipsPerronArch", "kind": "function", "doc": "Assesses the stationarity of time series data in each feature of the ML model using the Phillips-Perron test.
\n\nPurpose \n\nThe Phillips-Perron (PP) test is used to determine the stationarity of time series data for each feature in a\ndataset, which is crucial for forecasting tasks. It tests the null hypothesis that a time series is unit-root\nnon-stationary. This is vital for understanding the stochastic behavior of the data and ensuring the robustness and\nvalidity of predictions generated by regression analysis models.
\n\nTest Mechanism \n\nThe PP test is conducted for each feature in the dataset as follows:
\n\n\nA data frame is created from the dataset. \nFor each column, the Phillips-Perron method calculates the test statistic, p-value, lags used, and number of\nobservations. \nThe results are then stored for each feature, providing a metric that indicates the stationarity of the time\nseries data. \n \n\nSigns of High Risk \n\n\nA high p-value, indicating that the series has a unit root and is non-stationary. \nTest statistic values exceeding critical values, suggesting non-stationarity. \nHigh 'usedlag' value, pointing towards autocorrelation issues that may degrade model performance. \n \n\nStrengths \n\n\nResilience against heteroskedasticity in the error term. \nEffective for long time series data. \nHelps in determining whether the time series is stationary, aiding in the selection of suitable forecasting\nmodels. \n \n\nLimitations \n\n\nApplicable only within a univariate time series framework. \nRelies on asymptotic theory, which may reduce the test\u2019s power for small sample sizes. \nNon-stationary time series must be converted to stationary series through differencing, potentially leading to\nloss of important data points. \n \n", "signature": "(dataset : validmind . vm_models . dataset . dataset . VMDataset ): ", "funcdef": "def"}, "validmind.tests.data_validation.ProtectedClassesCombination": {"fullname": "validmind.tests.data_validation.ProtectedClassesCombination", "modulename": "validmind.tests.data_validation.ProtectedClassesCombination", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.ProtectedClassesCombination.ProtectedClassesCombination": {"fullname": "validmind.tests.data_validation.ProtectedClassesCombination.ProtectedClassesCombination", "modulename": "validmind.tests.data_validation.ProtectedClassesCombination", "qualname": "ProtectedClassesCombination", "kind": "function", "doc": "Visualizes combinations of protected classes and their corresponding error metric differences.
\n\nPurpose \n\nThis test aims to provide insights into how different combinations of protected classes affect various error metrics,\nparticularly the false negative rate (FNR) and false positive rate (FPR). By visualizing these combinations,\nit helps identify potential biases or disparities in model performance across different intersectional groups.
\n\nTest Mechanism \n\nThe test performs the following steps:
\n\n\nCombines the specified protected class columns to create a single multi-class category. \nCalculates error metrics (FNR, FPR, etc.) for each combination of protected classes. \nGenerates visualizations showing the distribution of these metrics across all class combinations. \n \n\nSigns of High Risk \n\n\nLarge disparities in FNR or FPR across different protected class combinations. \nConsistent patterns of higher error rates for specific combinations of protected attributes. \nUnexpected or unexplainable variations in error metrics between similar group combinations. \n \n\nStrengths \n\n\nProvides a comprehensive view of intersectional fairness across multiple protected attributes. \nAllows for easy identification of potentially problematic combinations of protected classes. \nVisualizations make it easier to spot patterns or outliers in model performance across groups. \n \n\nLimitations \n\n\nMay become complex and difficult to interpret with a large number of protected classes or combinations. \nDoes not provide statistical significance of observed differences. \nVisualization alone may not capture all nuances of intersectional fairness. \n \n", "signature": "(dataset , model , protected_classes = None ): ", "funcdef": "def"}, "validmind.tests.data_validation.ProtectedClassesDescription": {"fullname": "validmind.tests.data_validation.ProtectedClassesDescription", "modulename": "validmind.tests.data_validation.ProtectedClassesDescription", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.ProtectedClassesDescription.ProtectedClassesDescription": {"fullname": "validmind.tests.data_validation.ProtectedClassesDescription.ProtectedClassesDescription", "modulename": "validmind.tests.data_validation.ProtectedClassesDescription", "qualname": "ProtectedClassesDescription", "kind": "function", "doc": "Visualizes the distribution of protected classes in the dataset relative to the target variable\nand provides descriptive statistics.
\n\nPurpose \n\nThe ProtectedClassesDescription test aims to identify potential biases or significant differences in the\ndistribution of target outcomes across different protected classes. This visualization and statistical summary\nhelp in understanding the relationship between protected attributes and the target variable, which is crucial\nfor assessing fairness in machine learning models.
\n\nTest Mechanism \n\nThe function creates interactive stacked bar charts for each specified protected class using Plotly.\nAdditionally, it generates a single table of descriptive statistics for all protected classes, including:
\n\n\nProtected class and category \nCount and percentage of each category within the protected class \nMean, median, and mode of the target variable for each category \nStandard deviation of the target variable for each category \nMinimum and maximum values of the target variable for each category \n \n\nSigns of High Risk \n\n\nSignificant imbalances in the distribution of target outcomes across different categories of a protected class. \nLarge disparities in mean, median, or mode of the target variable across categories. \nUnderrepresentation or overrepresentation of certain groups within protected classes. \nHigh standard deviations in certain categories, indicating potential volatility or outliers. \n \n\nStrengths \n\n\nProvides both visual and statistical representation of potential biases in the dataset. \nAllows for easy identification of imbalances in target variable distribution across protected classes. \nInteractive plots enable detailed exploration of the data. \nConsolidated statistical summary provides quantitative measures to complement visual analysis. \nApplicable to both classification and regression tasks. \n \n\nLimitations \n\n\nDoes not provide advanced statistical measures of bias or fairness. \nMay become cluttered if there are many categories within a protected class or many unique target values. \nInterpretation may require domain expertise to understand the implications of observed disparities. \nDoes not account for intersectionality or complex interactions between multiple protected attributes. \n \n", "signature": "(dataset , protected_classes = None ): ", "funcdef": "def"}, "validmind.tests.data_validation.ProtectedClassesDisparity": {"fullname": "validmind.tests.data_validation.ProtectedClassesDisparity", "modulename": "validmind.tests.data_validation.ProtectedClassesDisparity", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.ProtectedClassesDisparity.ProtectedClassesDisparity": {"fullname": "validmind.tests.data_validation.ProtectedClassesDisparity.ProtectedClassesDisparity", "modulename": "validmind.tests.data_validation.ProtectedClassesDisparity", "qualname": "ProtectedClassesDisparity", "kind": "function", "doc": "Investigates disparities in model performance across different protected class segments.
\n\nPurpose \n\nThis test aims to identify and quantify potential biases in model outcomes by comparing various performance metrics\nacross different segments of protected classes. It helps in assessing whether the model produces discriminatory\noutcomes for certain groups, which is crucial for ensuring fairness in machine learning models.
\n\nTest Mechanism \n\nThe test performs the following steps:
\n\n\nCalculates performance metrics (e.g., false negative rate, false positive rate, true positive rate) for each segment\nof the specified protected classes. \nComputes disparity ratios by comparing these metrics between different segments and a reference group. \nGenerates visualizations showing the disparities and their relation to a user-defined disparity tolerance threshold. \nProduces a comprehensive table with various disparity metrics for detailed analysis. \n \n\nSigns of High Risk \n\n\nDisparity ratios exceeding the specified disparity tolerance threshold. \nConsistent patterns of higher error rates or lower performance for specific protected class segments. \nStatistically significant differences in performance metrics across segments. \n \n\nStrengths \n\n\nProvides a comprehensive view of model fairness across multiple protected attributes and metrics. \nAllows for easy identification of problematic disparities through visual and tabular representations. \nCustomizable disparity tolerance threshold to align with specific use-case requirements. \nApplicable to various performance metrics, offering a multi-faceted analysis of model fairness. \n \n\nLimitations \n\n\nRelies on a predefined reference group for each protected class, which may not always be the most appropriate choice. \nDoes not account for intersectionality between different protected attributes. \nThe interpretation of results may require domain expertise to understand the implications of observed disparities. \n \n", "signature": "(\tdataset , \tmodel , \tprotected_classes = None , \tdisparity_tolerance = 1.25 , \tmetrics = [ 'fnr' , 'fpr' , 'tpr' ] ): ", "funcdef": "def"}, "validmind.tests.data_validation.ProtectedClassesThresholdOptimizer": {"fullname": "validmind.tests.data_validation.ProtectedClassesThresholdOptimizer", "modulename": "validmind.tests.data_validation.ProtectedClassesThresholdOptimizer", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.ProtectedClassesThresholdOptimizer.ProtectedClassesThresholdOptimizer": {"fullname": "validmind.tests.data_validation.ProtectedClassesThresholdOptimizer.ProtectedClassesThresholdOptimizer", "modulename": "validmind.tests.data_validation.ProtectedClassesThresholdOptimizer", "qualname": "ProtectedClassesThresholdOptimizer", "kind": "function", "doc": "Obtains a classifier by applying group-specific thresholds to the provided estimator.
\n\nPurpose \n\nThis test aims to optimize the fairness of a machine learning model by applying different\nclassification thresholds for different protected groups. It helps in mitigating bias and\nachieving more equitable outcomes across different demographic groups.
\n\nTest Mechanism \n\nThe test uses Fairlearn's ThresholdOptimizer to:
\n\n\nFit an optimizer on the training data, considering protected classes. \nApply optimized thresholds to make predictions on the test data. \nCalculate and report various fairness metrics. \nVisualize the optimized thresholds. \n \n\nSigns of High Risk \n\n\nLarge disparities in fairness metrics (e.g., Demographic Parity Ratio, Equalized Odds Ratio)\nacross different protected groups. \nSignificant differences in False Positive Rates (FPR) or True Positive Rates (TPR) between groups. \nThresholds that vary widely across different protected groups. \n \n\nStrengths \n\n\nProvides a post-processing method to improve model fairness without modifying the original model. \nAllows for balancing multiple fairness criteria simultaneously. \nOffers visual insights into the threshold optimization process. \n \n\nLimitations \n\n\nMay lead to a decrease in overall model performance while improving fairness. \nRequires access to protected attribute information at prediction time. \nThe effectiveness can vary depending on the chosen fairness constraint and objective. \n \n", "signature": "(\tdataset , \tpipeline = None , \tprotected_classes = None , \tX_train = None , \ty_train = None ): ", "funcdef": "def"}, "validmind.tests.data_validation.ProtectedClassesThresholdOptimizer.initialize_and_fit_optimizer": {"fullname": "validmind.tests.data_validation.ProtectedClassesThresholdOptimizer.initialize_and_fit_optimizer", "modulename": "validmind.tests.data_validation.ProtectedClassesThresholdOptimizer", "qualname": "initialize_and_fit_optimizer", "kind": "function", "doc": "
\n", "signature": "(pipeline , X_train , y_train , protected_classes_df ): ", "funcdef": "def"}, "validmind.tests.data_validation.ProtectedClassesThresholdOptimizer.plot_thresholds": {"fullname": "validmind.tests.data_validation.ProtectedClassesThresholdOptimizer.plot_thresholds", "modulename": "validmind.tests.data_validation.ProtectedClassesThresholdOptimizer", "qualname": "plot_thresholds", "kind": "function", "doc": "
\n", "signature": "(threshold_optimizer ): ", "funcdef": "def"}, "validmind.tests.data_validation.ProtectedClassesThresholdOptimizer.make_predictions": {"fullname": "validmind.tests.data_validation.ProtectedClassesThresholdOptimizer.make_predictions", "modulename": "validmind.tests.data_validation.ProtectedClassesThresholdOptimizer", "qualname": "make_predictions", "kind": "function", "doc": "
\n", "signature": "(threshold_optimizer , test_df , protected_classes ): ", "funcdef": "def"}, "validmind.tests.data_validation.ProtectedClassesThresholdOptimizer.calculate_fairness_metrics": {"fullname": "validmind.tests.data_validation.ProtectedClassesThresholdOptimizer.calculate_fairness_metrics", "modulename": "validmind.tests.data_validation.ProtectedClassesThresholdOptimizer", "qualname": "calculate_fairness_metrics", "kind": "function", "doc": "
\n", "signature": "(test_df , target , y_pred_opt , protected_classes ): ", "funcdef": "def"}, "validmind.tests.data_validation.ProtectedClassesThresholdOptimizer.calculate_group_metrics": {"fullname": "validmind.tests.data_validation.ProtectedClassesThresholdOptimizer.calculate_group_metrics", "modulename": "validmind.tests.data_validation.ProtectedClassesThresholdOptimizer", "qualname": "calculate_group_metrics", "kind": "function", "doc": "
\n", "signature": "(test_df , target , y_pred_opt , protected_classes ): ", "funcdef": "def"}, "validmind.tests.data_validation.ProtectedClassesThresholdOptimizer.get_thresholds_by_group": {"fullname": "validmind.tests.data_validation.ProtectedClassesThresholdOptimizer.get_thresholds_by_group", "modulename": "validmind.tests.data_validation.ProtectedClassesThresholdOptimizer", "qualname": "get_thresholds_by_group", "kind": "function", "doc": "
\n", "signature": "(threshold_optimizer ): ", "funcdef": "def"}, "validmind.tests.data_validation.RollingStatsPlot": {"fullname": "validmind.tests.data_validation.RollingStatsPlot", "modulename": "validmind.tests.data_validation.RollingStatsPlot", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.RollingStatsPlot.plot_rolling_statistics": {"fullname": "validmind.tests.data_validation.RollingStatsPlot.plot_rolling_statistics", "modulename": "validmind.tests.data_validation.RollingStatsPlot", "qualname": "plot_rolling_statistics", "kind": "function", "doc": "
\n", "signature": "(df , col , window_size ): ", "funcdef": "def"}, "validmind.tests.data_validation.RollingStatsPlot.RollingStatsPlot": {"fullname": "validmind.tests.data_validation.RollingStatsPlot.RollingStatsPlot", "modulename": "validmind.tests.data_validation.RollingStatsPlot", "qualname": "RollingStatsPlot", "kind": "function", "doc": "Evaluates the stationarity of time series data by plotting its rolling mean and standard deviation over a specified\nwindow.
\n\nPurpose \n\nThe RollingStatsPlot metric is employed to gauge the stationarity of time series data in a given dataset. This\nmetric specifically evaluates the rolling mean and rolling standard deviation of the dataset over a pre-specified\nwindow size. The rolling mean provides an understanding of the average trend in the data, while the rolling\nstandard deviation gauges the volatility of the data within the window. It is critical in preparing time series\ndata for modeling as it reveals key insights into data behavior across time.
\n\nTest Mechanism \n\nThis mechanism is comprised of two steps. Initially, the rolling mean and standard deviation for each of the\ndataset's columns are calculated over a window size, which can be user-specified or by default set to 12 data\npoints. Then, the calculated rolling mean and standard deviation are visualized via separate plots, illustrating\nthe trends and volatility in the dataset. A straightforward check is conducted to ensure the existence of columns\nin the dataset, and to verify that the given dataset has been indexed by its date and time\u2014a necessary prerequisite\nfor time series analysis.
\n\nSigns of High Risk \n\n\nThe presence of non-stationary patterns in either the rolling mean or the rolling standard deviation plots, which\ncould indicate trends or seasonality in the data that may affect the performance of time series models. \nMissing columns in the dataset, which would prevent the execution of this metric correctly. \nThe detection of NaN values in the dataset, which may need to be addressed before the metric can proceed\nsuccessfully. \n \n\nStrengths \n\n\nOffers visualizations of trending behavior and volatility within the data, facilitating a broader understanding\nof the dataset's inherent characteristics. \nChecks of the dataset's integrity, such as the existence of all required columns and the availability of a\ndatetime index. \nAdjusts to accommodate various window sizes, thus allowing accurate analysis of data with differing temporal\ngranularities. \nConsiders each column of the data individually, thereby accommodating multi-feature datasets. \n \n\nLimitations \n\n\nFor all columns, a fixed-size window is utilized. This may not accurately capture patterns in datasets where\ndifferent features may require different optimal window sizes. \nRequires the dataset to be indexed by date and time, hence it may not be usable for datasets without a timestamp\nindex. \nPrimarily serves for data visualization as it does not facilitate any quantitative measures for stationarity,\nsuch as through statistical tests. Therefore, the interpretation is subjective and depends heavily on modeler\ndiscretion. \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \twindow_size : int = 12 ): ", "funcdef": "def"}, "validmind.tests.data_validation.RunsTest": {"fullname": "validmind.tests.data_validation.RunsTest", "modulename": "validmind.tests.data_validation.RunsTest", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.RunsTest.RunsTest": {"fullname": "validmind.tests.data_validation.RunsTest.RunsTest", "modulename": "validmind.tests.data_validation.RunsTest", "qualname": "RunsTest", "kind": "function", "doc": "Executes Runs Test on ML model to detect non-random patterns in output data sequence.
\n\nPurpose \n\nThe Runs Test is a statistical procedure used to determine whether the sequence of data extracted from the ML model\nbehaves randomly or not. Specifically, it analyzes runs, sequences of consecutive positives or negatives, in the\ndata to check if there are more or fewer runs than expected under the assumption of randomness. This can be an\nindication of some pattern, trend, or cycle in the model's output which may need attention.
\n\nTest Mechanism \n\nThe testing mechanism applies the Runs Test from the statsmodels module on each column of the training dataset. For\nevery feature in the dataset, a Runs Test is executed, whose output includes a Runs Statistic and P-value. A low\nP-value suggests that data arrangement in the feature is not likely to be random. The results are stored in a\ndictionary where the keys are the feature names, and the values are another dictionary storing the test statistic\nand the P-value for each feature.
\n\nSigns of High Risk \n\n\nHigh risk is indicated when the P-value is close to zero. \nIf the P-value is less than a predefined significance level (like 0.05), it suggests that the runs (series of\npositive or negative values) in the model's output are not random and are longer or shorter than what is expected\nunder a random scenario. \nThis would mean there's a high risk of non-random distribution of errors or model outcomes, suggesting potential\nissues with the model. \n \n\nStrengths \n\n\nStraightforward and fast for detecting non-random patterns in data sequence. \nValidates assumptions of randomness, which is valuable for checking error distributions in regression models,\ntrendless time series data, and ensuring a classifier doesn't favor one class over another. \nCan be applied to both classification and regression tasks, making it versatile. \n \n\nLimitations \n\n\nAssumes that the data is independently and identically distributed (i.i.d.), which might not be the case for many\nreal-world datasets. \nThe conclusion drawn from the low P-value indicating non-randomness does not provide information about the type\nor the source of the detected pattern. \nSensitive to extreme values (outliers), and overly large or small run sequences can influence the results. \nDoes not provide model performance evaluation; it is used to detect patterns in the sequence of outputs only. \n \n", "signature": "(dataset ): ", "funcdef": "def"}, "validmind.tests.data_validation.ScatterPlot": {"fullname": "validmind.tests.data_validation.ScatterPlot", "modulename": "validmind.tests.data_validation.ScatterPlot", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.ScatterPlot.ScatterPlot": {"fullname": "validmind.tests.data_validation.ScatterPlot.ScatterPlot", "modulename": "validmind.tests.data_validation.ScatterPlot", "qualname": "ScatterPlot", "kind": "function", "doc": "Assesses visual relationships, patterns, and outliers among features in a dataset through scatter plot matrices.
\n\nPurpose \n\nThe ScatterPlot test aims to visually analyze a given dataset by constructing a scatter plot matrix of its\nnumerical features. The primary goal is to uncover relationships, patterns, and outliers across different features\nto provide both quantitative and qualitative insights into multidimensional relationships within the dataset. This\nvisual assessment aids in understanding the efficacy of the chosen features for model training and their\nsuitability.
\n\nTest Mechanism \n\nUsing the Seaborn library, the ScatterPlot function creates the scatter plot matrix. The process involves\nretrieving all numerical columns from the dataset and generating a scatter matrix for these columns. The resulting\nscatter plot provides visual representations of feature relationships. The function also adjusts axis labels for\nreadability and returns the final plot as a Matplotlib Figure object for further analysis and visualization.
\n\nSigns of High Risk \n\n\nThe emergence of non-linear or random patterns across different feature pairs, suggesting complex relationships\nunsuitable for linear assumptions. \nLack of clear patterns or clusters, indicating weak or non-existent correlations among features, which could\nchallenge certain model types. \nPresence of outliers, as visual outliers can adversely influence the model's performance. \n \n\nStrengths \n\n\nProvides insight into the multidimensional relationships among multiple features. \nAssists in identifying trends, correlations, and outliers that could affect model performance. \nValidates assumptions made during model creation, such as linearity. \nVersatile for application in both regression and classification tasks. \nUsing Seaborn facilitates an intuitive and detailed visual exploration of data. \n \n\nLimitations \n\n\nScatter plot matrices may become cluttered and hard to decipher as the number of features increases. \nPrimarily reveals pairwise relationships and may fail to illuminate complex interactions involving three or more\nfeatures. \nBeing a visual tool, precision in quantitative analysis might be compromised. \nOutliers not clearly visible in plots can be missed, affecting model performance. \nAssumes that the dataset can fit into the computer's memory, which might not be valid for extremely large\ndatasets. \n \n", "signature": "(dataset ): ", "funcdef": "def"}, "validmind.tests.data_validation.ScoreBandDefaultRates": {"fullname": "validmind.tests.data_validation.ScoreBandDefaultRates", "modulename": "validmind.tests.data_validation.ScoreBandDefaultRates", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.ScoreBandDefaultRates.ScoreBandDefaultRates": {"fullname": "validmind.tests.data_validation.ScoreBandDefaultRates.ScoreBandDefaultRates", "modulename": "validmind.tests.data_validation.ScoreBandDefaultRates", "qualname": "ScoreBandDefaultRates", "kind": "function", "doc": "Analyzes default rates and population distribution across credit score bands.
\n\nPurpose \n\nThe Score Band Default Rates test evaluates the discriminatory power of credit scores by analyzing\ndefault rates across different score bands. This helps validate score effectiveness, supports\npolicy decisions, and provides insights into portfolio risk distribution.
\n\nTest Mechanism \n\nThe test segments the score distribution into bands and calculates key metrics for each band:
\n\n\nPopulation count and percentage in each band \nDefault rate within each band \nCumulative statistics across bands\nThe results show how well the scores separate good and bad accounts. \n \n\nSigns of High Risk \n\n\nNon-monotonic default rates across score bands \nInsufficient population in critical score bands \nUnexpected default rates for score ranges \nHigh concentration in specific score bands \nSimilar default rates across adjacent bands \nUnstable default rates in key decision bands \nExtreme population skewness \nPoor risk separation between bands \n \n\nStrengths \n\n\nClear view of score effectiveness \nSupports policy threshold decisions \nEasy to interpret and communicate \nDirectly links to business decisions \nShows risk segmentation power \nIdentifies potential score issues \nHelps validate scoring model \nSupports portfolio monitoring \n \n\nLimitations \n\n\nSensitive to band definition choices \nMay mask within-band variations \nRequires sufficient data in each band \nCannot capture non-linear patterns \nPoint-in-time analysis only \nNo temporal trend information \nAssumes band boundaries are appropriate \nMay oversimplify risk patterns \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tmodel : validmind . vm_models . model . VMModel , \tscore_column : str = 'score' , \tscore_bands : list = None ): ", "funcdef": "def"}, "validmind.tests.data_validation.SeasonalDecompose": {"fullname": "validmind.tests.data_validation.SeasonalDecompose", "modulename": "validmind.tests.data_validation.SeasonalDecompose", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.SeasonalDecompose.SeasonalDecompose": {"fullname": "validmind.tests.data_validation.SeasonalDecompose.SeasonalDecompose", "modulename": "validmind.tests.data_validation.SeasonalDecompose", "qualname": "SeasonalDecompose", "kind": "function", "doc": "Assesses patterns and seasonality in a time series dataset by decomposing its features into foundational components.
\n\nPurpose \n\nThe Seasonal Decompose test aims to decompose the features of a time series dataset into their fundamental\ncomponents: observed, trend, seasonal, and residuals. By utilizing the Seasonal Decomposition of Time Series by\nLoess (STL) method, the test identifies underlying patterns, predominantly seasonality, in the dataset's features.\nThis aids in developing a more comprehensive understanding of the dataset, which in turn facilitates more effective\nmodel validation.
\n\nTest Mechanism \n\nThe testing process leverages the seasonal_decompose function from the statsmodels.tsa.seasonal library to\nevaluate each feature in the dataset. It isolates each feature into four components\u2014observed, trend, seasonal, and\nresiduals\u2014and generates six subplot graphs per feature for visual interpretation. Prior to decomposition, the test\nscrutinizes and removes any non-finite values, ensuring the reliability of the analysis.
\n\nSigns of High Risk \n\n\nNon-Finiteness : Datasets with a high number of non-finite values may flag as high risk since these values are\nomitted before conducting the seasonal decomposition.Frequent Warnings : Chronic failure to infer the frequency for a scrutinized feature indicates high risk.High Seasonality : A significant seasonal component could potentially render forecasts unreliable due to\noverwhelming seasonal variation. \n\nStrengths \n\n\nSeasonality Detection : Accurately discerns hidden seasonality patterns in dataset features.Visualization : Facilitates interpretation and comprehension through graphical representations.Unrestricted Usage : Not confined to any specific regression model, promoting wide-ranging applicability. \n\nLimitations \n\n\nDependence on Assumptions : Assumes that dataset features are periodically distributed. Features with no\ninferable frequency are excluded from the test.Handling Non-Finite Values : Disregards non-finite values during analysis, potentially resulting in an\nincomplete understanding of the dataset.Unreliability with Noisy Datasets : Produces unreliable results when used with datasets that contain heavy\nnoise. \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tseasonal_model : str = 'additive' ): ", "funcdef": "def"}, "validmind.tests.data_validation.ShapiroWilk": {"fullname": "validmind.tests.data_validation.ShapiroWilk", "modulename": "validmind.tests.data_validation.ShapiroWilk", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.ShapiroWilk.ShapiroWilk": {"fullname": "validmind.tests.data_validation.ShapiroWilk.ShapiroWilk", "modulename": "validmind.tests.data_validation.ShapiroWilk", "qualname": "ShapiroWilk", "kind": "function", "doc": "Evaluates feature-wise normality of training data using the Shapiro-Wilk test.
\n\nPurpose \n\nThe Shapiro-Wilk test is utilized to investigate whether a particular dataset conforms to the standard normal\ndistribution. This analysis is crucial in machine learning modeling because the normality of the data can\nprofoundly impact the performance of the model. This metric is especially useful in evaluating various features of\nthe dataset in both classification and regression tasks.
\n\nTest Mechanism \n\nThe Shapiro-Wilk test is conducted on each feature column of the training dataset to determine if the data\ncontained fall within the normal distribution. The test presents a statistic and a p-value, with the p-value\nserving to validate or repudiate the null hypothesis, which is that the tested data is normally distributed.
\n\nSigns of High Risk \n\n\nA p-value that falls below 0.05 signifies a high risk as it discards the null hypothesis, indicating that the\ndata does not adhere to the normal distribution. \nFor machine learning models built on the presumption of data normality, such an outcome could result in subpar\nperformance or incorrect predictions. \n \n\nStrengths \n\n\nThe Shapiro-Wilk test is esteemed for its level of accuracy, thereby making it particularly well-suited to\ndatasets of small to moderate sizes. \nIt proves its versatility through its efficient functioning in both classification and regression tasks. \nBy separately testing each feature column, the Shapiro-Wilk test can raise an alarm if a specific feature does\nnot comply with the normality. \n \n\nLimitations \n\n\nThe Shapiro-Wilk test's sensitivity can be a disadvantage as it often rejects the null hypothesis (i.e., data is\nnormally distributed), even for minor deviations, especially in large datasets. This may lead to unwarranted 'false\nalarms' of high risk by deeming the data as not normally distributed even if it approximates normal distribution. \nExceptional care must be taken in managing missing data or outliers prior to testing as these can greatly skew\nthe results. \nLastly, the Shapiro-Wilk test is not optimally suited for processing data with pronounced skewness or kurtosis. \n \n", "signature": "(dataset ): ", "funcdef": "def"}, "validmind.tests.data_validation.Skewness": {"fullname": "validmind.tests.data_validation.Skewness", "modulename": "validmind.tests.data_validation.Skewness", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.Skewness.Skewness": {"fullname": "validmind.tests.data_validation.Skewness.Skewness", "modulename": "validmind.tests.data_validation.Skewness", "qualname": "Skewness", "kind": "function", "doc": "Evaluates the skewness of numerical data in a dataset to check against a defined threshold, aiming to ensure data\nquality and optimize model performance.
\n\nPurpose \n\nThe purpose of the Skewness test is to measure the asymmetry in the distribution of data within a predictive\nmachine learning model. Specifically, it evaluates the divergence of said distribution from a normal distribution.\nUnderstanding the level of skewness helps identify data quality issues, which are crucial for optimizing the\nperformance of traditional machine learning models in both classification and regression settings.
\n\nTest Mechanism \n\nThis test calculates the skewness of numerical columns in the dataset, focusing specifically on numerical data\ntypes. The calculated skewness value is then compared against a predetermined maximum threshold, which is set by\ndefault to 1. If the skewness value is less than this maximum threshold, the test passes; otherwise, it fails. The\ntest results, along with the skewness values and column names, are then recorded for further analysis.
\n\nSigns of High Risk \n\n\nSubstantial skewness levels that significantly exceed the maximum threshold. \nPersistent skewness in the data, indicating potential issues with the foundational assumptions of the machine\nlearning model. \nSubpar model performance, erroneous predictions, or biased inferences due to skewed data distributions. \n \n\nStrengths \n\n\nFast and efficient identification of unequal data distributions within a machine learning model. \nAdjustable maximum threshold parameter, allowing for customization based on user needs. \nProvides a clear quantitative measure to mitigate model risks related to data skewness. \n \n\nLimitations \n\n\nOnly evaluates numeric columns, potentially missing skewness or bias in non-numeric data. \nAssumes that data should follow a normal distribution, which may not always be applicable to real-world data. \nSubjective threshold for risk grading, requiring expert input and recurrent iterations for refinement. \n \n", "signature": "(dataset , max_threshold = 1 ): ", "funcdef": "def"}, "validmind.tests.data_validation.SpreadPlot": {"fullname": "validmind.tests.data_validation.SpreadPlot", "modulename": "validmind.tests.data_validation.SpreadPlot", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.SpreadPlot.SpreadPlot": {"fullname": "validmind.tests.data_validation.SpreadPlot.SpreadPlot", "modulename": "validmind.tests.data_validation.SpreadPlot", "qualname": "SpreadPlot", "kind": "function", "doc": "Assesses potential correlations between pairs of time series variables through visualization to enhance\nunderstanding of their relationships.
\n\nPurpose \n\nThe SpreadPlot test aims to graphically illustrate and analyze the relationships between pairs of time series\nvariables within a given dataset. This facilitated understanding helps in identifying and assessing potential time\nseries correlations, such as cointegration, between the variables.
\n\nTest Mechanism \n\nThe SpreadPlot test computes and represents the spread between each pair of time series variables in the dataset.\nSpecifically, the difference between two variables is calculated and presented as a line graph. This process is\niterated for each unique pair of variables in the dataset, allowing for comprehensive visualization of their\nrelationships.
\n\nSigns of High Risk \n\n\nLarge fluctuations in the spread over a given timespan. \nUnexpected patterns or trends that may signal potential risks in the underlying correlations between the\nvariables. \nPresence of significant missing data or extreme outlier values, which could potentially skew the spread and\nindicate high risk. \n \n\nStrengths \n\n\nAllows for thorough visual examination and interpretation of the correlations between time-series pairs. \nAids in revealing complex relationships like cointegration. \nEnhances interpretability by visualizing the relationships, thereby helping in spotting outliers and trends. \nCapable of handling numerous variable pairs from the dataset through a versatile and adaptable process. \n \n\nLimitations \n\n\nPrimarily serves as a visualization tool and does not offer quantitative measurements or statistics to\nobjectively determine relationships. \nHeavily relies on the quality and granularity of the data\u2014missing data or outliers can notably disturb the\ninterpretation of relationships. \nCan become inefficient or difficult to interpret with a high number of variables due to the profuse number of\nplots. \nMight not completely capture intricate non-linear relationships between the variables. \n \n", "signature": "(dataset : validmind . vm_models . dataset . dataset . VMDataset ): ", "funcdef": "def"}, "validmind.tests.data_validation.TabularCategoricalBarPlots": {"fullname": "validmind.tests.data_validation.TabularCategoricalBarPlots", "modulename": "validmind.tests.data_validation.TabularCategoricalBarPlots", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.TabularCategoricalBarPlots.TabularCategoricalBarPlots": {"fullname": "validmind.tests.data_validation.TabularCategoricalBarPlots.TabularCategoricalBarPlots", "modulename": "validmind.tests.data_validation.TabularCategoricalBarPlots", "qualname": "TabularCategoricalBarPlots", "kind": "function", "doc": "Generates and visualizes bar plots for each category in categorical features to evaluate the dataset's composition.
\n\nPurpose \n\nThe purpose of this metric is to visually analyze categorical data using bar plots. It is intended to evaluate the\ndataset's composition by displaying the counts of each category in each categorical feature.
\n\nTest Mechanism \n\nThe provided dataset is first checked to determine if it contains any categorical variables. If no categorical\ncolumns are found, the tool raises a ValueError. For each categorical variable in the dataset, a separate bar plot\nis generated. The number of occurrences for each category is calculated and displayed on the plot. If a dataset\ncontains multiple categorical columns, multiple bar plots are produced.
\n\nSigns of High Risk \n\n\nHigh risk could occur if the categorical variables exhibit an extreme imbalance, with categories having very few\ninstances possibly being underrepresented in the model, which could affect the model's performance and its ability\nto generalize. \nAnother sign of risk is if there are too many categories in a single variable, which could lead to overfitting\nand make the model complex. \n \n\nStrengths \n\n\nProvides a visual and intuitively understandable representation of categorical data. \nAids in the analysis of variable distributions. \nHelps in easily identifying imbalances or rare categories that could affect the model's performance. \n \n\nLimitations \n\n\nThis method only works with categorical data and won't apply to numerical variables. \nIt does not provide informative value when there are too many categories, as the bar chart could become cluttered\nand hard to interpret. \nOffers no insights into the model's performance or precision, but rather provides a descriptive analysis of the\ninput. \n \n", "signature": "(dataset : validmind . vm_models . dataset . dataset . VMDataset ): ", "funcdef": "def"}, "validmind.tests.data_validation.TabularDateTimeHistograms": {"fullname": "validmind.tests.data_validation.TabularDateTimeHistograms", "modulename": "validmind.tests.data_validation.TabularDateTimeHistograms", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.TabularDateTimeHistograms.TabularDateTimeHistograms": {"fullname": "validmind.tests.data_validation.TabularDateTimeHistograms.TabularDateTimeHistograms", "modulename": "validmind.tests.data_validation.TabularDateTimeHistograms", "qualname": "TabularDateTimeHistograms", "kind": "function", "doc": "Generates histograms to provide graphical insight into the distribution of time intervals in a model's datetime\ndata.
\n\nPurpose \n\nThe TabularDateTimeHistograms metric is designed to provide graphical insight into the distribution of time\nintervals in a machine learning model's datetime data. By plotting histograms of differences between consecutive\ndate entries in all datetime variables, it enables an examination of the underlying pattern of time series data and\nidentification of anomalies.
\n\nTest Mechanism \n\nThis test operates by first identifying all datetime columns and extracting them from the dataset. For each\ndatetime column, it next computes the differences (in days) between consecutive dates, excluding zero values, and\nvisualizes these differences in a histogram. The Plotly library's histogram function is used to generate\nhistograms, which are labeled appropriately and provide a graphical representation of the frequency of different\nday intervals in the dataset.
\n\nSigns of High Risk \n\n\nIf no datetime columns are detected in the dataset, this would lead to a ValueError. Hence, the absence of\ndatetime columns signifies a high risk. \nA severely skewed or irregular distribution depicted in the histogram may indicate possible complications with\nthe data, such as faulty timestamps or abnormalities. \n \n\nStrengths \n\n\nThe metric offers a visual overview of time interval frequencies within the dataset, supporting the recognition\nof inherent patterns. \nHistogram plots can aid in the detection of potential outliers and data anomalies, contributing to an assessment\nof data quality. \nThe metric is versatile, compatible with a range of task types, including classification and regression, and can\nwork with multiple datetime variables if present. \n \n\nLimitations \n\n\nA major weakness of this metric is its dependence on the visual examination of data, as it does not provide a\nmeasurable evaluation of the model. \nThe metric might overlook complex or multi-dimensional trends in the data. \nThe test is only applicable to datasets containing datetime columns and will fail if such columns are unavailable. \nThe interpretation of the histograms relies heavily on the domain expertise and experience of the reviewer. \n \n", "signature": "(dataset : validmind . vm_models . dataset . dataset . VMDataset ): ", "funcdef": "def"}, "validmind.tests.data_validation.TabularDescriptionTables": {"fullname": "validmind.tests.data_validation.TabularDescriptionTables", "modulename": "validmind.tests.data_validation.TabularDescriptionTables", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.TabularDescriptionTables.TabularDescriptionTables": {"fullname": "validmind.tests.data_validation.TabularDescriptionTables.TabularDescriptionTables", "modulename": "validmind.tests.data_validation.TabularDescriptionTables", "qualname": "TabularDescriptionTables", "kind": "function", "doc": "Summarizes key descriptive statistics for numerical, categorical, and datetime variables in a dataset.
\n\nPurpose \n\nThe main purpose of this metric is to gather and present the descriptive statistics of numerical, categorical, and\ndatetime variables present in a dataset. The attributes it measures include the count, mean, minimum and maximum\nvalues, percentage of missing values, data types of fields, and unique values for categorical fields, among others.
\n\nTest Mechanism \n\nThe test first segregates the variables in the dataset according to their data types (numerical, categorical, or\ndatetime). Then, it compiles summary statistics for each type of variable. The specifics of these statistics vary\ndepending on the type of variable:
\n\n\nFor numerical variables, the metric extracts descriptors like count, mean, minimum and maximum values, count of\nmissing values, and data types. \nFor categorical variables, it counts the number of unique values, displays unique values, counts missing values,\nand identifies data types. \nFor datetime variables, it counts the number of unique values, identifies the earliest and latest dates, counts\nmissing values, and identifies data types. \n \n\nSigns of High Risk \n\n\nMasses of missing values in the descriptive statistics results could hint at high risk or failure, indicating\npotential data collection, integrity, and quality issues. \nDetection of inappropriate distributions for numerical variables, like having negative values for variables that\nare always supposed to be positive. \nIdentifying inappropriate data types, like a continuous variable being encoded as a categorical type. \n \n\nStrengths \n\n\nProvides a comprehensive overview of the dataset. \nGives a snapshot into the essence of the numerical, categorical, and datetime fields. \nIdentifies potential data quality issues such as missing values or inconsistencies crucial for building credible\nmachine learning models. \nThe metadata, including the data type and missing value information, are vital for anyone including data\nscientists dealing with the dataset before the modeling process. \n \n\nLimitations \n\n\nIt does not perform any deeper statistical analysis or tests on the data. \nIt does not handle issues such as outliers, or relationships between variables. \nIt offers no insights into potential correlations or possible interactions between variables. \nIt does not investigate the potential impact of missing values on the performance of the machine learning models. \nIt does not explore potential transformation requirements that may be necessary to enhance the performance of the\nchosen algorithm. \n \n", "signature": "(dataset ): ", "funcdef": "def"}, "validmind.tests.data_validation.TabularDescriptionTables.get_summary_statistics_numerical": {"fullname": "validmind.tests.data_validation.TabularDescriptionTables.get_summary_statistics_numerical", "modulename": "validmind.tests.data_validation.TabularDescriptionTables", "qualname": "get_summary_statistics_numerical", "kind": "function", "doc": "
\n", "signature": "(dataset , numerical_fields ): ", "funcdef": "def"}, "validmind.tests.data_validation.TabularDescriptionTables.get_summary_statistics_categorical": {"fullname": "validmind.tests.data_validation.TabularDescriptionTables.get_summary_statistics_categorical", "modulename": "validmind.tests.data_validation.TabularDescriptionTables", "qualname": "get_summary_statistics_categorical", "kind": "function", "doc": "
\n", "signature": "(dataset , categorical_fields ): ", "funcdef": "def"}, "validmind.tests.data_validation.TabularDescriptionTables.get_summary_statistics_datetime": {"fullname": "validmind.tests.data_validation.TabularDescriptionTables.get_summary_statistics_datetime", "modulename": "validmind.tests.data_validation.TabularDescriptionTables", "qualname": "get_summary_statistics_datetime", "kind": "function", "doc": "
\n", "signature": "(dataset , datetime_fields ): ", "funcdef": "def"}, "validmind.tests.data_validation.TabularDescriptionTables.get_categorical_columns": {"fullname": "validmind.tests.data_validation.TabularDescriptionTables.get_categorical_columns", "modulename": "validmind.tests.data_validation.TabularDescriptionTables", "qualname": "get_categorical_columns", "kind": "function", "doc": "
\n", "signature": "(dataset ): ", "funcdef": "def"}, "validmind.tests.data_validation.TabularDescriptionTables.get_numerical_columns": {"fullname": "validmind.tests.data_validation.TabularDescriptionTables.get_numerical_columns", "modulename": "validmind.tests.data_validation.TabularDescriptionTables", "qualname": "get_numerical_columns", "kind": "function", "doc": "
\n", "signature": "(dataset ): ", "funcdef": "def"}, "validmind.tests.data_validation.TabularDescriptionTables.get_datetime_columns": {"fullname": "validmind.tests.data_validation.TabularDescriptionTables.get_datetime_columns", "modulename": "validmind.tests.data_validation.TabularDescriptionTables", "qualname": "get_datetime_columns", "kind": "function", "doc": "
\n", "signature": "(dataset ): ", "funcdef": "def"}, "validmind.tests.data_validation.TabularNumericalHistograms": {"fullname": "validmind.tests.data_validation.TabularNumericalHistograms", "modulename": "validmind.tests.data_validation.TabularNumericalHistograms", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.TabularNumericalHistograms.TabularNumericalHistograms": {"fullname": "validmind.tests.data_validation.TabularNumericalHistograms.TabularNumericalHistograms", "modulename": "validmind.tests.data_validation.TabularNumericalHistograms", "qualname": "TabularNumericalHistograms", "kind": "function", "doc": "Generates histograms for each numerical feature in a dataset to provide visual insights into data distribution and\ndetect potential issues.
\n\nPurpose \n\nThe purpose of this test is to provide visual analysis of numerical data through the generation of histograms for\neach numerical feature in the dataset. Histograms aid in the exploratory analysis of data, offering insight into\nthe distribution of the data, skewness, presence of outliers, and central tendencies. It helps in understanding if\nthe inputs to the model are normally distributed, which is a common assumption in many machine learning algorithms.
\n\nTest Mechanism \n\nThis test scans the provided dataset and extracts all the numerical columns. For each numerical column, it\nconstructs a histogram using plotly, with 50 bins. The deployment of histograms offers a robust visual aid,\nensuring unruffled identification and understanding of numerical data distribution patterns.
\n\nSigns of High Risk \n\n\nA high degree of skewness \nUnexpected data distributions \nExistence of extreme outliers in the histograms \n \n\nThese may indicate issues with the data that the model is receiving. If data for a numerical feature is expected to\nfollow a certain distribution (like a normal distribution) but does not, it could lead to sub-par performance by\nthe model. As such these instances should be treated as high-risk indicators.
\n\nStrengths \n\n\nProvides a simple, easy-to-interpret visualization of how data for each numerical attribute is distributed. \nHelps detect skewed values and outliers that could potentially harm the AI model's performance. \nCan be applied to large datasets and multiple numerical variables conveniently. \n \n\nLimitations \n\n\nOnly works with numerical data, thus ignoring non-numerical or categorical data. \nDoes not analyze relationships between different features, only the individual feature distributions. \nIs a univariate analysis and may miss patterns or anomalies that only appear when considering multiple variables\ntogether. \nDoes not provide any insight into how these features affect the output of the model; it is purely an input\nanalysis tool. \n \n", "signature": "(dataset : validmind . vm_models . dataset . dataset . VMDataset ): ", "funcdef": "def"}, "validmind.tests.data_validation.TargetRateBarPlots": {"fullname": "validmind.tests.data_validation.TargetRateBarPlots", "modulename": "validmind.tests.data_validation.TargetRateBarPlots", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.TargetRateBarPlots.TargetRateBarPlots": {"fullname": "validmind.tests.data_validation.TargetRateBarPlots.TargetRateBarPlots", "modulename": "validmind.tests.data_validation.TargetRateBarPlots", "qualname": "TargetRateBarPlots", "kind": "function", "doc": "Generates bar plots visualizing the default rates of categorical features for a classification machine learning\nmodel.
\n\nPurpose \n\nThis test, implemented as a metric, is designed to provide an intuitive, graphical summary of the decision-making\npatterns exhibited by a categorical classification machine learning model. The model's performance is evaluated\nusing bar plots depicting the ratio of target rates\u2014meaning the proportion of positive classes\u2014for different\ncategorical inputs. This allows for an easy, at-a-glance understanding of the model's accuracy.
\n\nTest Mechanism \n\nThe test involves creating a pair of bar plots for each categorical feature in the dataset. The first plot depicts\nthe frequency of each category in the dataset, with each category visually distinguished by its unique color. The\nsecond plot shows the mean target rate of each category (sourced from the \"default_column\"). Plotly, a Python\nlibrary, is used to generate these plots, with distinct plots created for each feature. If no specific columns are\nselected, the test will generate plots for each categorical column in the dataset.
\n\nSigns of High Risk \n\n\nInconsistent or non-binary values in the \"default_column\" could complicate or render impossible the calculation\nof average target rates. \nParticularly low or high target rates for a specific category might suggest that the model is misclassifying\ninstances of that category. \n \n\nStrengths \n\n\nThis test offers a visually interpretable breakdown of the model's decisions, providing an easy way to spot\nirregularities, inconsistencies, or patterns. \nIts flexibility allows for the inspection of one or multiple columns, as needed. \n \n\nLimitations \n\n\nThe readability of the bar plots drops as the number of distinct categories increases in the dataset, which can\nmake them harder to understand and less useful. \n \n", "signature": "(dataset : validmind . vm_models . dataset . dataset . VMDataset ): ", "funcdef": "def"}, "validmind.tests.data_validation.TimeSeriesDescription": {"fullname": "validmind.tests.data_validation.TimeSeriesDescription", "modulename": "validmind.tests.data_validation.TimeSeriesDescription", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.TimeSeriesDescription.TimeSeriesDescription": {"fullname": "validmind.tests.data_validation.TimeSeriesDescription.TimeSeriesDescription", "modulename": "validmind.tests.data_validation.TimeSeriesDescription", "qualname": "TimeSeriesDescription", "kind": "function", "doc": "Generates a detailed analysis for the provided time series dataset, summarizing key statistics to identify trends,\npatterns, and data quality issues.
\n\nPurpose \n\nThe TimeSeriesDescription function aims to analyze an individual time series by providing a summary of key\nstatistics. This helps in understanding trends, patterns, and data quality issues within the time series.
\n\nTest Mechanism \n\nThe function extracts the time series data and provides a summary of key statistics. The dataset is expected to\nhave a datetime index. The function checks this and raises an error if the index is not in datetime format. For\neach variable (column) in the dataset, appropriate statistics including start date, end date, frequency, number of\nmissing values, count, min, and max values are calculated.
\n\nSigns of High Risk \n\n\nIf the index of the dataset is not in datetime format, it could lead to errors in time-series analysis. \nInconsistent or missing data within the dataset might affect the analysis of trends and patterns. \n \n\nStrengths \n\n\nProvides a comprehensive summary of key statistics for each variable, helping to identify data quality issues\nsuch as missing values. \nHelps in understanding the distribution and range of the data by including min and max values. \n \n\nLimitations \n\n\nAssumes that the dataset is provided as a DataFrameDataset object with a .df attribute to access the pandas\nDataFrame. \nOnly analyzes datasets with a datetime index and will raise an error for other types of indices. \nDoes not handle large datasets efficiently; performance may degrade with very large datasets. \n \n", "signature": "(dataset ): ", "funcdef": "def"}, "validmind.tests.data_validation.TimeSeriesDescriptiveStatistics": {"fullname": "validmind.tests.data_validation.TimeSeriesDescriptiveStatistics", "modulename": "validmind.tests.data_validation.TimeSeriesDescriptiveStatistics", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.TimeSeriesDescriptiveStatistics.TimeSeriesDescriptiveStatistics": {"fullname": "validmind.tests.data_validation.TimeSeriesDescriptiveStatistics.TimeSeriesDescriptiveStatistics", "modulename": "validmind.tests.data_validation.TimeSeriesDescriptiveStatistics", "qualname": "TimeSeriesDescriptiveStatistics", "kind": "function", "doc": "Evaluates the descriptive statistics of a time series dataset to identify trends, patterns, and data quality issues.
\n\nPurpose \n\nThe purpose of the TimeSeriesDescriptiveStatistics function is to analyze an individual time series by providing a\nsummary of key descriptive statistics. This analysis helps in understanding trends, patterns, and data quality\nissues within the time series dataset.
\n\nTest Mechanism \n\nThe function extracts the time series data and provides a summary of key descriptive statistics. The dataset is\nexpected to have a datetime index, and the function will check this and raise an error if the index is not in a\ndatetime format. For each variable (column) in the dataset, appropriate statistics, including start date, end date,\nmin, mean, max, skewness, kurtosis, and count, are calculated.
\n\nSigns of High Risk \n\n\nIf the index of the dataset is not in datetime format, it could lead to errors in time-series analysis. \nInconsistent or missing data within the dataset might affect the analysis of trends and patterns. \n \n\nStrengths \n\n\nProvides a comprehensive summary of key descriptive statistics for each variable. \nHelps identify data quality issues and understand the distribution of the data. \n \n\nLimitations \n\n\nAssumes the dataset is provided as a DataFrameDataset object with a .df attribute to access the pandas DataFrame. \nOnly analyzes datasets with a datetime index and will raise an error for other types of indices. \nDoes not handle large datasets efficiently, and performance may degrade with very large datasets. \n \n", "signature": "(dataset ): ", "funcdef": "def"}, "validmind.tests.data_validation.TimeSeriesFrequency": {"fullname": "validmind.tests.data_validation.TimeSeriesFrequency", "modulename": "validmind.tests.data_validation.TimeSeriesFrequency", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.TimeSeriesFrequency.TimeSeriesFrequency": {"fullname": "validmind.tests.data_validation.TimeSeriesFrequency.TimeSeriesFrequency", "modulename": "validmind.tests.data_validation.TimeSeriesFrequency", "qualname": "TimeSeriesFrequency", "kind": "function", "doc": "Evaluates consistency of time series data frequency and generates a frequency plot.
\n\nPurpose \n\nThe purpose of the TimeSeriesFrequency test is to evaluate the consistency in the frequency of data points in a\ntime-series dataset. This test inspects the intervals or duration between each data point to determine if a fixed\npattern (such as daily, weekly, or monthly) exists. The identification of such patterns is crucial to time-series\nanalysis as any irregularities could lead to erroneous results and hinder the model's capacity for identifying\ntrends and patterns.
\n\nTest Mechanism \n\nInitially, the test checks if the dataframe index is in datetime format. Subsequently, it utilizes pandas'\ninfer_freq method to identify the frequency of each data series within the dataframe. The infer_freq method\nattempts to establish the frequency of a time series and returns both the frequency string and a dictionary\nrelating these strings to their respective labels. The test compares the frequencies of all datasets. If they share\na common frequency, the test passes, but it fails if they do not. Additionally, Plotly is used to create a\nfrequency plot, offering a visual depiction of the time differences between consecutive entries in the dataframe\nindex.
\n\nSigns of High Risk \n\n\nThe test fails, indicating multiple unique frequencies within the dataset. This failure could suggest irregular\nintervals between observations, potentially interrupting pattern recognition or trend analysis. \nThe presence of missing or null frequencies could be an indication of inconsistencies in data or gaps within the\ndata collection process. \n \n\nStrengths \n\n\nThis test uses a systematic approach to checking the consistency of data frequency within a time-series dataset. \nIt increases the model's reliability by asserting the consistency of observations over time, an essential factor\nin time-series analysis. \nThe test generates a visual plot, providing an intuitive representation of the dataset's frequency distribution,\nwhich caters to visual learners and aids in interpretation and explanation. \n \n\nLimitations \n\n\nThis test is only applicable to time-series datasets and hence not suitable for other types of datasets. \nThe infer_freq method might not always correctly infer frequency when faced with missing or irregular data\npoints. \nDepending on context or the model under development, mixed frequencies might sometimes be acceptable, but this\ntest considers them a failing condition. \n \n", "signature": "(dataset : validmind . vm_models . dataset . dataset . VMDataset ): ", "funcdef": "def"}, "validmind.tests.data_validation.TimeSeriesHistogram": {"fullname": "validmind.tests.data_validation.TimeSeriesHistogram", "modulename": "validmind.tests.data_validation.TimeSeriesHistogram", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.TimeSeriesHistogram.TimeSeriesHistogram": {"fullname": "validmind.tests.data_validation.TimeSeriesHistogram.TimeSeriesHistogram", "modulename": "validmind.tests.data_validation.TimeSeriesHistogram", "qualname": "TimeSeriesHistogram", "kind": "function", "doc": "Visualizes distribution of time-series data using histograms and Kernel Density Estimation (KDE) lines.
\n\nPurpose \n\nThe TimeSeriesHistogram test aims to perform a histogram analysis on time-series data to assess the distribution of\nvalues within a dataset over time. This test is useful for regression tasks and can be applied to various types of\ndata, such as internet traffic, stock prices, and weather data, providing insights into the probability\ndistribution, skewness, and kurtosis of the dataset.
\n\nTest Mechanism \n\nThis test operates on a specific column within the dataset that must have a datetime type index. For each column in\nthe dataset, a histogram is created using Plotly's histplot function. If the dataset includes more than one\ntime-series, a distinct histogram is plotted for each series. Additionally, a Kernel Density Estimate (KDE) line is\ndrawn for each histogram, visualizing the data's underlying probability distribution. The x and y-axis labels are\nhidden to focus solely on the data distribution.
\n\nSigns of High Risk \n\n\nThe dataset lacks a column with a datetime type index. \nThe specified columns do not exist within the dataset. \nHigh skewness or kurtosis in the data distribution, indicating potential bias. \nPresence of significant outliers in the data distribution. \n \n\nStrengths \n\n\nServes as a visual diagnostic tool for understanding data behavior and distribution trends. \nEffective for analyzing both single and multiple time-series data. \nKDE line provides a smooth estimate of the overall trend in data distribution. \n \n\nLimitations \n\n\nProvides a high-level view without specific numeric measures such as skewness or kurtosis. \nThe histogram loses some detail due to binning of data values. \nCannot handle non-numeric data columns. \nHistogram shape may be sensitive to the number of bins used. \n \n", "signature": "(dataset , nbins = 30 ): ", "funcdef": "def"}, "validmind.tests.data_validation.TimeSeriesLinePlot": {"fullname": "validmind.tests.data_validation.TimeSeriesLinePlot", "modulename": "validmind.tests.data_validation.TimeSeriesLinePlot", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.TimeSeriesLinePlot.TimeSeriesLinePlot": {"fullname": "validmind.tests.data_validation.TimeSeriesLinePlot.TimeSeriesLinePlot", "modulename": "validmind.tests.data_validation.TimeSeriesLinePlot", "qualname": "TimeSeriesLinePlot", "kind": "function", "doc": "Generates and analyses time-series data through line plots revealing trends, patterns, anomalies over time.
\n\nPurpose \n\nThe TimeSeriesLinePlot metric is designed to generate and analyze time series data through the creation of line\nplots. This assists in the initial inspection of the data by providing a visual representation of patterns, trends,\nseasonality, irregularity, and anomalies that may be present in the dataset over a period of time.
\n\nTest Mechanism \n\nThe mechanism for this Python class involves extracting the column names from the provided dataset and subsequently\ngenerating line plots for each column using the Plotly Python library. For every column in the dataset, a\ntime-series line plot is created where the values are plotted against the dataset's datetime index. It is important\nto note that indexes that are not of datetime type will result in a ValueError.
\n\nSigns of High Risk \n\n\nPresence of time-series data that does not have datetime indices. \nProvided columns do not exist in the provided dataset. \nThe detection of anomalous patterns or irregularities in the time-series plots, indicating potential high model\ninstability or probable predictive error. \n \n\nStrengths \n\n\nThe visual representation of complex time series data, which simplifies understanding and helps in recognizing\ntemporal trends, patterns, and anomalies. \nThe adaptability of the metric, which allows it to effectively work with multiple time series within the same\ndataset. \nEnables the identification of anomalies and irregular patterns through visual inspection, assisting in spotting\npotential data or model performance problems. \n \n\nLimitations \n\n\nThe effectiveness of the metric is heavily reliant on the quality and patterns of the provided time series data. \nExclusively a visual tool, it lacks the capability to provide quantitative measurements, making it less effective\nfor comparing and ranking multiple models or when specific numerical diagnostics are needed. \nThe metric necessitates that the time-specific data has been transformed into a datetime index, with the data\nformatted correctly. \nThe metric has an inherent limitation in that it cannot extract deeper statistical insights from the time series\ndata, which can limit its efficacy with complex data structures and phenomena. \n \n", "signature": "(dataset : validmind . vm_models . dataset . dataset . VMDataset ): ", "funcdef": "def"}, "validmind.tests.data_validation.TimeSeriesMissingValues": {"fullname": "validmind.tests.data_validation.TimeSeriesMissingValues", "modulename": "validmind.tests.data_validation.TimeSeriesMissingValues", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.TimeSeriesMissingValues.TimeSeriesMissingValues": {"fullname": "validmind.tests.data_validation.TimeSeriesMissingValues.TimeSeriesMissingValues", "modulename": "validmind.tests.data_validation.TimeSeriesMissingValues", "qualname": "TimeSeriesMissingValues", "kind": "function", "doc": "Validates time-series data quality by confirming the count of missing values is below a certain threshold.
\n\nPurpose \n\nThis test is designed to validate the quality of a historical time-series dataset by verifying that the number of\nmissing values is below a specified threshold. As time-series models greatly depend on the continuity and\ntemporality of data points, missing values could compromise the model's performance. Consequently, this test aims\nto ensure data quality and readiness for the machine learning model, safeguarding its predictive capacity.
\n\nTest Mechanism \n\nThe test method commences by validating if the dataset has a datetime index; if not, an error is raised. It\nestablishes a lower limit threshold for missing values and performs a missing values check on each column of the\ndataset. An object for the test result is created stating whether the number of missing values is within the\nspecified threshold. Additionally, the test calculates the percentage of missing values alongside the raw count.
\n\nSigns of High Risk \n\n\nThe number of missing values in any column of the dataset surpasses the threshold, marking a failure and a\nhigh-risk scenario. The reasons could range from incomplete data collection, faulty sensors to data preprocessing\nerrors. \n \n\nStrengths \n\n\nEffectively identifies missing values which could adversely affect the model\u2019s performance. \nApplicable and customizable through the threshold parameter across different data sets. \nGoes beyond raw numbers by calculating the percentage of missing values, offering a more relative understanding\nof data scarcity. \n \n\nLimitations \n\n\nAlthough it identifies missing values, the test does not provide solutions to handle them. \nThe test demands that the dataset should have a datetime index, hence limiting its use only to time series\nanalysis. \nThe test's sensitivity to the 'min_threshold' parameter may raise false alarms if set too strictly or may\noverlook problematic data if set too loosely. \nSolely focuses on the 'missingness' of the data and might fall short in addressing other aspects of data quality. \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tmin_threshold : int = 1 ): ", "funcdef": "def"}, "validmind.tests.data_validation.TimeSeriesOutliers": {"fullname": "validmind.tests.data_validation.TimeSeriesOutliers", "modulename": "validmind.tests.data_validation.TimeSeriesOutliers", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.TimeSeriesOutliers.TimeSeriesOutliers": {"fullname": "validmind.tests.data_validation.TimeSeriesOutliers.TimeSeriesOutliers", "modulename": "validmind.tests.data_validation.TimeSeriesOutliers", "qualname": "TimeSeriesOutliers", "kind": "function", "doc": "Identifies and visualizes outliers in time-series data using the z-score method.
\n\nPurpose \n\nThis test is designed to identify outliers in time-series data using the z-score method. It's vital for ensuring\ndata quality before modeling, as outliers can skew predictive models and significantly impact their overall\nperformance.
\n\nTest Mechanism \n\nThe test processes a given dataset which must have datetime indexing, checks if a 'zscore_threshold' parameter has\nbeen supplied, and identifies columns with numeric data types. After finding numeric columns, the implementer then\napplies the z-score method to each numeric column, identifying outliers based on the threshold provided. Each\noutlier is listed together with their variable name, z-score, timestamp, and relative threshold in a dictionary and\nconverted to a DataFrame for convenient output. Additionally, it produces visual plots for each time series\nillustrating outliers in the context of the broader dataset. The 'zscore_threshold' parameter sets the limit beyond\nwhich a data point will be labeled as an outlier. The default threshold is set at 3, indicating that any data point\nthat falls 3 standard deviations away from the mean will be marked as an outlier.
\n\nSigns of High Risk \n\n\nMany or substantial outliers are present within the dataset, indicating significant anomalies. \nData points with z-scores higher than the set threshold. \nPotential impact on the performance of machine learning models if outliers are not properly addressed. \n \n\nStrengths \n\n\nThe z-score method is a popular and robust method for identifying outliers in a dataset. \nSimplifies time series maintenance by requiring a datetime index. \nIdentifies outliers for each numeric feature individually. \nProvides an elaborate report showing variables, dates, z-scores, and pass/fail tests. \nOffers visual inspection for detected outliers through plots. \n \n\nLimitations \n\n\nThe test only identifies outliers in numeric columns, not in categorical variables. \nThe utility and accuracy of z-scores can be limited if the data doesn't follow a normal distribution. \nThe method relies on a subjective z-score threshold for deciding what constitutes an outlier, which might not\nalways be suitable depending on the dataset and use case. \nIt does not address possible ways to handle identified outliers in the data. \nThe requirement for a datetime index could limit its application. \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tzscore_threshold : int = 3 ): ", "funcdef": "def"}, "validmind.tests.data_validation.TooManyZeroValues": {"fullname": "validmind.tests.data_validation.TooManyZeroValues", "modulename": "validmind.tests.data_validation.TooManyZeroValues", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.TooManyZeroValues.TooManyZeroValues": {"fullname": "validmind.tests.data_validation.TooManyZeroValues.TooManyZeroValues", "modulename": "validmind.tests.data_validation.TooManyZeroValues", "qualname": "TooManyZeroValues", "kind": "function", "doc": "Identifies numerical columns in a dataset that contain an excessive number of zero values, defined by a threshold\npercentage.
\n\nPurpose \n\nThe 'TooManyZeroValues' test is utilized to identify numerical columns in the dataset that may present a quantity\nof zero values considered excessive. The aim is to detect situations where these may implicate data sparsity or a\nlack of variation, limiting their effectiveness within a machine learning model. The definition of 'too many' is\nquantified as a percentage of total values, with a default set to 3%.
\n\nTest Mechanism \n\nThis test is conducted by looping through each column in the dataset and categorizing those that pertain to\nnumerical data. On identifying a numerical column, the function computes the total quantity of zero values and\ntheir ratio to the total row count. Should the proportion exceed a pre-set threshold parameter, set by default at\n0.03 or 3%, the column is considered to have failed the test. The results for each column are summarized and\nreported, indicating the count and percentage of zero values for each numerical column, alongside a status\nindicating whether the column has passed or failed the test.
\n\nSigns of High Risk \n\n\nNumerical columns showing a high ratio of zero values when compared to the total count of rows (exceeding the\npredetermined threshold). \nColumns characterized by zero values across the board suggest a complete lack of data variation, signifying high\nrisk. \n \n\nStrengths \n\n\nAssists in highlighting columns featuring an excess of zero values that could otherwise go unnoticed within a\nlarge dataset. \nProvides the flexibility to alter the threshold that determines when the quantity of zero values becomes 'too\nmany', thus catering to specific needs of a particular analysis or model. \nOffers feedback in the form of both counts and percentages of zero values, which allows a closer inspection of\nthe distribution and proportion of zeros within a column. \nTargets specifically numerical data, thereby avoiding inappropriate application to non-numerical columns and\nmitigating the risk of false test failures. \n \n\nLimitations \n\n\nIs exclusively designed to check for zero values and doesn\u2019t assess the potential impact of other values that\ncould affect the dataset, such as extremely high or low figures, missing values, or outliers. \nLacks the ability to detect a repetitive pattern of zeros, which could be significant in time-series or\nlongitudinal data. \nZero values can actually be meaningful in some contexts; therefore, tagging them as 'too many' could potentially\nmisinterpret the data to some extent. \nThis test does not take into consideration the context of the dataset, and fails to recognize that within certain\ncolumns, a high number of zero values could be quite normal and not necessarily an indicator of poor data quality. \nCannot evaluate non-numerical or categorical columns, which might bring with them different types of concerns or\nissues. \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tmax_percent_threshold : float = 0.03 ): ", "funcdef": "def"}, "validmind.tests.data_validation.UniqueRows": {"fullname": "validmind.tests.data_validation.UniqueRows", "modulename": "validmind.tests.data_validation.UniqueRows", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.UniqueRows.UniqueRows": {"fullname": "validmind.tests.data_validation.UniqueRows.UniqueRows", "modulename": "validmind.tests.data_validation.UniqueRows", "qualname": "UniqueRows", "kind": "function", "doc": "Verifies the diversity of the dataset by ensuring that the count of unique rows exceeds a prescribed threshold.
\n\nPurpose \n\nThe UniqueRows test is designed to gauge the quality of the data supplied to the machine learning model by\nverifying that the count of distinct rows in the dataset exceeds a specific threshold, thereby ensuring a varied\ncollection of data. Diversity in data is essential for training an unbiased and robust model that excels when faced\nwith novel data.
\n\nTest Mechanism \n\nThe testing process starts with calculating the total number of rows in the dataset. Subsequently, the count of\nunique rows is determined for each column in the dataset. If the percentage of unique rows (calculated as the ratio\nof unique rows to the overall row count) is less than the prescribed minimum percentage threshold given as a\nfunction parameter, the test passes. The results are cached and a final pass or fail verdict is given based on\nwhether all columns have successfully passed the test.
\n\nSigns of High Risk \n\n\nA lack of diversity in data columns, demonstrated by a count of unique rows that falls short of the preset\nminimum percentage threshold, is indicative of high risk. \nThis lack of variety in the data signals potential issues with data quality, possibly leading to overfitting in\nthe model and issues with generalization, thus posing a significant risk. \n \n\nStrengths \n\n\nThe UniqueRows test is efficient in evaluating the data's diversity across each information column in the dataset. \nThis test provides a quick, systematic method to assess data quality based on uniqueness, which can be pivotal in\ndeveloping effective and unbiased machine learning models. \n \n\nLimitations \n\n\nA limitation of the UniqueRows test is its assumption that the data's quality is directly proportionate to its\nuniqueness, which may not always hold true. There might be contexts where certain non-unique rows are essential and\nshould not be overlooked. \nThe test does not consider the relative 'importance' of each column in predicting the output, treating all\ncolumns equally. \nThis test may not be suitable or useful for categorical variables, where the count of unique categories is\ninherently limited. \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tmin_percent_threshold : float = 1 ): ", "funcdef": "def"}, "validmind.tests.data_validation.WOEBinPlots": {"fullname": "validmind.tests.data_validation.WOEBinPlots", "modulename": "validmind.tests.data_validation.WOEBinPlots", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.WOEBinPlots.WOEBinPlots": {"fullname": "validmind.tests.data_validation.WOEBinPlots.WOEBinPlots", "modulename": "validmind.tests.data_validation.WOEBinPlots", "qualname": "WOEBinPlots", "kind": "function", "doc": "Generates visualizations of Weight of Evidence (WoE) and Information Value (IV) for understanding predictive power\nof categorical variables in a data set.
\n\nPurpose \n\nThis test is designed to visualize the Weight of Evidence (WoE) and Information Value (IV) for categorical\nvariables in a provided dataset. By showcasing the data distribution across different categories of each feature,\nit aids in understanding each variable's predictive power in the context of a classification-based machine learning\nmodel. Commonly used in credit scoring models, WoE and IV are robust statistical methods for evaluating a\nvariable's predictive power.
\n\nTest Mechanism \n\nThe test implementation follows defined steps. Initially, it selects non-numeric columns from the dataset and\nchanges them to string type, paving the way for accurate binning. It then performs an automated WoE binning\noperation on these selected features, effectively categorizing the potential values of a variable into distinct\nbins. After the binning process, the function generates two separate visualizations (a scatter chart for WoE values\nand a bar chart for IV) for each variable. These visual presentations are formed according to the spread of each\nmetric across various categories of each feature.
\n\nSigns of High Risk \n\n\nErrors occurring during the binning process. \nChallenges in converting non-numeric columns into string data type. \nMisbalance in the distribution of WoE and IV, with certain bins overtaking others conspicuously. This could\ndenote that the model is disproportionately dependent on certain variables or categories for predictions, an\nindication of potential risks to its robustness and generalizability. \n \n\nStrengths \n\n\nProvides a detailed visual representation of the relationship between feature categories and the target variable.\nThis grants an intuitive understanding of each feature's contribution to the model. \nAllows for easy identification of features with high impact, facilitating feature selection and enhancing\ncomprehension of the model's decision logic. \nWoE conversions are monotonic, upholding the rank ordering of the original data points, which simplifies analysis. \n \n\nLimitations \n\n\nThe method is largely reliant on the binning process, and an inappropriate binning threshold or bin number choice\nmight result in a misrepresentation of the variable's distribution. \nWhile excellent for categorical data, the encoding of continuous variables into categorical can sometimes lead to\ninformation loss. \nExtreme or outlier values can dramatically affect the computation of WoE and IV, skewing results. \nThe method requires a sufficient number of events per bin to generate a reliable information value and weight of\nevidence. \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tbreaks_adj : list = None , \tfig_height : int = 600 , \tfig_width : int = 500 ): ", "funcdef": "def"}, "validmind.tests.data_validation.WOEBinTable": {"fullname": "validmind.tests.data_validation.WOEBinTable", "modulename": "validmind.tests.data_validation.WOEBinTable", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.WOEBinTable.WOEBinTable": {"fullname": "validmind.tests.data_validation.WOEBinTable.WOEBinTable", "modulename": "validmind.tests.data_validation.WOEBinTable", "qualname": "WOEBinTable", "kind": "function", "doc": "Assesses the Weight of Evidence (WoE) and Information Value (IV) of each feature to evaluate its predictive power\nin a binary classification model.
\n\nPurpose \n\nThe Weight of Evidence (WoE) and Information Value (IV) test is designed to evaluate the predictive power of each\nfeature in a machine learning model. This test generates binned groups of values from each feature, computes the\nWoE and IV for each bin, and provides insights into the relationship between each feature and the target variable,\nillustrating their contribution to the model's predictive capabilities.
\n\nTest Mechanism \n\nThe test uses the scorecardpy.woebin method to perform automatic binning of the dataset based on WoE. The method\naccepts a list of break points for binning numeric variables through the parameter breaks_adj. If no breaks are\nprovided, it uses default binning. The bins are then used to calculate the WoE and IV values, effectively creating\na dataframe that includes the bin boundaries, WoE, and IV values for each feature. A target variable is required\nin the dataset to perform this analysis.
\n\nSigns of High Risk \n\n\nHigh IV values, indicating variables with excessive predictive power which might lead to overfitting. \nErrors during the binning process, potentially due to inappropriate data types or poorly defined bins. \n \n\nStrengths \n\n\nHighly effective for feature selection in binary classification problems, as it quantifies the predictive\ninformation within each feature concerning the binary outcome. \nThe WoE transformation creates a monotonic relationship between the target and independent variables. \n \n\nLimitations \n\n\nPrimarily designed for binary classification tasks, making it less applicable or reliable for multi-class\nclassification or regression tasks. \nPotential difficulties if the dataset has many features, non-binnable features, or non-numeric features. \nThe metric does not help in distinguishing whether the observed predictive factor is due to data randomness or a\ntrue phenomenon. \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tbreaks_adj : list = None ): ", "funcdef": "def"}, "validmind.tests.data_validation.ZivotAndrewsArch": {"fullname": "validmind.tests.data_validation.ZivotAndrewsArch", "modulename": "validmind.tests.data_validation.ZivotAndrewsArch", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.ZivotAndrewsArch.ZivotAndrewsArch": {"fullname": "validmind.tests.data_validation.ZivotAndrewsArch.ZivotAndrewsArch", "modulename": "validmind.tests.data_validation.ZivotAndrewsArch", "qualname": "ZivotAndrewsArch", "kind": "function", "doc": "Evaluates the order of integration and stationarity of time series data using the Zivot-Andrews unit root test.
\n\nPurpose \n\nThe Zivot-Andrews Arch metric is used to evaluate the order of integration for time series data in a machine\nlearning model. It's designed to test for stationarity, a crucial aspect of time series analysis, where data points\nare independent of time. Stationarity means that the statistical properties such as mean, variance, and\nautocorrelation are constant over time.
\n\nTest Mechanism \n\nThe Zivot-Andrews unit root test is performed on each feature in the dataset using the ZivotAndrews function from\nthe arch.unitroot module. This function returns several metrics for each feature, including the statistical\nvalue, p-value (probability value), the number of lags used, and the number of observations. The p-value is used to\ndecide on the null hypothesis (the time series has a unit root and is non-stationary) based on a chosen level of\nsignificance.
\n\nSigns of High Risk \n\n\nA high p-value suggests high risk, indicating insufficient evidence to reject the null hypothesis, implying that\nthe time series has a unit root and is non-stationary. \nNon-stationary time series data can lead to misleading statistics and unreliable machine learning models. \n \n\nStrengths \n\n\nDynamically tests for stationarity against structural breaks in time series data, offering robust evaluation of\nstationarity in features. \nEspecially beneficial with financial, economic, or other time-series data where data observations lack a\nconsistent pattern and structural breaks may occur. \n \n\nLimitations \n\n\nAssumes data is derived from a single-equation, autoregressive model, making it less appropriate for multivariate\ntime series data or data not aligning with this model. \nMay not account for unexpected shocks or changes in the series trend, both of which can significantly impact data\nstationarity. \n \n", "signature": "(dataset : validmind . vm_models . dataset . dataset . VMDataset ): ", "funcdef": "def"}, "validmind.tests.data_validation.nlp": {"fullname": "validmind.tests.data_validation.nlp", "modulename": "validmind.tests.data_validation.nlp", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.nlp.CommonWords": {"fullname": "validmind.tests.data_validation.nlp.CommonWords", "modulename": "validmind.tests.data_validation.nlp.CommonWords", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.nlp.CommonWords.CommonWords": {"fullname": "validmind.tests.data_validation.nlp.CommonWords.CommonWords", "modulename": "validmind.tests.data_validation.nlp.CommonWords", "qualname": "CommonWords", "kind": "function", "doc": "Assesses the most frequent non-stopwords in a text column for identifying prevalent language patterns.
\n\nPurpose \n\nThe CommonWords metric is used to identify and visualize the most prevalent words within a specified text column of\na dataset. This provides insights into the prevalent language patterns and vocabulary, especially useful in Natural\nLanguage Processing (NLP) tasks such as text classification and text summarization.
\n\nTest Mechanism \n\nThe test methodology involves splitting the specified text column's entries into words, collating them into a\ncorpus, and then counting the frequency of each word using the Counter. The forty most frequently occurring\nnon-stopwords are then visualized in an interactive bar chart using Plotly, where the x-axis represents the words,\nand the y-axis indicates their frequency of occurrence.
\n\nSigns of High Risk \n\n\nA lack of distinct words within the list, or the most common words being stopwords. \nFrequent occurrence of irrelevant or inappropriate words could point out a poorly curated or noisy dataset. \nAn error returned due to the absence of a valid Dataset object, indicating high risk as the metric cannot be\neffectively implemented without it. \n \n\nStrengths \n\n\nThe metric provides clear insights into the language features \u2013 specifically word frequency \u2013 of unstructured\ntext data. \nIt can reveal prominent vocabulary and language patterns, which prove vital for feature extraction in NLP tasks. \nThe interactive visualization helps in quickly capturing the patterns and understanding the data intuitively. \n \n\nLimitations \n\n\nThe test disregards semantic or context-related information as it solely focuses on word frequency. \nIt intentionally ignores stopwords, which might carry necessary significance in certain scenarios. \nThe applicability is limited to English-language text data as English stopwords are used for filtering, hence\ncannot account for data in other languages. \nThe metric requires a valid Dataset object, indicating a dependency condition that limits its broader\napplicability. \n \n", "signature": "(dataset : validmind . vm_models . dataset . dataset . VMDataset ): ", "funcdef": "def"}, "validmind.tests.data_validation.nlp.Hashtags": {"fullname": "validmind.tests.data_validation.nlp.Hashtags", "modulename": "validmind.tests.data_validation.nlp.Hashtags", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.nlp.Hashtags.Hashtags": {"fullname": "validmind.tests.data_validation.nlp.Hashtags.Hashtags", "modulename": "validmind.tests.data_validation.nlp.Hashtags", "qualname": "Hashtags", "kind": "function", "doc": "Assesses hashtag frequency in a text column, highlighting usage trends and potential dataset bias or spam.
\n\nPurpose \n\nThe Hashtags test is designed to measure the frequency of hashtags used within a given text column in a dataset. It\nis particularly useful for natural language processing tasks such as text classification and text summarization.\nThe goal is to identify common trends and patterns in the use of hashtags, which can serve as critical indicators\nor features within a machine learning model.
\n\nTest Mechanism \n\nThe test implements a regular expression (regex) to extract all hashtags from the specified text column. For each\nhashtag found, it makes a tally of its occurrences. It then outputs a list of the top N hashtags (default is 25,\nbut customizable), sorted by their counts in descending order. The results are also visualized in a bar plot, with\nfrequency counts on the y-axis and the corresponding hashtags on the x-axis.
\n\nSigns of High Risk \n\n\nA low diversity in the usage of hashtags, as indicated by a few hashtags being used disproportionately more than\nothers. \nRepeated usage of one or few hashtags can be indicative of spam or a biased dataset. \nIf there are no or extremely few hashtags found in the dataset, it perhaps signifies that the text data does not\ncontain structured social media data. \n \n\nStrengths \n\n\nProvides a concise visual representation of the frequency of hashtags, which can be critical for understanding\ntrends about a particular topic in text data. \nInstrumental in tasks specifically related to social media text analytics, such as opinion analysis and trend\ndiscovery. \nAdaptable, allowing the flexibility to determine the number of top hashtags to be analyzed. \n \n\nLimitations \n\n\nAssumes the presence of hashtags and therefore may not be applicable for text datasets that do not contain\nhashtags (e.g., formal documents, scientific literature). \nLanguage-specific limitations of hashtag formulations are not taken into account. \nDoes not account for typographical errors, variations, or synonyms in hashtags. \nDoes not provide context or sentiment associated with the hashtags, so the information provided may have limited\nutility on its own. \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \ttop_hashtags : int = 25 ): ", "funcdef": "def"}, "validmind.tests.data_validation.nlp.LanguageDetection": {"fullname": "validmind.tests.data_validation.nlp.LanguageDetection", "modulename": "validmind.tests.data_validation.nlp.LanguageDetection", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.nlp.LanguageDetection.LanguageDetection": {"fullname": "validmind.tests.data_validation.nlp.LanguageDetection.LanguageDetection", "modulename": "validmind.tests.data_validation.nlp.LanguageDetection", "qualname": "LanguageDetection", "kind": "function", "doc": "Assesses the diversity of languages in a textual dataset by detecting and visualizing the distribution of languages.
\n\nPurpose \n\nThe Language Detection test aims to identify and visualize the distribution of languages present within a textual\ndataset. This test helps in understanding the diversity of languages in the data, which is crucial for developing\nand validating multilingual models.
\n\nTest Mechanism \n\nThis test operates by:
\n\n\nChecking if the dataset has a specified text column. \nUsing a language detection library to determine the language of each text entry in the dataset. \nGenerating a histogram plot of the language distribution, with language codes on the x-axis and their frequencies\non the y-axis. \n \n\nIf the text column is not specified, a ValueError is raised to ensure proper dataset configuration.
\n\nSigns of High Risk \n\n\nA high proportion of entries returning \"Unknown\" language codes. \nDetection of unexpectedly diverse or incorrect language codes, indicating potential data quality issues. \nSignificant imbalance in language distribution, which might indicate potential biases in the dataset. \n \n\nStrengths \n\n\nProvides a visual representation of language diversity within the dataset. \nHelps identify data quality issues related to incorrect or unknown language detection. \nUseful for ensuring that multilingual models have adequate and appropriate representation from various languages. \n \n\nLimitations \n\n\nDependency on the accuracy of the language detection library, which may not be perfect. \nLanguages with similar structures or limited text length may be incorrectly classified. \nThe test returns \"Unknown\" for entries where language detection fails, which might mask underlying issues with\ncertain languages or text formats. \n \n", "signature": "(dataset ): ", "funcdef": "def"}, "validmind.tests.data_validation.nlp.Mentions": {"fullname": "validmind.tests.data_validation.nlp.Mentions", "modulename": "validmind.tests.data_validation.nlp.Mentions", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.nlp.Mentions.Mentions": {"fullname": "validmind.tests.data_validation.nlp.Mentions.Mentions", "modulename": "validmind.tests.data_validation.nlp.Mentions", "qualname": "Mentions", "kind": "function", "doc": "Calculates and visualizes frequencies of '@' prefixed mentions in a text-based dataset for NLP model analysis.
\n\nPurpose \n\nThe \"Mentions\" test is designed to gauge the quality of data in a Natural Language Processing (NLP) or text-focused\nMachine Learning model. The primary objective is to identify and calculate the frequency of 'mentions' within a\nchosen text column of a dataset. A 'mention' in this context refers to individual text elements that are prefixed\nby '@'. The output of this test reveals the most frequently mentioned entities or usernames, which can be integral\nfor applications such as social media analyses or customer sentiment analyses.
\n\nTest Mechanism \n\nThe test first verifies the existence of a text column in the provided dataset. It then employs a regular\nexpression pattern to extract mentions from the text. Subsequently, the frequency of each unique mention is\ncalculated. The test selects the most frequent mentions based on default or user-defined parameters, the default\nbeing the top 25, for representation. This process of thresholding forms the core of the test. A treemap plot\nvisualizes the test results, where the size of each rectangle corresponds to the frequency of a particular mention.
\n\nSigns of High Risk \n\n\nThe lack of a valid text column in the dataset, which would result in the failure of the test execution. \nThe absence of any mentions within the text data, indicating that there might not be any text associated with\n'@'. This situation could point toward sparse or poor-quality data, thereby hampering the model's generalization or\nlearning capabilities. \n \n\nStrengths \n\n\nThe test is specifically optimized for text-based datasets which gives it distinct power in the context of NLP. \nIt enables quick identification and visually appealing representation of the predominant elements or mentions. \nIt can provide crucial insights about the most frequently mentioned entities or usernames. \n \n\nLimitations \n\n\nThe test only recognizes mentions that are prefixed by '@', hence useful textual aspects not preceded by '@'\nmight be ignored. \nThis test isn't suited for datasets devoid of textual data. \nIt does not provide insights on less frequently occurring data or outliers, which means potentially significant\npatterns could be overlooked. \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \ttop_mentions : int = 25 ): ", "funcdef": "def"}, "validmind.tests.data_validation.nlp.PolarityAndSubjectivity": {"fullname": "validmind.tests.data_validation.nlp.PolarityAndSubjectivity", "modulename": "validmind.tests.data_validation.nlp.PolarityAndSubjectivity", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.nlp.PolarityAndSubjectivity.PolarityAndSubjectivity": {"fullname": "validmind.tests.data_validation.nlp.PolarityAndSubjectivity.PolarityAndSubjectivity", "modulename": "validmind.tests.data_validation.nlp.PolarityAndSubjectivity", "qualname": "PolarityAndSubjectivity", "kind": "function", "doc": "Analyzes the polarity and subjectivity of text data within a given dataset to visualize the sentiment distribution.
\n\nPurpose \n\nThe Polarity and Subjectivity test is designed to evaluate the sentiment expressed in textual data. By analyzing\nthese aspects, it helps to identify the emotional tone and subjectivity of the dataset, which could be crucial in\nunderstanding customer feedback, social media sentiments, or other text-related data.
\n\nTest Mechanism \n\nThis test uses TextBlob to compute the polarity and subjectivity scores of textual data in a given dataset. The\nmechanism includes:
\n\n\nIterating through each text entry in the specified column of the dataset. \nApplying the TextBlob library to compute the polarity (ranging from -1 for negative sentiment to +1 for positive\nsentiment) and subjectivity (ranging from 0 for objective to 1 for subjective) for each entry. \nCreating a scatter plot using Plotly to visualize the relationship between polarity and subjectivity. \n \n\nSigns of High Risk \n\n\nHigh concentration of negative polarity values indicating prevalent negative sentiments. \nHigh subjectivity scores suggesting the text data is largely opinion-based rather than factual. \nDisproportionate clusters of extreme scores (e.g., many points near -1 or +1 polarity). \n \n\nStrengths \n\n\nQuantifies sentiment and subjectivity which can provide actionable insights. \nVisualizes sentiment distribution, aiding in easy interpretation. \nUtilizes well-established TextBlob library for sentiment analysis. \n \n\nLimitations \n\n\nPolarity and subjectivity calculations may oversimplify nuanced text sentiments. \nReliance on TextBlob which may not be accurate for all domains or contexts. \nVisualization could become cluttered with very large datasets, making interpretation difficult. \n \n", "signature": "(dataset , threshold_subjectivity = 0.5 , threshold_polarity = 0 ): ", "funcdef": "def"}, "validmind.tests.data_validation.nlp.Punctuations": {"fullname": "validmind.tests.data_validation.nlp.Punctuations", "modulename": "validmind.tests.data_validation.nlp.Punctuations", "kind": "module", "doc": "Metrics functions for any Pandas-compatible datasets
\n"}, "validmind.tests.data_validation.nlp.Punctuations.Punctuations": {"fullname": "validmind.tests.data_validation.nlp.Punctuations.Punctuations", "modulename": "validmind.tests.data_validation.nlp.Punctuations", "qualname": "Punctuations", "kind": "function", "doc": "Analyzes and visualizes the frequency distribution of punctuation usage in a given text dataset.
\n\nPurpose \n\nThe Punctuations Metric's primary purpose is to analyze the frequency of punctuation usage within a given text\ndataset. This is often used in Natural Language Processing tasks, such as text classification and text\nsummarization.
\n\nTest Mechanism \n\nThe test begins by verifying that the input \"dataset\" is of the type VMDataset. The count_mode parameter must be\neither \"token\" (counts punctuation marks as individual tokens) or \"word\" (counts punctuation marks within words).\nFollowing that, a corpus is created from the dataset by splitting its text on spaces. Each unique punctuation\ncharacter in the text corpus is then tallied. The frequency distribution of each punctuation symbol is visualized\nas a bar graph, with these results being stored as Figures and associated with the main Punctuations object.
\n\nSigns of High Risk \n\n\nExcessive or unusual frequency of specific punctuation marks, potentially denoting dubious quality, data\ncorruption, or skewed data. \n \n\nStrengths \n\n\nProvides valuable insights into the distribution of punctuation usage in a text dataset. \nImportant in validating the quality, consistency, and nature of the data. \nCan provide hints about the style or tonality of the text corpus, such as informal and emotional context\nindicated by frequent exclamation marks. \n \n\nLimitations \n\n\nFocuses solely on punctuation usage, potentially missing other important textual characteristics. \nGeneral cultural or tonality assumptions based on punctuation distribution can be misguiding, as these vary\nacross different languages and contexts. \nLess effective with languages that use non-standard or different punctuation. \nVisualization may lack interpretability when there are many unique punctuation marks in the dataset. \n \n", "signature": "(dataset , count_mode = 'token' ): ", "funcdef": "def"}, "validmind.tests.data_validation.nlp.Sentiment": {"fullname": "validmind.tests.data_validation.nlp.Sentiment", "modulename": "validmind.tests.data_validation.nlp.Sentiment", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.nlp.Sentiment.Sentiment": {"fullname": "validmind.tests.data_validation.nlp.Sentiment.Sentiment", "modulename": "validmind.tests.data_validation.nlp.Sentiment", "qualname": "Sentiment", "kind": "function", "doc": "Analyzes the sentiment of text data within a dataset using the VADER sentiment analysis tool.
\n\nPurpose \n\nThe Sentiment test evaluates the overall sentiment of text data within a dataset. By analyzing sentiment scores, it\naims to ensure that the model is interpreting text data accurately and is not biased towards a particular sentiment.
\n\nTest Mechanism \n\nThis test uses the VADER (Valence Aware Dictionary and sEntiment Reasoner) SentimentIntensityAnalyzer. It processes\neach text entry in a specified column of the dataset to calculate the compound sentiment score, which represents\nthe overall sentiment polarity. The distribution of these sentiment scores is then visualized using a KDE (Kernel\nDensity Estimation) plot, highlighting any skewness or concentration in sentiment.
\n\nSigns of High Risk \n\n\nExtreme polarity in sentiment scores, indicating potential bias. \nUnusual concentration of sentiment scores in a specific range. \nSignificant deviation from expected sentiment distribution for the given text data. \n \n\nStrengths \n\n\nProvides a clear visual representation of sentiment distribution. \nUses a well-established sentiment analysis tool (VADER). \nCan handle a wide range of text data, making it flexible for various applications. \n \n\nLimitations \n\n\nMay not capture nuanced or context-specific sentiments. \nRelies heavily on the accuracy of the VADER sentiment analysis tool. \nVisualization alone may not provide comprehensive insights into underlying causes of sentiment distribution. \n \n", "signature": "(dataset ): ", "funcdef": "def"}, "validmind.tests.data_validation.nlp.StopWords": {"fullname": "validmind.tests.data_validation.nlp.StopWords", "modulename": "validmind.tests.data_validation.nlp.StopWords", "kind": "module", "doc": "Threshold based tests
\n"}, "validmind.tests.data_validation.nlp.StopWords.StopWords": {"fullname": "validmind.tests.data_validation.nlp.StopWords.StopWords", "modulename": "validmind.tests.data_validation.nlp.StopWords", "qualname": "StopWords", "kind": "function", "doc": "Evaluates and visualizes the frequency of English stop words in a text dataset against a defined threshold.
\n\nPurpose \n\nThe StopWords threshold test is a tool designed for assessing the quality of text data in an ML model. It focuses\non the identification and analysis of \"stop words\" in a given dataset. Stop words are frequent, common, yet\nsemantically insignificant words (for example: \"the\", \"and\", \"is\") in a language. This test evaluates the\nproportion of stop words to the total word count in the dataset, in essence, scrutinizing the frequency of stop\nword usage. The core objective is to highlight the prevalent stop words based on their usage frequency, which can\nbe instrumental in cleaning the data from noise and improving ML model performance.
\n\nTest Mechanism \n\nThe StopWords test initiates on receiving an input of a 'VMDataset' object. Absence of such an object will trigger\nan error. The methodology involves inspection of the text column of the VMDataset to create a 'corpus' (a\ncollection of written texts). Leveraging the Natural Language Toolkit's (NLTK) stop word repository, the test\nscreens the corpus for any stop words and documents their frequency. It further calculates the percentage usage of\neach stop word compared to the total word count in the corpus. This percentage is evaluated against a predefined\n'min_percent_threshold'. If this threshold is breached, the test returns a failed output. Top prevailing stop words\nalong with their usage percentages are returned, facilitated by a bar chart visualization of these stop words and\ntheir frequency.
\n\nSigns of High Risk \n\n\nA percentage of any stop words exceeding the predefined 'min_percent_threshold'. \nHigh frequency of stop words in the dataset which may adversely affect the application's analytical performance\ndue to noise creation. \n \n\nStrengths \n\n\nThe ability to scrutinize and quantify the usage of stop words. \nProvides insights into potential noise in the text data due to stop words. \nDirectly aids in enhancing model training efficiency. \nIncludes a bar chart visualization feature to easily interpret and action upon the stop words frequency\ninformation. \n \n\nLimitations \n\n\nThe test only supports English stop words, making it less effective with datasets of other languages. \nThe 'min_percent_threshold' parameter may require fine-tuning for different datasets, impacting the overall\neffectiveness of the test. \nContextual use of the stop words within the dataset is not considered, potentially overlooking their significance\nin certain contexts. \nThe test focuses specifically on the frequency of stop words, not providing direct measures of model performance\nor predictive accuracy. \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tmin_percent_threshold : float = 0.5 , \tnum_words : int = 25 ): ", "funcdef": "def"}, "validmind.tests.data_validation.nlp.TextDescription": {"fullname": "validmind.tests.data_validation.nlp.TextDescription", "modulename": "validmind.tests.data_validation.nlp.TextDescription", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.nlp.TextDescription.create_metrics_df": {"fullname": "validmind.tests.data_validation.nlp.TextDescription.create_metrics_df", "modulename": "validmind.tests.data_validation.nlp.TextDescription", "qualname": "create_metrics_df", "kind": "function", "doc": "
\n", "signature": "(df , text_column , unwanted_tokens , lang ): ", "funcdef": "def"}, "validmind.tests.data_validation.nlp.TextDescription.TextDescription": {"fullname": "validmind.tests.data_validation.nlp.TextDescription.TextDescription", "modulename": "validmind.tests.data_validation.nlp.TextDescription", "qualname": "TextDescription", "kind": "function", "doc": "Conducts comprehensive textual analysis on a dataset using NLTK to evaluate various parameters and generate\nvisualizations.
\n\nPurpose \n\nThe TextDescription test aims to conduct a thorough textual analysis of a dataset using the NLTK (Natural Language\nToolkit) library. It evaluates various metrics such as total words, total sentences, average sentence length, total\nparagraphs, total unique words, most common words, total punctuations, and lexical diversity. The goal is to\nunderstand the nature of the text and anticipate challenges machine learning models might face in text processing,\nlanguage understanding, or summarization tasks.
\n\nTest Mechanism \n\nThe test works by:
\n\n\nParsing the dataset and tokenizing the text into words, sentences, and paragraphs using NLTK. \nRemoving stopwords and unwanted tokens. \nCalculating parameters like total words, total sentences, average sentence length, total paragraphs, total unique\nwords, total punctuations, and lexical diversity. \nGenerating scatter plots to visualize correlations between various metrics (e.g., Total Words vs Total Sentences). \n \n\nSigns of High Risk \n\n\nAnomalies or increased complexity in lexical diversity. \nLonger sentences and paragraphs. \nHigh uniqueness of words. \nLarge number of unwanted tokens. \nMissing or erroneous visualizations. \n \n\nStrengths \n\n\nEssential for pre-processing text data in machine learning models. \nProvides a comprehensive breakdown of text data, aiding in understanding its complexity. \nGenerates visualizations to help comprehend text structure and complexity. \n \n\nLimitations \n\n\nHighly dependent on the NLTK library, limiting the test to supported languages. \nLimited customization for removing undesirable tokens and stop words. \nDoes not consider semantic or grammatical complexities. \nAssumes well-structured documents, which may result in inaccuracies with poorly formatted text. \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tunwanted_tokens : set = { "s'" , ' ' , 'dr' , "''" , 's' , '``' , 'mr' , 'mrs' , 'dollar' , 'ms' , 'us' , "'s" } , \tlang : str = 'english' ): ", "funcdef": "def"}, "validmind.tests.data_validation.nlp.Toxicity": {"fullname": "validmind.tests.data_validation.nlp.Toxicity", "modulename": "validmind.tests.data_validation.nlp.Toxicity", "kind": "module", "doc": "
\n"}, "validmind.tests.data_validation.nlp.Toxicity.Toxicity": {"fullname": "validmind.tests.data_validation.nlp.Toxicity.Toxicity", "modulename": "validmind.tests.data_validation.nlp.Toxicity", "qualname": "Toxicity", "kind": "function", "doc": "Assesses the toxicity of text data within a dataset to visualize the distribution of toxicity scores.
\n\nPurpose \n\nThe Toxicity test aims to evaluate the level of toxic content present in a text dataset by leveraging a pre-trained\ntoxicity model. It helps in identifying potentially harmful or offensive language that may negatively impact users\nor stakeholders.
\n\nTest Mechanism \n\nThis test uses a pre-trained toxicity evaluation model and applies it to each text entry in the specified column of\na dataset\u2019s dataframe. The procedure involves:
\n\n\nLoading a pre-trained toxicity model. \nExtracting the text from the specified column in the dataset. \nComputing toxicity scores for each text entry. \nGenerating a KDE (Kernel Density Estimate) plot to visualize the distribution of these toxicity scores. \n \n\nSigns of High Risk \n\n\nHigh concentration of high toxicity scores in the KDE plot. \nA significant proportion of text entries with toxicity scores above a predefined threshold. \nWide distribution of toxicity scores, indicating inconsistency in content quality. \n \n\nStrengths \n\n\nProvides a visual representation of toxicity distribution, making it easier to identify outliers. \nUses a robust pre-trained model for toxicity evaluation. \nCan process large text datasets efficiently. \n \n\nLimitations \n\n\nDepends on the accuracy and bias of the pre-trained toxicity model. \nDoes not provide context-specific insights, which may be necessary for nuanced understanding. \nMay not capture all forms of subtle or indirect toxic language. \n \n", "signature": "(dataset ): ", "funcdef": "def"}, "validmind.tests.model_validation": {"fullname": "validmind.tests.model_validation", "modulename": "validmind.tests.model_validation", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.BertScore": {"fullname": "validmind.tests.model_validation.BertScore", "modulename": "validmind.tests.model_validation.BertScore", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.BertScore.BertScore": {"fullname": "validmind.tests.model_validation.BertScore.BertScore", "modulename": "validmind.tests.model_validation.BertScore", "qualname": "BertScore", "kind": "function", "doc": "Assesses the quality of machine-generated text using BERTScore metrics and visualizes results through histograms\nand bar charts, alongside compiling a comprehensive table of descriptive statistics.
\n\nPurpose \n\nThis function is designed to assess the quality of text generated by machine learning models using BERTScore\nmetrics. BERTScore evaluates text generation models' performance by calculating precision, recall, and F1 score\nbased on BERT contextual embeddings.
\n\nTest Mechanism \n\nThe function starts by extracting the true and predicted values from the provided dataset and model. It then\ninitializes the BERTScore evaluator. For each pair of true and predicted texts, the function calculates the\nBERTScore metrics and compiles them into a dataframe. Histograms and bar charts are generated for each BERTScore\nmetric (Precision, Recall, and F1 Score) to visualize their distribution. Additionally, a table of descriptive\nstatistics (mean, median, standard deviation, minimum, and maximum) is compiled for each metric, providing a\ncomprehensive summary of the model's performance. The test uses the evaluation_model param to specify the\nhuggingface model to use for evaluation. microsoft/deberta-xlarge-mnli is the best-performing model but is\nvery large and may be slow without a GPU. microsoft/deberta-large-mnli is a smaller model that is faster to\nrun and distilbert-base-uncased is much lighter and can run on a CPU but is less accurate.
\n\nSigns of High Risk \n\n\nConsistently low scores across BERTScore metrics could indicate poor quality in the generated text, suggesting\nthat the model fails to capture the essential content of the reference texts. \nLow precision scores might suggest that the generated text contains a lot of redundant or irrelevant information. \nLow recall scores may indicate that important information from the reference text is being omitted. \nAn imbalanced performance between precision and recall, reflected by a low F1 Score, could signal issues in the\nmodel's ability to balance informativeness and conciseness. \n \n\nStrengths \n\n\nProvides a multifaceted evaluation of text quality through different BERTScore metrics, offering a detailed view\nof model performance. \nVisual representations (histograms and bar charts) make it easier to interpret the distribution and trends of the\nscores. \nDescriptive statistics offer a concise summary of the model's strengths and weaknesses in generating text. \n \n\nLimitations \n\n\nBERTScore relies on the contextual embeddings from BERT models, which may not fully capture all nuances of text\nsimilarity. \nThe evaluation relies on the availability of high-quality reference texts, which may not always be obtainable. \nWhile useful for comparison, BERTScore metrics alone do not provide a complete assessment of a model's\nperformance and should be supplemented with other metrics and qualitative analysis. \n \n", "signature": "(dataset , model , evaluation_model = 'distilbert-base-uncased' ): ", "funcdef": "def"}, "validmind.tests.model_validation.BleuScore": {"fullname": "validmind.tests.model_validation.BleuScore", "modulename": "validmind.tests.model_validation.BleuScore", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.BleuScore.BleuScore": {"fullname": "validmind.tests.model_validation.BleuScore.BleuScore", "modulename": "validmind.tests.model_validation.BleuScore", "qualname": "BleuScore", "kind": "function", "doc": "Evaluates the quality of machine-generated text using BLEU metrics and visualizes the results through histograms\nand bar charts, alongside compiling a comprehensive table of descriptive statistics for BLEU scores.
\n\nPurpose \n\nThis function is designed to assess the quality of text generated by machine learning models using the BLEU metric.\nBLEU, which stands for Bilingual Evaluation Understudy, is a metric used to evaluate the overlap of n-grams between\nthe machine-generated text and reference texts. This evaluation is crucial for tasks such as text summarization,\nmachine translation, and text generation, where the goal is to produce text that accurately reflects the content\nand meaning of human-crafted references.
\n\nTest Mechanism \n\nThe function starts by extracting the true and predicted values from the provided dataset and model. It then\ninitializes the BLEU evaluator. For each pair of true and predicted texts, the function calculates the BLEU scores\nand compiles them into a dataframe. Histograms and bar charts are generated for the BLEU scores to visualize their\ndistribution. Additionally, a table of descriptive statistics (mean, median, standard deviation, minimum, and\nmaximum) is compiled for the BLEU scores, providing a comprehensive summary of the model's performance.
\n\nSigns of High Risk \n\n\nConsistently low BLEU scores could indicate poor quality in the generated text, suggesting that the model fails\nto capture the essential content of the reference texts. \nLow precision scores might suggest that the generated text contains a lot of redundant or irrelevant information. \nLow recall scores may indicate that important information from the reference text is being omitted. \nAn imbalanced performance between precision and recall, reflected by a low BLEU score, could signal issues in the\nmodel's ability to balance informativeness and conciseness. \n \n\nStrengths \n\n\nProvides a straightforward and widely-used evaluation of text quality through BLEU scores. \nVisual representations (histograms and bar charts) make it easier to interpret the distribution and trends of the\nscores. \nDescriptive statistics offer a concise summary of the model's strengths and weaknesses in generating text. \n \n\nLimitations \n\n\nBLEU metrics primarily focus on n-gram overlap and may not fully capture semantic coherence, fluency, or\ngrammatical quality of the text. \nThe evaluation relies on the availability of high-quality reference texts, which may not always be obtainable. \nWhile useful for comparison, BLEU scores alone do not provide a complete assessment of a model's performance and\nshould be supplemented with other metrics and qualitative analysis. \n \n", "signature": "(dataset , model ): ", "funcdef": "def"}, "validmind.tests.model_validation.ClusterSizeDistribution": {"fullname": "validmind.tests.model_validation.ClusterSizeDistribution", "modulename": "validmind.tests.model_validation.ClusterSizeDistribution", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.ClusterSizeDistribution.ClusterSizeDistribution": {"fullname": "validmind.tests.model_validation.ClusterSizeDistribution.ClusterSizeDistribution", "modulename": "validmind.tests.model_validation.ClusterSizeDistribution", "qualname": "ClusterSizeDistribution", "kind": "function", "doc": "Assesses the performance of clustering models by comparing the distribution of cluster sizes in model predictions\nwith the actual data.
\n\nPurpose \n\nThe Cluster Size Distribution test aims to assess the performance of clustering models by comparing the\ndistribution of cluster sizes in the model's predictions with the actual data. This comparison helps determine if\nthe clustering model's output aligns well with the true cluster distribution, providing insights into the model's\naccuracy and performance.
\n\nTest Mechanism \n\nThe test mechanism involves the following steps:
\n\n\nRun the clustering model on the provided dataset to obtain predictions. \nConvert both the actual and predicted outputs into pandas dataframes. \nUse pandas built-in functions to derive the cluster size distributions from these dataframes. \nConstruct two histograms: one for the actual cluster size distribution and one for the predicted distribution. \nPlot the histograms side-by-side for visual comparison. \n \n\nSigns of High Risk \n\n\nDiscrepancies between the actual cluster size distribution and the predicted cluster size distribution. \nIrregular distribution of data across clusters in the predicted outcomes. \nHigh number of outlier clusters suggesting the model struggles to correctly group data. \n \n\nStrengths \n\n\nProvides a visual and intuitive way to compare the clustering model's performance against actual data. \nEffectively reveals where the model may be over- or underestimating cluster sizes. \nVersatile as it works well with any clustering model. \n \n\nLimitations \n\n\nAssumes that the actual cluster distribution is optimal, which may not always be the case. \nRelies heavily on visual comparison, which could be subjective and may not offer a precise numerical measure of\nperformance. \nMay not fully capture other important aspects of clustering, such as cluster density, distances between clusters,\nand the shape of clusters. \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tmodel : validmind . vm_models . model . VMModel ): ", "funcdef": "def"}, "validmind.tests.model_validation.ContextualRecall": {"fullname": "validmind.tests.model_validation.ContextualRecall", "modulename": "validmind.tests.model_validation.ContextualRecall", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.ContextualRecall.ContextualRecall": {"fullname": "validmind.tests.model_validation.ContextualRecall.ContextualRecall", "modulename": "validmind.tests.model_validation.ContextualRecall", "qualname": "ContextualRecall", "kind": "function", "doc": "Evaluates a Natural Language Generation model's ability to generate contextually relevant and factually correct\ntext, visualizing the results through histograms and bar charts, alongside compiling a comprehensive table of\ndescriptive statistics for contextual recall scores.
\n\nPurpose \n\nThe Contextual Recall metric is used to evaluate the ability of a natural language generation (NLG) model to\ngenerate text that appropriately reflects the given context or prompt. It measures the model's capability to\nremember and reproduce the main context in its resulting output. This metric is critical in natural language\nprocessing tasks, as the coherency and contextuality of the generated text are essential.
\n\nTest Mechanism \n\nThe function starts by extracting the true and predicted values from the provided dataset and model. It then\ntokenizes the reference and candidate texts into discernible words or tokens using NLTK. The token overlap between\nthe reference and candidate texts is identified, and the Contextual Recall score is computed by dividing the number\nof overlapping tokens by the total number of tokens in the reference text. Scores are calculated for each test\ndataset instance, resulting in an array of scores. These scores are visualized using a histogram and a bar chart to\nshow score variations across different rows. Additionally, a table of descriptive statistics (mean, median,\nstandard deviation, minimum, and maximum) is compiled for the contextual recall scores, providing a comprehensive\nsummary of the model's performance.
\n\nSigns of High Risk \n\n\nLow contextual recall scores could indicate that the model is not effectively reflecting the original context in\nits output, leading to incoherent or contextually misaligned text. \nA consistent trend of low recall scores could suggest underperformance of the model. \n \n\nStrengths \n\n\nProvides a quantifiable measure of a model's adherence to the context and factual elements of the generated\nnarrative. \nVisual representations (histograms and bar charts) make it easier to interpret the distribution and trends of\ncontextual recall scores. \nDescriptive statistics offer a concise summary of the model's performance in generating contextually relevant\ntexts. \n \n\nLimitations \n\n\nThe focus on word overlap could result in high scores for texts that use many common words, even when these texts\nlack coherence or meaningful context. \nThis metric does not consider the order of words, which could lead to overestimated scores for scrambled outputs. \nModels that effectively use infrequent words might be undervalued, as these words might not overlap as often. \n \n", "signature": "(dataset , model ): ", "funcdef": "def"}, "validmind.tests.model_validation.FeaturesAUC": {"fullname": "validmind.tests.model_validation.FeaturesAUC", "modulename": "validmind.tests.model_validation.FeaturesAUC", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.FeaturesAUC.FeaturesAUC": {"fullname": "validmind.tests.model_validation.FeaturesAUC.FeaturesAUC", "modulename": "validmind.tests.model_validation.FeaturesAUC", "qualname": "FeaturesAUC", "kind": "function", "doc": "Evaluates the discriminatory power of each individual feature within a binary classification model by calculating\nthe Area Under the Curve (AUC) for each feature separately.
\n\nPurpose \n\nThe central objective of this metric is to quantify how well each feature on its own can differentiate between the\ntwo classes in a binary classification problem. It serves as a univariate analysis tool that can help in\npre-modeling feature selection or post-modeling interpretation.
\n\nTest Mechanism \n\nFor each feature, the metric treats the feature values as raw scores to compute the AUC against the actual binary\noutcomes. It provides an AUC value for each feature, offering a simple yet powerful indication of each feature's\nunivariate classification strength.
\n\nSigns of High Risk \n\n\nA feature with a low AUC score may not be contributing significantly to the differentiation between the two\nclasses, which could be a concern if it is expected to be predictive. \nConversely, a surprisingly high AUC for a feature not believed to be informative may suggest data leakage or\nother issues with the data. \n \n\nStrengths \n\n\nBy isolating each feature, it highlights the individual contribution of features to the classification task\nwithout the influence of other variables. \nUseful for both initial feature evaluation and for providing insights into the model's reliance on individual\nfeatures after model training. \n \n\nLimitations \n\n\nDoes not reflect the combined effects of features or any interaction between them, which can be critical in\ncertain models. \nThe AUC values are calculated without considering the model's use of the features, which could lead to different\ninterpretations of feature importance when considering the model holistically. \nThis metric is applicable only to binary classification tasks and cannot be directly extended to multiclass\nclassification or regression without modifications. \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tfontsize : int = 12 , \tfigure_height : int = 500 ): ", "funcdef": "def"}, "validmind.tests.model_validation.MeteorScore": {"fullname": "validmind.tests.model_validation.MeteorScore", "modulename": "validmind.tests.model_validation.MeteorScore", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.MeteorScore.MeteorScore": {"fullname": "validmind.tests.model_validation.MeteorScore.MeteorScore", "modulename": "validmind.tests.model_validation.MeteorScore", "qualname": "MeteorScore", "kind": "function", "doc": "Assesses the quality of machine-generated translations by comparing them to human-produced references using the\nMETEOR score, which evaluates precision, recall, and word order.
\n\nPurpose \n\nThe METEOR (Metric for Evaluation of Translation with Explicit ORdering) score is designed to evaluate the quality\nof machine translations by comparing them against reference translations. It emphasizes both the accuracy and\nfluency of translations, incorporating precision, recall, and word order into its assessment.
\n\nTest Mechanism \n\nThe function starts by extracting the true and predicted values from the provided dataset and model. The METEOR\nscore is computed for each pair of machine-generated translation (prediction) and its corresponding human-produced\nreference. This is done by considering unigram matches between the translations, including matches based on surface\nforms, stemmed forms, and synonyms. The score is a combination of unigram precision and recall, adjusted for word\norder through a fragmentation penalty. Scores are compiled into a dataframe, and histograms and bar charts are\ngenerated to visualize the distribution of METEOR scores. Additionally, a table of descriptive statistics (mean,\nmedian, standard deviation, minimum, and maximum) is compiled for the METEOR scores, providing a comprehensive\nsummary of the model's performance.
\n\nSigns of High Risk \n\n\nLower METEOR scores can indicate a lack of alignment between the machine-generated translations and their\nhuman-produced references, highlighting potential deficiencies in both the accuracy and fluency of translations. \nSignificant discrepancies in word order or an excessive fragmentation penalty could signal issues with how the\ntranslation model processes and reconstructs sentence structures, potentially compromising the natural flow of\ntranslated text. \nPersistent underperformance across a variety of text types or linguistic contexts might suggest a broader\ninability of the model to adapt to the nuances of different languages or dialects, pointing towards gaps in its\ntraining or inherent limitations. \n \n\nStrengths \n\n\nIncorporates a balanced consideration of precision and recall, weighted towards recall to reflect the importance\nof content coverage in translations. \nDirectly accounts for word order, offering a nuanced evaluation of translation fluency beyond simple lexical\nmatching. \nAdapts to various forms of lexical similarity, including synonyms and stemmed forms, allowing for flexible\nmatching. \n \n\nLimitations \n\n\nWhile comprehensive, the complexity of METEOR's calculation can make it computationally intensive, especially for\nlarge datasets. \nThe use of external resources for synonym and stemming matching may introduce variability based on the resources'\nquality and relevance to the specific translation task. \n \n", "signature": "(dataset , model ): ", "funcdef": "def"}, "validmind.tests.model_validation.ModelMetadata": {"fullname": "validmind.tests.model_validation.ModelMetadata", "modulename": "validmind.tests.model_validation.ModelMetadata", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.ModelMetadata.ModelMetadata": {"fullname": "validmind.tests.model_validation.ModelMetadata.ModelMetadata", "modulename": "validmind.tests.model_validation.ModelMetadata", "qualname": "ModelMetadata", "kind": "function", "doc": "Compare metadata of different models and generate a summary table with the results.
\n\nPurpose : The purpose of this function is to compare the metadata of different models, including information about their architecture, framework, framework version, and programming language.
\n\nTest Mechanism : The function retrieves the metadata for each model using get_model_info, renames columns according to a predefined set of labels, and compiles this information into a summary table.
\n\nSigns of High Risk :
\n\n\nInconsistent or missing metadata across models can indicate potential issues in model documentation or management. \nSignificant differences in framework versions or programming languages might pose challenges in model integration and deployment. \n \n\nStrengths :
\n\n\nProvides a clear comparison of essential model metadata. \nStandardizes metadata labels for easier interpretation and comparison. \nHelps identify potential compatibility or consistency issues across models. \n \n\nLimitations :
\n\n\nAssumes that the get_model_info function returns all necessary metadata fields. \nRelies on the correctness and completeness of the metadata provided by each model. \nDoes not include detailed parameter information, focusing instead on high-level metadata. \n \n", "signature": "(model ): ", "funcdef": "def"}, "validmind.tests.model_validation.ModelPredictionResiduals": {"fullname": "validmind.tests.model_validation.ModelPredictionResiduals", "modulename": "validmind.tests.model_validation.ModelPredictionResiduals", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.ModelPredictionResiduals.ModelPredictionResiduals": {"fullname": "validmind.tests.model_validation.ModelPredictionResiduals.ModelPredictionResiduals", "modulename": "validmind.tests.model_validation.ModelPredictionResiduals", "qualname": "ModelPredictionResiduals", "kind": "function", "doc": "Assesses normality and behavior of residuals in regression models through visualization and statistical tests.
\n\nPurpose \n\nThe Model Prediction Residuals test aims to visualize the residuals of model predictions and assess their normality\nusing the Kolmogorov-Smirnov (KS) test. It helps to identify potential issues related to model assumptions and\neffectiveness.
\n\nTest Mechanism \n\nThe function calculates residuals and generates\ntwo figures: one for the time series of residuals and one for the histogram of residuals.\nIt also calculates the KS test for normality and summarizes the results in a table.
\n\nSigns of High Risk \n\n\nResiduals are not normally distributed, indicating potential issues with model assumptions. \nHigh skewness or kurtosis in the residuals, which may suggest model misspecification. \n \n\nStrengths \n\n\nProvides clear visualizations of residuals over time and their distribution. \nIncludes statistical tests to assess the normality of residuals. \nHelps in identifying potential model misspecifications and assumption violations. \n \n\nLimitations \n\n\nAssumes that the dataset is provided as a DataFrameDataset object with a .df attribute to access the pandas\nDataFrame. \nOnly generates plots for datasets with a datetime index, resulting in errors for other types of indices. \n \n", "signature": "(\tdataset , \tmodel , \tnbins = 100 , \tp_value_threshold = 0.05 , \tstart_date = None , \tend_date = None ): ", "funcdef": "def"}, "validmind.tests.model_validation.RegardScore": {"fullname": "validmind.tests.model_validation.RegardScore", "modulename": "validmind.tests.model_validation.RegardScore", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.RegardScore.RegardScore": {"fullname": "validmind.tests.model_validation.RegardScore.RegardScore", "modulename": "validmind.tests.model_validation.RegardScore", "qualname": "RegardScore", "kind": "function", "doc": "Assesses the sentiment and potential biases in text generated by NLP models by computing and visualizing regard\nscores.
\n\nPurpose \n\nThe RegardScore test aims to evaluate the levels of regard (positive, negative, neutral, or other) in texts\ngenerated by NLP models. It helps in understanding the sentiment and bias present in the generated content.
\n\nTest Mechanism \n\nThis test extracts the true and predicted values from the provided dataset and model. It then computes the regard\nscores for each text instance using a preloaded regard evaluation tool. The scores are compiled into dataframes,\nand visualizations such as histograms and bar charts are generated to display the distribution of regard scores.\nAdditionally, descriptive statistics (mean, median, standard deviation, minimum, and maximum) are calculated for\nthe regard scores, providing a comprehensive overview of the model's performance.
\n\nSigns of High Risk \n\n\nNoticeable skewness in the histogram, especially when comparing the predicted regard scores with the target\nregard scores, can indicate biases or inconsistencies in the model. \nLack of neutral scores in the model's predictions, despite a balanced distribution in the target data, might\nsignal an issue. \n \n\nStrengths \n\n\nProvides a clear evaluation of regard levels in generated texts, aiding in ensuring content appropriateness. \nVisual representations (histograms and bar charts) make it easier to interpret the distribution and trends of\nregard scores. \nDescriptive statistics offer a concise summary of the model's performance in generating texts with balanced\nsentiments. \n \n\nLimitations \n\n\nThe accuracy of the regard scores is contingent upon the underlying regard tool. \nThe scores provide a broad overview but do not specify which portions or tokens of the text are responsible for\nhigh regard. \nSupplementary, in-depth analysis might be needed for granular insights. \n \n", "signature": "(dataset , model ): ", "funcdef": "def"}, "validmind.tests.model_validation.RegressionResidualsPlot": {"fullname": "validmind.tests.model_validation.RegressionResidualsPlot", "modulename": "validmind.tests.model_validation.RegressionResidualsPlot", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.RegressionResidualsPlot.RegressionResidualsPlot": {"fullname": "validmind.tests.model_validation.RegressionResidualsPlot.RegressionResidualsPlot", "modulename": "validmind.tests.model_validation.RegressionResidualsPlot", "qualname": "RegressionResidualsPlot", "kind": "function", "doc": "Evaluates regression model performance using residual distribution and actual vs. predicted plots.
\n\nPurpose \n\nThe RegressionResidualsPlot metric aims to evaluate the performance of regression models. By generating and\nanalyzing two plots \u2013 a distribution of residuals and a scatter plot of actual versus predicted values \u2013 this tool\nhelps to visually appraise how well the model predicts and the nature of errors it makes.
\n\nTest Mechanism \n\nThe process begins by extracting the true output values (y_true) and the model's predicted values (y_pred).\nResiduals are computed by subtracting predicted from true values. These residuals are then visualized using a\nhistogram to display their distribution. Additionally, a scatter plot is derived to compare true values against\npredicted values, together with a \"Perfect Fit\" line, which represents an ideal match (predicted values equal\nactual values), facilitating the assessment of the model's predictive accuracy.
\n\nSigns of High Risk \n\n\nResiduals showing a non-normal distribution, especially those with frequent extreme values. \nSignificant deviations of predicted values from actual values in the scatter plot. \nSparse density of data points near the \"Perfect Fit\" line in the scatter plot, indicating poor prediction\naccuracy. \nVisible patterns or trends in the residuals plot, suggesting the model's failure to capture the underlying data\nstructure adequately. \n \n\nStrengths \n\n\nProvides a direct, visually intuitive assessment of a regression model\u2019s accuracy and handling of data. \nVisual plots can highlight issues of underfitting or overfitting. \nCan reveal systematic deviations or trends that purely numerical metrics might miss. \nApplicable across various regression model types. \n \n\nLimitations \n\n\nRelies on visual interpretation, which can be subjective and less precise than numerical evaluations. \nMay be difficult to interpret in cases with multi-dimensional outputs due to the plots\u2019 two-dimensional nature. \nOverlapping data points in the residuals plot can complicate interpretation efforts. \nDoes not summarize model performance into a single quantifiable metric, which might be needed for comparative or\nsummary analyses. \n \n", "signature": "(\tmodel : validmind . vm_models . model . VMModel , \tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tbin_size : float = 0.1 ): ", "funcdef": "def"}, "validmind.tests.model_validation.RougeScore": {"fullname": "validmind.tests.model_validation.RougeScore", "modulename": "validmind.tests.model_validation.RougeScore", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.RougeScore.RougeScore": {"fullname": "validmind.tests.model_validation.RougeScore.RougeScore", "modulename": "validmind.tests.model_validation.RougeScore", "qualname": "RougeScore", "kind": "function", "doc": "Assesses the quality of machine-generated text using ROUGE metrics and visualizes the results to provide\ncomprehensive performance insights.
\n\nPurpose \n\nThe ROUGE Score test is designed to evaluate the quality of text generated by machine learning models using various\nROUGE metrics. ROUGE, which stands for Recall-Oriented Understudy for Gisting Evaluation, measures the overlap of\nn-grams, word sequences, and word pairs between machine-generated text and reference texts. This evaluation is\ncrucial for tasks like text summarization, machine translation, and text generation, where the goal is to produce\ntext that accurately reflects the content and meaning of human-crafted references.
\n\nTest Mechanism \n\nThe test extracts the true and predicted values from the provided dataset and model. It initializes the ROUGE\nevaluator with the specified metric (e.g., ROUGE-1). For each pair of true and predicted texts, it calculates the\nROUGE scores and compiles them into a dataframe. Histograms and bar charts are generated for each ROUGE metric\n(Precision, Recall, and F1 Score) to visualize their distribution. Additionally, a table of descriptive statistics\n(mean, median, standard deviation, minimum, and maximum) is compiled for each metric, providing a comprehensive\nsummary of the model's performance.
\n\nSigns of High Risk \n\n\nConsistently low scores across ROUGE metrics could indicate poor quality in the generated text, suggesting that\nthe model fails to capture the essential content of the reference texts. \nLow precision scores might suggest that the generated text contains a lot of redundant or irrelevant information. \nLow recall scores may indicate that important information from the reference text is being omitted. \nAn imbalanced performance between precision and recall, reflected by a low F1 Score, could signal issues in the\nmodel's ability to balance informativeness and conciseness. \n \n\nStrengths \n\n\nProvides a multifaceted evaluation of text quality through different ROUGE metrics, offering a detailed view of\nmodel performance. \nVisual representations (histograms and bar charts) make it easier to interpret the distribution and trends of the\nscores. \nDescriptive statistics offer a concise summary of the model's strengths and weaknesses in generating text. \n \n\nLimitations \n\n\nROUGE metrics primarily focus on n-gram overlap and may not fully capture semantic coherence, fluency, or\ngrammatical quality of the text. \nThe evaluation relies on the availability of high-quality reference texts, which may not always be obtainable. \nWhile useful for comparison, ROUGE scores alone do not provide a complete assessment of a model's performance and\nshould be supplemented with other metrics and qualitative analysis. \n \n", "signature": "(dataset , model , metric = 'rouge-1' ): ", "funcdef": "def"}, "validmind.tests.model_validation.TimeSeriesPredictionWithCI": {"fullname": "validmind.tests.model_validation.TimeSeriesPredictionWithCI", "modulename": "validmind.tests.model_validation.TimeSeriesPredictionWithCI", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.TimeSeriesPredictionWithCI.TimeSeriesPredictionWithCI": {"fullname": "validmind.tests.model_validation.TimeSeriesPredictionWithCI.TimeSeriesPredictionWithCI", "modulename": "validmind.tests.model_validation.TimeSeriesPredictionWithCI", "qualname": "TimeSeriesPredictionWithCI", "kind": "function", "doc": "Assesses predictive accuracy and uncertainty in time series models, highlighting breaches beyond confidence\nintervals.
\n\nPurpose \n\nThe purpose of the Time Series Prediction with Confidence Intervals (CI) test is to visualize the actual versus\npredicted values for time series data, including confidence intervals, and to compute and report the number of\nbreaches beyond these intervals. This helps in evaluating the reliability and accuracy of the model's predictions.
\n\nTest Mechanism \n\nThe function performs the following steps:
\n\n\nCalculates the standard deviation of prediction errors. \nDetermines the confidence intervals using a specified confidence level, typically 95%. \nCounts the number of actual values that fall outside the confidence intervals, referred to as breaches. \nGenerates a plot visualizing the actual values, predicted values, and confidence intervals. \nReturns a DataFrame summarizing the breach information, including the total breaches, upper breaches, and lower\nbreaches. \n \n\nSigns of High Risk \n\n\nA high number of breaches indicates that the model's predictions are not reliable within the specified confidence\nlevel. \nSignificant deviations between actual and predicted values may highlight model inadequacies or issues with data\nquality. \n \n\nStrengths \n\n\nProvides a visual representation of prediction accuracy and the uncertainty around predictions. \nIncludes a statistical measure of prediction reliability through confidence intervals. \nComputes and reports breaches, offering a quantitative assessment of prediction performance. \n \n\nLimitations \n\n\nAssumes that the dataset is provided as a DataFrameDataset object with a datetime index. \nRequires that dataset.y_pred(model) returns the predicted values for the model. \nThe calculation of confidence intervals assumes normally distributed errors, which may not hold for all datasets. \n \n", "signature": "(dataset , model , confidence = 0.95 ): ", "funcdef": "def"}, "validmind.tests.model_validation.TimeSeriesPredictionsPlot": {"fullname": "validmind.tests.model_validation.TimeSeriesPredictionsPlot", "modulename": "validmind.tests.model_validation.TimeSeriesPredictionsPlot", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.TimeSeriesPredictionsPlot.TimeSeriesPredictionsPlot": {"fullname": "validmind.tests.model_validation.TimeSeriesPredictionsPlot.TimeSeriesPredictionsPlot", "modulename": "validmind.tests.model_validation.TimeSeriesPredictionsPlot", "qualname": "TimeSeriesPredictionsPlot", "kind": "function", "doc": "Plot actual vs predicted values for time series data and generate a visual comparison for the model.
\n\nPurpose \n\nThe purpose of this function is to visualize the actual versus predicted values for time\nseries data for a single model.
\n\nTest Mechanism \n\nThe function plots the actual values from the dataset and overlays the predicted\nvalues from the model using Plotly for interactive visualization.
\n\n\nLarge discrepancies between actual and predicted values indicate poor model performance. \nSystematic deviations in predicted values can highlight model bias or issues with data patterns. \n \n\nStrengths \n\n\nProvides a clear visual comparison of model predictions against actual values. \nUses Plotly for interactive and visually appealing plots. \n \n\nLimitations \n\n\nAssumes that the dataset is provided as a DataFrameDataset object with a datetime index. \nRequires that dataset.y_pred(model) returns the predicted values for the model. \n \n", "signature": "(dataset , model ): ", "funcdef": "def"}, "validmind.tests.model_validation.TimeSeriesR2SquareBySegments": {"fullname": "validmind.tests.model_validation.TimeSeriesR2SquareBySegments", "modulename": "validmind.tests.model_validation.TimeSeriesR2SquareBySegments", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.TimeSeriesR2SquareBySegments.TimeSeriesR2SquareBySegments": {"fullname": "validmind.tests.model_validation.TimeSeriesR2SquareBySegments.TimeSeriesR2SquareBySegments", "modulename": "validmind.tests.model_validation.TimeSeriesR2SquareBySegments", "qualname": "TimeSeriesR2SquareBySegments", "kind": "function", "doc": "Evaluates the R-Squared values of regression models over specified time segments in time series data to assess\nsegment-wise model performance.
\n\nPurpose \n\nThe TimeSeriesR2SquareBySegments test aims to evaluate the R-Squared values for several regression models across\ndifferent segments of time series data. This helps in determining how well the models explain the variability in\nthe data within each specific time segment.
\n\nTest Mechanism \n\n\nProvides a visual representation of model performance across different time segments. \nAllows for identification of segments where the model performs poorly. \nCalculating the R-Squared values for each segment. \nGenerating a bar chart to visually represent the R-Squared values across different models and segments. \n \n\nSigns of High Risk \n\n\nSignificantly low R-Squared values for certain time segments, indicating poor model performance in those periods. \nLarge variability in R-Squared values across different segments for the same model, suggesting inconsistent\nperformance. \n \n\nStrengths \n\n\nProvides a visual representation of how well models perform over different time periods. \nHelps identify time segments where models may need improvement or retraining. \nFacilitates comparison between multiple models in a straightforward manner. \n \n\nLimitations \n\n\nAssumes datasets are provided as DataFrameDataset objects with the attributes y, y_pred, and\nfeature_columns. \nRequires that dataset.y_pred(model) returns predicted values for the model. \nAssumes that both y_true and y_pred are pandas Series with datetime indices, which may not always be the case. \nMay not account for more nuanced temporal dependencies within the segments. \n \n", "signature": "(dataset , model , segments = None ): ", "funcdef": "def"}, "validmind.tests.model_validation.TokenDisparity": {"fullname": "validmind.tests.model_validation.TokenDisparity", "modulename": "validmind.tests.model_validation.TokenDisparity", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.TokenDisparity.TokenDisparity": {"fullname": "validmind.tests.model_validation.TokenDisparity.TokenDisparity", "modulename": "validmind.tests.model_validation.TokenDisparity", "qualname": "TokenDisparity", "kind": "function", "doc": "Evaluates the token disparity between reference and generated texts, visualizing the results through histograms and\nbar charts, alongside compiling a comprehensive table of descriptive statistics for token counts.
\n\nPurpose \n\nThe Token Disparity test aims to assess the difference in the number of tokens between reference texts and texts\ngenerated by the model. Understanding token disparity is essential for evaluating how well the generated content\nmatches the expected length and richness of the reference texts.
\n\nTest Mechanism \n\nThe test extracts true and predicted values from the dataset and model. It computes the number of tokens in each\nreference and generated text. The results are visualized using histograms and bar charts to display the\ndistribution of token counts. Additionally, a table of descriptive statistics, including the mean, median, standard\ndeviation, minimum, and maximum token counts, is compiled to provide a detailed summary of token usage.
\n\nSigns of High Risk \n\n\nSignificant disparity in token counts between reference and generated texts could indicate issues with text\ngeneration quality, such as verbosity or lack of detail. \nConsistently low token counts in generated texts compared to references might suggest that the model is producing\nincomplete or overly concise outputs. \n \n\nStrengths \n\n\nProvides a simple yet effective evaluation of text length and token usage. \nVisual representations (histograms and bar charts) make it easier to interpret the distribution and trends of\ntoken counts. \nDescriptive statistics offer a concise summary of the model's performance in generating texts of appropriate\nlength. \n \n\nLimitations \n\n\nToken counts alone do not provide a complete assessment of text quality and should be supplemented with other\nmetrics and qualitative analysis. \n \n", "signature": "(dataset , model ): ", "funcdef": "def"}, "validmind.tests.model_validation.ToxicityScore": {"fullname": "validmind.tests.model_validation.ToxicityScore", "modulename": "validmind.tests.model_validation.ToxicityScore", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.ToxicityScore.ToxicityScore": {"fullname": "validmind.tests.model_validation.ToxicityScore.ToxicityScore", "modulename": "validmind.tests.model_validation.ToxicityScore", "qualname": "ToxicityScore", "kind": "function", "doc": "Assesses the toxicity levels of texts generated by NLP models to identify and mitigate harmful or offensive content.
\n\nPurpose \n\nThe ToxicityScore metric is designed to evaluate the toxicity levels of texts generated by models. This is crucial\nfor identifying and mitigating harmful or offensive content in machine-generated texts.
\n\nTest Mechanism \n\nThe function starts by extracting the input, true, and predicted values from the provided dataset and model. The\ntoxicity score is computed for each text using a preloaded toxicity evaluation tool. The scores are compiled into\ndataframes, and histograms and bar charts are generated to visualize the distribution of toxicity scores.\nAdditionally, a table of descriptive statistics (mean, median, standard deviation, minimum, and maximum) is\ncompiled for the toxicity scores, providing a comprehensive summary of the model's performance.
\n\nSigns of High Risk \n\n\nDrastic spikes in toxicity scores indicate potentially toxic content within the associated text segment. \nPersistent high toxicity scores across multiple texts may suggest systemic issues in the model's text generation\nprocess. \n \n\nStrengths \n\n\nProvides a clear evaluation of toxicity levels in generated texts, helping to ensure content safety and\nappropriateness. \nVisual representations (histograms and bar charts) make it easier to interpret the distribution and trends of\ntoxicity scores. \nDescriptive statistics offer a concise summary of the model's performance in generating non-toxic texts. \n \n\nLimitations \n\n\nThe accuracy of the toxicity scores is contingent upon the underlying toxicity tool. \nThe scores provide a broad overview but do not specify which portions or tokens of the text are responsible for\nhigh toxicity. \nSupplementary, in-depth analysis might be needed for granular insights. \n \n", "signature": "(dataset , model ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn": {"fullname": "validmind.tests.model_validation.sklearn", "modulename": "validmind.tests.model_validation.sklearn", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.sklearn.AdjustedMutualInformation": {"fullname": "validmind.tests.model_validation.sklearn.AdjustedMutualInformation", "modulename": "validmind.tests.model_validation.sklearn.AdjustedMutualInformation", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.sklearn.AdjustedMutualInformation.AdjustedMutualInformation": {"fullname": "validmind.tests.model_validation.sklearn.AdjustedMutualInformation.AdjustedMutualInformation", "modulename": "validmind.tests.model_validation.sklearn.AdjustedMutualInformation", "qualname": "AdjustedMutualInformation", "kind": "function", "doc": "Evaluates clustering model performance by measuring mutual information between true and predicted labels, adjusting\nfor chance.
\n\nPurpose \n\nThe purpose of this metric (Adjusted Mutual Information) is to evaluate the performance of a machine learning\nmodel, more specifically, a clustering model. It measures the mutual information between the true labels and the\nones predicted by the model, adjusting for chance.
\n\nTest Mechanism \n\nThe Adjusted Mutual Information (AMI) uses sklearn's adjusted_mutual_info_score function. This function\ncalculates the mutual information between the true labels and the ones predicted while correcting for the chance\ncorrelation expected due to random label assignments. This test requires the model, the training dataset, and the\ntest dataset as inputs.
\n\nSigns of High Risk \n\n\nLow Adjusted Mutual Information Score: This score ranges between 0 and 1. A low score (closer to 0) can indicate\npoor model performance as the predicted labels do not align well with the true labels. \nIn case of high-dimensional data, if the algorithm shows high scores, this could also be a potential risk as AMI\nmay not perform reliably. \n \n\nStrengths \n\n\nThe AMI metric takes into account the randomness of the predicted labels, which makes it more robust than the\nsimple Mutual Information. \nThe scale of AMI is not dependent on the sizes of the clustering, allowing for comparability between different\ndatasets or models. \nGood for comparing the output of clustering algorithms where the number of clusters is not known a priori. \n \n\nLimitations \n\n\nAdjusted Mutual Information does not take into account the continuous nature of some data. As a result, it may\nnot be the best choice for regression or other continuous types of tasks. \nAMI has the drawback of being biased towards clusterings with a higher number of clusters. \nIn comparison to other metrics, AMI can be slower to compute. \nThe interpretability of the score can be complex as it depends on the understanding of information theory\nconcepts. \n \n", "signature": "(\tmodel : validmind . vm_models . model . VMModel , \tdataset : validmind . vm_models . dataset . dataset . VMDataset ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.AdjustedRandIndex": {"fullname": "validmind.tests.model_validation.sklearn.AdjustedRandIndex", "modulename": "validmind.tests.model_validation.sklearn.AdjustedRandIndex", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.sklearn.AdjustedRandIndex.AdjustedRandIndex": {"fullname": "validmind.tests.model_validation.sklearn.AdjustedRandIndex.AdjustedRandIndex", "modulename": "validmind.tests.model_validation.sklearn.AdjustedRandIndex", "qualname": "AdjustedRandIndex", "kind": "function", "doc": "Measures the similarity between two data clusters using the Adjusted Rand Index (ARI) metric in clustering machine\nlearning models.
\n\nPurpose \n\nThe Adjusted Rand Index (ARI) metric is intended to measure the similarity between two data clusters. This metric\nis specifically used for clustering machine learning models to quantify how well the model is clustering and\nproducing data groups. It involves comparing the model's produced clusters against the actual (true) clusters found\nin the dataset.
\n\nTest Mechanism \n\nThe Adjusted Rand Index (ARI) is calculated using the adjusted_rand_score method from the sklearn.metrics\nmodule in Python. The test requires inputs including the model itself and the model's training and test datasets.\nThe model's computed clusters and the true clusters are compared, and the similarities are measured to compute the\nARI.
\n\nSigns of High Risk \n\n\nIf the ARI is close to zero, it signifies that the model's cluster assignments are random and do not match the\nactual dataset clusters, indicating a high risk. \nAn ARI of less than zero indicates that the model's clustering performance is worse than random. \n \n\nStrengths \n\n\nARI is normalized and provides a consistent metric between -1 and +1, irrespective of raw cluster sizes or\ndataset size variations. \nIt does not require a ground truth for computation, making it ideal for unsupervised learning model evaluations. \nIt penalizes for false positives and false negatives, providing a robust measure of clustering quality. \n \n\nLimitations \n\n\nIn real-world situations, true clustering is often unknown, which can hinder the practical application of the ARI. \nThe ARI requires all individual data instances to be independent, which may not always hold true. \nIt may be difficult to interpret the implications of an ARI score without context or a benchmark, as it is\nheavily dependent on the characteristics of the dataset used. \n \n", "signature": "(\tmodel : validmind . vm_models . model . VMModel , \tdataset : validmind . vm_models . dataset . dataset . VMDataset ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.CalibrationCurve": {"fullname": "validmind.tests.model_validation.sklearn.CalibrationCurve", "modulename": "validmind.tests.model_validation.sklearn.CalibrationCurve", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.sklearn.CalibrationCurve.CalibrationCurve": {"fullname": "validmind.tests.model_validation.sklearn.CalibrationCurve.CalibrationCurve", "modulename": "validmind.tests.model_validation.sklearn.CalibrationCurve", "qualname": "CalibrationCurve", "kind": "function", "doc": "Evaluates the calibration of probability estimates by comparing predicted probabilities against observed\nfrequencies.
\n\nPurpose \n\nThe Calibration Curve test assesses how well a model's predicted probabilities align with actual\nobserved frequencies. This is crucial for applications requiring accurate probability estimates,\nsuch as risk assessment, decision-making systems, and cost-sensitive applications where probability\ncalibration directly impacts business decisions.
\n\nTest Mechanism \n\nThe test uses sklearn's calibration_curve function to:
\n\n\nSort predictions into bins based on predicted probabilities \nCalculate the mean predicted probability in each bin \nCompare against the observed frequency of positive cases \nPlot the results against the perfect calibration line (y=x)\nThe resulting curve shows how well the predicted probabilities match empirical probabilities. \n \n\nSigns of High Risk \n\n\nSignificant deviation from the perfect calibration line \nSystematic overconfidence (predictions too close to 0 or 1) \nSystematic underconfidence (predictions clustered around 0.5) \nEmpty or sparse bins indicating poor probability coverage \nSharp discontinuities in the calibration curve \nDifferent calibration patterns across different probability ranges \nConsistent over/under estimation in critical probability regions \nLarge confidence intervals in certain probability ranges \n \n\nStrengths \n\n\nVisual and intuitive interpretation of probability quality \nIdentifies systematic biases in probability estimates \nSupports probability threshold selection \nHelps understand model confidence patterns \nApplicable across different classification models \nEnables comparison between different models \nGuides potential need for recalibration \nCritical for risk-sensitive applications \n \n\nLimitations \n\n\nSensitive to the number of bins chosen \nRequires sufficient samples in each bin for reliable estimates \nMay mask local calibration issues within bins \nDoes not account for feature-dependent calibration issues \nLimited to binary classification problems \nCannot detect all forms of miscalibration \nAssumes bin boundaries are appropriate for the problem \nMay be affected by class imbalance \n \n", "signature": "(\tmodel : validmind . vm_models . model . VMModel , \tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tn_bins : int = 10 ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.ClassifierPerformance": {"fullname": "validmind.tests.model_validation.sklearn.ClassifierPerformance", "modulename": "validmind.tests.model_validation.sklearn.ClassifierPerformance", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.sklearn.ClassifierPerformance.multiclass_roc_auc_score": {"fullname": "validmind.tests.model_validation.sklearn.ClassifierPerformance.multiclass_roc_auc_score", "modulename": "validmind.tests.model_validation.sklearn.ClassifierPerformance", "qualname": "multiclass_roc_auc_score", "kind": "function", "doc": "
\n", "signature": "(y_test , y_pred , average = 'macro' ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.ClassifierPerformance.ClassifierPerformance": {"fullname": "validmind.tests.model_validation.sklearn.ClassifierPerformance.ClassifierPerformance", "modulename": "validmind.tests.model_validation.sklearn.ClassifierPerformance", "qualname": "ClassifierPerformance", "kind": "function", "doc": "Evaluates performance of binary or multiclass classification models using precision, recall, F1-Score, accuracy,\nand ROC AUC scores.
\n\nPurpose \n\nThe Classifier Performance test is designed to evaluate the performance of Machine Learning classification models.\nIt accomplishes this by computing precision, recall, F1-Score, and accuracy, as well as the ROC AUC (Receiver\noperating characteristic - Area under the curve) scores, thereby providing a comprehensive analytic view of the\nmodels' performance. The test is adaptable, handling binary and multiclass models equally effectively.
\n\nTest Mechanism \n\nThe test produces a report that includes precision, recall, F1-Score, and accuracy, by leveraging the\nclassification_report from scikit-learn's metrics module. For multiclass models, macro and weighted averages for\nthese scores are also calculated. Additionally, the ROC AUC scores are calculated and included in the report using\nthe multiclass_roc_auc_score function. The outcome of the test (report format) differs based on whether the model\nis binary or multiclass.
\n\nSigns of High Risk \n\n\nLow values for precision, recall, F1-Score, accuracy, and ROC AUC, indicating poor performance. \nImbalance in precision and recall scores. \nA low ROC AUC score, especially scores close to 0.5 or lower, suggesting a failing model. \n \n\nStrengths \n\n\nVersatile, capable of assessing both binary and multiclass models. \nUtilizes a variety of commonly employed performance metrics, offering a comprehensive view of model performance. \nThe use of ROC-AUC as a metric is beneficial for evaluating unbalanced datasets. \n \n\nLimitations \n\n\nAssumes correctly identified labels for binary classification models. \nSpecifically designed for classification models and not suitable for regression models. \nMay provide limited insights if the test dataset does not represent real-world scenarios adequately. \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tmodel : validmind . vm_models . model . VMModel , \taverage : str = 'macro' ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.ClassifierThresholdOptimization": {"fullname": "validmind.tests.model_validation.sklearn.ClassifierThresholdOptimization", "modulename": "validmind.tests.model_validation.sklearn.ClassifierThresholdOptimization", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.sklearn.ClassifierThresholdOptimization.find_optimal_threshold": {"fullname": "validmind.tests.model_validation.sklearn.ClassifierThresholdOptimization.find_optimal_threshold", "modulename": "validmind.tests.model_validation.sklearn.ClassifierThresholdOptimization", "qualname": "find_optimal_threshold", "kind": "function", "doc": "Find the optimal classification threshold using various methods.
\n\nArguments: \n\n\ny_true: True binary labelsy_prob: Predicted probabilitiesmethod: Method to use for finding optimal thresholdtarget_recall: Required if method='target_recall' \n\nReturns: \n\n\n dict: Dictionary containing threshold and metrics
\n \n", "signature": "(y_true , y_prob , method = 'youden' , target_recall = None ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.ClassifierThresholdOptimization.ClassifierThresholdOptimization": {"fullname": "validmind.tests.model_validation.sklearn.ClassifierThresholdOptimization.ClassifierThresholdOptimization", "modulename": "validmind.tests.model_validation.sklearn.ClassifierThresholdOptimization", "qualname": "ClassifierThresholdOptimization", "kind": "function", "doc": "Analyzes and visualizes different threshold optimization methods for binary classification models.
\n\nPurpose \n\nThe Classifier Threshold Optimization test identifies optimal decision thresholds using various\nmethods to balance different performance metrics. This helps adapt the model's decision boundary\nto specific business requirements, such as minimizing false positives in fraud detection or\nachieving target recall in medical diagnosis.
\n\nTest Mechanism \n\nThe test implements multiple threshold optimization methods:
\n\n\nYouden's J statistic (maximizing sensitivity + specificity - 1) \nF1-score optimization (balancing precision and recall) \nPrecision-Recall equality point \nTarget recall achievement \nNaive (0.5) threshold\nFor each method, it computes ROC and PR curves, identifies optimal points, and provides\ncomprehensive performance metrics at each threshold. \n \n\nSigns of High Risk \n\n\nLarge discrepancies between different optimization methods \nOptimal thresholds far from the default 0.5 \nPoor performance metrics across all thresholds \nSignificant gap between achieved and target recall \nUnstable thresholds across different methods \nExtreme trade-offs between precision and recall \nThreshold optimization showing minimal impact \nBusiness metrics not improving with optimization \n \n\nStrengths \n\n\nMultiple optimization strategies for different needs \nVisual and numerical results for comparison \nSupport for business-driven optimization (target recall) \nComprehensive performance metrics at each threshold \nIntegration with ROC and PR curves \nHandles class imbalance through various metrics \nEnables informed threshold selection \nSupports cost-sensitive decision making \n \n\nLimitations \n\n\nAssumes cost of false positives/negatives are known \nMay need adjustment for highly imbalanced datasets \nThreshold might not be stable across different samples \nCannot handle multi-class problems directly \nOptimization methods may conflict with business needs \nRequires sufficient validation data \nMay not capture temporal changes in optimal threshold \nSingle threshold may not be optimal for all subgroups \n \n\nArguments: \n\n\ndataset: VMDataset containing features and targetmodel: VMModel containing predictionsmethods: List of methods to compare (default: ['youden', 'f1', 'precision_recall'])target_recall: Target recall value if using 'target_recall' method \n\nReturns: \n\n\n Dictionary containing:\n - table: DataFrame comparing different threshold optimization methods\n (using weighted averages for precision, recall, and f1)\n - figure: Plotly figure showing ROC and PR curves with optimal thresholds
\n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tmodel : validmind . vm_models . model . VMModel , \tmethods = None , \ttarget_recall = None ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.ClusterCosineSimilarity": {"fullname": "validmind.tests.model_validation.sklearn.ClusterCosineSimilarity", "modulename": "validmind.tests.model_validation.sklearn.ClusterCosineSimilarity", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.sklearn.ClusterCosineSimilarity.ClusterCosineSimilarity": {"fullname": "validmind.tests.model_validation.sklearn.ClusterCosineSimilarity.ClusterCosineSimilarity", "modulename": "validmind.tests.model_validation.sklearn.ClusterCosineSimilarity", "qualname": "ClusterCosineSimilarity", "kind": "function", "doc": "Measures the intra-cluster similarity of a clustering model using cosine similarity.
\n\nPurpose \n\nThe purpose of this metric is to measure how similar the data points within each cluster of a clustering model are.\nThis is done using cosine similarity, which compares the multi-dimensional direction (but not magnitude) of data\nvectors. From a Model Risk Management perspective, this metric is used to quantitatively validate that clusters\nformed by a model have high intra-cluster similarity.
\n\nTest Mechanism \n\nThis test works by first extracting the true and predicted clusters of the model's training data. Then, it computes\nthe centroid (average data point) of each cluster. Next, it calculates the cosine similarity between each data\npoint within a cluster and its respective centroid. Finally, it outputs the mean cosine similarity of each cluster,\nhighlighting how similar, on average, data points in a cluster are to the cluster's centroid.
\n\nSigns of High Risk \n\n\nLow mean cosine similarity for one or more clusters: If the mean cosine similarity is low, the data points within\nthe respective cluster have high variance in their directions. This can be indicative of poor clustering,\nsuggesting that the model might not be suitably separating the data into distinct patterns. \nHigh disparity between mean cosine similarity values across clusters: If there's a significant difference in mean\ncosine similarity across different clusters, this could indicate imbalance in how the model forms clusters. \n \n\nStrengths \n\n\nCosine similarity operates in a multi-dimensional space, making it effective for measuring similarity in high\ndimensional datasets, typical for many machine learning problems. \nIt provides an agnostic view of the cluster performance by only considering the direction (and not the magnitude)\nof each vector. \nThis metric is not dependent on the scale of the variables, making it equally effective on different scales. \n \n\nLimitations \n\n\nCosine similarity does not consider magnitudes (i.e. lengths) of vectors, only their direction. This means it may\noverlook instances where clusters have been adequately separated in terms of magnitude. \nThis method summarily assumes that centroids represent the average behavior of data points in each cluster. This\nmight not always be true, especially in clusters with high amounts of variance or non-spherical shapes. \nIt primarily works with continuous variables and is not suitable for binary or categorical variables. \nLastly, although rare, perfect perpendicular vectors (cosine similarity = 0) could be within the same cluster,\nwhich may give an inaccurate representation of a 'bad' cluster due to low cosine similarity score. \n \n", "signature": "(\tmodel : validmind . vm_models . model . VMModel , \tdataset : validmind . vm_models . dataset . dataset . VMDataset ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.ClusterPerformanceMetrics": {"fullname": "validmind.tests.model_validation.sklearn.ClusterPerformanceMetrics", "modulename": "validmind.tests.model_validation.sklearn.ClusterPerformanceMetrics", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.sklearn.ClusterPerformanceMetrics.ClusterPerformanceMetrics": {"fullname": "validmind.tests.model_validation.sklearn.ClusterPerformanceMetrics.ClusterPerformanceMetrics", "modulename": "validmind.tests.model_validation.sklearn.ClusterPerformanceMetrics", "qualname": "ClusterPerformanceMetrics", "kind": "function", "doc": "Evaluates the performance of clustering machine learning models using multiple established metrics.
\n\nPurpose \n\nThe ClusterPerformanceMetrics test is used to assess the performance and validity of clustering machine learning\nmodels. It evaluates homogeneity, completeness, V measure score, the Adjusted Rand Index, the Adjusted Mutual\nInformation, and the Fowlkes-Mallows score of the model. These metrics provide a holistic understanding of the\nmodel's ability to accurately form clusters of the given dataset.
\n\nTest Mechanism \n\nThe ClusterPerformanceMetrics test runs a clustering ML model over a given dataset and then calculates six\nmetrics using the Scikit-learn metrics computation functions: Homogeneity Score, Completeness Score, V Measure,\nAdjusted Rand Index (ARI), Adjusted Mutual Information (AMI), and Fowlkes-Mallows Score. It then returns the result\nas a summary, presenting the metric values for both training and testing datasets.
\n\nSigns of High Risk \n\n\nLow Homogeneity Score: Indicates that the clusters formed contain a variety of classes, resulting in less pure\nclusters. \nLow Completeness Score: Suggests that class instances are scattered across multiple clusters rather than being\ngathered in a single cluster. \nLow V Measure: Reports a low overall clustering performance. \nARI close to 0 or Negative: Implies that clustering results are random or disagree with the true labels. \nAMI close to 0: Means that clustering labels are random compared with the true labels. \nLow Fowlkes-Mallows score: Signifies less precise and poor clustering performance in terms of precision and\nrecall. \n \n\nStrengths \n\n\nProvides a comprehensive view of clustering model performance by examining multiple clustering metrics. \nUses established and widely accepted metrics from scikit-learn, providing reliability in the results. \nAble to provide performance metrics for both training and testing datasets. \nClearly defined and human-readable descriptions of each score make it easy to understand what each score\nrepresents. \n \n\nLimitations \n\n\nOnly applies to clustering models; not suitable for other types of machine learning models. \nDoes not test for overfitting or underfitting in the clustering model. \nAll the scores rely on ground truth labels, the absence or inaccuracy of which can lead to misleading results. \nDoes not consider aspects like computational efficiency of the model or its capability to handle high dimensional\ndata. \n \n", "signature": "(\tmodel : validmind . vm_models . model . VMModel , \tdataset : validmind . vm_models . dataset . dataset . VMDataset ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.CompletenessScore": {"fullname": "validmind.tests.model_validation.sklearn.CompletenessScore", "modulename": "validmind.tests.model_validation.sklearn.CompletenessScore", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.sklearn.CompletenessScore.CompletenessScore": {"fullname": "validmind.tests.model_validation.sklearn.CompletenessScore.CompletenessScore", "modulename": "validmind.tests.model_validation.sklearn.CompletenessScore", "qualname": "CompletenessScore", "kind": "function", "doc": "Evaluates a clustering model's capacity to categorize instances from a single class into the same cluster.
\n\nPurpose \n\nThe Completeness Score metric is used to assess the performance of clustering models. It measures the extent to\nwhich all the data points that are members of a given class are elements of the same cluster. The aim is to\ndetermine the capability of the model to categorize all instances from a single class into the same cluster.
\n\nTest Mechanism \n\nThis test takes three inputs, a model and its associated training and testing datasets. It invokes the\ncompleteness_score function from the sklearn library on the labels predicted by the model. High scores indicate\nthat data points from the same class generally appear in the same cluster, while low scores suggest the opposite.
\n\nSigns of High Risk \n\n\nLow completeness score: This suggests that the model struggles to group instances from the same class into one\ncluster, indicating poor clustering performance. \n \n\nStrengths \n\n\nThe Completeness Score provides an effective method for assessing the performance of a clustering model,\nspecifically its ability to group class instances together. \nThis test metric conveniently relies on the capabilities provided by the sklearn library, ensuring consistent and\nreliable test results. \n \n\nLimitations \n\n\nThis metric only evaluates a specific aspect of clustering, meaning it may not provide a holistic or complete\nview of the model's performance. \nIt cannot assess the effectiveness of the model in differentiating between separate classes, as it is solely\nfocused on how well data points from the same class are grouped. \nThe Completeness Score only applies to clustering models; it cannot be used for other types of machine learning\nmodels. \n \n", "signature": "(\tmodel : validmind . vm_models . model . VMModel , \tdataset : validmind . vm_models . dataset . dataset . VMDataset ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.ConfusionMatrix": {"fullname": "validmind.tests.model_validation.sklearn.ConfusionMatrix", "modulename": "validmind.tests.model_validation.sklearn.ConfusionMatrix", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.sklearn.ConfusionMatrix.ConfusionMatrix": {"fullname": "validmind.tests.model_validation.sklearn.ConfusionMatrix.ConfusionMatrix", "modulename": "validmind.tests.model_validation.sklearn.ConfusionMatrix", "qualname": "ConfusionMatrix", "kind": "function", "doc": "Evaluates and visually represents the classification ML model's predictive performance using a Confusion Matrix\nheatmap.
\n\nPurpose \n\nThe Confusion Matrix tester is designed to assess the performance of a classification Machine Learning model. This\nperformance is evaluated based on how well the model is able to correctly classify True Positives, True Negatives,\nFalse Positives, and False Negatives - fundamental aspects of model accuracy.
\n\nTest Mechanism \n\nThe mechanism used involves taking the predicted results (y_test_predict) from the classification model and\ncomparing them against the actual values (y_test_true). A confusion matrix is built using the unique labels\nextracted from y_test_true, employing scikit-learn's metrics. The matrix is then visually rendered with the help\nof Plotly's create_annotated_heatmap function. A heatmap is created which provides a two-dimensional graphical\nrepresentation of the model's performance, showcasing distributions of True Positives (TP), True Negatives (TN),\nFalse Positives (FP), and False Negatives (FN).
\n\nSigns of High Risk \n\n\nHigh numbers of False Positives (FP) and False Negatives (FN), depicting that the model is not effectively\nclassifying the values. \nLow numbers of True Positives (TP) and True Negatives (TN), implying that the model is struggling with correctly\nidentifying class labels. \n \n\nStrengths \n\n\nIt provides a simplified yet comprehensive visual snapshot of the classification model's predictive performance. \nIt distinctly brings out True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives\n(FN), thus making it easier to focus on potential areas of improvement. \nThe matrix is beneficial in dealing with multi-class classification problems as it can provide a simple view of\ncomplex model performances. \nIt aids in understanding the different types of errors that the model could potentially make, as it provides\nin-depth insights into Type-I and Type-II errors. \n \n\nLimitations \n\n\nIn cases of unbalanced classes, the effectiveness of the confusion matrix might be lessened. It may wrongly\ninterpret the accuracy of a model that is essentially just predicting the majority class. \nIt does not provide a single unified statistic that could evaluate the overall performance of the model.\nDifferent aspects of the model's performance are evaluated separately instead. \nIt mainly serves as a descriptive tool and does not offer the capability for statistical hypothesis testing. \nRisks of misinterpretation exist because the matrix doesn't directly provide precision, recall, or F1-score data.\nThese metrics have to be computed separately. \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tmodel : validmind . vm_models . model . VMModel , \tthreshold : float = 0.5 ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.FeatureImportance": {"fullname": "validmind.tests.model_validation.sklearn.FeatureImportance", "modulename": "validmind.tests.model_validation.sklearn.FeatureImportance", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.sklearn.FeatureImportance.FeatureImportance": {"fullname": "validmind.tests.model_validation.sklearn.FeatureImportance.FeatureImportance", "modulename": "validmind.tests.model_validation.sklearn.FeatureImportance", "qualname": "FeatureImportance", "kind": "function", "doc": "Compute feature importance scores for a given model and generate a summary table\nwith the top important features.
\n\nPurpose \n\nThe Feature Importance Comparison test is designed to compare the feature importance scores for different models\nwhen applied to various datasets. By doing so, it aims to identify the most impactful features and assess the\nconsistency of feature importance across models.
\n\nTest Mechanism \n\nThis test works by iterating through each dataset-model pair and calculating permutation feature importance (PFI)\nscores. It then generates a summary table containing the top num_features important features for each model. The\nprocess involves:
\n\n\nExtracting features and target data from each dataset. \nComputing PFI scores using sklearn.inspection.permutation_importance. \nSorting and selecting the top features based on their importance scores. \nCompiling these features into a summary table for comparison. \n \n\nSigns of High Risk \n\n\nKey features expected to be important are ranked low, indicating potential issues with model training or data\nquality. \nHigh variance in feature importance scores across different models, suggesting instability in feature selection. \n \n\nStrengths \n\n\nProvides a clear comparison of the most important features for each model. \nUses permutation importance, which is a model-agnostic method and can be applied to any estimator. \n \n\nLimitations \n\n\nAssumes that the dataset is provided as a DataFrameDataset object with x_df and y_df methods to access\nfeature and target data. \nRequires that model.model is compatible with sklearn.inspection.permutation_importance. \nThe function's output is dependent on the number of features specified by num_features, which defaults to 3 but\ncan be adjusted. \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tmodel : validmind . vm_models . model . VMModel , \tnum_features : int = 3 ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.FowlkesMallowsScore": {"fullname": "validmind.tests.model_validation.sklearn.FowlkesMallowsScore", "modulename": "validmind.tests.model_validation.sklearn.FowlkesMallowsScore", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.sklearn.FowlkesMallowsScore.FowlkesMallowsScore": {"fullname": "validmind.tests.model_validation.sklearn.FowlkesMallowsScore.FowlkesMallowsScore", "modulename": "validmind.tests.model_validation.sklearn.FowlkesMallowsScore", "qualname": "FowlkesMallowsScore", "kind": "function", "doc": "Evaluates the similarity between predicted and actual cluster assignments in a model using the Fowlkes-Mallows\nscore.
\n\nPurpose \n\nThe FowlkesMallowsScore is a performance metric used to validate clustering algorithms within machine learning\nmodels. The score intends to evaluate the matching grade between two clusters. It measures the similarity between\nthe predicted and actual cluster assignments, thus gauging the accuracy of the model's clustering capability.
\n\nTest Mechanism \n\nThe FowlkesMallowsScore method applies the fowlkes_mallows_score function from the sklearn library to evaluate\nthe model's accuracy in clustering different types of data. The test fetches the datasets from the model's training\nand testing datasets as inputs then compares the resulting clusters against the previously known clusters to obtain\na score. A high score indicates a better clustering performance by the model.
\n\nSigns of High Risk \n\n\nA low Fowlkes-Mallows score (near zero): This indicates that the model's clustering capability is poor and the\nalgorithm isn't properly grouping data. \nInconsistently low scores across different datasets: This may indicate that the model's clustering performance is\nnot robust and the model may fail when applied to unseen data. \n \n\nStrengths \n\n\nThe Fowlkes-Mallows score is a simple and effective method for evaluating the performance of clustering\nalgorithms. \nThis metric takes into account both precision and recall in its calculation, therefore providing a balanced and\ncomprehensive measure of model performance. \nThe Fowlkes-Mallows score is non-biased meaning it treats False Positives and False Negatives equally. \n \n\nLimitations \n\n\nAs a pairwise-based method, this score can be computationally intensive for large datasets and can become\nunfeasible as the size of the dataset increases. \nThe Fowlkes-Mallows score works best with balanced distribution of samples across clusters. If this condition is\nnot met, the score can be skewed. \nIt does not handle mismatching numbers of clusters between the true and predicted labels. As such, it may return\nmisleading results if the predicted labels suggest a different number of clusters than what is in the true labels. \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tmodel : validmind . vm_models . model . VMModel ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.HomogeneityScore": {"fullname": "validmind.tests.model_validation.sklearn.HomogeneityScore", "modulename": "validmind.tests.model_validation.sklearn.HomogeneityScore", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.sklearn.HomogeneityScore.HomogeneityScore": {"fullname": "validmind.tests.model_validation.sklearn.HomogeneityScore.HomogeneityScore", "modulename": "validmind.tests.model_validation.sklearn.HomogeneityScore", "qualname": "HomogeneityScore", "kind": "function", "doc": "Assesses clustering homogeneity by comparing true and predicted labels, scoring from 0 (heterogeneous) to 1\n(homogeneous).
\n\nPurpose \n\nThe Homogeneity Score encapsulated in this performance test is used to measure the homogeneity of the clusters\nformed by a machine learning model. In simple terms, a clustering result satisfies homogeneity if all of its\nclusters contain only points which are members of a single class.
\n\nTest Mechanism \n\nThis test uses the homogeneity_score function from the sklearn.metrics library to compare the ground truth\nclass labels of the training and testing sets with the labels predicted by the given model. The returned score is a\nmetric of the clustering accuracy, and ranges from 0.0 to 1.0, with 1.0 denoting the highest possible degree of\nhomogeneity.
\n\nSigns of High Risk \n\n\nA score close to 0: This denotes that clusters are highly heterogenous and points within the same cluster might\nnot belong to the same class. \nA significantly lower score for testing data compared to the score for training data: This can indicate\noverfitting, where the model has learned to perfectly match the training data but fails to perform well on unseen\ndata. \n \n\nStrengths \n\n\nIt provides a simple quantitative measure of the degree to which clusters contain points from only one class. \nUseful for validating clustering solutions where the ground truth \u2014 class membership of points \u2014 is known. \nIt's agnostic to the absolute labels, and cares only that the points within the same cluster have the same class\nlabel. \n \n\nLimitations \n\n\nThe Homogeneity Score is not useful for clustering solutions where the ground truth labels are not known. \nIt doesn\u2019t work well with differently sized clusters since it gives predominance to larger clusters. \nThe score does not address the actual number of clusters formed, or the evenness of cluster sizes. It only checks\nthe homogeneity within the given clusters created by the model. \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tmodel : validmind . vm_models . model . VMModel ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.HyperParametersTuning": {"fullname": "validmind.tests.model_validation.sklearn.HyperParametersTuning", "modulename": "validmind.tests.model_validation.sklearn.HyperParametersTuning", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.sklearn.HyperParametersTuning.custom_recall": {"fullname": "validmind.tests.model_validation.sklearn.HyperParametersTuning.custom_recall", "modulename": "validmind.tests.model_validation.sklearn.HyperParametersTuning", "qualname": "custom_recall", "kind": "function", "doc": "
\n", "signature": "(y_true , y_pred_proba , threshold = 0.5 ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.HyperParametersTuning.HyperParametersTuning": {"fullname": "validmind.tests.model_validation.sklearn.HyperParametersTuning.HyperParametersTuning", "modulename": "validmind.tests.model_validation.sklearn.HyperParametersTuning", "qualname": "HyperParametersTuning", "kind": "function", "doc": "Performs exhaustive grid search over specified parameter ranges to find optimal model configurations\nacross different metrics and decision thresholds.
\n\nPurpose \n\nThe Hyperparameter Tuning test systematically explores the model's parameter space to identify optimal\nconfigurations. It supports multiple optimization metrics and decision thresholds, providing a comprehensive\nview of how different parameter combinations affect various aspects of model performance.
\n\nTest Mechanism \n\nThe test uses scikit-learn's GridSearchCV to perform cross-validation for each parameter combination.\nFor each specified threshold and optimization metric, it creates a scoring dictionary with\nthreshold-adjusted metrics, performs grid search with cross-validation, records best parameters and\ncorresponding scores, and combines results into a comparative table. This process is repeated for each\noptimization metric to provide a comprehensive view of model performance under different configurations.
\n\nSigns of High Risk \n\n\nLarge performance variations across different parameter combinations \nSignificant discrepancies between different optimization metrics \nBest parameters at the edges of the parameter grid \nUnstable performance across different thresholds \nOverly complex model configurations (risk of overfitting) \nVery different optimal parameters for different metrics \nCross-validation scores showing high variance \nExtreme parameter values in best configurations \n \n\nStrengths \n\n\nComprehensive exploration of parameter space \nSupports multiple optimization metrics \nAllows threshold optimization \nProvides comparative view across different configurations \nUses cross-validation for robust evaluation \nHelps understand trade-offs between different metrics \nEnables systematic parameter selection \nSupports both classification and clustering tasks \n \n\nLimitations \n\n\nComputationally expensive for large parameter grids \nMay not find global optimum (limited to grid points) \nCannot handle dependencies between parameters \nMemory intensive for large datasets \nLimited to scikit-learn compatible models \nCross-validation splits may not preserve time series structure \nGrid search may miss optimal values between grid points \nResource intensive for high-dimensional parameter spaces \n \n", "signature": "(\tmodel : validmind . vm_models . model . VMModel , \tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tparam_grid : dict , \tscoring : Union [ str , List , Dict ] = None , \tthresholds : Union [ float , List [ float ]] = None , \tfit_params : dict = None ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.KMeansClustersOptimization": {"fullname": "validmind.tests.model_validation.sklearn.KMeansClustersOptimization", "modulename": "validmind.tests.model_validation.sklearn.KMeansClustersOptimization", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.sklearn.KMeansClustersOptimization.KMeansClustersOptimization": {"fullname": "validmind.tests.model_validation.sklearn.KMeansClustersOptimization.KMeansClustersOptimization", "modulename": "validmind.tests.model_validation.sklearn.KMeansClustersOptimization", "qualname": "KMeansClustersOptimization", "kind": "function", "doc": "Optimizes the number of clusters in K-means models using Elbow and Silhouette methods.
\n\nPurpose \n\nThis metric is used to optimize the number of clusters used in K-means clustering models. It intends to measure and\nevaluate the optimal number of clusters by leveraging two methodologies, namely the Elbow method and the Silhouette\nmethod. This is crucial as an inappropriate number of clusters can either overly simplify or overcomplicate the\nstructure of the data, thereby undermining the effectiveness of the model.
\n\nTest Mechanism \n\nThe test mechanism involves iterating over a predefined range of cluster numbers and applying both the Elbow method\nand the Silhouette method. The Elbow method computes the sum of the minimum euclidean distances between data points\nand their respective cluster centers (distortion). This value decreases as the number of clusters increases; the\noptimal number is typically at the 'elbow' point where the decrease in distortion becomes less pronounced.\nMeanwhile, the Silhouette method calculates the average silhouette score for each data point in the dataset,\nproviding a measure of how similar each item is to its own cluster compared to other clusters. The optimal number\nof clusters under this method is the one that maximizes the average silhouette score. The results of both methods\nare plotted for visual inspection.
\n\nSigns of High Risk \n\n\nA high distortion value or a low silhouette average score for the optimal number of clusters. \nNo clear 'elbow' point or plateau observed in the distortion plot, or a uniformly low silhouette average score\nacross different numbers of clusters, suggesting the data is not amenable to clustering. \nAn optimal cluster number that is unreasonably high or low, suggestive of overfitting or underfitting,\nrespectively. \n \n\nStrengths \n\n\nProvides both a visual and quantitative method to determine the optimal number of clusters. \nLeverages two different methods (Elbow and Silhouette), thereby affording robustness and versatility in assessing\nthe data's clusterability. \nFacilitates improved model performance by allowing for an informed selection of the number of clusters. \n \n\nLimitations \n\n\nAssumes that a suitable number of clusters exists in the data, which may not always be true, especially for\ncomplex or noisy data. \nBoth methods may fail to provide definitive answers when the data lacks clear cluster structures. \nMight not be straightforward to determine the 'elbow' point or maximize the silhouette average score, especially\nin larger and complicated datasets. \nAssumes spherical clusters (due to using the Euclidean distance in the Elbow method), which might not align with\nthe actual structure of the data. \n \n", "signature": "(\tmodel : validmind . vm_models . model . VMModel , \tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tn_clusters : Optional [ List [ int ]] = None ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.MinimumAccuracy": {"fullname": "validmind.tests.model_validation.sklearn.MinimumAccuracy", "modulename": "validmind.tests.model_validation.sklearn.MinimumAccuracy", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.sklearn.MinimumAccuracy.MinimumAccuracy": {"fullname": "validmind.tests.model_validation.sklearn.MinimumAccuracy.MinimumAccuracy", "modulename": "validmind.tests.model_validation.sklearn.MinimumAccuracy", "qualname": "MinimumAccuracy", "kind": "function", "doc": "Checks if the model's prediction accuracy meets or surpasses a specified threshold.
\n\nPurpose \n\nThe Minimum Accuracy test\u2019s objective is to verify whether the model's prediction accuracy on a specific dataset\nmeets or surpasses a predetermined minimum threshold. Accuracy, which is simply the ratio of correct predictions to\ntotal predictions, is a key metric for evaluating the model's performance. Considering binary as well as multiclass\nclassifications, accurate labeling becomes indispensable.
\n\nTest Mechanism \n\nThe test mechanism involves contrasting the model's accuracy score with a preset minimum threshold value, with the\ndefault being 0.7. The accuracy score is computed utilizing sklearn\u2019s accuracy_score method, where the true\nlabels y_true and predicted labels class_pred are compared. If the accuracy score is above the threshold, the\ntest receives a passing mark. The test returns the result along with the accuracy score and threshold used for the\ntest.
\n\nSigns of High Risk \n\n\nModel fails to achieve or surpass the predefined score threshold. \nPersistent scores below the threshold, indicating a high risk of inaccurate predictions. \n \n\nStrengths \n\n\nSimplicity, presenting a straightforward measure of holistic model performance across all classes. \nParticularly advantageous when classes are balanced. \nVersatile, as it can be implemented on both binary and multiclass classification tasks. \n \n\nLimitations \n\n\nMisleading accuracy scores when classes in the dataset are highly imbalanced. \nFavoritism towards the majority class, giving an inaccurate perception of model performance. \nInability to measure the model's precision, recall, or capacity to manage false positives or false negatives. \nFocused on overall correctness and may not be sufficient for all types of model analytics. \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tmodel : validmind . vm_models . model . VMModel , \tmin_threshold : float = 0.7 ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.MinimumF1Score": {"fullname": "validmind.tests.model_validation.sklearn.MinimumF1Score", "modulename": "validmind.tests.model_validation.sklearn.MinimumF1Score", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.sklearn.MinimumF1Score.MinimumF1Score": {"fullname": "validmind.tests.model_validation.sklearn.MinimumF1Score.MinimumF1Score", "modulename": "validmind.tests.model_validation.sklearn.MinimumF1Score", "qualname": "MinimumF1Score", "kind": "function", "doc": "Assesses if the model's F1 score on the validation set meets a predefined minimum threshold, ensuring balanced\nperformance between precision and recall.
\n\nPurpose \n\nThe main objective of this test is to ensure that the F1 score, a balanced measure of precision and recall, of the\nmodel meets or surpasses a predefined threshold on the validation dataset. The F1 score is highly useful for\ngauging model performance in classification tasks, especially in cases where the distribution of positive and\nnegative classes is skewed.
\n\nTest Mechanism \n\nThe F1 score for the validation dataset is computed through scikit-learn's metrics in Python. The scoring mechanism\ndiffers based on the classification problem: for multi-class problems, macro averaging is used, and for binary\nclassification, the built-in f1_score calculation is used. The obtained F1 score is then assessed against the\npredefined minimum F1 score that is expected from the model.
\n\nSigns of High Risk \n\n\nIf a model returns an F1 score that is less than the established threshold, it is regarded as high risk. \nA low F1 score might suggest that the model is not finding an optimal balance between precision and recall,\nfailing to effectively identify positive classes while minimizing false positives. \n \n\nStrengths \n\n\nProvides a balanced measure of a model's performance by accounting for both false positives and false negatives. \nParticularly advantageous in scenarios with imbalanced class distribution, where accuracy can be misleading. \nFlexibility in setting the threshold value allows tailored minimum acceptable performance standards. \n \n\nLimitations \n\n\nMay not be suitable for all types of models and machine learning tasks. \nThe F1 score assumes an equal cost for false positives and false negatives, which may not be true in some\nreal-world scenarios. \nPractitioners might need to rely on other metrics such as precision, recall, or the ROC-AUC score that align more\nclosely with specific requirements. \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tmodel : validmind . vm_models . model . VMModel , \tmin_threshold : float = 0.5 ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.MinimumROCAUCScore": {"fullname": "validmind.tests.model_validation.sklearn.MinimumROCAUCScore", "modulename": "validmind.tests.model_validation.sklearn.MinimumROCAUCScore", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.sklearn.MinimumROCAUCScore.MinimumROCAUCScore": {"fullname": "validmind.tests.model_validation.sklearn.MinimumROCAUCScore.MinimumROCAUCScore", "modulename": "validmind.tests.model_validation.sklearn.MinimumROCAUCScore", "qualname": "MinimumROCAUCScore", "kind": "function", "doc": "Validates model by checking if the ROC AUC score meets or surpasses a specified threshold.
\n\nPurpose \n\nThe Minimum ROC AUC Score test is used to determine the model's performance by ensuring that the Receiver Operating\nCharacteristic Area Under the Curve (ROC AUC) score on the validation dataset meets or exceeds a predefined\nthreshold. The ROC AUC score indicates how well the model can distinguish between different classes, making it a\ncrucial measure in binary and multiclass classification tasks.
\n\nTest Mechanism \n\nThis test implementation calculates the multiclass ROC AUC score on the true target values and the model's\npredictions. The test converts the multi-class target variables into binary format using LabelBinarizer before\ncomputing the score. If this ROC AUC score is higher than the predefined threshold (defaulted to 0.5), the test\npasses; otherwise, it fails. The results, including the ROC AUC score, the threshold, and whether the test passed\nor failed, are then stored in a ThresholdTestResult object.
\n\nSigns of High Risk \n\n\nA high risk or failure in the model's performance as related to this metric would be represented by a low ROC AUC\nscore, specifically any score lower than the predefined minimum threshold. This suggests that the model is\nstruggling to distinguish between different classes effectively. \n \n\nStrengths \n\n\nThe test considers both the true positive rate and false positive rate, providing a comprehensive performance\nmeasure. \nROC AUC score is threshold-independent meaning it measures the model's quality across various classification\nthresholds. \nWorks robustly with binary as well as multi-class classification problems. \n \n\nLimitations \n\n\nROC AUC may not be useful if the class distribution is highly imbalanced; it could perform well in terms of AUC\nbut still fail to predict the minority class. \nThe test does not provide insight into what specific aspects of the model are causing poor performance if the ROC\nAUC score is unsatisfactory. \nThe use of macro average for multiclass ROC AUC score implies equal weightage to each class, which might not be\nappropriate if the classes are imbalanced. \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tmodel : validmind . vm_models . model . VMModel , \tmin_threshold : float = 0.5 ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.ModelParameters": {"fullname": "validmind.tests.model_validation.sklearn.ModelParameters", "modulename": "validmind.tests.model_validation.sklearn.ModelParameters", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.sklearn.ModelParameters.ModelParameters": {"fullname": "validmind.tests.model_validation.sklearn.ModelParameters.ModelParameters", "modulename": "validmind.tests.model_validation.sklearn.ModelParameters", "qualname": "ModelParameters", "kind": "function", "doc": "Extracts and displays model parameters in a structured format for transparency and reproducibility.
\n\nPurpose \n\nThe Model Parameters test is designed to provide transparency into model configuration and ensure\nreproducibility of machine learning models. It accomplishes this by extracting and presenting all\nrelevant parameters that define the model's behavior, making it easier to audit, validate, and\nreproduce model training.
\n\nTest Mechanism \n\nThe test leverages scikit-learn's API convention of get_params() to extract model parameters. It\nproduces a structured DataFrame containing parameter names and their corresponding values. For models\nthat follow scikit-learn's API (including XGBoost, RandomForest, and other estimators), all\nparameters are automatically extracted and displayed.
\n\nSigns of High Risk \n\n\nMissing crucial parameters that should be explicitly set \nExtreme parameter values that could indicate overfitting (e.g., unlimited tree depth) \nInconsistent parameters across different versions of the same model type \nParameter combinations known to cause instability or poor performance \nDefault values used for critical parameters that should be tuned \n \n\nStrengths \n\n\nUniversal compatibility with scikit-learn API-compliant models \nEnsures transparency in model configuration \nFacilitates model reproducibility and version control \nEnables systematic parameter auditing \nSupports both classification and regression models \nHelps identify potential configuration issues \n \n\nLimitations \n\n\nOnly works with models implementing scikit-learn's get_params() method \nCannot capture dynamic parameters set during model training \nDoes not validate parameter values for model-specific appropriateness \nParameter meanings and impacts may vary across different model types \nCannot detect indirect parameter interactions or their effects on model performance \n \n", "signature": "(model , model_params = None ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.ModelsPerformanceComparison": {"fullname": "validmind.tests.model_validation.sklearn.ModelsPerformanceComparison", "modulename": "validmind.tests.model_validation.sklearn.ModelsPerformanceComparison", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.sklearn.ModelsPerformanceComparison.ModelsPerformanceComparison": {"fullname": "validmind.tests.model_validation.sklearn.ModelsPerformanceComparison.ModelsPerformanceComparison", "modulename": "validmind.tests.model_validation.sklearn.ModelsPerformanceComparison", "qualname": "ModelsPerformanceComparison", "kind": "function", "doc": "Evaluates and compares the performance of multiple Machine Learning models using various metrics like accuracy,\nprecision, recall, and F1 score.
\n\nPurpose \n\nThe Models Performance Comparison test aims to evaluate and compare the performance of various Machine Learning\nmodels using test data. It employs multiple metrics such as accuracy, precision, recall, and the F1 score, among\nothers, to assess model performance and assist in selecting the most effective model for the designated task.
\n\nTest Mechanism \n\nThe test employs Scikit-learn\u2019s performance metrics to evaluate each model's performance for both binary and\nmulticlass classification tasks. To compare performances, the test runs each model against the test dataset, then\nproduces a comprehensive classification report. This report includes metrics such as accuracy, precision, recall,\nand the F1 score. Based on whether the task at hand is binary or multiclass classification, it calculates metrics\nfor all the classes and their weighted averages, macro averages, and per-class metrics. The test will be skipped if\nno models are supplied.
\n\nSigns of High Risk \n\n\nLow scores in accuracy, precision, recall, and F1 metrics indicate a potentially high risk. \nA low area under the Receiver Operating Characteristic (ROC) curve (roc_auc score) is another possible indicator\nof high risk. \nIf the metrics scores are significantly lower than alternative models, this might suggest a high risk of failure. \n \n\nStrengths \n\n\nProvides a simple way to compare the performance of multiple models, accommodating both binary and multiclass\nclassification tasks. \nOffers a holistic view of model performance through a comprehensive report of key performance metrics. \nThe inclusion of the ROC AUC score is advantageous, as this robust performance metric can effectively handle\nclass imbalance issues. \n \n\nLimitations \n\n\nMay not be suitable for more complex performance evaluations that consider factors such as prediction speed,\ncomputational cost, or business-specific constraints. \nThe test's reliability depends on the provided test dataset; hence, the selected models' performance could vary\nwith unseen data or changes in the data distribution. \nThe ROC AUC score might not be as meaningful or easily interpretable for multilabel/multiclass tasks. \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tmodels : list [ validmind . vm_models . model . VMModel ] ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.OverfitDiagnosis": {"fullname": "validmind.tests.model_validation.sklearn.OverfitDiagnosis", "modulename": "validmind.tests.model_validation.sklearn.OverfitDiagnosis", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.sklearn.OverfitDiagnosis.OverfitDiagnosis": {"fullname": "validmind.tests.model_validation.sklearn.OverfitDiagnosis.OverfitDiagnosis", "modulename": "validmind.tests.model_validation.sklearn.OverfitDiagnosis", "qualname": "OverfitDiagnosis", "kind": "function", "doc": "Assesses potential overfitting in a model's predictions, identifying regions where performance between training and\ntesting sets deviates significantly.
\n\nPurpose \n\nThe Overfit Diagnosis test aims to identify areas in a model's predictions where there is a significant difference\nin performance between the training and testing sets. This test helps to pinpoint specific regions or feature\nsegments where the model may be overfitting.
\n\nTest Mechanism \n\nThis test compares the model's performance on training versus test data, grouped by feature columns. It calculates\nthe difference between the training and test performance for each group and identifies regions where this\ndifference exceeds a specified threshold:
\n\n\nThe test works for both classification and regression models. \nIt defaults to using the AUC metric for classification models and the MSE metric for regression models. \nThe threshold for identifying overfitting regions is set to 0.04 by default. \nThe test calculates the performance metrics for each feature segment and plots regions where the performance gap\nexceeds the threshold. \n \n\nSigns of High Risk \n\n\nSignificant gaps between training and test performance metrics for specific feature segments. \nMultiple regions with performance gaps exceeding the defined threshold. \nHigher than expected differences in predicted versus actual values in the test set compared to the training set. \n \n\nStrengths \n\n\nIdentifies specific areas where overfitting occurs. \nSupports multiple performance metrics, providing flexibility. \nApplicable to both classification and regression models. \nVisualization of overfitting segments aids in better understanding and debugging. \n \n\nLimitations \n\n\nThe default threshold may not be suitable for all use cases and requires tuning. \nMay not capture more subtle forms of overfitting that do not exceed the threshold. \nAssumes that the binning of features adequately represents the data segments. \n \n", "signature": "(\tmodel : validmind . vm_models . model . VMModel , \tdatasets : List [ validmind . vm_models . dataset . dataset . VMDataset ] , \tmetric : str = None , \tcut_off_threshold : float = 0.04 ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.PermutationFeatureImportance": {"fullname": "validmind.tests.model_validation.sklearn.PermutationFeatureImportance", "modulename": "validmind.tests.model_validation.sklearn.PermutationFeatureImportance", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.sklearn.PermutationFeatureImportance.PermutationFeatureImportance": {"fullname": "validmind.tests.model_validation.sklearn.PermutationFeatureImportance.PermutationFeatureImportance", "modulename": "validmind.tests.model_validation.sklearn.PermutationFeatureImportance", "qualname": "PermutationFeatureImportance", "kind": "function", "doc": "Assesses the significance of each feature in a model by evaluating the impact on model performance when feature\nvalues are randomly rearranged.
\n\nPurpose \n\nThe Permutation Feature Importance (PFI) metric aims to assess the importance of each feature used by the Machine\nLearning model. The significance is measured by evaluating the decrease in the model's performance when the\nfeature's values are randomly arranged.
\n\nTest Mechanism \n\nPFI is calculated via the permutation_importance method from the sklearn.inspection module. This method\nshuffles the columns of the feature dataset and measures the impact on the model's performance. A significant\ndecrease in performance after permutating a feature's values deems the feature as important. On the other hand, if\nperformance remains the same, the feature is likely not important. The output of the PFI metric is a figure\nillustrating the importance of each feature.
\n\nSigns of High Risk \n\n\nThe model heavily relies on a feature with highly variable or easily permutable values, indicating instability. \nA feature deemed unimportant by the model but expected to have a significant effect on the outcome based on\ndomain knowledge is not influencing the model's predictions. \n \n\nStrengths \n\n\nProvides insights into the importance of different features and may reveal underlying data structure. \nCan indicate overfitting if a particular feature or set of features overly impacts the model's predictions. \nModel-agnostic and can be used with any classifier that provides a measure of prediction accuracy before and\nafter feature permutation. \n \n\nLimitations \n\n\nDoes not imply causality; it only presents the amount of information that a feature provides for the prediction\ntask. \nDoes not account for interactions between features. If features are correlated, the permutation importance may\nallocate importance to one and not the other. \nCannot interact with certain libraries like statsmodels, pytorch, catboost, etc., thus limiting its applicability. \n \n", "signature": "(\tmodel : validmind . vm_models . model . VMModel , \tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tfontsize : Optional [ int ] = None , \tfigure_height : Optional [ int ] = None ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.PopulationStabilityIndex": {"fullname": "validmind.tests.model_validation.sklearn.PopulationStabilityIndex", "modulename": "validmind.tests.model_validation.sklearn.PopulationStabilityIndex", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.sklearn.PopulationStabilityIndex.calculate_psi": {"fullname": "validmind.tests.model_validation.sklearn.PopulationStabilityIndex.calculate_psi", "modulename": "validmind.tests.model_validation.sklearn.PopulationStabilityIndex", "qualname": "calculate_psi", "kind": "function", "doc": "Taken from:\nhttps://towardsdatascience.com/checking-model-stability-and-population-shift-with-psi-and-csi-6d12af008783
\n", "signature": "(score_initial , score_new , num_bins = 10 , mode = 'fixed' ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.PopulationStabilityIndex.PopulationStabilityIndex": {"fullname": "validmind.tests.model_validation.sklearn.PopulationStabilityIndex.PopulationStabilityIndex", "modulename": "validmind.tests.model_validation.sklearn.PopulationStabilityIndex", "qualname": "PopulationStabilityIndex", "kind": "function", "doc": "Assesses the Population Stability Index (PSI) to quantify the stability of an ML model's predictions across\ndifferent datasets.
\n\nPurpose \n\nThe Population Stability Index (PSI) serves as a quantitative assessment for evaluating the stability of a machine\nlearning model's output distributions when comparing two different datasets. Typically, these would be a\ndevelopment and a validation dataset or two datasets collected at different periods. The PSI provides a measurable\nindication of any significant shift in the model's performance over time or noticeable changes in the\ncharacteristics of the population the model is making predictions for.
\n\nTest Mechanism \n\nThe implementation of the PSI in this script involves calculating the PSI for each feature between the training and\ntest datasets. Data from both datasets is sorted and placed into either a predetermined number of bins or\nquantiles. The boundaries for these bins are initially determined based on the distribution of the training data.\nThe contents of each bin are calculated and their respective proportions determined. Subsequently, the PSI is\nderived for each bin through a logarithmic transformation of the ratio of the proportions of data for each feature\nin the training and test datasets. The PSI, along with the proportions of data in each bin for both datasets, are\ndisplayed in a summary table, a grouped bar chart, and a scatter plot.
\n\nSigns of High Risk \n\n\nA high PSI value is a clear indicator of high risk. Such a value suggests a significant shift in the model\npredictions or severe changes in the characteristics of the underlying population. \nThis ultimately suggests that the model may not be performing as well as expected and that it may be less\nreliable for making future predictions. \n \n\nStrengths \n\n\nThe PSI provides a quantitative measure of the stability of a model over time or across different samples, making\nit an invaluable tool for evaluating changes in a model's performance. \nIt allows for direct comparisons across different features based on the PSI value. \nThe calculation and interpretation of the PSI are straightforward, facilitating its use in model risk management. \nThe use of visual aids such as tables and charts further simplifies the comprehension and interpretation of the\nPSI. \n \n\nLimitations \n\n\nThe PSI test does not account for the interdependence between features: features that are dependent on one\nanother may show similar shifts in their distributions, which in turn may result in similar PSI values. \nThe PSI test does not inherently provide insights into why there are differences in distributions or why the PSI\nvalues may have changed. \nThe test may not handle features with significant outliers adequately. \nAdditionally, the PSI test is performed on model predictions, not on the underlying data distributions which can\nlead to misinterpretations. Any changes in PSI could be due to shifts in the model (model drift), changes in the\nrelationships between features and the target variable (concept drift), or both. However, distinguishing between\nthese causes is non-trivial. \n \n", "signature": "(\tdatasets : List [ validmind . vm_models . dataset . dataset . VMDataset ] , \tmodel : validmind . vm_models . model . VMModel , \tnum_bins : int = 10 , \tmode : str = 'fixed' ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.PrecisionRecallCurve": {"fullname": "validmind.tests.model_validation.sklearn.PrecisionRecallCurve", "modulename": "validmind.tests.model_validation.sklearn.PrecisionRecallCurve", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.sklearn.PrecisionRecallCurve.PrecisionRecallCurve": {"fullname": "validmind.tests.model_validation.sklearn.PrecisionRecallCurve.PrecisionRecallCurve", "modulename": "validmind.tests.model_validation.sklearn.PrecisionRecallCurve", "qualname": "PrecisionRecallCurve", "kind": "function", "doc": "Evaluates the precision-recall trade-off for binary classification models and visualizes the Precision-Recall curve.
\n\nPurpose \n\nThe Precision Recall Curve metric is intended to evaluate the trade-off between precision and recall in\nclassification models, particularly binary classification models. It assesses the model's capacity to produce\naccurate results (high precision), as well as its ability to capture a majority of all positive instances (high\nrecall).
\n\nTest Mechanism \n\nThe test extracts ground truth labels and prediction probabilities from the model's test dataset. It applies the\nprecision_recall_curve method from the sklearn metrics module to these extracted labels and predictions, which\ncomputes a precision-recall pair for each possible threshold. This calculation results in an array of precision and\nrecall scores that can be plotted against each other to form the Precision-Recall Curve. This curve is then\nvisually represented by using Plotly's scatter plot.
\n\nSigns of High Risk \n\n\nA lower area under the Precision-Recall Curve signifies high risk. \nThis corresponds to a model yielding a high amount of false positives (low precision) and/or false negatives (low\nrecall). \nIf the curve is closer to the bottom left of the plot, rather than being closer to the top right corner, it can\nbe a sign of high risk. \n \n\nStrengths \n\n\nThis metric aptly represents the balance between precision (minimizing false positives) and recall (minimizing\nfalse negatives), which is especially critical in scenarios where both values are significant. \nThrough the graphic representation, it enables an intuitive understanding of the model's performance across\ndifferent threshold levels. \n \n\nLimitations \n\n\nThis metric is only applicable to binary classification models - it raises errors for multiclass classification\nmodels or Foundation models. \nIt may not fully represent the overall accuracy of the model if the cost of false positives and false negatives\nare extremely different, or if the dataset is heavily imbalanced. \n \n", "signature": "(\tmodel : validmind . vm_models . model . VMModel , \tdataset : validmind . vm_models . dataset . dataset . VMDataset ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.ROCCurve": {"fullname": "validmind.tests.model_validation.sklearn.ROCCurve", "modulename": "validmind.tests.model_validation.sklearn.ROCCurve", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.sklearn.ROCCurve.ROCCurve": {"fullname": "validmind.tests.model_validation.sklearn.ROCCurve.ROCCurve", "modulename": "validmind.tests.model_validation.sklearn.ROCCurve", "qualname": "ROCCurve", "kind": "function", "doc": "Evaluates binary classification model performance by generating and plotting the Receiver Operating Characteristic\n(ROC) curve and calculating the Area Under Curve (AUC) score.
\n\nPurpose \n\nThe Receiver Operating Characteristic (ROC) curve is designed to evaluate the performance of binary classification\nmodels. This curve illustrates the balance between the True Positive Rate (TPR) and False Positive Rate (FPR)\nacross various threshold levels. In combination with the Area Under the Curve (AUC), the ROC curve aims to measure\nthe model's discrimination ability between the two defined classes in a binary classification problem (e.g.,\ndefault vs non-default). Ideally, a higher AUC score signifies superior model performance in accurately\ndistinguishing between the positive and negative classes.
\n\nTest Mechanism \n\nFirst, this script selects the target model and datasets that require binary classification. It then calculates the\npredicted probabilities for the test set, and uses this data, along with the true outcomes, to generate and plot\nthe ROC curve. Additionally, it includes a line signifying randomness (AUC of 0.5). The AUC score for the model's\nROC curve is also computed, presenting a numerical estimation of the model's performance. If any Infinite values\nare detected in the ROC threshold, these are effectively eliminated. The resulting ROC curve, AUC score, and\nthresholds are consequently saved for future reference.
\n\nSigns of High Risk \n\n\nA high risk is potentially linked to the model's performance if the AUC score drops below or nears 0.5. \nAnother warning sign would be the ROC curve lying closer to the line of randomness, indicating no discriminative\nability. \nFor the model to be deemed competent at its classification tasks, it is crucial that the AUC score is\nsignificantly above 0.5. \n \n\nStrengths \n\n\nThe ROC Curve offers an inclusive visual depiction of a model's discriminative power throughout all conceivable\nclassification thresholds, unlike other metrics that solely disclose model performance at one fixed threshold. \nDespite the proportions of the dataset, the AUC Score, which represents the entire ROC curve as a single data\npoint, continues to be consistent, proving to be the ideal choice for such situations. \n \n\nLimitations \n\n\nThe primary limitation is that this test is exclusively structured for binary classification tasks, thus limiting\nits application towards other model types. \nFurthermore, its performance might be subpar with models that output probabilities highly skewed towards 0 or 1. \nAt the extreme, the ROC curve could reflect high performance even when the majority of classifications are\nincorrect, provided that the model's ranking format is retained. This phenomenon is commonly termed the \"Class\nImbalance Problem\". \n \n", "signature": "(\tmodel : validmind . vm_models . model . VMModel , \tdataset : validmind . vm_models . dataset . dataset . VMDataset ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.RegressionErrors": {"fullname": "validmind.tests.model_validation.sklearn.RegressionErrors", "modulename": "validmind.tests.model_validation.sklearn.RegressionErrors", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.sklearn.RegressionErrors.RegressionErrors": {"fullname": "validmind.tests.model_validation.sklearn.RegressionErrors.RegressionErrors", "modulename": "validmind.tests.model_validation.sklearn.RegressionErrors", "qualname": "RegressionErrors", "kind": "function", "doc": "Assesses the performance and error distribution of a regression model using various error metrics.
\n\nPurpose \n\nThe purpose of the Regression Errors test is to measure the performance of a regression model by calculating\nseveral error metrics. This evaluation helps determine the model's accuracy and potential issues like overfitting\nor bias by analyzing differences in error metrics between the training and testing datasets.
\n\nTest Mechanism \n\nThe test computes the following error metrics:
\n\n\nMean Absolute Error (MAE) : Average of the absolute differences between true values and predicted values.Mean Squared Error (MSE) : Average of the squared differences between true values and predicted values.Root Mean Squared Error (RMSE) : Square root of the mean squared error.Mean Absolute Percentage Error (MAPE) : Average of the absolute differences between true values and predicted\nvalues, divided by the true values, and expressed as a percentage.Mean Bias Deviation (MBD) : Average bias between true values and predicted values. \n\nThese metrics are calculated separately for the training and testing datasets and compared to identify\ndiscrepancies.
\n\nSigns of High Risk \n\n\nHigh values for MAE, MSE, RMSE, or MAPE indicating poor model performance. \nLarge differences in error metrics between the training and testing datasets, suggesting overfitting. \nSignificant deviation of MBD from zero, indicating systematic bias in model predictions. \n \n\nStrengths \n\n\nProvides a comprehensive overview of model performance through multiple error metrics. \nIndividual metrics offer specific insights, e.g., MAE for interpretability, MSE for emphasizing larger errors. \nRMSE is useful for being in the same unit as the target variable. \nMAPE allows the error to be expressed as a percentage. \nMBD detects systematic bias in model predictions. \n \n\nLimitations \n\n\nMAE and MSE are sensitive to outliers. \nRMSE heavily penalizes larger errors, which might not always be desirable. \nMAPE can be misleading when actual values are near zero. \nMBD may not be suitable if bias varies with the magnitude of actual values. \nThese metrics may not capture all nuances of model performance and should be interpreted with domain-specific\ncontext. \n \n", "signature": "(model , dataset ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.RegressionErrorsComparison": {"fullname": "validmind.tests.model_validation.sklearn.RegressionErrorsComparison", "modulename": "validmind.tests.model_validation.sklearn.RegressionErrorsComparison", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.sklearn.RegressionErrorsComparison.RegressionErrorsComparison": {"fullname": "validmind.tests.model_validation.sklearn.RegressionErrorsComparison.RegressionErrorsComparison", "modulename": "validmind.tests.model_validation.sklearn.RegressionErrorsComparison", "qualname": "RegressionErrorsComparison", "kind": "function", "doc": "Assesses multiple regression error metrics to compare model performance across different datasets, emphasizing\nsystematic overestimation or underestimation and large percentage errors.
\n\nPurpose \n\nThe purpose of this test is to compare regression errors for different models applied to various datasets. It aims\nto examine model performance using multiple error metrics, thereby identifying areas where models may be\nunderperforming or exhibiting bias.
\n\nTest Mechanism \n\nThe function iterates through each dataset-model pair and calculates various error metrics, including Mean Absolute\nError (MAE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Mean Bias Deviation (MBD). The\nresults are summarized in a table, which provides a comprehensive view of each model's performance on the datasets.
\n\nSigns of High Risk \n\n\nHigh Mean Absolute Error (MAE) or Mean Squared Error (MSE), indicating poor model performance. \nHigh Mean Absolute Percentage Error (MAPE), suggesting large percentage errors, especially problematic if the\ntrue values are small. \nMean Bias Deviation (MBD) significantly different from zero, indicating systematic overestimation or\nunderestimation by the model. \n \n\nStrengths \n\n\nProvides multiple error metrics to assess model performance from different perspectives. \nIncludes a check to avoid division by zero when calculating MAPE. \n \n\nLimitations \n\n\nAssumes that the dataset is provided as a DataFrameDataset object with y, y_pred, and feature_columns\nattributes. \nRelies on the logger from validmind.logging to warn about zero values in y_true, which should be correctly\nimplemented and imported. \nRequires that dataset.y_pred(model) returns the predicted values for the model. \n \n", "signature": "(datasets , models ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.RegressionPerformance": {"fullname": "validmind.tests.model_validation.sklearn.RegressionPerformance", "modulename": "validmind.tests.model_validation.sklearn.RegressionPerformance", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.sklearn.RegressionPerformance.RegressionPerformance": {"fullname": "validmind.tests.model_validation.sklearn.RegressionPerformance.RegressionPerformance", "modulename": "validmind.tests.model_validation.sklearn.RegressionPerformance", "qualname": "RegressionPerformance", "kind": "function", "doc": "Evaluates the performance of a regression model using five different metrics: MAE, MSE, RMSE, MAPE, and MBD.
\n\nPurpose \n\nThe Regression Models Performance Comparison metric is used to measure the performance of regression models. It\ncalculates multiple evaluation metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE),\nRoot Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Bias Deviation (MBD), thereby\nenabling a comprehensive view of model performance.
\n\nTest Mechanism \n\nThe test uses the sklearn library to calculate the MAE, MSE, RMSE, MAPE, and MBD. These calculations encapsulate both\nthe direction and the magnitude of error in predictions, thereby providing a multi-faceted view of model accuracy.
\n\nSigns of High Risk \n\n\nHigh values of MAE, MSE, RMSE, and MAPE, which indicate a high error rate and imply a larger departure of the\nmodel's predictions from the true values. \nA large value of MBD, which shows a consistent bias in the model\u2019s predictions. \n \n\nStrengths \n\n\nThe metric evaluates models on five different metrics offering a comprehensive analysis of model performance. \nIt is designed to handle regression tasks and can be seamlessly integrated with libraries like sklearn. \n \n\nLimitations \n\n\nThe metric only evaluates regression models and does not evaluate classification models. \nThe test assumes that the models have been trained and tested appropriately prior to evaluation. It does not\nhandle pre-processing, feature selection, or other stages in the model lifecycle. \n \n", "signature": "(\tmodel : validmind . vm_models . model . VMModel , \tdataset : validmind . vm_models . dataset . dataset . VMDataset ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.RegressionR2Square": {"fullname": "validmind.tests.model_validation.sklearn.RegressionR2Square", "modulename": "validmind.tests.model_validation.sklearn.RegressionR2Square", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.sklearn.RegressionR2Square.RegressionR2Square": {"fullname": "validmind.tests.model_validation.sklearn.RegressionR2Square.RegressionR2Square", "modulename": "validmind.tests.model_validation.sklearn.RegressionR2Square", "qualname": "RegressionR2Square", "kind": "function", "doc": "Assesses the overall goodness-of-fit of a regression model by evaluating R-squared (R2) and Adjusted R-squared (Adj\nR2) scores to determine the model's explanatory power over the dependent variable.
\n\nPurpose \n\nThe purpose of the RegressionR2Square Metric test is to measure the overall goodness-of-fit of a regression model.\nSpecifically, this Python-based test evaluates the R-squared (R2) and Adjusted R-squared (Adj R2) scores, which are\nstatistical measures used to assess the strength of the relationship between the model's predictors and the\nresponse variable.
\n\nTest Mechanism \n\nThe test deploys the r2_score method from the Scikit-learn metrics module to measure the R2 score on both\ntraining and test sets. This score reflects the proportion of the variance in the dependent variable that is\npredictable from the independent variables. The test also calculates the Adjusted R2 score, which accounts for the\nnumber of predictors in the model to penalize model complexity and reduce overfitting. The Adjusted R2 score will\nbe smaller if unnecessary predictors are included in the model.
\n\nSigns of High Risk \n\n\nLow R2 or Adjusted R2 scores, suggesting that the model does not explain much variation in the dependent variable. \nSignificant discrepancy between R2 scores on the training set and test set, indicating overfitting and poor\ngeneralization to unseen data. \n \n\nStrengths \n\n\nWidely-used measure in regression analysis, providing a sound general indication of model performance. \nEasy to interpret and understand, as it represents the proportion of the dependent variable's variance explained\nby the independent variables. \nAdjusted R2 score helps control overfitting by penalizing unnecessary predictors. \n \n\nLimitations \n\n\nSensitive to the inclusion of unnecessary predictors even though Adjusted R2 penalizes complexity. \nLess reliable in cases of non-linear relationships or when the underlying assumptions of linear regression are\nviolated. \nDoes not provide insight on whether the correct regression model was used or if key assumptions have been met. \n \n", "signature": "(dataset , model ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.RegressionR2SquareComparison": {"fullname": "validmind.tests.model_validation.sklearn.RegressionR2SquareComparison", "modulename": "validmind.tests.model_validation.sklearn.RegressionR2SquareComparison", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.sklearn.RegressionR2SquareComparison.RegressionR2SquareComparison": {"fullname": "validmind.tests.model_validation.sklearn.RegressionR2SquareComparison.RegressionR2SquareComparison", "modulename": "validmind.tests.model_validation.sklearn.RegressionR2SquareComparison", "qualname": "RegressionR2SquareComparison", "kind": "function", "doc": "Compares R-Squared and Adjusted R-Squared values for different regression models across multiple datasets to assess\nmodel performance and relevance of features.
\n\nPurpose \n\nThe Regression R2 Square Comparison test aims to compare the R-Squared and Adjusted R-Squared values for different\nregression models across various datasets. It helps in assessing how well each model explains the variability in\nthe dataset, and whether the models include irrelevant features.
\n\nTest Mechanism \n\nThis test operates by:
\n\n\nIterating through each dataset-model pair. \nCalculating the R-Squared values to measure how much of the variability in the dataset is explained by the model. \nCalculating the Adjusted R-Squared values, which adjust the R-Squared based on the number of predictors in the\nmodel, making it more reliable when comparing models with different numbers of features. \nGenerating a summary table containing these values for each combination of dataset and model. \n \n\nSigns of High Risk \n\n\nIf the R-Squared values are significantly low, it indicates the model isn't explaining much of the variability in\nthe dataset. \nA significant difference between R-Squared and Adjusted R-Squared values might indicate that the model includes\nirrelevant features. \n \n\nStrengths \n\n\nProvides a quantitative measure of model performance in terms of variance explained. \nAdjusted R-Squared accounts for the number of predictors, making it a more reliable measure when comparing models\nwith different numbers of features. \nUseful for time-series forecasting and regression tasks. \n \n\nLimitations \n\n\nAssumes the dataset is provided as a DataFrameDataset object with y, y_pred, and feature_columns attributes. \nRelies on adj_r2_score from the statsmodels.statsutils module, which needs to be correctly implemented and\nimported. \nRequires that dataset.y_pred(model) returns the predicted values for the model. \n \n", "signature": "(datasets , models ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.RobustnessDiagnosis": {"fullname": "validmind.tests.model_validation.sklearn.RobustnessDiagnosis", "modulename": "validmind.tests.model_validation.sklearn.RobustnessDiagnosis", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.sklearn.RobustnessDiagnosis.RobustnessDiagnosis": {"fullname": "validmind.tests.model_validation.sklearn.RobustnessDiagnosis.RobustnessDiagnosis", "modulename": "validmind.tests.model_validation.sklearn.RobustnessDiagnosis", "qualname": "RobustnessDiagnosis", "kind": "function", "doc": "Assesses the robustness of a machine learning model by evaluating performance decay under noisy conditions.
\n\nPurpose \n\nThe Robustness Diagnosis test aims to evaluate the resilience of a machine learning model when subjected to\nperturbations or noise in its input data. This is essential for understanding the model's ability to handle\nreal-world scenarios where data may be imperfect or corrupted.
\n\nTest Mechanism \n\nThis test introduces Gaussian noise to the numeric input features of the datasets at varying scales of standard\ndeviation. The performance of the model is then measured using a specified metric. The process includes:
\n\n\nAdding Gaussian noise to numerical input features based on scaling factors. \nEvaluating the model's performance on the perturbed data using metrics like AUC for classification tasks and MSE\nfor regression tasks. \nAggregating and plotting the results to visualize performance decay relative to perturbation size. \n \n\nSigns of High Risk \n\n\nA significant drop in performance metrics with minimal noise. \nPerformance decay values exceeding the specified threshold. \nConsistent failure to meet performance standards across multiple perturbation scales. \n \n\nStrengths \n\n\nProvides insights into the model's robustness against noisy or corrupted data. \nUtilizes a variety of performance metrics suitable for both classification and regression tasks. \nVisualization helps in understanding the extent of performance degradation. \n \n\nLimitations \n\n\nGaussian noise might not adequately represent all types of real-world data perturbations. \nPerformance thresholds are somewhat arbitrary and might need tuning. \nThe test may not account for more complex or unstructured noise patterns that could affect model robustness. \n \n", "signature": "(\tdatasets : List [ validmind . vm_models . dataset . dataset . VMDataset ] , \tmodel : validmind . vm_models . model . VMModel , \tmetric : str = None , \tscaling_factor_std_dev_list : List [ float ] = [ 0.1 , 0.2 , 0.3 , 0.4 , 0.5 ] , \tperformance_decay_threshold : float = 0.05 ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.SHAPGlobalImportance": {"fullname": "validmind.tests.model_validation.sklearn.SHAPGlobalImportance", "modulename": "validmind.tests.model_validation.sklearn.SHAPGlobalImportance", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.sklearn.SHAPGlobalImportance.select_shap_values": {"fullname": "validmind.tests.model_validation.sklearn.SHAPGlobalImportance.select_shap_values", "modulename": "validmind.tests.model_validation.sklearn.SHAPGlobalImportance", "qualname": "select_shap_values", "kind": "function", "doc": "Selects SHAP values for binary or multiclass classification.
\n\nFor regression models, returns the SHAP values directly as there are no classes.
\n\nArguments: \n\n\nshap_values: The SHAP values returned by the SHAP explainer. For multiclass\nclassification, this will be a list where each element corresponds to a class.\nFor regression, this will be a single array of SHAP values.class_of_interest: The class index for which to retrieve SHAP values. If None\n(default), the function will assume binary classification and use class 1\nby default. \n\nReturns: \n\n\n The SHAP values for the specified class (classification) or for the regression\n output.
\n \n\nRaises: \n\n\nValueError: If class_of_interest is specified and is out of bounds for the\nnumber of classes. \n", "signature": "(shap_values , class_of_interest ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.SHAPGlobalImportance.generate_shap_plot": {"fullname": "validmind.tests.model_validation.sklearn.SHAPGlobalImportance.generate_shap_plot", "modulename": "validmind.tests.model_validation.sklearn.SHAPGlobalImportance", "qualname": "generate_shap_plot", "kind": "function", "doc": "Plots two types of SHAP global importance (SHAP).
\n\nArguments: \n\n\ntype_: The type of SHAP plot to generate. Must be \"mean\" or \"summary\".shap_values: The SHAP values to plot.x_test: The test data used to generate the SHAP values. \n\nReturns: \n\n\n The generated plot.
\n \n", "signature": "(type_ , shap_values , x_test ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.SHAPGlobalImportance.SHAPGlobalImportance": {"fullname": "validmind.tests.model_validation.sklearn.SHAPGlobalImportance.SHAPGlobalImportance", "modulename": "validmind.tests.model_validation.sklearn.SHAPGlobalImportance", "qualname": "SHAPGlobalImportance", "kind": "function", "doc": "Evaluates and visualizes global feature importance using SHAP values for model explanation and risk identification.
\n\nPurpose \n\nThe SHAP (SHapley Additive exPlanations) Global Importance metric aims to elucidate model outcomes by attributing\nthem to the contributing features. It assigns a quantifiable global importance to each feature via their respective\nabsolute Shapley values, thereby making it suitable for tasks like classification (both binary and multiclass).\nThis metric forms an essential part of model risk management.
\n\nTest Mechanism \n\nThe exam begins with the selection of a suitable explainer which aligns with the model's type. For tree-based\nmodels like XGBClassifier, RandomForestClassifier, CatBoostClassifier, TreeExplainer is used whereas for linear\nmodels like LogisticRegression, XGBRegressor, LinearRegression, it is the LinearExplainer. Once the explainer\ncalculates the Shapley values, these values are visualized using two specific graphical representations:
\n\n\nMean Importance Plot: This graph portrays the significance of individual features based on their absolute\nShapley values. It calculates the average of these absolute Shapley values across all instances to highlight the\nglobal importance of features.
Summary Plot: This visual tool combines the feature importance with their effects. Every dot on this chart\nrepresents a Shapley value for a certain feature in a specific case. The vertical axis is denoted by the feature\nwhereas the horizontal one corresponds to the Shapley value. A color gradient indicates the value of the feature,\ngradually changing from low to high. Features are systematically organized in accordance with their importance.
\n\nSigns of High Risk \n\n\nOveremphasis on certain features in SHAP importance plots, thus hinting at the possibility of model overfitting \nAnomalies such as unexpected or illogical features showing high importance, which might suggest that the model's\ndecisions are rooted in incorrect or undesirable reasoning \nA SHAP summary plot filled with high variability or scattered data points, indicating a cause for concern \n \n\nStrengths \n\n\nSHAP does more than just illustrating global feature significance, it offers a detailed perspective on how\ndifferent features shape the model's decision-making logic for each instance. \nIt provides clear insights into model behavior. \n \n\nLimitations \n\n\nHigh-dimensional data can convolute interpretations. \nAssociating importance with tangible real-world impact still involves a certain degree of subjectivity. \n \n", "signature": "(\tmodel : validmind . vm_models . model . VMModel , \tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tkernel_explainer_samples : int = 10 , \ttree_or_linear_explainer_samples : int = 200 , \tclass_of_interest : int = None ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.ScoreProbabilityAlignment": {"fullname": "validmind.tests.model_validation.sklearn.ScoreProbabilityAlignment", "modulename": "validmind.tests.model_validation.sklearn.ScoreProbabilityAlignment", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.sklearn.ScoreProbabilityAlignment.ScoreProbabilityAlignment": {"fullname": "validmind.tests.model_validation.sklearn.ScoreProbabilityAlignment.ScoreProbabilityAlignment", "modulename": "validmind.tests.model_validation.sklearn.ScoreProbabilityAlignment", "qualname": "ScoreProbabilityAlignment", "kind": "function", "doc": "Analyzes the alignment between credit scores and predicted probabilities.
\n\nPurpose \n\nThe Score-Probability Alignment test evaluates how well credit scores align with\npredicted default probabilities. This helps validate score scaling, identify potential\ncalibration issues, and ensure scores reflect risk appropriately.
\n\nTest Mechanism \n\nThe test:
\n\n\nGroups scores into bins \nCalculates average predicted probability per bin \nTests monotonicity of relationship \nAnalyzes probability distribution within score bands \n \n\nSigns of High Risk \n\n\nNon-monotonic relationship between scores and probabilities \nLarge probability variations within score bands \nUnexpected probability jumps between adjacent bands \nPoor alignment with expected odds-to-score relationship \nInconsistent probability patterns across score ranges \nClustering of probabilities at extreme values \nScore bands with similar probability profiles \nUnstable probability estimates in key decision bands \n \n\nStrengths \n\n\nDirect validation of score-to-probability relationship \nIdentifies potential calibration issues \nSupports score band validation \nHelps understand model behavior \nUseful for policy setting \nVisual and numerical results \nEasy to interpret \nSupports regulatory documentation \n \n\nLimitations \n\n\nSensitive to bin selection \nRequires sufficient data per bin \nMay mask within-bin variations \nPoint-in-time analysis only \nCannot detect all forms of miscalibration \nAssumes scores should align with probabilities \nMay oversimplify complex relationships \nLimited to binary outcomes \n \n", "signature": "(\tmodel : validmind . vm_models . model . VMModel , \tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tscore_column : str = 'score' , \tn_bins : int = 10 ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.SilhouettePlot": {"fullname": "validmind.tests.model_validation.sklearn.SilhouettePlot", "modulename": "validmind.tests.model_validation.sklearn.SilhouettePlot", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.sklearn.SilhouettePlot.SilhouettePlot": {"fullname": "validmind.tests.model_validation.sklearn.SilhouettePlot.SilhouettePlot", "modulename": "validmind.tests.model_validation.sklearn.SilhouettePlot", "qualname": "SilhouettePlot", "kind": "function", "doc": "Calculates and visualizes Silhouette Score, assessing the degree of data point suitability to its cluster in ML\nmodels.
\n\nPurpose \n\nThis test calculates the Silhouette Score, which is a model performance metric used in clustering applications.\nPrimarily, the Silhouette Score evaluates how similar a data point is to its own cluster compared to other\nclusters. The metric ranges between -1 and 1, where a high value indicates that the object is well matched to its\nown cluster and poorly matched to neighboring clusters. Thus, the goal is to achieve a high Silhouette Score,\nimplying well-separated clusters.
\n\nTest Mechanism \n\nThe test first extracts the true and predicted labels from the model's training data. The test runs the Silhouette\nScore function, which takes as input the training dataset features and the predicted labels, subsequently\ncalculating the average score. This average Silhouette Score is printed for reference. The script then calculates\nthe silhouette coefficients for each data point, helping to form the Silhouette Plot. Each cluster is represented\nin this plot, with color distinguishing between different clusters. A red dashed line indicates the average\nSilhouette Score. The Silhouette Scores are also collected into a structured table, facilitating model performance\nanalysis and comparison.
\n\nSigns of High Risk \n\n\nA low Silhouette Score, potentially indicating that the clusters are not well separated and that data points may\nnot be fitting well to their respective clusters. \nA Silhouette Plot displaying overlapping clusters or the absence of clear distinctions between clusters visually\nalso suggests poor clustering performance. \n \n\nStrengths \n\n\nThe Silhouette Score provides a clear and quantitative measure of how well data points have been grouped into\nclusters, offering insights into model performance. \nThe Silhouette Plot provides an intuitive, graphical representation of the clustering mechanism, aiding visual\nassessments of model performance. \nIt does not require ground truth labels, so it's useful when true cluster assignments are not known. \n \n\nLimitations \n\n\nThe Silhouette Score may be susceptible to the influence of outliers, which could impact its accuracy and\nreliability. \nIt assumes the clusters are convex and isotropic, which might not be the case with complex datasets. \nDue to the average nature of the Silhouette Score, the metric does not account for individual data point\nassignment nuances, so potentially relevant details may be omitted. \nComputationally expensive for large datasets, as it requires pairwise distance computations. \n \n", "signature": "(\tmodel : validmind . vm_models . model . VMModel , \tdataset : validmind . vm_models . dataset . dataset . VMDataset ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.TrainingTestDegradation": {"fullname": "validmind.tests.model_validation.sklearn.TrainingTestDegradation", "modulename": "validmind.tests.model_validation.sklearn.TrainingTestDegradation", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.sklearn.TrainingTestDegradation.TrainingTestDegradation": {"fullname": "validmind.tests.model_validation.sklearn.TrainingTestDegradation.TrainingTestDegradation", "modulename": "validmind.tests.model_validation.sklearn.TrainingTestDegradation", "qualname": "TrainingTestDegradation", "kind": "function", "doc": "Tests if model performance degradation between training and test datasets exceeds a predefined threshold.
\n\nPurpose \n\nThe TrainingTestDegradation class serves as a test to verify that the degradation in performance between the\ntraining and test datasets does not exceed a predefined threshold. This test measures the model's ability to\ngeneralize from its training data to unseen test data, assessing key classification metrics such as accuracy,\nprecision, recall, and f1 score to verify the model's robustness and reliability.
\n\nTest Mechanism \n\nThe code applies several predefined metrics, including accuracy, precision, recall, and f1 scores, to the model's\npredictions for both the training and test datasets. It calculates the degradation as the difference between the\ntraining score and test score divided by the training score. The test is considered successful if the degradation\nfor each metric is less than the preset maximum threshold of 10%. The results are summarized in a table showing\neach metric's train score, test score, degradation percentage, and pass/fail status.
\n\nSigns of High Risk \n\n\nA degradation percentage that exceeds the maximum allowed threshold of 10% for any of the evaluated metrics. \nA high difference or gap between the metric scores on the training and the test datasets. \nThe 'Pass/Fail' column displaying 'Fail' for any of the evaluated metrics. \n \n\nStrengths \n\n\nProvides a quantitative measure of the model's ability to generalize to unseen data, which is key for predicting\nits practical real-world performance. \nBy evaluating multiple metrics, it takes into account different facets of model performance and enables a more\nholistic evaluation. \nThe use of a variable predefined threshold allows the flexibility to adjust the acceptability criteria for\ndifferent scenarios. \n \n\nLimitations \n\n\nThe test compares raw performance on training and test data but does not factor in the nature of the data. Areas\nwith less representation in the training set might still perform poorly on unseen data. \nIt requires good coverage and balance in the test and training datasets to produce reliable results, which may\nnot always be available. \nThe test is currently only designed for classification tasks. \n \n", "signature": "(\tdatasets : List [ validmind . vm_models . dataset . dataset . VMDataset ] , \tmodel : validmind . vm_models . model . VMModel , \tmax_threshold : float = 0.1 ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.VMeasure": {"fullname": "validmind.tests.model_validation.sklearn.VMeasure", "modulename": "validmind.tests.model_validation.sklearn.VMeasure", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.sklearn.VMeasure.VMeasure": {"fullname": "validmind.tests.model_validation.sklearn.VMeasure.VMeasure", "modulename": "validmind.tests.model_validation.sklearn.VMeasure", "qualname": "VMeasure", "kind": "function", "doc": "Evaluates homogeneity and completeness of a clustering model using the V Measure Score.
\n\nPurpose \n\nThe purpose of this metric, V Measure Score (V Score), is to evaluate the performance of a clustering model. It\nmeasures the homogeneity and completeness of a set of cluster labels, where homogeneity refers to each cluster\ncontaining only members of a single class and completeness meaning all members of a given class are assigned to the\nsame cluster.
\n\nTest Mechanism \n\nClusterVMeasure is a class that inherits from another class, ClusterPerformance. It uses the v_measure_score\nfunction from the sklearn module's metrics package. The required inputs to perform this metric are the model, train\ndataset, and test dataset. The test is appropriate for models tasked with clustering.
\n\nSigns of High Risk \n\n\nLow V Measure Score: A low V Measure Score indicates that the clustering model has poor homogeneity or\ncompleteness, or both. This might signal that the model is failing to correctly cluster the data. \n \n\nStrengths \n\n\nThe V Measure Score is a harmonic mean between homogeneity and completeness. This ensures that both attributes\nare taken into account when evaluating the model, providing an overall measure of its cluster validity. \nThe metric does not require knowledge of the ground truth classes when measuring homogeneity and completeness,\nmaking it applicable in instances where such information is unavailable. \n \n\nLimitations \n\n\nThe V Measure Score can be influenced by the number of clusters, which means that it might not always reflect the\nquality of the clustering. Partitioning the data into many small clusters could lead to high homogeneity but low\ncompleteness, leading to a low V Measure Score even if the clustering might be useful. \nIt assumes equal importance of homogeneity and completeness. In some applications, one may be more important than\nthe other. The V Measure Score does not provide flexibility in assigning different weights to homogeneity and\ncompleteness. \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tmodel : validmind . vm_models . model . VMModel ): ", "funcdef": "def"}, "validmind.tests.model_validation.sklearn.WeakspotsDiagnosis": {"fullname": "validmind.tests.model_validation.sklearn.WeakspotsDiagnosis", "modulename": "validmind.tests.model_validation.sklearn.WeakspotsDiagnosis", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.sklearn.WeakspotsDiagnosis.WeakspotsDiagnosis": {"fullname": "validmind.tests.model_validation.sklearn.WeakspotsDiagnosis.WeakspotsDiagnosis", "modulename": "validmind.tests.model_validation.sklearn.WeakspotsDiagnosis", "qualname": "WeakspotsDiagnosis", "kind": "function", "doc": "Identifies and visualizes weak spots in a machine learning model's performance across various sections of the\nfeature space.
\n\nPurpose \n\nThe weak spots test is applied to evaluate the performance of a machine learning model within specific regions of\nits feature space. This test slices the feature space into various sections, evaluating the model's outputs within\neach section against specific performance metrics (e.g., accuracy, precision, recall, and F1 scores). The ultimate\naim is to identify areas where the model's performance falls below the set thresholds, thereby exposing its\npossible weaknesses and limitations.
\n\nTest Mechanism \n\nThe test mechanism adopts an approach of dividing the feature space of the training dataset into numerous bins. The\nmodel's performance metrics (accuracy, precision, recall, F1 scores) are then computed for each bin on both the\ntraining and test datasets. A \"weak spot\" is identified if any of the performance metrics fall below a\npredetermined threshold for a particular bin on the test dataset. The test results are visually plotted as bar\ncharts for each performance metric, indicating the bins which fail to meet the established threshold.
\n\nSigns of High Risk \n\n\nAny performance metric of the model dropping below the set thresholds. \nSignificant disparity in performance between the training and test datasets within a bin could be an indication\nof overfitting. \nRegions or slices with consistently low performance metrics. Such instances could mean that the model struggles\nto handle specific types of input data adequately, resulting in potentially inaccurate predictions. \n \n\nStrengths \n\n\nThe test helps pinpoint precise regions of the feature space where the model's performance is below par, allowing\nfor more targeted improvements to the model. \nThe graphical presentation of the performance metrics offers an intuitive way to understand the model's\nperformance across different feature areas. \nThe test exhibits flexibility, letting users set different thresholds for various performance metrics according\nto the specific requirements of the application. \n \n\nLimitations \n\n\nThe binning system utilized for the feature space in the test could over-simplify the model's behavior within\neach bin. The granularity of this slicing depends on the chosen 'bins' parameter and can sometimes be arbitrary. \nThe effectiveness of this test largely hinges on the selection of thresholds for the performance metrics, which\nmay not hold universally applicable and could be subjected to the specifications of a particular model and\napplication. \nThe test is unable to handle datasets with a text column, limiting its application to numerical or categorical\ndata types only. \nDespite its usefulness in highlighting problematic regions, the test does not offer direct suggestions for model\nimprovement. \n \n", "signature": "(\tdatasets : List [ validmind . vm_models . dataset . dataset . VMDataset ] , \tmodel : validmind . vm_models . model . VMModel , \tfeatures_columns : Optional [ List [ str ]] = None , \tmetrics : Optional [ Dict [ str , Callable ]] = None , \tthresholds : Optional [ Dict [ str , float ]] = None ): ", "funcdef": "def"}, "validmind.tests.model_validation.statsmodels": {"fullname": "validmind.tests.model_validation.statsmodels", "modulename": "validmind.tests.model_validation.statsmodels", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.statsmodels.AutoARIMA": {"fullname": "validmind.tests.model_validation.statsmodels.AutoARIMA", "modulename": "validmind.tests.model_validation.statsmodels.AutoARIMA", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.statsmodels.AutoARIMA.AutoARIMA": {"fullname": "validmind.tests.model_validation.statsmodels.AutoARIMA.AutoARIMA", "modulename": "validmind.tests.model_validation.statsmodels.AutoARIMA", "qualname": "AutoARIMA", "kind": "function", "doc": "Evaluates ARIMA models for time-series forecasting, ranking them using Bayesian and Akaike Information Criteria.
\n\nPurpose \n\nThe AutoARIMA validation test is designed to evaluate and rank AutoRegressive Integrated Moving Average (ARIMA)\nmodels. These models are primarily used for forecasting time-series data. The validation test automatically fits\nmultiple ARIMA models, with varying parameters, to every variable within the given dataset. The models are then\nranked based on their Bayesian Information Criterion (BIC) and Akaike Information Criterion (AIC) values, which\nprovide a basis for the efficient model selection process.
\n\nTest Mechanism \n\nThis metric proceeds by generating an array of feasible combinations of ARIMA model parameters which are within a\nprescribed limit. These limits include max_p, max_d, max_q; they represent the autoregressive, differencing,\nand moving average components respectively. Upon applying these sets of parameters, the validation test fits each\nARIMA model to the time-series data provided. For each model, it subsequently proceeds to calculate and record both\nthe BIC and AIC values, which serve as performance indicators for the model fit. Prior to this parameter fitting\nprocess, the Augmented Dickey-Fuller test for data stationarity is conducted on the data series. If a series is\nfound to be non-stationary, a warning message is sent out, given that ARIMA models necessitate input series to be\nstationary.
\n\nSigns of High Risk \n\n\nIf the p-value of the Augmented Dickey-Fuller test for a variable exceeds 0.05, a warning is logged. This warning\nindicates that the series might not be stationary, leading to potentially inaccurate results. \nConsistent failure in fitting ARIMA models (as made evident through logged errors) might disclose issues with\neither the data or model stability. \n \n\nStrengths \n\n\nThe AutoARIMA validation test simplifies the often complex task of selecting the most suitable ARIMA model based\non BIC and AIC criteria. \nThe mechanism incorporates a check for non-stationarity within the data, which is a critical prerequisite for\nARIMA models. \nThe exhaustive search through all possible combinations of model parameters enhances the likelihood of\nidentifying the best-fit model. \n \n\nLimitations \n\n\nThis validation test can be computationally costly as it involves creating and fitting multiple ARIMA models for\nevery variable. \nAlthough the test checks for non-stationarity and logs warnings where present, it does not apply any\ntransformations to the data to establish stationarity. \nThe selection of models leans solely on BIC and AIC criteria, which may not yield the best predictive model in\nall scenarios. \nThe test is only applicable to regression tasks involving time-series data, and may not work effectively for\nother types of machine learning tasks. \n \n", "signature": "(\tmodel : validmind . vm_models . model . VMModel , \tdataset : validmind . vm_models . dataset . dataset . VMDataset ): ", "funcdef": "def"}, "validmind.tests.model_validation.statsmodels.CumulativePredictionProbabilities": {"fullname": "validmind.tests.model_validation.statsmodels.CumulativePredictionProbabilities", "modulename": "validmind.tests.model_validation.statsmodels.CumulativePredictionProbabilities", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.statsmodels.CumulativePredictionProbabilities.CumulativePredictionProbabilities": {"fullname": "validmind.tests.model_validation.statsmodels.CumulativePredictionProbabilities.CumulativePredictionProbabilities", "modulename": "validmind.tests.model_validation.statsmodels.CumulativePredictionProbabilities", "qualname": "CumulativePredictionProbabilities", "kind": "function", "doc": "Visualizes cumulative probabilities of positive and negative classes for both training and testing in classification models.
\n\nPurpose \n\nThis metric is utilized to evaluate the distribution of predicted probabilities for positive and negative classes\nin a classification model. It provides a visual assessment of the model's behavior by plotting the cumulative\nprobabilities for positive and negative classes across both the training and test datasets.
\n\nTest Mechanism \n\nThe classification model is evaluated by first computing the predicted probabilities for each instance in both\nthe training and test datasets, which are then added as a new column in these sets. The cumulative probabilities\nfor positive and negative classes are subsequently calculated and sorted in ascending order. Cumulative\ndistributions of these probabilities are created for both positive and negative classes across both training and\ntest datasets. These cumulative probabilities are represented visually in a plot, containing two subplots - one for\nthe training data and the other for the test data, with lines representing cumulative distributions of positive and\nnegative classes.
\n\nSigns of High Risk \n\n\nImbalanced distribution of probabilities for either positive or negative classes. \nNotable discrepancies or significant differences between the cumulative probability distributions for the\ntraining data versus the test data. \nMarked discrepancies or large differences between the cumulative probability distributions for positive and\nnegative classes. \n \n\nStrengths \n\n\nProvides a visual illustration of data, which enhances the ease of understanding and interpreting the model's\nbehavior. \nAllows for the comparison of model's behavior across training and testing datasets, providing insights about how\nwell the model is generalized. \nDifferentiates between positive and negative classes and their respective distribution patterns, aiding in\nproblem diagnosis. \n \n\nLimitations \n\n\nExclusive to classification tasks and specifically to classification models. \nGraphical results necessitate human interpretation and may not be directly applicable for automated risk\ndetection. \nThe method does not give a solitary quantifiable measure of model risk, instead, it offers a visual\nrepresentation and broad distributional information. \nIf the training and test datasets are not representative of the overall data distribution, the metric could\nprovide misleading results. \n \n", "signature": "(dataset , model , title = 'Cumulative Probabilities' ): ", "funcdef": "def"}, "validmind.tests.model_validation.statsmodels.DurbinWatsonTest": {"fullname": "validmind.tests.model_validation.statsmodels.DurbinWatsonTest", "modulename": "validmind.tests.model_validation.statsmodels.DurbinWatsonTest", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.statsmodels.DurbinWatsonTest.DurbinWatsonTest": {"fullname": "validmind.tests.model_validation.statsmodels.DurbinWatsonTest.DurbinWatsonTest", "modulename": "validmind.tests.model_validation.statsmodels.DurbinWatsonTest", "qualname": "DurbinWatsonTest", "kind": "function", "doc": "Assesses autocorrelation in time series data features using the Durbin-Watson statistic.
\n\nPurpose \n\nThe Durbin-Watson Test metric detects autocorrelation in time series data (where a set of data values influences\ntheir predecessors). Autocorrelation is a crucial factor for regression tasks as these often assume the\nindependence of residuals. A model with significant autocorrelation may give unreliable predictions.
\n\nTest Mechanism \n\nUtilizing the durbin_watson function in the statsmodels Python library, the Durbin-Watson (DW) Test metric\ngenerates a statistical value for each feature of the training dataset. The function is looped over all columns of\nthe dataset, calculating and caching the DW value for each column for further analysis. A DW metric value nearing 2\nindicates no autocorrelation. Conversely, values approaching 0 suggest positive autocorrelation, and those leaning\ntowards 4 imply negative autocorrelation.
\n\nSigns of High Risk \n\n\nIf a feature's DW value significantly deviates from 2, it could signal a high risk due to potential\nautocorrelation issues in the dataset. \nA value closer to 0 could imply positive autocorrelation, while a value nearer to 4 could point to negative\nautocorrelation, both leading to potentially unreliable prediction models. \n \n\nStrengths \n\n\nThe metric specializes in identifying autocorrelation in prediction model residuals. \nAutocorrelation detection assists in diagnosing violation of various modeling technique assumptions, particularly\nin regression analysis and time-series data modeling. \n \n\nLimitations \n\n\nThe Durbin-Watson Test mainly detects linear autocorrelation and could overlook other types of relationships. \nThe metric is highly sensitive to data points order. Shuffling the order could lead to notably different results. \nThe test only checks for first-order autocorrelation (between a variable and its immediate predecessor) and fails\nto detect higher-order autocorrelation. \n \n", "signature": "(dataset , model , threshold = [ 1.5 , 2.5 ] ): ", "funcdef": "def"}, "validmind.tests.model_validation.statsmodels.GINITable": {"fullname": "validmind.tests.model_validation.statsmodels.GINITable", "modulename": "validmind.tests.model_validation.statsmodels.GINITable", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.statsmodels.GINITable.GINITable": {"fullname": "validmind.tests.model_validation.statsmodels.GINITable.GINITable", "modulename": "validmind.tests.model_validation.statsmodels.GINITable", "qualname": "GINITable", "kind": "function", "doc": "Evaluates classification model performance using AUC, GINI, and KS metrics for training and test datasets.
\n\nPurpose \n\nThe 'GINITable' metric is designed to evaluate the performance of a classification model by emphasizing its\ndiscriminatory power. Specifically, it calculates and presents three important metrics - the Area under the ROC\nCurve (AUC), the GINI coefficient, and the Kolmogorov-Smirnov (KS) statistic - for both training and test datasets.
\n\nTest Mechanism \n\nUsing a dictionary for storing performance metrics for both the training and test datasets, the 'GINITable' metric\ncalculates each of these metrics sequentially. The Area under the ROC Curve (AUC) is calculated via the\nroc_auc_score function from the Scikit-Learn library. The GINI coefficient, a measure of statistical dispersion,\nis then computed by doubling the AUC and subtracting 1. Finally, the Kolmogorov-Smirnov (KS) statistic is\ncalculated via the roc_curve function from Scikit-Learn, with the False Positive Rate (FPR) subtracted from the\nTrue Positive Rate (TPR) and the maximum value taken from the resulting data. These metrics are then stored in a\npandas DataFrame for convenient visualization.
\n\nSigns of High Risk \n\n\nLow values for performance metrics may suggest a reduction in model performance, particularly a low AUC which\nindicates poor classification performance, or a low GINI coefficient, which could suggest a decreased ability to\ndiscriminate different classes. \nA high KS value may be an indicator of potential overfitting, as this generally signifies a substantial\ndivergence between positive and negative distributions. \nSignificant discrepancies between the performance on the training dataset and the test dataset may present\nanother signal of high risk. \n \n\nStrengths \n\n\nOffers three key performance metrics (AUC, GINI, and KS) in one test, providing a more comprehensive evaluation\nof the model. \nProvides a direct comparison between the model's performance on training and testing datasets, which aids in\nidentifying potential underfitting or overfitting. \nThe applied metrics are class-distribution invariant, thereby remaining effective for evaluating model\nperformance even when dealing with imbalanced datasets. \nPresents the metrics in a user-friendly table format for easy comprehension and analysis. \n \n\nLimitations \n\n\nThe GINI coefficient and KS statistic are both dependent on the AUC value. Therefore, any errors in the\ncalculation of the latter will adversely impact the former metrics too. \nMainly suited for binary classification models and may require modifications for effective application in\nmulti-class scenarios. \nThe metrics used are threshold-dependent and may exhibit high variability based on the chosen cut-off points. \nThe test does not incorporate a method to efficiently handle missing or inefficiently processed data, which could\nlead to inaccuracies in the metrics if the data is not appropriately preprocessed. \n \n", "signature": "(dataset , model ): ", "funcdef": "def"}, "validmind.tests.model_validation.statsmodels.KolmogorovSmirnov": {"fullname": "validmind.tests.model_validation.statsmodels.KolmogorovSmirnov", "modulename": "validmind.tests.model_validation.statsmodels.KolmogorovSmirnov", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.statsmodels.KolmogorovSmirnov.KolmogorovSmirnov": {"fullname": "validmind.tests.model_validation.statsmodels.KolmogorovSmirnov.KolmogorovSmirnov", "modulename": "validmind.tests.model_validation.statsmodels.KolmogorovSmirnov", "qualname": "KolmogorovSmirnov", "kind": "function", "doc": "Assesses whether each feature in the dataset aligns with a normal distribution using the Kolmogorov-Smirnov test.
\n\nPurpose \n\nThe Kolmogorov-Smirnov (KS) test evaluates the distribution of features in a dataset to determine their alignment\nwith a normal distribution. This is important because many statistical methods and machine learning models assume\nnormality in the data distribution.
\n\nTest Mechanism \n\nThis test calculates the KS statistic and corresponding p-value for each feature in the dataset. It does so by\ncomparing the cumulative distribution function of the feature with an ideal normal distribution. The KS statistic\nand p-value for each feature are then stored in a dictionary. The p-value threshold to reject the normal\ndistribution hypothesis is not preset, providing flexibility for different applications.
\n\nSigns of High Risk \n\n\nElevated KS statistic for a feature combined with a low p-value, indicating a significant divergence from a\nnormal distribution. \nFeatures with notable deviations that could create problems if the model assumes normality in data distribution. \n \n\nStrengths \n\n\nThe KS test is sensitive to differences in the location and shape of empirical cumulative distribution functions. \nIt is non-parametric and adaptable to various datasets, as it does not assume any specific data distribution. \nProvides detailed insights into the distribution of individual features. \n \n\nLimitations \n\n\nThe test's sensitivity to disparities in the tails of data distribution might cause false alarms about\nnon-normality. \nLess effective for multivariate distributions, as it is designed for univariate distributions. \nDoes not identify specific types of non-normality, such as skewness or kurtosis, which could impact model fitting. \n \n", "signature": "(\tmodel : validmind . vm_models . model . VMModel , \tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tdist : str = 'norm' ): ", "funcdef": "def"}, "validmind.tests.model_validation.statsmodels.Lilliefors": {"fullname": "validmind.tests.model_validation.statsmodels.Lilliefors", "modulename": "validmind.tests.model_validation.statsmodels.Lilliefors", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.statsmodels.Lilliefors.Lilliefors": {"fullname": "validmind.tests.model_validation.statsmodels.Lilliefors.Lilliefors", "modulename": "validmind.tests.model_validation.statsmodels.Lilliefors", "qualname": "Lilliefors", "kind": "function", "doc": "Assesses the normality of feature distributions in an ML model's training dataset using the Lilliefors test.
\n\nPurpose \n\nThe purpose of this metric is to utilize the Lilliefors test, named in honor of the Swedish statistician Hubert\nLilliefors, in order to assess whether the features of the machine learning model's training dataset conform to a\nnormal distribution. This is done because the assumption of normal distribution plays a vital role in numerous\nstatistical procedures as well as numerous machine learning models. Should the features fail to follow a normal\ndistribution, some model types may not operate at optimal efficiency. This can potentially lead to inaccurate\npredictions.
\n\nTest Mechanism \n\nThe application of this test happens across all feature columns within the training dataset. For each feature, the\nLilliefors test returns a test statistic and p-value. The test statistic quantifies how far the feature's\ndistribution is from an ideal normal distribution, whereas the p-value aids in determining the statistical\nrelevance of this deviation. The final results are stored within a dictionary, the keys of which correspond to the\nname of the feature column, and the values being another dictionary which houses the test statistic and p-value.
\n\nSigns of High Risk \n\n\nIf the p-value corresponding to a specific feature sinks below a pre-established significance level, generally\nset at 0.05, then it can be deduced that the distribution of that feature significantly deviates from a normal\ndistribution. This can present a high risk for models that assume normality, as these models may perform\ninaccurately or inefficiently in the presence of such a feature. \n \n\nStrengths \n\n\nOne advantage of the Lilliefors test is its utility irrespective of whether the mean and variance of the normal\ndistribution are known in advance. This makes it a more robust option in real-world situations where these values\nmight not be known. \nThe test has the ability to screen every feature column, offering a holistic view of the dataset. \n \n\nLimitations \n\n\nDespite the practical applications of the Lilliefors test in validating normality, it does come with some\nlimitations. \nIt is only capable of testing unidimensional data, thus rendering it ineffective for datasets with interactions\nbetween features or multi-dimensional phenomena. \nThe test might not be as sensitive as some other tests (like the Anderson-Darling test) in detecting deviations\nfrom a normal distribution. \nLike any other statistical test, Lilliefors test may also produce false positives or negatives. Hence, banking\nsolely on this test, without considering other characteristics of the data, may give rise to risks. \n \n", "signature": "(dataset : validmind . vm_models . dataset . dataset . VMDataset ): ", "funcdef": "def"}, "validmind.tests.model_validation.statsmodels.PredictionProbabilitiesHistogram": {"fullname": "validmind.tests.model_validation.statsmodels.PredictionProbabilitiesHistogram", "modulename": "validmind.tests.model_validation.statsmodels.PredictionProbabilitiesHistogram", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.statsmodels.PredictionProbabilitiesHistogram.PredictionProbabilitiesHistogram": {"fullname": "validmind.tests.model_validation.statsmodels.PredictionProbabilitiesHistogram.PredictionProbabilitiesHistogram", "modulename": "validmind.tests.model_validation.statsmodels.PredictionProbabilitiesHistogram", "qualname": "PredictionProbabilitiesHistogram", "kind": "function", "doc": "Assesses the predictive probability distribution for binary classification to evaluate model performance and\npotential overfitting or bias.
\n\nPurpose \n\nThe Prediction Probabilities Histogram test is designed to generate histograms displaying the Probability of\nDefault (PD) predictions for both positive and negative classes in training and testing datasets. This helps in\nevaluating the performance of a classification model.
\n\nTest Mechanism \n\nThe metric follows these steps to execute the test:
\n\n\nExtracts the target column from both the train and test datasets. \nUses the model's predict function to calculate probabilities. \nAdds these probabilities as a new column to the training and testing dataframes. \nGenerates histograms for each class (0 or 1) within the training and testing datasets. \nSets different opacities for the histograms to enhance visualization. \nOverlays the four histograms (two for training and two for testing) on two different subplot frames. \nReturns a plotly graph object displaying the visualization. \n \n\nSigns of High Risk \n\n\nSignificant discrepancies between the histograms of training and testing data. \nLarge disparities between the histograms for the positive and negative classes. \nPotential overfitting or bias indicated by significant issues. \nUnevenly distributed probabilities suggesting inaccurate model predictions. \n \n\nStrengths \n\n\nOffers a visual representation of the PD predictions made by the model, aiding in understanding its behavior. \nAssesses both the training and testing datasets, adding depth to model validation. \nHighlights disparities between classes, providing insights into class imbalance or data skewness. \nEffectively visualizes risk spread, which is particularly beneficial for credit risk prediction. \n \n\nLimitations \n\n\nSpecifically tailored for binary classification scenarios and not suited for multi-class classification tasks. \nProvides a robust visual representation but lacks a quantifiable measure to assess model performance. \n \n", "signature": "(dataset , model , title = 'Histogram of Predictive Probabilities' ): ", "funcdef": "def"}, "validmind.tests.model_validation.statsmodels.RegressionCoeffs": {"fullname": "validmind.tests.model_validation.statsmodels.RegressionCoeffs", "modulename": "validmind.tests.model_validation.statsmodels.RegressionCoeffs", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.statsmodels.RegressionCoeffs.RegressionCoeffs": {"fullname": "validmind.tests.model_validation.statsmodels.RegressionCoeffs.RegressionCoeffs", "modulename": "validmind.tests.model_validation.statsmodels.RegressionCoeffs", "qualname": "RegressionCoeffs", "kind": "function", "doc": "Assesses the significance and uncertainty of predictor variables in a regression model through visualization of\ncoefficients and their 95% confidence intervals.
\n\nPurpose \n\nThe RegressionCoeffs metric visualizes the estimated regression coefficients alongside their 95% confidence intervals,\nproviding insights into the impact and significance of predictor variables on the response variable. This visualization\nhelps to understand the variability and uncertainty in the model's estimates, aiding in the evaluation of the\nsignificance of each predictor.
\n\nTest Mechanism \n\nThe function operates by extracting the estimated coefficients and their standard errors from the regression model.\nUsing these, it calculates the confidence intervals at a 95% confidence level, which indicates the range within which\nthe true coefficient value is expected to fall 95% of the time. The confidence intervals are computed using the\nZ-value associated with the 95% confidence level. The coefficients and their confidence intervals are then visualized\nin a bar plot. The x-axis represents the predictor variables, the y-axis represents the estimated coefficients, and\nthe error bars depict the confidence intervals.
\n\nSigns of High Risk \n\n\nThe confidence interval for a coefficient contains the zero value, suggesting that the predictor may not significantly\ncontribute to the model. \nMultiple coefficients with confidence intervals that include zero, potentially indicating issues with model reliability. \nVery wide confidence intervals, which may suggest high uncertainty in the coefficient estimates and potential model\ninstability. \n \n\nStrengths \n\n\nProvides a clear visualization that allows for easy interpretation of the significance and impact of predictor\nvariables. \nIncludes confidence intervals, which provide additional information about the uncertainty surrounding each coefficient\nestimate. \n \n\nLimitations \n\n\nThe method assumes normality of residuals and independence of observations, assumptions that may not always hold true\nin practice. \nIt does not address issues related to multi-collinearity among predictor variables, which can affect the interpretation\nof coefficients. \nThis metric is limited to regression tasks using tabular data and is not applicable to other types of machine learning\ntasks or data structures. \n \n", "signature": "(model ): ", "funcdef": "def"}, "validmind.tests.model_validation.statsmodels.RegressionFeatureSignificance": {"fullname": "validmind.tests.model_validation.statsmodels.RegressionFeatureSignificance", "modulename": "validmind.tests.model_validation.statsmodels.RegressionFeatureSignificance", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.statsmodels.RegressionFeatureSignificance.RegressionFeatureSignificance": {"fullname": "validmind.tests.model_validation.statsmodels.RegressionFeatureSignificance.RegressionFeatureSignificance", "modulename": "validmind.tests.model_validation.statsmodels.RegressionFeatureSignificance", "qualname": "RegressionFeatureSignificance", "kind": "function", "doc": "Assesses and visualizes the statistical significance of features in a regression model.
\n\nPurpose \n\nThe Regression Feature Significance metric assesses the significance of each feature in a given set of regression\nmodel. It creates a visualization displaying p-values for every feature of the model, assisting model developers\nin understanding which features are most influential in their model.
\n\nTest Mechanism \n\nThe test mechanism involves extracting the model's coefficients and p-values for each feature, and then plotting these\nvalues. The x-axis on the plot contains the p-values while the y-axis denotes the coefficients of each feature. A\nvertical red line is drawn at the threshold for p-value significance, which is 0.05 by default. Any features with\np-values to the left of this line are considered statistically significant at the chosen level.
\n\nSigns of High Risk \n\n\nAny feature with a high p-value (greater than the threshold) is considered a potential high risk, as it suggests\nthe feature is not statistically significant and may not be reliably contributing to the model's predictions. \nA high number of such features may indicate problems with the model validation, variable selection, and overall\nreliability of the model predictions. \n \n\nStrengths \n\n\nHelps identify the features that significantly contribute to a model's prediction, providing insights into the\nfeature importance. \nProvides tangible, easy-to-understand visualizations to interpret the feature significance. \n \n\nLimitations \n\n\nThis metric assumes model features are independent, which may not always be the case. Multicollinearity (high\ncorrelation amongst predictors) can cause high variance and unreliable statistical tests of significance. \nThe p-value strategy for feature selection doesn't take into account the magnitude of the effect, focusing solely\non whether the feature is likely non-zero. \nThis test is specific to regression models and wouldn't be suitable for other types of ML models. \nP-value thresholds are somewhat arbitrary and do not always indicate practical significance, only statistical\nsignificance. \n \n", "signature": "(\tmodel : validmind . vm_models . model . VMModel , \tfontsize : int = 10 , \tp_threshold : float = 0.05 ): ", "funcdef": "def"}, "validmind.tests.model_validation.statsmodels.RegressionModelForecastPlot": {"fullname": "validmind.tests.model_validation.statsmodels.RegressionModelForecastPlot", "modulename": "validmind.tests.model_validation.statsmodels.RegressionModelForecastPlot", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.statsmodels.RegressionModelForecastPlot.RegressionModelForecastPlot": {"fullname": "validmind.tests.model_validation.statsmodels.RegressionModelForecastPlot.RegressionModelForecastPlot", "modulename": "validmind.tests.model_validation.statsmodels.RegressionModelForecastPlot", "qualname": "RegressionModelForecastPlot", "kind": "function", "doc": "Generates plots to visually compare the forecasted outcomes of a regression model against actual observed values over\na specified date range.
\n\nPurpose \n\nThis metric is useful for time-series models or any model where the outcome changes over time, allowing direct\ncomparison of predicted vs actual values. It can help identify overfitting or underfitting situations as well as\ngeneral model performance.
\n\nTest Mechanism \n\nThis test generates a plot with the x-axis representing the date ranging from the specified \"start_date\" to the\n\"end_date\", while the y-axis shows the value of the outcome variable. Two lines are plotted: one representing the\nforecasted values and the other representing the observed values. The \"start_date\" and \"end_date\" can be parameters\nof this test; if these parameters are not provided, they are set to the minimum and maximum date available in the\ndataset.
\n\nSigns of High Risk \n\n\nHigh risk or failure signs could be deduced visually from the plots if the forecasted line significantly deviates\nfrom the observed line, indicating the model's predicted values are not matching actual outcomes. \nA model that struggles to handle the edge conditions like maximum and minimum data points could also be\nconsidered a sign of risk. \n \n\nStrengths \n\n\nVisualization: The plot provides an intuitive and clear illustration of how well the forecast matches the actual\nvalues, making it straightforward even for non-technical stakeholders to interpret. \nFlexibility: It allows comparison for multiple models and for specified time periods. \nModel Evaluation: It can be useful in identifying overfitting or underfitting situations, as these will manifest\nas discrepancies between the forecasted and observed values. \n \n\nLimitations \n\n\nInterpretation Bias: Interpretation of the plot is subjective and can lead to different conclusions by different\nevaluators. \nLack of Precision: Visual representation might not provide precise values of the deviation. \nInapplicability: Limited to cases where the order of data points (time-series) matters, it might not be of much\nuse in problems that are not related to time series prediction. \n \n", "signature": "(\tmodel : validmind . vm_models . model . VMModel , \tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tstart_date : Optional [ str ] = None , \tend_date : Optional [ str ] = None ): ", "funcdef": "def"}, "validmind.tests.model_validation.statsmodels.RegressionModelForecastPlotLevels": {"fullname": "validmind.tests.model_validation.statsmodels.RegressionModelForecastPlotLevels", "modulename": "validmind.tests.model_validation.statsmodels.RegressionModelForecastPlotLevels", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.statsmodels.RegressionModelForecastPlotLevels.integrate_diff": {"fullname": "validmind.tests.model_validation.statsmodels.RegressionModelForecastPlotLevels.integrate_diff", "modulename": "validmind.tests.model_validation.statsmodels.RegressionModelForecastPlotLevels", "qualname": "integrate_diff", "kind": "function", "doc": "
\n", "signature": "(series_diff , start_value ): ", "funcdef": "def"}, "validmind.tests.model_validation.statsmodels.RegressionModelForecastPlotLevels.RegressionModelForecastPlotLevels": {"fullname": "validmind.tests.model_validation.statsmodels.RegressionModelForecastPlotLevels.RegressionModelForecastPlotLevels", "modulename": "validmind.tests.model_validation.statsmodels.RegressionModelForecastPlotLevels", "qualname": "RegressionModelForecastPlotLevels", "kind": "function", "doc": "Assesses the alignment between forecasted and observed values in regression models through visual plots
\n\nPurpose \n\nThis test aims to visually assess the performance of a regression model by comparing its forecasted values against\nthe actual observed values for both the raw and transformed (integrated) data. This helps determine the accuracy\nof the model and can help identify overfitting or underfitting. The integration is applied to highlight the trend\nrather than the absolute level.
\n\nTest Mechanism \n\nThis test generates two plots:
\n\n\nRaw data vs forecast \nTransformed data vs forecast \n \n\nThe transformed data is created by performing a cumulative sum on the raw data.
\n\nSigns of High Risk \n\n\nSignificant deviation between forecasted and observed values. \nPatterns suggesting overfitting or underfitting. \nLarge discrepancies in the plotted forecasts, indicating potential issues with model generalizability and\nprecision. \n \n\nStrengths \n\n\nProvides an intuitive, visual way to assess multiple regression models, aiding in easier interpretation and\nevaluation of forecast accuracy. \n \n\nLimitations \n\n\nRelies heavily on visual interpretation, which may vary between individuals. \nDoes not provide a numerical metric to quantify forecast accuracy, relying solely on visual assessment. \n \n", "signature": "(\tmodel : validmind . vm_models . model . VMModel , \tdataset : validmind . vm_models . dataset . dataset . VMDataset ): ", "funcdef": "def"}, "validmind.tests.model_validation.statsmodels.RegressionModelSensitivityPlot": {"fullname": "validmind.tests.model_validation.statsmodels.RegressionModelSensitivityPlot", "modulename": "validmind.tests.model_validation.statsmodels.RegressionModelSensitivityPlot", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.statsmodels.RegressionModelSensitivityPlot.integrate_diff": {"fullname": "validmind.tests.model_validation.statsmodels.RegressionModelSensitivityPlot.integrate_diff", "modulename": "validmind.tests.model_validation.statsmodels.RegressionModelSensitivityPlot", "qualname": "integrate_diff", "kind": "function", "doc": "
\n", "signature": "(series_diff , start_value ): ", "funcdef": "def"}, "validmind.tests.model_validation.statsmodels.RegressionModelSensitivityPlot.RegressionModelSensitivityPlot": {"fullname": "validmind.tests.model_validation.statsmodels.RegressionModelSensitivityPlot.RegressionModelSensitivityPlot", "modulename": "validmind.tests.model_validation.statsmodels.RegressionModelSensitivityPlot", "qualname": "RegressionModelSensitivityPlot", "kind": "function", "doc": "Assesses the sensitivity of a regression model to changes in independent variables by applying shocks and\nvisualizing the impact.
\n\nPurpose \n\nThe Regression Sensitivity Plot test is designed to perform sensitivity analysis on regression models. This test\naims to measure the impact of slight changes (shocks) applied to individual variables on the system's outcome while\nkeeping all other variables constant. By doing so, it analyzes the effects of each independent variable on the\ndependent variable within the regression model, helping identify significant risk factors that could substantially\ninfluence the model's output.
\n\nTest Mechanism \n\nThis test operates by initially applying shocks of varying magnitudes, defined by specific parameters, to each of\nthe model's features, one at a time. With all other variables held constant, a new prediction is made for each\ndataset subjected to shocks. Any changes in the model's predictions are directly attributed to the shocks applied.\nIf the transformation parameter is set to \"integrate,\" initial predictions and target values undergo transformation\nvia an integration function before being plotted. Finally, a plot demonstrating observed values against predicted\nvalues for each model is generated, showcasing a distinct line graph illustrating predictions for each shock.
\n\nSigns of High Risk \n\n\nDrastic alterations in model predictions due to minor shocks to an individual variable, indicating high\nsensitivity and potential over-dependence on that variable. \nUnusually high or unpredictable shifts in response to shocks, suggesting potential model instability or\noverfitting. \n \n\nStrengths \n\n\nHelps identify variables that strongly influence model outcomes, aiding in understanding feature importance. \nGenerates visual plots, making results easily interpretable even to non-technical stakeholders. \nUseful in identifying overfitting and detecting unstable models that react excessively to minor variable changes. \n \n\nLimitations \n\n\nOperates on the assumption that all other variables remain unchanged during the application of a shock, which may\nnot reflect real-world interdependencies. \nBest compatible with linear models and may not effectively evaluate the sensitivity of non-linear models. \nProvides a visual representation without a numerical risk measure, potentially introducing subjectivity in\ninterpretation. \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tmodel : validmind . vm_models . model . VMModel , \tshocks : List [ float ] = [ 0.1 ] , \ttransformation : Optional [ str ] = None ): ", "funcdef": "def"}, "validmind.tests.model_validation.statsmodels.RegressionModelSummary": {"fullname": "validmind.tests.model_validation.statsmodels.RegressionModelSummary", "modulename": "validmind.tests.model_validation.statsmodels.RegressionModelSummary", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.statsmodels.RegressionModelSummary.RegressionModelSummary": {"fullname": "validmind.tests.model_validation.statsmodels.RegressionModelSummary.RegressionModelSummary", "modulename": "validmind.tests.model_validation.statsmodels.RegressionModelSummary", "qualname": "RegressionModelSummary", "kind": "function", "doc": "Evaluates regression model performance using metrics including R-Squared, Adjusted R-Squared, MSE, and RMSE.
\n\nPurpose \n\nThe Regression Model Summary test evaluates the performance of regression models by measuring their predictive\nability regarding dependent variables given changes in the independent variables. It uses conventional regression\nmetrics such as R-Squared, Adjusted R-Squared, Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) to\nassess the model's accuracy and fit.
\n\nTest Mechanism \n\nThis test uses the sklearn library to calculate the R-Squared, Adjusted R-Squared, MSE, and RMSE. It outputs a\ntable with the results of these metrics along with the feature columns used by the model.
\n\nSigns of High Risk \n\n\nLow R-Squared and Adjusted R-Squared values. \nHigh MSE and RMSE values. \n \n\nStrengths \n\n\nOffers an extensive evaluation of regression models by combining four key measures of model accuracy and fit. \nProvides a comprehensive view of the model's performance. \nBoth the R-Squared and Adjusted R-Squared measures are readily interpretable. \n \n\nLimitations \n\n\nRMSE and MSE might be sensitive to outliers. \nA high R-Squared or Adjusted R-Squared may not necessarily indicate a good model, especially in cases of\noverfitting. \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tmodel : validmind . vm_models . model . VMModel ): ", "funcdef": "def"}, "validmind.tests.model_validation.statsmodels.RegressionPermutationFeatureImportance": {"fullname": "validmind.tests.model_validation.statsmodels.RegressionPermutationFeatureImportance", "modulename": "validmind.tests.model_validation.statsmodels.RegressionPermutationFeatureImportance", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.statsmodels.RegressionPermutationFeatureImportance.RegressionPermutationFeatureImportance": {"fullname": "validmind.tests.model_validation.statsmodels.RegressionPermutationFeatureImportance.RegressionPermutationFeatureImportance", "modulename": "validmind.tests.model_validation.statsmodels.RegressionPermutationFeatureImportance", "qualname": "RegressionPermutationFeatureImportance", "kind": "function", "doc": "Assesses the significance of each feature in a model by evaluating the impact on model performance when feature\nvalues are randomly rearranged.
\n\nPurpose \n\nThe primary purpose of this metric is to determine which features significantly impact the performance of a\nregression model developed using statsmodels. The metric measures how much the prediction accuracy deteriorates\nwhen each feature's values are permuted.
\n\nTest Mechanism \n\nThis metric shuffles the values of each feature one at a time in the dataset, computes the model's performance\nafter each permutation, and compares it to the baseline performance. A significant decrease in performance\nindicates the importance of the feature.
\n\nSigns of High Risk \n\n\nSignificant reliance on a feature that, when permuted, leads to a substantial decrease in performance, suggesting\noverfitting or high model dependency on that feature. \nFeatures identified as unimportant despite known impacts from domain knowledge, suggesting potential issues in\nmodel training or data preprocessing. \n \n\nStrengths \n\n\nDirectly assesses the impact of each feature on model performance, providing clear insights into model\ndependencies. \nModel-agnostic within the scope of statsmodels, applicable to any regression model that outputs predictions. \n \n\nLimitations \n\n\nThe metric is specific to statsmodels and cannot be used with other types of models without adaptation. \nIt does not capture interactions between features, which can lead to underestimating the importance of correlated\nfeatures. \nAssumes independence of features when calculating importance, which might not always hold true. \n \n", "signature": "(\tdataset : validmind . vm_models . dataset . dataset . VMDataset , \tmodel : validmind . vm_models . model . VMModel , \tfontsize : int = 12 , \tfigure_height : int = 500 ): ", "funcdef": "def"}, "validmind.tests.model_validation.statsmodels.ScorecardHistogram": {"fullname": "validmind.tests.model_validation.statsmodels.ScorecardHistogram", "modulename": "validmind.tests.model_validation.statsmodels.ScorecardHistogram", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.statsmodels.ScorecardHistogram.ScorecardHistogram": {"fullname": "validmind.tests.model_validation.statsmodels.ScorecardHistogram.ScorecardHistogram", "modulename": "validmind.tests.model_validation.statsmodels.ScorecardHistogram", "qualname": "ScorecardHistogram", "kind": "function", "doc": "The Scorecard Histogram test evaluates the distribution of credit scores between default and non-default instances,\nproviding critical insights into the performance and generalizability of credit-risk models.
\n\nPurpose \n\nThe Scorecard Histogram test metric provides a visual interpretation of the credit scores generated by a machine\nlearning model for credit-risk classification tasks. It aims to compare the alignment of the model's scoring\ndecisions with the actual outcomes of credit loan applications. It helps in identifying potential discrepancies\nbetween the model's predictions and real-world risk levels.
\n\nTest Mechanism \n\nThis metric uses logistic regression to generate a histogram of credit scores for both default (negative class) and\nnon-default (positive class) instances. Using both training and test datasets, the metric calculates the credit\nscore of each instance with a scorecard method, considering the impact of different features on the likelihood of\ndefault. It includes the default point to odds (PDO) scaling factor and predefined target score and odds settings.\nHistograms for training and test sets are computed and plotted separately to offer insights into the model's\ngeneralizability to unseen data.
\n\nSigns of High Risk \n\n\nDiscrepancies between the distributions of training and testing data, indicating a model's poor generalization\nability \nSkewed distributions favoring specific scores or classes, representing potential bias \n \n\nStrengths \n\n\nProvides a visual interpretation of the model's credit scoring system, enhancing comprehension of model behavior \nEnables a direct comparison between actual and predicted scores for both training and testing data \nIts intuitive visualization helps understand the model's ability to differentiate between positive and negative\nclasses \nCan unveil patterns or anomalies not easily discerned through numerical metrics alone \n \n\nLimitations \n\n\nDespite its value for visual interpretation, it doesn't quantify the performance of the model and therefore may\nlack precision for thorough model evaluation \nThe quality of input data can strongly influence the metric, as bias or noise in the data will affect both the\nscore calculation and resultant histogram \nIts specificity to credit scoring models limits its applicability across a wider variety of machine learning\ntasks and models \nThe metric's effectiveness is somewhat tied to the subjective interpretation of the analyst, relying on their\njudgment of the characteristics and implications of the plot. \n \n", "signature": "(dataset , title = 'Histogram of Scores' , score_column = 'score' ): ", "funcdef": "def"}, "validmind.tests.model_validation.statsmodels.statsutils": {"fullname": "validmind.tests.model_validation.statsmodels.statsutils", "modulename": "validmind.tests.model_validation.statsmodels.statsutils", "kind": "module", "doc": "
\n"}, "validmind.tests.model_validation.statsmodels.statsutils.adj_r2_score": {"fullname": "validmind.tests.model_validation.statsmodels.statsutils.adj_r2_score", "modulename": "validmind.tests.model_validation.statsmodels.statsutils", "qualname": "adj_r2_score", "kind": "function", "doc": "Adjusted R2 Score
\n", "signature": "(\tactual : numpy . ndarray , \tpredicted : numpy . ndarray , \trowcount : int , \tfeaturecount : int ): ", "funcdef": "def"}, "validmind.tests.prompt_validation": {"fullname": "validmind.tests.prompt_validation", "modulename": "validmind.tests.prompt_validation", "kind": "module", "doc": "
\n"}, "validmind.tests.prompt_validation.Bias": {"fullname": "validmind.tests.prompt_validation.Bias", "modulename": "validmind.tests.prompt_validation.Bias", "kind": "module", "doc": "
\n"}, "validmind.tests.prompt_validation.Bias.Bias": {"fullname": "validmind.tests.prompt_validation.Bias.Bias", "modulename": "validmind.tests.prompt_validation.Bias", "qualname": "Bias", "kind": "function", "doc": "Assesses potential bias in a Large Language Model by analyzing the distribution and order of exemplars in the\nprompt.
\n\nPurpose \n\nThe Bias Evaluation test calculates if and how the order and distribution of exemplars (examples) in a few-shot\nlearning prompt affect the output of a Large Language Model (LLM). The results of this evaluation can be used to\nfine-tune the model's performance and manage any unintended biases in its results.
\n\nTest Mechanism \n\nThis test uses two checks:
\n\n\nDistribution of Exemplars: The number of positive vs. negative examples in a prompt is varied. The test then\nexamines the LLM's classification of a neutral or ambiguous statement under these circumstances.Order of Exemplars: The sequence in which positive and negative examples are presented to the model is\nmodified. Their resultant effect on the LLM's response is studied. \n\nFor each test case, the LLM grades the input prompt on a scale of 1 to 10. It evaluates whether the examples in the\nprompt could produce biased responses. The test only passes if the score meets or exceeds a predetermined minimum\nthreshold. This threshold is set at 7 by default but can be modified as per the requirements via the test\nparameters.
\n\nSigns of High Risk \n\n\nA skewed result favoring either positive or negative responses may suggest potential bias in the model. This skew\ncould be caused by an unbalanced distribution of positive and negative exemplars. \nIf the score given by the model is less than the set minimum threshold, it might indicate a risk of high bias and\nhence poor performance. \n \n\nStrengths \n\n\nThis test provides a quantitative measure of potential bias, offering clear guidelines for developers about\nwhether their Large Language Model (LLM) contains significant bias. \nIt is useful in evaluating the impartiality of the model based on the distribution and sequence of examples. \nThe flexibility to adjust the minimum required threshold allows tailoring this test to stricter or more lenient\nbias standards. \n \n\nLimitations \n\n\nThe test may not pick up on more subtle forms of bias or biases that are not directly related to the distribution\nor order of exemplars. \nThe test's effectiveness will decrease if the quality or balance of positive and negative exemplars is not\nrepresentative of the problem space the model is intended to solve. \nThe use of a grading mechanism to gauge bias may not be entirely accurate in every case, particularly when the\ndifference between threshold and score is narrow. \n \n", "signature": "(model , min_threshold = 7 ): ", "funcdef": "def"}, "validmind.tests.prompt_validation.Clarity": {"fullname": "validmind.tests.prompt_validation.Clarity", "modulename": "validmind.tests.prompt_validation.Clarity", "kind": "module", "doc": "
\n"}, "validmind.tests.prompt_validation.Clarity.Clarity": {"fullname": "validmind.tests.prompt_validation.Clarity.Clarity", "modulename": "validmind.tests.prompt_validation.Clarity", "qualname": "Clarity", "kind": "function", "doc": "Evaluates and scores the clarity of prompts in a Large Language Model based on specified guidelines.
\n\nPurpose \n\nThe Clarity evaluation metric is used to assess how clear the prompts of a Large Language Model (LLM) are. This\nassessment is particularly important because clear prompts assist the LLM in more accurately interpreting and\nresponding to instructions.
\n\nTest Mechanism \n\nThe evaluation uses an LLM to scrutinize the clarity of prompts, factoring in considerations such as the inclusion\nof relevant details, persona adoption, step-by-step instructions, usage of examples, and specification of desired\noutput length. Each prompt is rated on a clarity scale of 1 to 10, and any prompt scoring at or above the preset\nthreshold (default of 7) will be marked as clear. It is important to note that this threshold can be adjusted via\ntest parameters, providing flexibility in the evaluation process.
\n\nSigns of High Risk \n\n\nPrompts that consistently score below the clarity threshold \nRepeated failure of prompts to adhere to guidelines for clarity, including detail inclusion, persona adoption,\nexplicit step-by-step instructions, use of examples, and specification of output length \n \n\nStrengths \n\n\nEncourages the development of more effective prompts that aid the LLM in interpreting instructions accurately \nApplies a quantifiable measure (a score from 1 to 10) to evaluate the clarity of prompts \nThreshold for clarity is adjustable, allowing for flexible evaluation depending on the context \n \n\nLimitations \n\n\nScoring system is subjective and relies on the AI\u2019s interpretation of 'clarity' \nThe test assumes that all required factors (detail inclusion, persona adoption, step-by-step instructions, use of\nexamples, and specification of output length) contribute equally to clarity, which might not always be the case \nThe evaluation may not be as effective if used on non-textual models \n \n", "signature": "(model , min_threshold = 7 ): ", "funcdef": "def"}, "validmind.tests.prompt_validation.Conciseness": {"fullname": "validmind.tests.prompt_validation.Conciseness", "modulename": "validmind.tests.prompt_validation.Conciseness", "kind": "module", "doc": "
\n"}, "validmind.tests.prompt_validation.Conciseness.Conciseness": {"fullname": "validmind.tests.prompt_validation.Conciseness.Conciseness", "modulename": "validmind.tests.prompt_validation.Conciseness", "qualname": "Conciseness", "kind": "function", "doc": "Analyzes and grades the conciseness of prompts provided to a Large Language Model.
\n\nPurpose \n\nThe Conciseness Assessment is designed to evaluate the brevity and succinctness of prompts provided to a Language\nLearning Model (LLM). A concise prompt strikes a balance between offering clear instructions and eliminating\nredundant or unnecessary information, ensuring that the LLM receives relevant input without being overwhelmed.
\n\nTest Mechanism \n\nUsing an LLM, this test conducts a conciseness analysis on input prompts. The analysis grades the prompt on a scale\nfrom 1 to 10, where the grade reflects how well the prompt delivers clear instructions without being verbose.\nPrompts that score equal to or above a predefined threshold (default set to 7) are deemed successfully concise.\nThis threshold can be adjusted to meet specific requirements.
\n\nSigns of High Risk \n\n\nPrompts that consistently score below the predefined threshold. \nPrompts that are overly wordy or contain unnecessary information. \nPrompts that create confusion or ambiguity due to excess or unnecessary information. \n \n\nStrengths \n\n\nEnsures clarity and effectiveness of the prompts. \nPromotes brevity and preciseness in prompts without sacrificing essential information. \nUseful for models like LLMs, where input prompt length and clarity greatly influence model performance. \nProvides a quantifiable measure of prompt conciseness. \n \n\nLimitations \n\n\nThe conciseness score is based on an AI's assessment, which might not fully capture human interpretation of\nconciseness. \nThe predefined threshold for conciseness could be subjective and might need adjustment based on application. \nThe test is dependent on the LLM\u2019s understanding of conciseness, which might vary from model to model. \n \n", "signature": "(model , min_threshold = 7 ): ", "funcdef": "def"}, "validmind.tests.prompt_validation.Delimitation": {"fullname": "validmind.tests.prompt_validation.Delimitation", "modulename": "validmind.tests.prompt_validation.Delimitation", "kind": "module", "doc": "
\n"}, "validmind.tests.prompt_validation.Delimitation.Delimitation": {"fullname": "validmind.tests.prompt_validation.Delimitation.Delimitation", "modulename": "validmind.tests.prompt_validation.Delimitation", "qualname": "Delimitation", "kind": "function", "doc": "Evaluates the proper use of delimiters in prompts provided to Large Language Models.
\n\nPurpose \n\nThe Delimitation Test aims to assess whether prompts provided to the Language Learning Model (LLM) correctly use\ndelimiters to mark different sections of the input. Well-delimited prompts help simplify the interpretation process\nfor the LLM, ensuring that the responses are precise and accurate.
\n\nTest Mechanism \n\nThe test employs an LLM to examine prompts for appropriate use of delimiters such as triple quotation marks, XML\ntags, and section titles. Each prompt is assigned a score from 1 to 10 based on its delimitation integrity. Prompts\nwith scores equal to or above the preset threshold (which is 7 by default, although it can be adjusted as\nnecessary) pass the test.
\n\nSigns of High Risk \n\n\nPrompts missing, improperly placed, or incorrectly used delimiters, leading to misinterpretation by the LLM. \nHigh-risk scenarios with complex prompts involving multiple tasks or diverse data where correct delimitation is\ncrucial. \nScores below the threshold, indicating a high risk. \n \n\nStrengths \n\n\nEnsures clarity in demarcating different components of given prompts. \nReduces ambiguity in understanding prompts, especially for complex tasks. \nProvides a quantified insight into the appropriateness of delimiter usage, aiding continuous improvement. \n \n\nLimitations \n\n\nOnly checks for the presence and placement of delimiters, not whether the correct delimiter type is used for the\nspecific data or task. \nMay not fully reveal the impacts of poor delimitation on the LLM's final performance. \nThe preset score threshold may not be refined enough for complex tasks and prompts, requiring regular manual\nadjustment. \n \n", "signature": "(model , min_threshold = 7 ): ", "funcdef": "def"}, "validmind.tests.prompt_validation.NegativeInstruction": {"fullname": "validmind.tests.prompt_validation.NegativeInstruction", "modulename": "validmind.tests.prompt_validation.NegativeInstruction", "kind": "module", "doc": "
\n"}, "validmind.tests.prompt_validation.NegativeInstruction.NegativeInstruction": {"fullname": "validmind.tests.prompt_validation.NegativeInstruction.NegativeInstruction", "modulename": "validmind.tests.prompt_validation.NegativeInstruction", "qualname": "NegativeInstruction", "kind": "function", "doc": "Evaluates and grades the use of affirmative, proactive language over negative instructions in LLM prompts.
\n\nPurpose \n\nThe Negative Instruction test is utilized to scrutinize the prompts given to a Large Language Model (LLM). The\nobjective is to ensure these prompts are expressed using proactive, affirmative language. The focus is on\ninstructions indicating what needs to be done rather than what needs to be avoided, thereby guiding the LLM more\nefficiently towards the desired output.
\n\nTest Mechanism \n\nAn LLM is employed to evaluate each prompt. The prompt is graded based on its use of positive instructions with\nscores ranging between 1-10. This grade reflects how effectively the prompt leverages affirmative language while\nshying away from negative or restrictive instructions. A prompt that attains a grade equal to or above a\npredetermined threshold (7 by default) is regarded as adhering effectively to the best practices of positive\ninstruction. This threshold can be custom-tailored through the test parameters.
\n\nSigns of High Risk \n\n\nLow score obtained from the LLM analysis, indicating heavy reliance on negative instructions in the prompts. \nFailure to surpass the preset minimum threshold. \nThe LLM generates ambiguous or undesirable outputs as a consequence of the negative instructions used in the\nprompt. \n \n\nStrengths \n\n\nEncourages the usage of affirmative, proactive language in prompts, aiding in more accurate and advantageous\nmodel responses. \nThe test result provides a comprehensible score, helping to understand how well a prompt follows the positive\ninstruction best practices. \n \n\nLimitations \n\n\nDespite an adequate score, a prompt could still be misleading or could lead to undesired responses due to factors\nnot covered by this test. \nThe test necessitates an LLM for evaluation, which might not be available or feasible in certain scenarios. \nA numeric scoring system, while straightforward, may oversimplify complex issues related to prompt designing and\ninstruction clarity. \nThe effectiveness of the test hinges significantly on the predetermined threshold level, which can be subjective\nand may need to be adjusted according to specific use-cases. \n \n", "signature": "(model , min_threshold = 7 ): ", "funcdef": "def"}, "validmind.tests.prompt_validation.Robustness": {"fullname": "validmind.tests.prompt_validation.Robustness", "modulename": "validmind.tests.prompt_validation.Robustness", "kind": "module", "doc": "
\n"}, "validmind.tests.prompt_validation.Robustness.Robustness": {"fullname": "validmind.tests.prompt_validation.Robustness.Robustness", "modulename": "validmind.tests.prompt_validation.Robustness", "qualname": "Robustness", "kind": "function", "doc": "Assesses the robustness of prompts provided to a Large Language Model under varying conditions and contexts. This test\nspecifically measures the model's ability to generate correct classifications with the given prompt even when the\ninputs are edge cases or otherwise difficult to classify.
\n\nPurpose \n\nThe Robustness test is meant to evaluate the resilience and reliability of prompts provided to a Language Learning\nModel (LLM). The aim of this test is to guarantee that the prompts consistently generate accurate and expected\noutputs, even in diverse or challenging scenarios. This test is only applicable to LLM-powered text classification\ntasks where the prompt has a single input variable.
\n\nTest Mechanism \n\nThe Robustness test appraises prompts under various conditions, alterations, and contexts to ascertain their\nstability in producing consistent responses from the LLM. Factors evaluated include different phrasings, inclusion\nof potential distracting elements, and various input complexities. By default, the test generates 10 inputs for a\nprompt but can be adjusted according to test parameters.
\n\nSigns of High Risk \n\n\nIf the output from the tests diverges extensively from the expected results, this indicates high risk. \nWhen the prompt doesn't give a consistent performance across various tests. \nA high risk is indicated when the prompt is susceptible to breaking, especially when the output is expected to be\nof a specific type. \n \n\nStrengths \n\n\nThe robustness test helps to ensure stable performance of the LLM prompts and lowers the chances of generating\nunexpected or off-target outputs. \nThis test is vital for applications where predictability and reliability of the LLM\u2019s output are crucial. \n \n\nLimitations \n\n\nCurrently, the test only supports single-variable prompts, which restricts its application to more complex models. \nWhen there are too many target classes (over 10), the test is skipped, which can leave potential vulnerabilities\nunchecked in complex multi-class models. \nThe test may not account for all potential conditions or alterations that could show up in practical use\nscenarios. \n \n", "signature": "(model , dataset , num_tests = 10 ): ", "funcdef": "def"}, "validmind.tests.prompt_validation.Specificity": {"fullname": "validmind.tests.prompt_validation.Specificity", "modulename": "validmind.tests.prompt_validation.Specificity", "kind": "module", "doc": "
\n"}, "validmind.tests.prompt_validation.Specificity.Specificity": {"fullname": "validmind.tests.prompt_validation.Specificity.Specificity", "modulename": "validmind.tests.prompt_validation.Specificity", "qualname": "Specificity", "kind": "function", "doc": "Evaluates and scores the specificity of prompts provided to a Large Language Model (LLM), based on clarity, detail,\nand relevance.
\n\nPurpose \n\nThe Specificity Test evaluates the clarity, precision, and effectiveness of the prompts provided to a Language\nModel (LLM). It aims to ensure that the instructions embedded in a prompt are indisputably clear and relevant,\nthereby helping to remove ambiguity and steer the LLM towards desired outputs. This level of specificity\nsignificantly affects the accuracy and relevance of LLM outputs.
\n\nTest Mechanism \n\nThe Specificity Test employs an LLM to grade each prompt based on clarity, detail, and relevance parameters within\na specificity scale that extends from 1 to 10. On this scale, prompts scoring equal to or more than a predefined\nthreshold (set to 7 by default) pass the evaluation, while those scoring below this threshold fail it. Users can\nadjust this threshold as per their requirements.
\n\nSigns of High Risk \n\n\nPrompts scoring consistently below the established threshold \nVague or ambiguous prompts that do not provide clear direction to the LLM \nOverly verbose prompts that may confuse the LLM instead of providing clear guidance \n \n\nStrengths \n\n\nEnables precise and clear communication with the LLM to achieve desired outputs \nServes as a crucial means to measure the effectiveness of prompts \nHighly customizable, allowing users to set their threshold based on specific use cases \n \n\nLimitations \n\n\nThis test doesn't consider the content comprehension capability of the LLM \nHigh specificity score doesn't guarantee a high-quality response from the LLM, as the model's performance is also\ndependent on various other factors \nStriking a balance between specificity and verbosity can be challenging, as overly detailed prompts might confuse\nor mislead the model \n \n", "signature": "(model , min_threshold = 7 ): ", "funcdef": "def"}, "validmind.tests.prompt_validation.ai_powered_test": {"fullname": "validmind.tests.prompt_validation.ai_powered_test", "modulename": "validmind.tests.prompt_validation.ai_powered_test", "kind": "module", "doc": "
\n"}, "validmind.tests.prompt_validation.ai_powered_test.call_model": {"fullname": "validmind.tests.prompt_validation.ai_powered_test.call_model", "modulename": "validmind.tests.prompt_validation.ai_powered_test", "qualname": "call_model", "kind": "function", "doc": "Call LLM with the given prompts and return the response
\n", "signature": "(\tsystem_prompt : str , \tuser_prompt : str , \ttemperature : float = 0.0 , \tseed : int = 42 ): ", "funcdef": "def"}, "validmind.tests.prompt_validation.ai_powered_test.get_score": {"fullname": "validmind.tests.prompt_validation.ai_powered_test.get_score", "modulename": "validmind.tests.prompt_validation.ai_powered_test", "qualname": "get_score", "kind": "function", "doc": "Get just the score from the response string\n TODO: use json response mode instead of this
\n\ne.g. \"Score: 8\nExplanation: \" -> 8
\n", "signature": "(response : str ): ", "funcdef": "def"}, "validmind.tests.prompt_validation.ai_powered_test.get_explanation": {"fullname": "validmind.tests.prompt_validation.ai_powered_test.get_explanation", "modulename": "validmind.tests.prompt_validation.ai_powered_test", "qualname": "get_explanation", "kind": "function", "doc": "Get just the explanation from the response string\n TODO: use json response mode instead of this
\n\ne.g. \"Score: 8\nExplanation: \" -> \"\"
\n", "signature": "(response : str ): ", "funcdef": "def"}, "validmind.unit_metrics": {"fullname": "validmind.unit_metrics", "modulename": "validmind.unit_metrics", "kind": "module", "doc": "
\n"}, "validmind.unit_metrics.list_metrics": {"fullname": "validmind.unit_metrics.list_metrics", "modulename": "validmind.unit_metrics", "qualname": "list_metrics", "kind": "function", "doc": "List all metrics
\n", "signature": "(** kwargs ): ", "funcdef": "def"}, "validmind.unit_metrics.describe_metric": {"fullname": "validmind.unit_metrics.describe_metric", "modulename": "validmind.unit_metrics", "qualname": "describe_metric", "kind": "function", "doc": "Describe a metric
\n", "signature": "(metric_id : str , ** kwargs ): ", "funcdef": "def"}, "validmind.unit_metrics.run_metric": {"fullname": "validmind.unit_metrics.run_metric", "modulename": "validmind.unit_metrics", "qualname": "run_metric", "kind": "function", "doc": "Run a metric
\n", "signature": "(metric_id : str , ** kwargs ): ", "funcdef": "def"}, "validmind.vm_models": {"fullname": "validmind.vm_models", "modulename": "validmind.vm_models", "kind": "module", "doc": "Models entrypoint
\n"}, "validmind.vm_models.VMInput": {"fullname": "validmind.vm_models.VMInput", "modulename": "validmind.vm_models", "qualname": "VMInput", "kind": "class", "doc": "Base class for ValidMind Input types
\n", "bases": "abc.ABC"}, "validmind.vm_models.VMInput.with_options": {"fullname": "validmind.vm_models.VMInput.with_options", "modulename": "validmind.vm_models", "qualname": "VMInput.with_options", "kind": "function", "doc": "Allows for setting options on the input object that are passed by the user\nwhen using the input to run a test or set of tests
\n\nTo allow options, just override this method in the subclass (see VMDataset)\nand ensure that it returns a new instance of the input with the specified options\nset.
\n\nArguments: \n\n\n**kwargs: Arbitrary keyword arguments that will be passed to the input object \n\nReturns: \n\n\n VMInput: A new instance of the input with the specified options set
\n \n", "signature": "(self , ** kwargs ) -> validmind . vm_models . input . VMInput : ", "funcdef": "def"}, "validmind.vm_models.VMDataset": {"fullname": "validmind.vm_models.VMDataset", "modulename": "validmind.vm_models", "qualname": "VMDataset", "kind": "class", "doc": "Base class for VM datasets
\n\nChild classes should be used to support new dataset types (tensor, polars etc)\nby converting the user's dataset into a numpy array collecting metadata like\ncolumn names and then call this (parent) class __init__ method.
\n\nThis way we can support multiple dataset types but under the hood we only\nneed to work with numpy arrays and pandas dataframes in this class.
\n\nAttributes: \n\n\nraw_dataset (np.ndarray): The raw dataset as a NumPy array.input_id (str): Identifier for the dataset.index (np.ndarray): The raw dataset index as a NumPy array.columns (Set[str]): The column names of the dataset.target_column (str): The target column name of the dataset.feature_columns (List[str]): The feature column names of the dataset.feature_columns_numeric (List[str]): The numeric feature column names of the dataset.feature_columns_categorical (List[str]): The categorical feature column names of the dataset.text_column (str): The text column name of the dataset for NLP tasks.target_class_labels (Dict): The class labels for the target columns.df (pd.DataFrame): The dataset as a pandas DataFrame.extra_columns (Dict): Extra columns to include in the dataset. \n", "bases": "validmind.vm_models.input.VMInput"}, "validmind.vm_models.VMDataset.__init__": {"fullname": "validmind.vm_models.VMDataset.__init__", "modulename": "validmind.vm_models", "qualname": "VMDataset.__init__", "kind": "function", "doc": "Initializes a VMDataset instance.
\n\nArguments: \n\n\nraw_dataset (np.ndarray): The raw dataset as a NumPy array.input_id (str): Identifier for the dataset.model (VMModel): Model associated with the dataset.index (np.ndarray): The raw dataset index as a NumPy array.index_name (str): The raw dataset index name as a NumPy array.date_time_index (bool): Whether the index is a datetime index.columns (List[str], optional): The column names of the dataset. Defaults to None.target_column (str, optional): The target column name of the dataset. Defaults to None.feature_columns (str, optional): The feature column names of the dataset. Defaults to None.text_column (str, optional): The text column name of the dataset for nlp tasks. Defaults to None.target_class_labels (Dict, optional): The class labels for the target columns. Defaults to None. \n", "signature": "(\traw_dataset : numpy . ndarray , \tinput_id : str = None , \tmodel : validmind . vm_models . model . VMModel = None , \tindex : numpy . ndarray = None , \tindex_name : str = None , \tdate_time_index : bool = False , \tcolumns : list = None , \ttarget_column : str = None , \tfeature_columns : list = None , \ttext_column : str = None , \textra_columns : dict = None , \ttarget_class_labels : dict = None ) "}, "validmind.vm_models.VMDataset.with_options": {"fullname": "validmind.vm_models.VMDataset.with_options", "modulename": "validmind.vm_models", "qualname": "VMDataset.with_options", "kind": "function", "doc": "Support options provided when passing an input to run_test or run_test_suite
\n\nExample:
\n\n\n
# to only use a certain subset of columns in the dataset: \nrun_test ( \n "validmind.SomeTestID" , \n inputs = { \n "dataset" : { \n "input_id" : "my_dataset_id" , \n "columns" : [ "col1" , "col2" ], \n } \n } \n) \n\n# behind the scenes, this retrieves the dataset object (VMDataset) from the registry \n# and then calls the `with_options()` method and passes `{"columns": ...}` \n\n
Arguments: \n\n\n**kwargs: Options:\n\ncolumns: Filter columns in the dataset \n \n\nReturns: \n\n\n VMDataset: A new instance of the dataset with only the specified columns
\n \n", "signature": "(self , ** kwargs ) -> validmind . vm_models . dataset . dataset . VMDataset : ", "funcdef": "def"}, "validmind.vm_models.VMDataset.assign_predictions": {"fullname": "validmind.vm_models.VMDataset.assign_predictions", "modulename": "validmind.vm_models", "qualname": "VMDataset.assign_predictions", "kind": "function", "doc": "Assign predictions and probabilities to the dataset.
\n\nArguments: \n\n\nmodel (VMModel): The model used to generate the predictions.prediction_column (str, optional): The name of the column containing the predictions. Defaults to None.prediction_values (list, optional): The values of the predictions. Defaults to None.probability_column (str, optional): The name of the column containing the probabilities. Defaults to None.probability_values (list, optional): The values of the probabilities. Defaults to None.prediction_probabilities (list, optional): DEPRECATED: The values of the probabilities. Defaults to None.kwargs: Additional keyword arguments that will get passed through to the model's predict method. \n", "signature": "(\tself , \tmodel : validmind . vm_models . model . VMModel , \tprediction_column : str = None , \tprediction_values : list = None , \tprobability_column : str = None , \tprobability_values : list = None , \tprediction_probabilities : list = None , \t** kwargs ): ", "funcdef": "def"}, "validmind.vm_models.VMDataset.prediction_column": {"fullname": "validmind.vm_models.VMDataset.prediction_column", "modulename": "validmind.vm_models", "qualname": "VMDataset.prediction_column", "kind": "function", "doc": "Get or set the prediction column for a model.
\n", "signature": "(\tself , \tmodel : validmind . vm_models . model . VMModel , \tcolumn_name : str = None ) -> str : ", "funcdef": "def"}, "validmind.vm_models.VMDataset.probability_column": {"fullname": "validmind.vm_models.VMDataset.probability_column", "modulename": "validmind.vm_models", "qualname": "VMDataset.probability_column", "kind": "function", "doc": "Get or set the probability column for a model.
\n", "signature": "(\tself , \tmodel : validmind . vm_models . model . VMModel , \tcolumn_name : str = None ) -> str : ", "funcdef": "def"}, "validmind.vm_models.VMDataset.add_extra_column": {"fullname": "validmind.vm_models.VMDataset.add_extra_column", "modulename": "validmind.vm_models", "qualname": "VMDataset.add_extra_column", "kind": "function", "doc": "Adds an extra column to the dataset without modifying the dataset features and target columns.
\n\nArguments: \n\n\ncolumn_name (str): The name of the extra column.column_values (np.ndarray, optional): The values of the extra column. \n", "signature": "(self , column_name , column_values = None ): ", "funcdef": "def"}, "validmind.vm_models.VMDataset.df": {"fullname": "validmind.vm_models.VMDataset.df", "modulename": "validmind.vm_models", "qualname": "VMDataset.df", "kind": "variable", "doc": "Returns the dataset as a pandas DataFrame.
\n\nReturns: \n\n\n pd.DataFrame: The dataset as a pandas DataFrame.
\n \n", "annotation": ": pandas.core.frame.DataFrame"}, "validmind.vm_models.VMDataset.x": {"fullname": "validmind.vm_models.VMDataset.x", "modulename": "validmind.vm_models", "qualname": "VMDataset.x", "kind": "variable", "doc": "Returns the input features (X) of the dataset.
\n\nReturns: \n\n\n np.ndarray: The input features.
\n \n", "annotation": ": numpy.ndarray"}, "validmind.vm_models.VMDataset.y": {"fullname": "validmind.vm_models.VMDataset.y", "modulename": "validmind.vm_models", "qualname": "VMDataset.y", "kind": "variable", "doc": "Returns the target variables (y) of the dataset.
\n\nReturns: \n\n\n np.ndarray: The target variables.
\n \n", "annotation": ": numpy.ndarray"}, "validmind.vm_models.VMDataset.y_pred": {"fullname": "validmind.vm_models.VMDataset.y_pred", "modulename": "validmind.vm_models", "qualname": "VMDataset.y_pred", "kind": "function", "doc": "Returns the predictions for a given model.
\n\nAttempts to stack complex prediction types (e.g., embeddings) into a single,\nmulti-dimensional array.
\n\nArguments: \n\n\nmodel (VMModel): The model whose predictions are sought. \n\nReturns: \n\n\n np.ndarray: The predictions for the model
\n \n", "signature": "(self , model ) -> numpy . ndarray : ", "funcdef": "def"}, "validmind.vm_models.VMDataset.y_prob": {"fullname": "validmind.vm_models.VMDataset.y_prob", "modulename": "validmind.vm_models", "qualname": "VMDataset.y_prob", "kind": "function", "doc": "Returns the probabilities for a given model.
\n\nArguments: \n\n\nmodel (str): The ID of the model whose predictions are sought. \n\nReturns: \n\n\n np.ndarray: The probability variables.
\n \n", "signature": "(self , model ) -> numpy . ndarray : ", "funcdef": "def"}, "validmind.vm_models.VMDataset.x_df": {"fullname": "validmind.vm_models.VMDataset.x_df", "modulename": "validmind.vm_models", "qualname": "VMDataset.x_df", "kind": "function", "doc": "Returns a dataframe containing only the feature columns
\n", "signature": "(self ): ", "funcdef": "def"}, "validmind.vm_models.VMDataset.y_df": {"fullname": "validmind.vm_models.VMDataset.y_df", "modulename": "validmind.vm_models", "qualname": "VMDataset.y_df", "kind": "function", "doc": "Returns a dataframe containing the target column
\n", "signature": "(self ) -> pandas . core . frame . DataFrame : ", "funcdef": "def"}, "validmind.vm_models.VMDataset.y_pred_df": {"fullname": "validmind.vm_models.VMDataset.y_pred_df", "modulename": "validmind.vm_models", "qualname": "VMDataset.y_pred_df", "kind": "function", "doc": "Returns a dataframe containing the predictions for a given model
\n", "signature": "(self , model ) -> pandas . core . frame . DataFrame : ", "funcdef": "def"}, "validmind.vm_models.VMDataset.y_prob_df": {"fullname": "validmind.vm_models.VMDataset.y_prob_df", "modulename": "validmind.vm_models", "qualname": "VMDataset.y_prob_df", "kind": "function", "doc": "Returns a dataframe containing the probabilities for a given model
\n", "signature": "(self , model ) -> pandas . core . frame . DataFrame : ", "funcdef": "def"}, "validmind.vm_models.VMDataset.target_classes": {"fullname": "validmind.vm_models.VMDataset.target_classes", "modulename": "validmind.vm_models", "qualname": "VMDataset.target_classes", "kind": "function", "doc": "Returns the target class labels or unique values of the target column.
\n", "signature": "(self ): ", "funcdef": "def"}, "validmind.vm_models.VMModel": {"fullname": "validmind.vm_models.VMModel", "modulename": "validmind.vm_models", "qualname": "VMModel", "kind": "class", "doc": "An base class that wraps a trained model instance and its associated data.
\n\nAttributes: \n\n\nmodel (object, optional): The trained model instance. Defaults to None.input_id (str, optional): The input ID for the model. Defaults to None.attributes (ModelAttributes, optional): The attributes of the model. Defaults to None.name (str, optional): The name of the model. Defaults to the class name. \n", "bases": "validmind.vm_models.input.VMInput"}, "validmind.vm_models.VMModel.serialize": {"fullname": "validmind.vm_models.VMModel.serialize", "modulename": "validmind.vm_models", "qualname": "VMModel.serialize", "kind": "function", "doc": "Serializes the model to a dictionary so it can be sent to the API
\n", "signature": "(self ): ", "funcdef": "def"}, "validmind.vm_models.VMModel.predict_proba": {"fullname": "validmind.vm_models.VMModel.predict_proba", "modulename": "validmind.vm_models", "qualname": "VMModel.predict_proba", "kind": "function", "doc": "Predict probabilties - must be implemented by subclass if needed
\n", "signature": "(self , * args , ** kwargs ): ", "funcdef": "def"}, "validmind.vm_models.VMModel.predict": {"fullname": "validmind.vm_models.VMModel.predict", "modulename": "validmind.vm_models", "qualname": "VMModel.predict", "kind": "function", "doc": "Predict method for the model. This is a wrapper around the model's
\n", "signature": "(self , * args , ** kwargs ): ", "funcdef": "def"}, "validmind.vm_models.Figure": {"fullname": "validmind.vm_models.Figure", "modulename": "validmind.vm_models", "qualname": "Figure", "kind": "class", "doc": "Figure objects track the schema supported by the ValidMind API
\n"}, "validmind.vm_models.Figure.__init__": {"fullname": "validmind.vm_models.Figure.__init__", "modulename": "validmind.vm_models", "qualname": "Figure.__init__", "kind": "function", "doc": "
\n", "signature": "(\tkey : str , \tfigure : Union [ matplotlib . figure . Figure , plotly . graph_objs . _figure . Figure , plotly . graph_objs . _figurewidget . FigureWidget , bytes ] , \tref_id : str , \t_type : str = 'plot' ) "}, "validmind.vm_models.Figure.to_widget": {"fullname": "validmind.vm_models.Figure.to_widget", "modulename": "validmind.vm_models", "qualname": "Figure.to_widget", "kind": "function", "doc": "Returns the ipywidget compatible representation of the figure. Ideally\nwe would render images as-is, but Plotly FigureWidgets don't work well\non Google Colab when they are combined with ipywidgets.
\n", "signature": "(self ): ", "funcdef": "def"}, "validmind.vm_models.Figure.serialize": {"fullname": "validmind.vm_models.Figure.serialize", "modulename": "validmind.vm_models", "qualname": "Figure.serialize", "kind": "function", "doc": "Serializes the Figure to a dictionary so it can be sent to the API
\n", "signature": "(self ): ", "funcdef": "def"}, "validmind.vm_models.Figure.serialize_files": {"fullname": "validmind.vm_models.Figure.serialize_files", "modulename": "validmind.vm_models", "qualname": "Figure.serialize_files", "kind": "function", "doc": "Creates a requests-compatible files object to be sent to the API
\n", "signature": "(self ): ", "funcdef": "def"}, "validmind.vm_models.ModelAttributes": {"fullname": "validmind.vm_models.ModelAttributes", "modulename": "validmind.vm_models", "qualname": "ModelAttributes", "kind": "class", "doc": "Model attributes definition
\n"}, "validmind.vm_models.ModelAttributes.__init__": {"fullname": "validmind.vm_models.ModelAttributes.__init__", "modulename": "validmind.vm_models", "qualname": "ModelAttributes.__init__", "kind": "function", "doc": "
\n", "signature": "(\tarchitecture : str = None , \tframework : str = None , \tframework_version : str = None , \tlanguage : str = None , \ttask : validmind . vm_models . model . ModelTask = None ) "}, "validmind.vm_models.ModelAttributes.from_dict": {"fullname": "validmind.vm_models.ModelAttributes.from_dict", "modulename": "validmind.vm_models", "qualname": "ModelAttributes.from_dict", "kind": "function", "doc": "Creates a ModelAttributes instance from a dictionary
\n", "signature": "(cls , data ): ", "funcdef": "def"}, "validmind.vm_models.ResultTable": {"fullname": "validmind.vm_models.ResultTable", "modulename": "validmind.vm_models", "qualname": "ResultTable", "kind": "class", "doc": "A dataclass that holds the table summary of result
\n"}, "validmind.vm_models.ResultTable.__init__": {"fullname": "validmind.vm_models.ResultTable.__init__", "modulename": "validmind.vm_models", "qualname": "ResultTable.__init__", "kind": "function", "doc": "
\n", "signature": "(\tdata : Union [ List [ Any ], pandas . core . frame . DataFrame ] , \ttitle : Optional [ str ] = None ) "}, "validmind.vm_models.ResultTable.serialize": {"fullname": "validmind.vm_models.ResultTable.serialize", "modulename": "validmind.vm_models", "qualname": "ResultTable.serialize", "kind": "function", "doc": "
\n", "signature": "(self ): ", "funcdef": "def"}, "validmind.vm_models.TestResult": {"fullname": "validmind.vm_models.TestResult", "modulename": "validmind.vm_models", "qualname": "TestResult", "kind": "class", "doc": "Test result
\n", "bases": "validmind.vm_models.result.result.Result"}, "validmind.vm_models.TestResult.__init__": {"fullname": "validmind.vm_models.TestResult.__init__", "modulename": "validmind.vm_models", "qualname": "TestResult.__init__", "kind": "function", "doc": "
\n", "signature": "(\tresult_id : str = None , \tname : str = 'Test Result' , \tref_id : str = None , \ttitle : Optional [ str ] = None , \tdoc : Optional [ str ] = None , \tdescription : Union [ str , validmind . ai . utils . DescriptionFuture , NoneType ] = None , \tmetric : Union [ int , float , NoneType ] = None , \ttables : Optional [ List [ validmind . vm_models . result . result . ResultTable ]] = None , \traw_data : Optional [ validmind . vm_models . result . result . RawData ] = None , \tfigures : Optional [ List [ validmind . vm_models . figure . Figure ]] = None , \tpassed : Optional [ bool ] = None , \tparams : Optional [ Dict [ str , Any ]] = None , \tinputs : Optional [ Dict [ str , Union [ List [ validmind . vm_models . input . VMInput ], validmind . vm_models . input . VMInput ]]] = None , \tmetadata : Optional [ Dict [ str , Any ]] = None , \t_was_description_generated : bool = False , \t_unsafe : bool = False , \t_client_config_cache : Optional [ Any ] = None ) "}, "validmind.vm_models.TestResult.test_name": {"fullname": "validmind.vm_models.TestResult.test_name", "modulename": "validmind.vm_models", "qualname": "TestResult.test_name", "kind": "variable", "doc": "Get the test name, using custom title if available.
\n", "annotation": ": str"}, "validmind.vm_models.TestResult.add_table": {"fullname": "validmind.vm_models.TestResult.add_table", "modulename": "validmind.vm_models", "qualname": "TestResult.add_table", "kind": "function", "doc": "Add a new table to the result
\n\nArguments: \n\n\ntable (Union[ResultTable, pd.DataFrame, List[Dict[str, Any]]]): The table to addtitle (Optional[str]): The title of the table (can optionally be provided for\npd.DataFrame and List[Dict[str, Any]] tables) \n", "signature": "(\tself , \ttable : Union [ validmind . vm_models . result . result . ResultTable , pandas . core . frame . DataFrame , List [ Dict [ str , Any ]]] , \ttitle : Optional [ str ] = None ): ", "funcdef": "def"}, "validmind.vm_models.TestResult.remove_table": {"fullname": "validmind.vm_models.TestResult.remove_table", "modulename": "validmind.vm_models", "qualname": "TestResult.remove_table", "kind": "function", "doc": "Remove a table from the result by index
\n\nArguments: \n\n\nindex (int): The index of the table to remove (default is 0) \n", "signature": "(self , index : int ): ", "funcdef": "def"}, "validmind.vm_models.TestResult.add_figure": {"fullname": "validmind.vm_models.TestResult.add_figure", "modulename": "validmind.vm_models", "qualname": "TestResult.add_figure", "kind": "function", "doc": "Add a new figure to the result
\n\nArguments: \n\n\nfigure (Union[matplotlib.figure.Figure, go.Figure, go.FigureWidget,\nbytes, Figure]): The figure to add (can be either a VM Figure object,\na raw figure object from the supported libraries, or a png image as\nraw bytes) \n \n", "signature": "(\tself , \tfigure : Union [ matplotlib . figure . Figure , plotly . graph_objs . _figure . Figure , plotly . graph_objs . _figurewidget . FigureWidget , bytes , validmind . vm_models . figure . Figure ] ): ", "funcdef": "def"}, "validmind.vm_models.TestResult.remove_figure": {"fullname": "validmind.vm_models.TestResult.remove_figure", "modulename": "validmind.vm_models", "qualname": "TestResult.remove_figure", "kind": "function", "doc": "Remove a figure from the result by index
\n\nArguments: \n\n\nindex (int): The index of the figure to remove (default is 0) \n", "signature": "(self , index : int = 0 ): ", "funcdef": "def"}, "validmind.vm_models.TestResult.to_widget": {"fullname": "validmind.vm_models.TestResult.to_widget", "modulename": "validmind.vm_models", "qualname": "TestResult.to_widget", "kind": "function", "doc": "Create an ipywdiget representation of the result... Must be overridden by subclasses
\n", "signature": "(self ): ", "funcdef": "def"}, "validmind.vm_models.TestResult.check_result_id_exist": {"fullname": "validmind.vm_models.TestResult.check_result_id_exist", "modulename": "validmind.vm_models", "qualname": "TestResult.check_result_id_exist", "kind": "function", "doc": "Check if the result_id exists in any test block across all sections
\n", "signature": "(self ): ", "funcdef": "def"}, "validmind.vm_models.TestResult.serialize": {"fullname": "validmind.vm_models.TestResult.serialize", "modulename": "validmind.vm_models", "qualname": "TestResult.serialize", "kind": "function", "doc": "Serialize the result for the API
\n", "signature": "(self ): ", "funcdef": "def"}, "validmind.vm_models.TestResult.log_async": {"fullname": "validmind.vm_models.TestResult.log_async", "modulename": "validmind.vm_models", "qualname": "TestResult.log_async", "kind": "function", "doc": "
\n", "signature": "(\tself , \tsection_id : str = None , \tposition : int = None , \tunsafe : bool = False ): ", "funcdef": "async def"}, "validmind.vm_models.TestResult.log": {"fullname": "validmind.vm_models.TestResult.log", "modulename": "validmind.vm_models", "qualname": "TestResult.log", "kind": "function", "doc": "Log the result to ValidMind
\n\nArguments: \n\n\nsection_id (str): The section ID within the model document to insert the\ntest resultposition (int): The position (index) within the section to insert the test\nresultunsafe (bool): If True, log the result even if it contains sensitive data\ni.e. raw data from input datasets \n", "signature": "(\tself , \tsection_id : str = None , \tposition : int = None , \tunsafe : bool = False ): ", "funcdef": "def"}, "validmind.vm_models.TestSuite": {"fullname": "validmind.vm_models.TestSuite", "modulename": "validmind.vm_models", "qualname": "TestSuite", "kind": "class", "doc": "Base class for test suites. Test suites are used to define a grouping of tests that\ncan be run as a suite against datasets and models. Test Suites can be defined by\ninheriting from this base class and defining the list of tests as a class variable.
\n\nTests can be a flat list of strings or may be nested into sections by using a dict
\n"}, "validmind.vm_models.TestSuite.__init__": {"fullname": "validmind.vm_models.TestSuite.__init__", "modulename": "validmind.vm_models", "qualname": "TestSuite.__init__", "kind": "function", "doc": "
\n", "signature": "(\tsections : List [ validmind . vm_models . test_suite . test_suite . TestSuiteSection ] = None ) "}, "validmind.vm_models.TestSuite.get_tests": {"fullname": "validmind.vm_models.TestSuite.get_tests", "modulename": "validmind.vm_models", "qualname": "TestSuite.get_tests", "kind": "function", "doc": "Get all test suite test objects from all sections
\n", "signature": "(self ) -> List [ str ] : ", "funcdef": "def"}, "validmind.vm_models.TestSuite.num_tests": {"fullname": "validmind.vm_models.TestSuite.num_tests", "modulename": "validmind.vm_models", "qualname": "TestSuite.num_tests", "kind": "function", "doc": "Returns the total number of tests in the test suite
\n", "signature": "(self ) -> int : ", "funcdef": "def"}, "validmind.vm_models.TestSuite.get_default_config": {"fullname": "validmind.vm_models.TestSuite.get_default_config", "modulename": "validmind.vm_models", "qualname": "TestSuite.get_default_config", "kind": "function", "doc": "Returns the default configuration for the test suite
\n\nEach test in a test suite can accept parameters and those parameters can have\ndefault values. Both the parameters and their defaults are set in the test\nclass and a config object can be passed to the test suite's run method to\noverride the defaults. This function returns a dictionary containing the\nparameters and their default values for every test to allow users to view\nand set values
\n\nReturns: \n\n\n dict: A dictionary of test names and their default parameters
\n \n", "signature": "(self ) -> dict : ", "funcdef": "def"}, "validmind.vm_models.TestSuiteRunner": {"fullname": "validmind.vm_models.TestSuiteRunner", "modulename": "validmind.vm_models", "qualname": "TestSuiteRunner", "kind": "class", "doc": "Runs a test suite
\n"}, "validmind.vm_models.TestSuiteRunner.__init__": {"fullname": "validmind.vm_models.TestSuiteRunner.__init__", "modulename": "validmind.vm_models", "qualname": "TestSuiteRunner.__init__", "kind": "function", "doc": "
\n", "signature": "(\tsuite : validmind . vm_models . test_suite . test_suite . TestSuite , \tconfig : dict = None , \tinputs : dict = None ) "}, "validmind.vm_models.TestSuiteRunner.log_results": {"fullname": "validmind.vm_models.TestSuiteRunner.log_results", "modulename": "validmind.vm_models", "qualname": "TestSuiteRunner.log_results", "kind": "function", "doc": "Logs the results of the test suite to ValidMind
\n\nThis method will be called after the test suite has been run and all results have been\ncollected. This method will log the results to ValidMind.
\n", "signature": "(self ): ", "funcdef": "async def"}, "validmind.vm_models.TestSuiteRunner.summarize": {"fullname": "validmind.vm_models.TestSuiteRunner.summarize", "modulename": "validmind.vm_models", "qualname": "TestSuiteRunner.summarize", "kind": "function", "doc": "
\n", "signature": "(self , show_link : bool = True ): ", "funcdef": "def"}, "validmind.vm_models.TestSuiteRunner.run": {"fullname": "validmind.vm_models.TestSuiteRunner.run", "modulename": "validmind.vm_models", "qualname": "TestSuiteRunner.run", "kind": "function", "doc": "Runs the test suite, renders the summary and sends the results to ValidMind
\n\nArguments: \n\n\nsend (bool, optional): Whether to send the results to ValidMind.\nDefaults to True.fail_fast (bool, optional): Whether to stop running tests after the first\nfailure. Defaults to False. \n", "signature": "(self , send : bool = True , fail_fast : bool = False ): ", "funcdef": "def"}}, "docInfo": {"validmind": {"qualname": 0, "fullname": 1, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 323}, "validmind.init": {"qualname": 1, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 172, "bases": 0, "doc": 207}, "validmind.reload": {"qualname": 1, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 7, "bases": 0, "doc": 12}, "validmind.init_dataset": {"qualname": 2, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 259, "bases": 0, "doc": 291}, "validmind.init_model": {"qualname": 2, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 142, "bases": 0, "doc": 187}, "validmind.init_r_model": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 60, "bases": 0, "doc": 213}, "validmind.preview_template": {"qualname": 2, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 7, "bases": 0, "doc": 56}, "validmind.run_documentation_tests": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 70, "bases": 0, "doc": 219}, "validmind.log_metric": {"qualname": 2, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 149, "bases": 0, "doc": 183}, "validmind.get_test_suite": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 86, "bases": 0, "doc": 169}, "validmind.run_test_suite": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 67, "bases": 0, "doc": 278}, "validmind.print_env": {"qualname": 2, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 7, "bases": 0, "doc": 33}, "validmind.tags": {"qualname": 1, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 13, "bases": 0, "doc": 30}, "validmind.tasks": {"qualname": 1, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 13, "bases": 0, "doc": 37}, "validmind.test": {"qualname": 1, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 13, "bases": 0, "doc": 245}, "validmind.RawData": {"qualname": 1, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 9}, "validmind.RawData.__init__": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 28, "bases": 0, "doc": 52}, "validmind.RawData.inspect": {"qualname": 2, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 28, "bases": 0, "doc": 6}, "validmind.RawData.serialize": {"qualname": 2, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 3}, "validmind.datasets": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 13}, "validmind.datasets.classification": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 7}, "validmind.datasets.classification.customer_churn": {"qualname": 0, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "validmind.datasets.classification.customer_churn.load_data": {"qualname": 2, "fullname": 7, "annotation": 0, "default_value": 0, "signature": 17, "bases": 0, "doc": 3}, "validmind.datasets.classification.customer_churn.preprocess": {"qualname": 1, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 3}, "validmind.datasets.classification.customer_churn.get_demo_test_config": {"qualname": 4, "fullname": 9, "annotation": 0, "default_value": 0, "signature": 17, "bases": 0, "doc": 148}, "validmind.datasets.classification.taiwan_credit": {"qualname": 0, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "validmind.datasets.classification.taiwan_credit.load_data": {"qualname": 2, "fullname": 7, "annotation": 0, "default_value": 0, "signature": 7, "bases": 0, "doc": 3}, "validmind.datasets.classification.taiwan_credit.preprocess": {"qualname": 1, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 3}, "validmind.datasets.credit_risk": {"qualname": 0, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 8}, "validmind.datasets.credit_risk.lending_club": {"qualname": 0, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "validmind.datasets.credit_risk.lending_club.load_data": {"qualname": 2, "fullname": 8, "annotation": 0, "default_value": 0, "signature": 30, "bases": 0, "doc": 59}, "validmind.datasets.credit_risk.lending_club.preprocess": {"qualname": 1, "fullname": 7, "annotation": 0, "default_value": 0, "signature": 21, "bases": 0, "doc": 3}, "validmind.datasets.credit_risk.lending_club.feature_engineering": {"qualname": 2, "fullname": 8, "annotation": 0, "default_value": 0, "signature": 21, "bases": 0, "doc": 3}, "validmind.datasets.credit_risk.lending_club.woe_encoding": {"qualname": 2, "fullname": 8, "annotation": 0, "default_value": 0, "signature": 21, "bases": 0, "doc": 3}, "validmind.datasets.credit_risk.lending_club.split": {"qualname": 1, "fullname": 7, "annotation": 0, "default_value": 0, "signature": 60, "bases": 0, "doc": 103}, "validmind.datasets.credit_risk.lending_club.compute_scores": {"qualname": 2, "fullname": 8, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 3}, "validmind.datasets.credit_risk.lending_club.get_demo_test_config": {"qualname": 4, "fullname": 10, "annotation": 0, "default_value": 0, "signature": 28, "bases": 0, "doc": 45}, "validmind.datasets.credit_risk.lending_club.load_scorecard": {"qualname": 2, "fullname": 8, "annotation": 0, "default_value": 0, "signature": 7, "bases": 0, "doc": 3}, "validmind.datasets.credit_risk.lending_club.init_vm_objects": {"qualname": 3, "fullname": 9, "annotation": 0, "default_value": 0, "signature": 11, 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"validmind.tests.model_validation.sklearn.RegressionErrorsComparison.RegressionErrorsComparison": {"tf": 1.7320508075688772}, "validmind.tests.model_validation.statsmodels.RegressionCoeffs.RegressionCoeffs": {"tf": 1.4142135623730951}, "validmind.tests.model_validation.statsmodels.RegressionFeatureSignificance.RegressionFeatureSignificance": {"tf": 1}}, "df": 11, "s": {"docs": {"validmind.tests.data_validation.TooManyZeroValues.TooManyZeroValues": {"tf": 1.4142135623730951}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"validmind.tests.data_validation.TimeSeriesOutliers.TimeSeriesOutliers": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"validmind.tests.data_validation.ZivotAndrewsArch.ZivotAndrewsArch": {"tf": 1.7320508075688772}}, "df": 1, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "s": {"docs": {"validmind.tests.data_validation.ZivotAndrewsArch.ZivotAndrewsArch": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}, "pipeline": ["trimmer"], "_isPrebuiltIndex": true};
-
- // mirrored in build-search-index.js (part 1)
- // Also split on html tags. this is a cheap heuristic, but good enough.
- elasticlunr.tokenizer.setSeperator(/[\s\-.;&_'"=,()]+|<[^>]*>/);
-
- let searchIndex;
- if (docs._isPrebuiltIndex) {
- console.info("using precompiled search index");
- searchIndex = elasticlunr.Index.load(docs);
- } else {
- console.time("building search index");
- // mirrored in build-search-index.js (part 2)
- searchIndex = elasticlunr(function () {
- this.pipeline.remove(elasticlunr.stemmer);
- this.pipeline.remove(elasticlunr.stopWordFilter);
- this.addField("qualname");
- this.addField("fullname");
- this.addField("annotation");
- this.addField("default_value");
- this.addField("signature");
- this.addField("bases");
- this.addField("doc");
- this.setRef("fullname");
- });
- for (let doc of docs) {
- searchIndex.addDoc(doc);
- }
- console.timeEnd("building search index");
- }
-
- return (term) => searchIndex.search(term, {
- fields: {
- qualname: {boost: 4},
- fullname: {boost: 2},
- annotation: {boost: 2},
- default_value: {boost: 2},
- signature: {boost: 2},
- bases: {boost: 2},
- doc: {boost: 1},
- },
- expand: true
- });
-})();
\ No newline at end of file
diff --git a/docs/_build/validmind.html b/docs/_build/validmind.html
deleted file mode 100644
index 7a79bb4ad..000000000
--- a/docs/_build/validmind.html
+++ /dev/null
@@ -1,893 +0,0 @@
-
-
-
- validmind API documentation
-
-
-
-
-
- ValidMind Library
-
-
- The ValidMind Library is a suite of developer tools and methods designed to automate the documentation and validation of your models.
-
-
Designed to be model agnostic, the ValidMind Library provides all the standard functionality without requiring you to rewrite any functions as long as your model is built in Python.
-
-
With a rich array of documentation tools and test suites, from documenting descriptions of your datasets to testing your models for weak spots and overfit areas, the ValidMind Library helps you automate model documentation by feeding the ValidMind Platform with documentation artifacts and test results.
-
-
To install the ValidMind Library:
-
-
-
pip install validmind
-
-
-
-
To initialize the ValidMind Library, paste the code snippet with the model identifier credentials directly into your development source code, replacing this example with your own:
-
-
-
import validmind as vm
-
-vm . init (
- api_host = "https://api.dev.vm.validmind.ai/api/v1/tracking/tracking" ,
- api_key = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" ,
- api_secret = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" ,
- project = "<project-identifier>"
-)
-
-
-
-
After you have pasted the code snippet into your development source code and executed the code, the Python Library API will register with ValidMind. You can now use the ValidMind Library to document and test your models, and to upload to the ValidMind Platform.
-
-
-
- __version__ =
-'2.8.12'
-
-
-
-
-
-
-
-
-
-
-
- def
- init ( project : Optional [ str ] = None , api_key : Optional [ str ] = None , api_secret : Optional [ str ] = None , api_host : Optional [ str ] = None , model : Optional [ str ] = None , monitoring : bool = False , generate_descriptions : Optional [ bool ] = None ):
-
-
-
-
-
- Initializes the API client instances and calls the /ping endpoint to ensure
-the provided credentials are valid and we can connect to the ValidMind API.
-
-
If the API key and secret are not provided, the client will attempt to
-retrieve them from the environment variables VM_API_KEY and VM_API_SECRET.
-
-
Arguments:
-
-
-project (str, optional): The project CUID. Alias for model. Defaults to None. [DEPRECATED]model (str, optional): The model CUID. Defaults to None.api_key (str, optional): The API key. Defaults to None.api_secret (str, optional): The API secret. Defaults to None.api_host (str, optional): The API host. Defaults to None.monitoring (bool): The ongoing monitoring flag. Defaults to False.generate_descriptions (bool): Whether to use GenAI to generate test result descriptions. Defaults to True.
-
-
Raises:
-
-
-ValueError: If the API key and secret are not provided
-
-
-
-
- def
- reload ():
-
-
-
-
-
- Reconnect to the ValidMind API and reload the project configuration
-
-
-
-
-
def
-
init_dataset ( dataset , model = None , index = None , index_name : str = None , date_time_index : bool = False , columns : list = None , text_column : str = None , target_column : str = None , feature_columns : list = None , extra_columns : dict = None , class_labels : dict = None , type : str = None , input_id : str = None , __log = True ) -> validmind.vm_models.VMDataset
-
-
-
Initializes a VM Dataset, which can then be passed to other functions
-that can perform additional analysis and tests on the data. This function
-also ensures we are reading a valid dataset type.
-
-
The following dataset types are supported:
-
-
-Pandas DataFrame
-Polars DataFrame
-Numpy ndarray
-Torch TensorDataset
-
-
-
Arguments:
-
-
-dataset : dataset from various python librariesmodel (VMModel): ValidMind model objecttargets (vm.vm.DatasetTargets): A list of target variablestarget_column (str): The name of the target column in the datasetfeature_columns (list): A list of names of feature columns in the datasetextra_columns (dictionary): A dictionary containing the names of theprediction_column and group_by_columns in the dataset
-class_labels (dict): A list of class labels for classification problemstype (str): The type of dataset (one of DATASET_TYPES)input_id (str): The input ID for the dataset (e.g. "my_dataset"). By default,
-this will be set to dataset but if you are passing this dataset as a
-test input using some other key than dataset, then you should set
-this to the same key.
-
-
Raises:
-
-
-ValueError: If the dataset type is not supported
-
-
Returns:
-
-
- vm.vm.Dataset: A VM Dataset instance
-
-
-
-
-
-
def
-
init_model ( model : object = None , input_id : str = 'model' , attributes : dict = None , predict_fn : < built - in function callable > = None , __log = True , ** kwargs ) -> validmind.vm_models.VMModel
-
-
-
Initializes a VM Model, which can then be passed to other functions
-that can perform additional analysis and tests on the data. This function
-also ensures we are creating a model supported libraries.
-
-
Arguments:
-
-
-model: A trained model or VMModel instanceinput_id (str): The input ID for the model (e.g. "my_model"). By default,
-this will be set to model but if you are passing this model as a
-test input using some other key than model, then you should set
-this to the same key.attributes (dict): A dictionary of model attributespredict_fn (callable): A function that takes an input and returns a prediction**kwargs: Additional arguments to pass to the model
-
-
Raises:
-
-
-ValueError: If the model type is not supported
-
-
Returns:
-
-
- vm.VMModel: A VM Model instance
-
-
-
-
-
-
- Initializes a VM Model for an R model
-
-
R models must be saved to disk and the filetype depends on the model type...
-Currently we support the following model types:
-
-
-LogisticRegression glm model in R: saved as an RDS file with saveRDS
-LinearRegression lm model in R: saved as an RDS file with saveRDS
-XGBClassifier: saved as a .json or .bin file with xgb.save
-XGBRegressor: saved as a .json or .bin file with xgb.save
-
-
-
LogisticRegression and LinearRegression models are converted to sklearn models by extracting
-the coefficients and intercept from the R model. XGB models are loaded using the xgboost
-since xgb models saved in .json or .bin format can be loaded directly with either Python or R
-
-
Arguments:
-
-
-model_path (str): The path to the R model saved as an RDS or XGB filemodel_type (str): The type of the model (one of R_MODEL_TYPES)
-
-
Returns:
-
-
- vm.vm.Model: A VM Model instance
-
-
-
-
-
- def
- preview_template ():
-
-
-
-
-
- Preview the documentation template for the current project
-
-
This function will display the documentation template for the current project. If
-the project has not been initialized, then an error will be raised.
-
-
Raises:
-
-
-ValueError: If the project has not been initialized
-
-
-
-
- def
- run_documentation_tests ( section = None , send = True , fail_fast = False , inputs = None , config = None , ** kwargs ):
-
-
-
-
-
- Collect and run all the tests associated with a template
-
-
This function will analyze the current project's documentation template and collect
-all the tests associated with it into a test suite. It will then run the test
-suite, log the results to the ValidMind API, and display them to the user.
-
-
Arguments:
-
-
-section (str or list, optional): The section(s) to preview. Defaults to None.send (bool, optional): Whether to send the results to the ValidMind API. Defaults to True.fail_fast (bool, optional): Whether to stop running tests after the first failure. Defaults to False.inputs (dict, optional): A dictionary of test inputs to pass to the TestSuiteconfig: A dictionary of test parameters to override the defaults**kwargs: backwards compatibility for passing in test inputs using keyword arguments
-
-
Returns:
-
-
- TestSuite or dict: The completed TestSuite instance or a dictionary of TestSuites if section is a list.
-
-
-
Raises:
-
-
-ValueError: If the project has not been initialized
-
-
-
-
- def
- log_metric ( key : str , value : float , inputs : Optional [ List [ str ]] = None , params : Optional [ Dict [ str , Any ]] = None , recorded_at : Optional [ str ] = None , thresholds : Optional [ Dict [ str , Any ]] = None ):
-
-
-
-
-
- Logs a unit metric
-
-
Unit metrics are key-value pairs where the key is the metric name and the value is
-a scalar (int or float). These key-value pairs are associated with the currently
-selected model (inventory model in the ValidMind Platform) and keys can be logged
-to over time to create a history of the metric. On the ValidMind Platform, these metrics
-will be used to create plots/visualizations for documentation and dashboards etc.
-
-
Arguments:
-
-
-key (str): The metric keyvalue (float): The metric valueinputs (list, optional): A list of input IDs that were used to compute the metric.params (dict, optional): Dictionary of parameters used to compute the metric.recorded_at (str, optional): The timestamp of the metric. Server will use
-current time if not provided.thresholds (dict, optional): Dictionary of thresholds for the metric.
-
-
-
-
-
- Gets a TestSuite object for the current project or a specific test suite
-
-
This function provides an interface to retrieve the TestSuite instance for the
-current project or a specific TestSuite instance identified by test_suite_id.
-The project Test Suite will contain sections for every section in the project's
-documentation template and these Test Suite Sections will contain all the tests
-associated with that template section.
-
-
Arguments:
-
-
-test_suite_id (str, optional): The test suite name. If not passed, then the
-project's test suite will be returned. Defaults to None.section (str, optional): The section of the documentation template from which
-to retrieve the test suite. This only applies if test_suite_id is None.
-Defaults to None.args: Additional arguments to pass to the TestSuitekwargs: Additional keyword arguments to pass to the TestSuite
-
-
-
-
- def
- run_test_suite ( test_suite_id , send = True , fail_fast = False , config = None , inputs = None , ** kwargs ):
-
-
-
-
-
- High Level function for running a test suite
-
-
This function provides a high level interface for running a test suite. A test suite is
-a collection of tests. This function will automatically find the correct test suite
-class based on the test_suite_id, initialize each of the tests, and run them.
-
-
Arguments:
-
-
-test_suite_id (str): The test suite name (e.g. 'classifier_full_suite')config (dict, optional): A dictionary of parameters to pass to the tests in the
-test suite. Defaults to None.send (bool, optional): Whether to post the test results to the API. send=False
-is useful for testing. Defaults to True.fail_fast (bool, optional): Whether to stop running tests after the first failure. Defaults to False.inputs (dict, optional): A dictionary of test inputs to pass to the TestSuite e.g. model, dataset
-models etc. These inputs will be accessible by any test in the test suite. See the test
-documentation or vm.describe_test() for more details on the inputs required for each.**kwargs: backwards compatibility for passing in test inputs using keyword arguments
-
-
Raises:
-
-
-ValueError: If the test suite name is not found or if there is an error initializing the test suite
-
-
Returns:
-
-
- TestSuite: the TestSuite instance
-
-
-
-
-
- def
- print_env ():
-
-
-
-
-
- Prints a log of the running environment for debugging.
-
-
Output includes: ValidMind Library version, operating system details, installed dependencies, and the ISO 8601 timestamp at log creation.
-
-
-
-
-
- def
- tasks (* tasks ):
-
-
-
-
-
- Decorator for specifying the task types that a test is designed for.
-
-
Arguments:
-
-
-*tasks: The task types that the test is designed for.
-
-
-
-
- def
- test (func_or_id ):
-
-
-
-
-
- Decorator for creating and registering custom tests
-
-
This decorator registers the function it wraps as a test function within ValidMind
-under the provided ID. Once decorated, the function can be run using the
-validmind.tests.run_test
-
-
The function can take two different types of arguments:
-
-
-Inputs: ValidMind model or dataset (or list of models/datasets). These arguments
-must use the following names: model, models, dataset, datasets.
-Parameters: Any additional keyword arguments of any type (must have a default
-value) that can have any name.
-
-
-
The function should return one of the following types:
-
-
-Table: Either a list of dictionaries or a pandas DataFrame
-Plot: Either a matplotlib figure or a plotly figure
-Scalar: A single number (int or float)
-Boolean: A single boolean value indicating whether the test passed or failed
-
-
-
The function may also include a docstring. This docstring will be used and logged
-as the metric's description.
-
-
Arguments:
-
-
-func: The function to decoratetest_id: The identifier for the metric. If not provided, the function name is used.
-
-
Returns:
-
-
- The decorated function.
-
-
-
-
-
- class
- RawData :
-
-
-
-
-
- Holds raw data for a test result
-
-
-
- RawData (log : bool = False , ** kwargs
-
-
-
-
-
-
Create a new RawData object
-
-
Arguments:
-
-
-log (bool): If True, log the raw data to ValidMind**kwargs: Keyword arguments to set as attributes e.g.
-RawData(log=True, dataset_duplicates=df_duplicates)
-
-
-
-
-
-
- def
- inspect (self , show : bool = True ):
-
-
-
-
-
-
-
-
-
-
-
- def
- serialize (self ):
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/__version__.html b/docs/_build/validmind/__version__.html
deleted file mode 100644
index 588b7c178..000000000
--- a/docs/_build/validmind/__version__.html
+++ /dev/null
@@ -1,239 +0,0 @@
-
-
-
- validmind.__version__ API documentation
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/datasets.html b/docs/_build/validmind/datasets.html
deleted file mode 100644
index 1140bf359..000000000
--- a/docs/_build/validmind/datasets.html
+++ /dev/null
@@ -1,244 +0,0 @@
-
-
-
- validmind.datasets API documentation
-
-
-
-
-
-
- Example datasets that can be used with the ValidMind Library.
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/datasets/classification.html b/docs/_build/validmind/datasets/classification.html
deleted file mode 100644
index 1840aa734..000000000
--- a/docs/_build/validmind/datasets/classification.html
+++ /dev/null
@@ -1,242 +0,0 @@
-
-
-
- validmind.datasets.classification API documentation
-
-
-
-
-
-
- Entrypoint for classification datasets.
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/datasets/classification/customer_churn.html b/docs/_build/validmind/datasets/classification/customer_churn.html
deleted file mode 100644
index 61d35bf7d..000000000
--- a/docs/_build/validmind/datasets/classification/customer_churn.html
+++ /dev/null
@@ -1,311 +0,0 @@
-
-
-
- validmind.datasets.classification.customer_churn API documentation
-
-
-
-
-
-
-
- def
- load_data (full_dataset = False ):
-
-
-
-
-
-
-
-
-
-
-
- def
- preprocess (df ):
-
-
-
-
-
-
-
-
-
-
-
- def
- get_demo_test_config (test_suite = None ):
-
-
-
-
-
- Returns input configuration for the default documentation
-template assigned to this demo model
-
-
The default documentation template uses the following inputs:
-
-
-raw_dataset
-train_dataset
-test_dataset
-model
-
-
-
We assign the following inputs depending on the input config expected
-by each test:
-
-
-When a test expects a "dataset" we use the raw_dataset
-When a tets expects "datasets" we use the train_dataset and test_dataset
-When a test expects a "model" we use the model
-When a test expects "model" and "dataset" we use the model and test_dataset
-The only exception is ClassifierPerformance since that runs twice: once
-with the train_dataset (in sample) and once with the test_dataset (out of sample)
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/datasets/classification/taiwan_credit.html b/docs/_build/validmind/datasets/classification/taiwan_credit.html
deleted file mode 100644
index 7cdba8403..000000000
--- a/docs/_build/validmind/datasets/classification/taiwan_credit.html
+++ /dev/null
@@ -1,271 +0,0 @@
-
-
-
- validmind.datasets.classification.taiwan_credit API documentation
-
-
-
-
-
-
-
- def
- load_data ():
-
-
-
-
-
-
-
-
-
-
-
- def
- preprocess (df ):
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/datasets/credit_risk.html b/docs/_build/validmind/datasets/credit_risk.html
deleted file mode 100644
index 798b3db30..000000000
--- a/docs/_build/validmind/datasets/credit_risk.html
+++ /dev/null
@@ -1,242 +0,0 @@
-
-
-
- validmind.datasets.credit_risk API documentation
-
-
-
-
-
-
- Entrypoint for credit risk datasets.
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/datasets/credit_risk/lending_club.html b/docs/_build/validmind/datasets/credit_risk/lending_club.html
deleted file mode 100644
index 0af0633a6..000000000
--- a/docs/_build/validmind/datasets/credit_risk/lending_club.html
+++ /dev/null
@@ -1,448 +0,0 @@
-
-
-
- validmind.datasets.credit_risk.lending_club API documentation
-
-
-
-
-
-
-
- def
- load_data (source = 'online' , verbose = True ):
-
-
-
-
-
- Load data from either an online source or offline files, automatically dropping specified columns for offline data.
-
-
Parameters
-
-
-source : 'online' for online data, 'offline' for offline files. Defaults to 'online'.
-
-
Returns
-
-
- DataFrame containing the loaded data.
-
-
-
-
-
- def
- preprocess (df , verbose = True ):
-
-
-
-
-
-
-
-
-
-
-
- def
- feature_engineering (df , verbose = True ):
-
-
-
-
-
-
-
-
-
-
-
- def
- woe_encoding (df , verbose = True ):
-
-
-
-
-
-
-
-
-
-
-
- def
- split ( df , validation_size = None , test_size = 0.2 , add_constant = False , verbose = True ):
-
-
-
-
-
- Split dataset into train, validation (optional), and test sets.
-
-
Arguments:
-
-
-df: Input DataFramevalidation_split: If None, returns train/test split. If float, returns train/val/test splittest_size: Proportion of data for test set (default: 0.2)add_constant: Whether to add constant column for statsmodels (default: False)
-
-
Returns:
-
-
- If validation_size is None:
- train_df, test_df
- If validation_size is float:
- train_df, validation_df, test_df
-
-
-
-
-
- def
- compute_scores (probabilities ):
-
-
-
-
-
-
-
-
-
-
-
- def
- get_demo_test_config (x_test = None , y_test = None ):
-
-
-
-
-
- Get demo test configuration.
-
-
Arguments:
-
-
-x_test: Test features DataFramey_test: Test target Series
-
-
Returns:
-
-
- dict: Test configuration dictionary
-
-
-
-
-
- def
- load_scorecard ():
-
-
-
-
-
-
-
-
-
-
-
- def
- init_vm_objects (scorecard ):
-
-
-
-
-
-
-
-
-
-
-
- def
- load_test_config (scorecard ):
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/datasets/credit_risk/lending_club_bias.html b/docs/_build/validmind/datasets/credit_risk/lending_club_bias.html
deleted file mode 100644
index 8fa32bcaf..000000000
--- a/docs/_build/validmind/datasets/credit_risk/lending_club_bias.html
+++ /dev/null
@@ -1,311 +0,0 @@
-
-
-
- validmind.datasets.credit_risk.lending_club_bias API documentation
-
-
-
-
-
-
-
- def
- load_data ():
-
-
-
-
-
- Load data from the specified CSV file.
-
-
Returns
-
-
- DataFrame containing the loaded data.
-
-
-
-
-
- def
- preprocess (df ):
-
-
-
-
-
-
-
-
-
-
-
- def
- split (df , test_size = 0.3 ):
-
-
-
-
-
-
-
-
-
-
-
- def
- compute_scores (probabilities ):
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/datasets/nlp.html b/docs/_build/validmind/datasets/nlp.html
deleted file mode 100644
index 3ba47adf3..000000000
--- a/docs/_build/validmind/datasets/nlp.html
+++ /dev/null
@@ -1,242 +0,0 @@
-
-
-
- validmind.datasets.nlp API documentation
-
-
-
-
-
-
- Example datasets that can be used with the ValidMind Library.
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/datasets/nlp/cnn_dailymail.html b/docs/_build/validmind/datasets/nlp/cnn_dailymail.html
deleted file mode 100644
index 925ca6e95..000000000
--- a/docs/_build/validmind/datasets/nlp/cnn_dailymail.html
+++ /dev/null
@@ -1,288 +0,0 @@
-
-
-
- validmind.datasets.nlp.cnn_dailymail API documentation
-
-
-
-
-
-
-
- def
- load_data (source = 'online' , dataset_size = None ):
-
-
-
-
-
- Load data from either online source or offline files.
-
-
Parameters
-
-
-source : 'online' for online data, 'offline' for offline data. Defaults to 'online'.dataset_size : Applicable if source is 'offline'. '300k' or '500k' for dataset size. Defaults to None.
-
-
Returns
-
-
- DataFrame containing the loaded data.
-
-
-
-
-
- def
- display_nice (df , num_rows = None ):
-
-
-
-
-
- Primary function to format and display a DataFrame.
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/datasets/nlp/twitter_covid_19.html b/docs/_build/validmind/datasets/nlp/twitter_covid_19.html
deleted file mode 100644
index 03434fbcc..000000000
--- a/docs/_build/validmind/datasets/nlp/twitter_covid_19.html
+++ /dev/null
@@ -1,255 +0,0 @@
-
-
-
- validmind.datasets.nlp.twitter_covid_19 API documentation
-
-
-
-
-
-
-
- def
- load_data (full_dataset = False ):
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/datasets/regression.html b/docs/_build/validmind/datasets/regression.html
deleted file mode 100644
index 3b1094871..000000000
--- a/docs/_build/validmind/datasets/regression.html
+++ /dev/null
@@ -1,242 +0,0 @@
-
-
-
- validmind.datasets.regression API documentation
-
-
-
-
-
-
- Entrypoint for regression datasets
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/datasets/regression/fred.html b/docs/_build/validmind/datasets/regression/fred.html
deleted file mode 100644
index 3d57548c1..000000000
--- a/docs/_build/validmind/datasets/regression/fred.html
+++ /dev/null
@@ -1,386 +0,0 @@
-
-
-
- validmind.datasets.regression.fred API documentation
-
-
-
-
-
-
-
- def
- load_all_data ():
-
-
-
-
-
-
-
-
-
-
-
- def
- load_data ():
-
-
-
-
-
-
-
-
-
-
-
- def
- load_processed_data ():
-
-
-
-
-
-
-
-
-
-
-
- def
- preprocess (df , split_option = 'train_test_val' , train_size = 0.6 , test_size = 0.2 ):
-
-
-
-
-
- Split a time series DataFrame into train, validation, and test sets.
-
-
Arguments:
-
-
-df (pandas.DataFrame): The time series DataFrame to be split.split_option (str): The split option to choose from: 'train_test_val' (default) or 'train_test'.train_size (float): The proportion of the dataset to include in the training set. Default is 0.6.test_size (float): The proportion of the dataset to include in the test set. Default is 0.2.
-
-
Returns:
-
-
- train_df (pandas.DataFrame): The training set.
- validation_df (pandas.DataFrame): The validation set (only returned if split_option is 'train_test_val').
- test_df (pandas.DataFrame): The test set.
-
-
-
-
-
-
- def
- load_model (model_name ):
-
-
-
-
-
-
-
-
-
-
-
- def
- load_train_dataset (model_path ):
-
-
-
-
-
-
-
-
-
-
-
- def
- load_test_dataset (model_name ):
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/datasets/regression/lending_club.html b/docs/_build/validmind/datasets/regression/lending_club.html
deleted file mode 100644
index e082a2ff0..000000000
--- a/docs/_build/validmind/datasets/regression/lending_club.html
+++ /dev/null
@@ -1,306 +0,0 @@
-
-
-
- validmind.datasets.regression.lending_club API documentation
-
-
-
-
-
-
-
- def
- load_data ():
-
-
-
-
-
-
-
-
-
-
-
- def
- preprocess (df , split_option = 'train_test_val' , train_size = 0.6 , test_size = 0.2 ):
-
-
-
-
-
- Split a time series DataFrame into train, validation, and test sets.
-
-
Arguments:
-
-
-df (pandas.DataFrame): The time series DataFrame to be split.split_option (str): The split option to choose from: 'train_test_val' (default) or 'train_test'.train_size (float): The proportion of the dataset to include in the training set. Default is 0.6.test_size (float): The proportion of the dataset to include in the test set. Default is 0.2.
-
-
Returns:
-
-
- train_df (pandas.DataFrame): The training set.
- validation_df (pandas.DataFrame): The validation set (only returned if split_option is 'train_test_val').
- test_df (pandas.DataFrame): The test set.
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/errors.html b/docs/_build/validmind/errors.html
deleted file mode 100644
index 84ece3866..000000000
--- a/docs/_build/validmind/errors.html
+++ /dev/null
@@ -1,1719 +0,0 @@
-
-
-
- validmind.errors API documentation
-
-
-
-
-
-
- This module contains all the custom errors that are used in the ValidMind Library.
-
-
The following base errors are defined for others:
-
-
-BaseError
-APIRequestError
-
-
-
-
-
- class
- BaseError (builtins.Exception ):
-
-
-
-
-
- Common base class for all non-exit exceptions.
-
-
-
- BaseError (message = ''
-
-
-
-
-
-
-
-
-
-
- def
- description (self , * args , ** kwargs ):
-
-
-
-
-
-
-
-
-
Inherited Members
-
-
builtins.BaseException
- with_traceback
- add_note
-
-
-
-
-
-
-
-
class
-
APIRequestError (BaseError Generic error for API request errors that are not known.
-
-
Inherited Members
-
-
-
builtins.BaseException
- with_traceback
- add_note
-
-
-
-
-
-
-
-
class
-
GetTestSuiteError (BaseError When the test suite could not be found.
-
-
Inherited Members
-
-
-
builtins.BaseException
- with_traceback
- add_note
-
-
-
-
-
-
-
-
class
-
MissingCacheResultsArgumentsError (BaseError When the cache_results function is missing arguments.
-
-
Inherited Members
-
-
-
builtins.BaseException
- with_traceback
- add_note
-
-
-
-
-
-
-
-
class
-
MissingOrInvalidModelPredictFnError (BaseError When the pytorch model is missing a predict function or its predict
-method does not have the expected arguments.
-
-
Inherited Members
-
-
-
builtins.BaseException
- with_traceback
- add_note
-
-
-
-
-
-
-
-
class
-
InitializeTestSuiteError (BaseError When the test suite was found but could not be initialized.
-
-
Inherited Members
-
-
-
builtins.BaseException
- with_traceback
- add_note
-
-
-
-
-
-
-
-
- Generic error for API request errors that are not known.
-
-
-
- def
- description (self , * args , ** kwargs ):
-
-
-
-
-
-
-
-
-
Inherited Members
-
-
-
builtins.BaseException
- with_traceback
- add_note
-
-
-
-
-
-
-
-
- When an invalid text content_id is sent to the API.
-
-
Inherited Members
-
-
-
builtins.BaseException
- with_traceback
- add_note
-
-
-
-
-
-
-
-
- When an invalid metric results object is sent to the API.
-
-
Inherited Members
-
-
-
builtins.BaseException
- with_traceback
- add_note
-
-
-
-
-
-
-
-
- Generic error for API request errors that are not known.
-
-
-
- def
- description (self , * args , ** kwargs ):
-
-
-
-
-
-
-
-
-
Inherited Members
-
-
-
builtins.BaseException
- with_traceback
- add_note
-
-
-
-
-
-
-
-
- When a POST/PUT request is made with an invalid request body.
-
-
Inherited Members
-
-
-
builtins.BaseException
- with_traceback
- add_note
-
-
-
-
-
-
-
-
- When an invalid test results object is sent to the API.
-
-
Inherited Members
-
-
-
builtins.BaseException
- with_traceback
- add_note
-
-
-
-
-
-
-
-
class
-
InvalidTestParametersError (BaseError When an invalid parameters for the test.
-
-
Inherited Members
-
-
-
builtins.BaseException
- with_traceback
- add_note
-
-
-
-
-
-
-
-
-
- When an invalid Metadat (Text) object is sent to the API.
-
-
Inherited Members
-
-
-
builtins.BaseException
- with_traceback
- add_note
-
-
-
-
-
-
-
-
-
class
-
InvalidXGBoostTrainedModelError (BaseError When an invalid XGBoost trained model is used when calling init_r_model.
-
-
Inherited Members
-
-
-
builtins.BaseException
- with_traceback
- add_note
-
-
-
-
-
-
-
-
- Exception raised when an error occurs while loading a test
-
-
-
- LoadTestError (message : str , original_error : Optional [ Exception ] = None
-
-
-
-
-
-
-
-
-
Inherited Members
-
-
-
builtins.BaseException
- with_traceback
- add_note
-
-
-
-
-
-
-
-
class
-
MismatchingClassLabelsError (BaseError When the class labels found in the dataset don't match the provided target labels.
-
-
Inherited Members
-
-
-
builtins.BaseException
- with_traceback
- add_note
-
-
-
-
-
-
-
-
class
-
MissingAPICredentialsError (BaseError Common base class for all non-exit exceptions.
-
-
-
- def
- description (self , * args , ** kwargs ):
-
-
-
-
-
-
-
-
-
Inherited Members
-
-
-
builtins.BaseException
- with_traceback
- add_note
-
-
-
-
-
-
-
-
class
-
MissingClassLabelError (BaseError When the one or more class labels are missing from provided dataset targets.
-
-
Inherited Members
-
-
-
builtins.BaseException
- with_traceback
- add_note
-
-
-
-
-
-
-
-
class
-
MissingDocumentationTemplate (BaseError When the client config is missing the documentation template.
-
-
Inherited Members
-
-
-
builtins.BaseException
- with_traceback
- add_note
-
-
-
-
-
-
-
-
-
class
-
MissingDependencyError (BaseError When a required dependency is missing.
-
-
-
- MissingDependencyError (message = '' , required_dependencies = None , extra = None
-
-
-
-
-
-
Arguments:
-
-
-message (str): The error message.required_dependencies (list): A list of required dependencies.extra (str): The particular validmind extra that will install the missing dependencies.
-
-
-
-
-
Inherited Members
-
-
-
builtins.BaseException
- with_traceback
- add_note
-
-
-
-
-
-
-
-
-
- When a Text object is sent to the API without a content_id.
-
-
Inherited Members
-
-
-
builtins.BaseException
- with_traceback
- add_note
-
-
-
-
-
-
-
-
- When a Text object is sent to the API without a "text" attribute.
-
-
Inherited Members
-
-
-
builtins.BaseException
- with_traceback
- add_note
-
-
-
-
-
-
-
-
class
-
MissingModelIdError (BaseError Common base class for all non-exit exceptions.
-
-
-
- def
- description (self , * args , ** kwargs ):
-
-
-
-
-
-
-
-
-
Inherited Members
-
-
-
builtins.BaseException
- with_traceback
- add_note
-
-
-
-
-
-
-
-
-
class
-
UnsupportedColumnTypeError (BaseError When an unsupported column type is found on a dataset.
-
-
Inherited Members
-
-
-
builtins.BaseException
- with_traceback
- add_note
-
-
-
-
-
-
-
-
class
-
UnsupportedDatasetError (BaseError When an unsupported dataset is used.
-
-
Inherited Members
-
-
-
builtins.BaseException
- with_traceback
- add_note
-
-
-
-
-
-
-
-
-
class
-
UnsupportedRModelError (BaseError When an unsupported R model is used.
-
-
Inherited Members
-
-
-
builtins.BaseException
- with_traceback
- add_note
-
-
-
-
-
-
-
-
class
-
UnsupportedModelError (BaseError When an unsupported model is used.
-
-
Inherited Members
-
-
-
builtins.BaseException
- with_traceback
- add_note
-
-
-
-
-
-
-
-
class
-
UnsupportedModelForSHAPError (BaseError When an unsupported model is used for SHAP importance.
-
-
Inherited Members
-
-
-
builtins.BaseException
- with_traceback
- add_note
-
-
-
-
-
-
-
-
- Useful error to throw when a test cannot be executed.
-
-
Inherited Members
-
-
-
builtins.BaseException
- with_traceback
- add_note
-
-
-
-
-
-
-
- def
- raise_api_error (error_string ):
-
-
-
-
-
- Safely try to parse JSON from the response message in case the API
-returns a non-JSON string or if the API returns a non-standard error
-
-
-
-
- def
- should_raise_on_fail_fast (error ) -> bool :
-
-
-
-
-
- Determine whether an error should be raised when fail_fast is True.
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/test_suites.html b/docs/_build/validmind/test_suites.html
deleted file mode 100644
index 36e0cd629..000000000
--- a/docs/_build/validmind/test_suites.html
+++ /dev/null
@@ -1,372 +0,0 @@
-
-
-
- validmind.test_suites API documentation
-
-
-
-
-
-
- Entrypoint for test suites.
-
-
-
-
- def
- get_by_id (test_suite_id : str ):
-
-
-
-
-
- Returns the test suite by ID
-
-
-
-
- def
- list_suites (pretty : bool = True ):
-
-
-
-
-
- Returns a list of all available test suites
-
-
-
-
- def
- describe_suite (test_suite_id : str , verbose = False ):
-
-
-
-
-
- Describes a Test Suite by ID
-
-
Arguments:
-
-
-test_suite_id: Test Suite IDverbose: If True, describe all plans and tests in the Test Suite
-
-
Returns:
-
-
- pandas.DataFrame: A formatted table with the Test Suite description
-
-
-
-
-
- def
- describe_test_suite (test_suite_id : str , verbose = False ):
-
-
-
-
-
- Describes a Test Suite by ID
-
-
Arguments:
-
-
-test_suite_id: Test Suite IDverbose: If True, describe all plans and tests in the Test Suite
-
-
Returns:
-
-
- pandas.DataFrame: A formatted table with the Test Suite description
-
-
-
-
-
-
- Registers a custom test suite
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/test_suites/classifier.html b/docs/_build/validmind/test_suites/classifier.html
deleted file mode 100644
index e85a35b4b..000000000
--- a/docs/_build/validmind/test_suites/classifier.html
+++ /dev/null
@@ -1,409 +0,0 @@
-
-
-
- validmind.test_suites.classifier API documentation
-
-
-
-
-
-
- Test suites for sklearn-compatible classifier models
-
-
Ideal setup is to have the API client to read a
-custom test suite from the project's configuration
-
-
-
-
- class
- ClassifierMetrics (validmind.vm_models.test_suite.test_suite.TestSuite ):
-
-
-
-
-
- Test suite for sklearn classifier metrics
-
-
Inherited Members
-
-
validmind.vm_models.test_suite.test_suite.TestSuite
- TestSuite
- get_tests
- num_tests
- get_default_config
-
-
-
-
-
-
-
-
- class
- ClassifierDiagnosis (validmind.vm_models.test_suite.test_suite.TestSuite ):
-
-
-
-
-
- Test suite for sklearn classifier model diagnosis tests
-
-
Inherited Members
-
-
validmind.vm_models.test_suite.test_suite.TestSuite
- TestSuite
- get_tests
- num_tests
- get_default_config
-
-
-
-
-
-
-
- class
- ClassifierModelValidation (validmind.vm_models.test_suite.test_suite.TestSuite ):
-
-
-
-
-
- Test suite for binary classification models.
-
-
Inherited Members
-
-
validmind.vm_models.test_suite.test_suite.TestSuite
- TestSuite
- get_tests
- num_tests
- get_default_config
-
-
-
-
-
-
-
- class
- ClassifierFullSuite (validmind.vm_models.test_suite.test_suite.TestSuite ):
-
-
-
-
-
- Full test suite for binary classification models.
-
-
Inherited Members
-
-
validmind.vm_models.test_suite.test_suite.TestSuite
- TestSuite
- get_tests
- num_tests
- get_default_config
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/test_suites/cluster.html b/docs/_build/validmind/test_suites/cluster.html
deleted file mode 100644
index 305265952..000000000
--- a/docs/_build/validmind/test_suites/cluster.html
+++ /dev/null
@@ -1,343 +0,0 @@
-
-
-
- validmind.test_suites.cluster API documentation
-
-
-
-
-
-
- Test suites for sklearn-compatible clustering models
-
-
Ideal setup is to have the API client to read a
-custom test suite from the project's configuration
-
-
-
-
- class
- ClusterMetrics (validmind.vm_models.test_suite.test_suite.TestSuite ):
-
-
-
-
-
- Test suite for sklearn clustering metrics
-
-
Inherited Members
-
-
validmind.vm_models.test_suite.test_suite.TestSuite
- TestSuite
- get_tests
- num_tests
- get_default_config
-
-
-
-
-
-
-
-
- class
- ClusterFullSuite (validmind.vm_models.test_suite.test_suite.TestSuite ):
-
-
-
-
-
- Full test suite for clustering models.
-
-
Inherited Members
-
-
validmind.vm_models.test_suite.test_suite.TestSuite
- TestSuite
- get_tests
- num_tests
- get_default_config
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/test_suites/embeddings.html b/docs/_build/validmind/test_suites/embeddings.html
deleted file mode 100644
index e068e3622..000000000
--- a/docs/_build/validmind/test_suites/embeddings.html
+++ /dev/null
@@ -1,343 +0,0 @@
-
-
-
- validmind.test_suites.embeddings API documentation
-
-
-
-
-
-
- Test suites for embeddings models
-
-
Ideal setup is to have the API client to read a
-custom test suite from the project's configuration
-
-
-
-
- class
- EmbeddingsMetrics (validmind.vm_models.test_suite.test_suite.TestSuite ):
-
-
-
-
-
- Test suite for embeddings metrics
-
-
Inherited Members
-
-
validmind.vm_models.test_suite.test_suite.TestSuite
- TestSuite
- get_tests
- num_tests
- get_default_config
-
-
-
-
-
-
-
-
- class
- EmbeddingsFullSuite (validmind.vm_models.test_suite.test_suite.TestSuite ):
-
-
-
-
-
- Full test suite for embeddings models.
-
-
Inherited Members
-
-
validmind.vm_models.test_suite.test_suite.TestSuite
- TestSuite
- get_tests
- num_tests
- get_default_config
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/test_suites/llm.html b/docs/_build/validmind/test_suites/llm.html
deleted file mode 100644
index b7a9df7a7..000000000
--- a/docs/_build/validmind/test_suites/llm.html
+++ /dev/null
@@ -1,307 +0,0 @@
-
-
-
- validmind.test_suites.llm API documentation
-
-
-
-
-
-
-
- class
- PromptValidation (validmind.vm_models.test_suite.test_suite.TestSuite ):
-
-
-
-
-
- Test suite for prompt validation
-
-
Inherited Members
-
-
validmind.vm_models.test_suite.test_suite.TestSuite
- TestSuite
- get_tests
- num_tests
- get_default_config
-
-
-
-
-
-
-
- class
- LLMClassifierFullSuite (validmind.vm_models.test_suite.test_suite.TestSuite ):
-
-
-
-
-
- Full test suite for LLM classification models.
-
-
Inherited Members
-
-
validmind.vm_models.test_suite.test_suite.TestSuite
- TestSuite
- get_tests
- num_tests
- get_default_config
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/test_suites/nlp.html b/docs/_build/validmind/test_suites/nlp.html
deleted file mode 100644
index e92f8c45b..000000000
--- a/docs/_build/validmind/test_suites/nlp.html
+++ /dev/null
@@ -1,274 +0,0 @@
-
-
-
- validmind.test_suites.nlp API documentation
-
-
-
-
-
-
- Test suites for NLP models
-
-
-
-
- class
- NLPClassifierFullSuite (validmind.vm_models.test_suite.test_suite.TestSuite ):
-
-
-
-
-
- Full test suite for NLP classification models.
-
-
Inherited Members
-
-
validmind.vm_models.test_suite.test_suite.TestSuite
- TestSuite
- get_tests
- num_tests
- get_default_config
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/test_suites/parameters_optimization.html b/docs/_build/validmind/test_suites/parameters_optimization.html
deleted file mode 100644
index 528f0d262..000000000
--- a/docs/_build/validmind/test_suites/parameters_optimization.html
+++ /dev/null
@@ -1,277 +0,0 @@
-
-
-
- validmind.test_suites.parameters_optimization API documentation
-
-
-
-
-
-
- Test suites for sklearn-compatible hyper parameters tunning
-
-
Ideal setup is to have the API client to read a
-custom test suite from the project's configuration
-
-
-
-
- class
- KmeansParametersOptimization (validmind.vm_models.test_suite.test_suite.TestSuite ):
-
-
-
-
-
- Test suite for sklearn hyperparameters optimization
-
-
Inherited Members
-
-
validmind.vm_models.test_suite.test_suite.TestSuite
- TestSuite
- get_tests
- num_tests
- get_default_config
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/test_suites/regression.html b/docs/_build/validmind/test_suites/regression.html
deleted file mode 100644
index 09268ada9..000000000
--- a/docs/_build/validmind/test_suites/regression.html
+++ /dev/null
@@ -1,338 +0,0 @@
-
-
-
- validmind.test_suites.regression API documentation
-
-
-
-
-
-
-
- class
- RegressionMetrics (validmind.vm_models.test_suite.test_suite.TestSuite ):
-
-
-
-
-
- Test suite for performance metrics of regression metrics
-
-
Inherited Members
-
-
validmind.vm_models.test_suite.test_suite.TestSuite
- TestSuite
- get_tests
- num_tests
- get_default_config
-
-
-
-
-
-
-
-
- class
- RegressionFullSuite (validmind.vm_models.test_suite.test_suite.TestSuite ):
-
-
-
-
-
- Full test suite for regression models.
-
-
Inherited Members
-
-
validmind.vm_models.test_suite.test_suite.TestSuite
- TestSuite
- get_tests
- num_tests
- get_default_config
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/test_suites/statsmodels_timeseries.html b/docs/_build/validmind/test_suites/statsmodels_timeseries.html
deleted file mode 100644
index 13f39a871..000000000
--- a/docs/_build/validmind/test_suites/statsmodels_timeseries.html
+++ /dev/null
@@ -1,307 +0,0 @@
-
-
-
- validmind.test_suites.statsmodels_timeseries API documentation
-
-
-
-
-
-
- Time Series Test Suites from statsmodels
-
-
-
-
- class
- RegressionModelDescription (validmind.vm_models.test_suite.test_suite.TestSuite ):
-
-
-
-
-
- Test suite for performance metric of regression model of statsmodels library
-
-
Inherited Members
-
-
validmind.vm_models.test_suite.test_suite.TestSuite
- TestSuite
- get_tests
- num_tests
- get_default_config
-
-
-
-
-
-
-
- class
- RegressionModelsEvaluation (validmind.vm_models.test_suite.test_suite.TestSuite ):
-
-
-
-
-
- Test suite for metrics comparison of regression model of statsmodels library
-
-
Inherited Members
-
-
validmind.vm_models.test_suite.test_suite.TestSuite
- TestSuite
- get_tests
- num_tests
- get_default_config
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/test_suites/summarization.html b/docs/_build/validmind/test_suites/summarization.html
deleted file mode 100644
index a4a36876a..000000000
--- a/docs/_build/validmind/test_suites/summarization.html
+++ /dev/null
@@ -1,274 +0,0 @@
-
-
-
- validmind.test_suites.summarization API documentation
-
-
-
-
-
-
- Test suites for llm summarization models
-
-
-
-
- class
- SummarizationMetrics (validmind.vm_models.test_suite.test_suite.TestSuite ):
-
-
-
-
-
- Test suite for Summarization metrics
-
-
Inherited Members
-
-
validmind.vm_models.test_suite.test_suite.TestSuite
- TestSuite
- get_tests
- num_tests
- get_default_config
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/test_suites/tabular_datasets.html b/docs/_build/validmind/test_suites/tabular_datasets.html
deleted file mode 100644
index f5104c467..000000000
--- a/docs/_build/validmind/test_suites/tabular_datasets.html
+++ /dev/null
@@ -1,341 +0,0 @@
-
-
-
- validmind.test_suites.tabular_datasets API documentation
-
-
-
-
-
-
- Test suites for tabular datasets
-
-
-
-
- class
- TabularDatasetDescription (validmind.vm_models.test_suite.test_suite.TestSuite ):
-
-
-
-
-
- Test suite to extract metadata and descriptive
-statistics from a tabular dataset
-
-
Inherited Members
-
-
validmind.vm_models.test_suite.test_suite.TestSuite
- TestSuite
- get_tests
- num_tests
- get_default_config
-
-
-
-
-
-
-
- class
- TabularDataQuality (validmind.vm_models.test_suite.test_suite.TestSuite ):
-
-
-
-
-
- Test suite for data quality on tabular datasets
-
-
Inherited Members
-
-
validmind.vm_models.test_suite.test_suite.TestSuite
- TestSuite
- get_tests
- num_tests
- get_default_config
-
-
-
-
-
-
-
- class
- TabularDataset (validmind.vm_models.test_suite.test_suite.TestSuite ):
-
-
-
-
-
- Test suite for tabular datasets.
-
-
Inherited Members
-
-
validmind.vm_models.test_suite.test_suite.TestSuite
- TestSuite
- get_tests
- num_tests
- get_default_config
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/test_suites/text_data.html b/docs/_build/validmind/test_suites/text_data.html
deleted file mode 100644
index 05062a4bc..000000000
--- a/docs/_build/validmind/test_suites/text_data.html
+++ /dev/null
@@ -1,274 +0,0 @@
-
-
-
- validmind.test_suites.text_data API documentation
-
-
-
-
-
-
- Test suites for text datasets
-
-
-
-
- class
- TextDataQuality (validmind.vm_models.test_suite.test_suite.TestSuite ):
-
-
-
-
-
- Test suite for data quality on text data
-
-
Inherited Members
-
-
validmind.vm_models.test_suite.test_suite.TestSuite
- TestSuite
- get_tests
- num_tests
- get_default_config
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/test_suites/time_series.html b/docs/_build/validmind/test_suites/time_series.html
deleted file mode 100644
index b35d76215..000000000
--- a/docs/_build/validmind/test_suites/time_series.html
+++ /dev/null
@@ -1,424 +0,0 @@
-
-
-
- validmind.test_suites.time_series API documentation
-
-
-
-
-
-
- Time Series Test Suites
-
-
-
-
- class
- TimeSeriesDataQuality (validmind.vm_models.test_suite.test_suite.TestSuite ):
-
-
-
-
-
- Test suite for data quality on time series datasets
-
-
Inherited Members
-
-
validmind.vm_models.test_suite.test_suite.TestSuite
- TestSuite
- get_tests
- num_tests
- get_default_config
-
-
-
-
-
-
-
- class
- TimeSeriesUnivariate (validmind.vm_models.test_suite.test_suite.TestSuite ):
-
-
-
-
-
- This test suite provides a preliminary understanding of the target variable(s)
-used in the time series dataset. It visualizations that present the raw time
-series data and a histogram of the target variable(s).
-
-
The raw time series data provides a visual inspection of the target variable's
-behavior over time. This helps to identify any patterns or trends in the data,
-as well as any potential outliers or anomalies. The histogram of the target
-variable displays the distribution of values, providing insight into the range
-and frequency of values observed in the data.
-
-
Inherited Members
-
-
validmind.vm_models.test_suite.test_suite.TestSuite
- TestSuite
- get_tests
- num_tests
- get_default_config
-
-
-
-
-
-
-
- class
- TimeSeriesMultivariate (validmind.vm_models.test_suite.test_suite.TestSuite ):
-
-
-
-
-
- This test suite provides a preliminary understanding of the features
-and relationship in multivariate dataset. It presents various
-multivariate visualizations that can help identify patterns, trends,
-and relationships between pairs of variables. The visualizations are
-designed to explore the relationships between multiple features
-simultaneously. They allow you to quickly identify any patterns or
-trends in the data, as well as any potential outliers or anomalies.
-The individual feature distribution can also be explored to provide
-insight into the range and frequency of values observed in the data.
-This multivariate analysis test suite aims to provide an overview of
-the data structure and guide further exploration and modeling.
-
-
Inherited Members
-
-
validmind.vm_models.test_suite.test_suite.TestSuite
- TestSuite
- get_tests
- num_tests
- get_default_config
-
-
-
-
-
-
-
- class
- TimeSeriesDataset (validmind.vm_models.test_suite.test_suite.TestSuite ):
-
-
-
-
-
- Test suite for time series datasets.
-
-
Inherited Members
-
-
validmind.vm_models.test_suite.test_suite.TestSuite
- TestSuite
- get_tests
- num_tests
- get_default_config
-
-
-
-
-
-
-
- class
- TimeSeriesModelValidation (validmind.vm_models.test_suite.test_suite.TestSuite ):
-
-
-
-
-
- Test suite for time series model validation.
-
-
Inherited Members
-
-
validmind.vm_models.test_suite.test_suite.TestSuite
- TestSuite
- get_tests
- num_tests
- get_default_config
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests.html b/docs/_build/validmind/tests.html
deleted file mode 100644
index a24e7d5ac..000000000
--- a/docs/_build/validmind/tests.html
+++ /dev/null
@@ -1,880 +0,0 @@
-
-
-
- validmind.tests API documentation
-
-
-
-
-
-
-
- def
- list_tests (filter = None , task = None , tags = None , pretty = True , truncate = True ):
-
-
-
-
-
- List all tests in the tests directory.
-
-
Arguments:
-
-
-filter (str, optional): Find tests where the ID, tasks or tags match the
-filter string. Defaults to None.task (str, optional): Find tests that match the task. Can be used to
-narrow down matches from the filter string. Defaults to None.tags (list, optional): Find tests that match list of tags. Can be used to
-narrow down matches from the filter string. Defaults to None.pretty (bool, optional): If True, returns a pandas DataFrame with a
-formatted table. Defaults to True.truncate (bool, optional): If True, truncates the test description to the first
-line. Defaults to True. (only used if pretty=True)
-
-
Returns:
-
-
- list or pandas.DataFrame: A list of all tests or a formatted table.
-
-
-
-
-
- def
- load_test ( test_id : str , test_func : < built - in function callable > = None , reload : bool = False ):
-
-
-
-
-
- Load a test by test ID
-
-
Test IDs are in the format namespace.path_to_module.TestClassOrFuncName[:tag].
-The tag is optional and is used to distinguish between multiple results from the
-same test.
-
-
Arguments:
-
-
-test_id (str): The test ID in the format namespace.path_to_module.TestName[:tag]test_func (callable, optional): The test function to load. If not provided, the
-test will be loaded from the test provider. Defaults to None.
-
-
-
-
- def
- describe_test ( test_id : Union [ Literal [ 'validmind.data_validation.ACFandPACFPlot' , 'validmind.data_validation.ADF' , 'validmind.data_validation.AutoAR' , 'validmind.data_validation.AutoMA' , 'validmind.data_validation.AutoStationarity' , 'validmind.data_validation.BivariateScatterPlots' , 'validmind.data_validation.BoxPierce' , 'validmind.data_validation.ChiSquaredFeaturesTable' , 'validmind.data_validation.ClassImbalance' , 'validmind.data_validation.DatasetDescription' , 'validmind.data_validation.DatasetSplit' , 'validmind.data_validation.DescriptiveStatistics' , 'validmind.data_validation.DickeyFullerGLS' , 'validmind.data_validation.Duplicates' , 'validmind.data_validation.EngleGrangerCoint' , 'validmind.data_validation.FeatureTargetCorrelationPlot' , 'validmind.data_validation.HighCardinality' , 'validmind.data_validation.HighPearsonCorrelation' , 'validmind.data_validation.IQROutliersBarPlot' , 'validmind.data_validation.IQROutliersTable' , 'validmind.data_validation.IsolationForestOutliers' , 'validmind.data_validation.JarqueBera' , 'validmind.data_validation.KPSS' , 'validmind.data_validation.LJungBox' , 'validmind.data_validation.LaggedCorrelationHeatmap' , 'validmind.data_validation.MissingValues' , 'validmind.data_validation.MissingValuesBarPlot' , 'validmind.data_validation.MutualInformation' , 'validmind.data_validation.PearsonCorrelationMatrix' , 'validmind.data_validation.PhillipsPerronArch' , 'validmind.data_validation.ProtectedClassesCombination' , 'validmind.data_validation.ProtectedClassesDescription' , 'validmind.data_validation.ProtectedClassesDisparity' , 'validmind.data_validation.ProtectedClassesThresholdOptimizer' , 'validmind.data_validation.RollingStatsPlot' , 'validmind.data_validation.RunsTest' , 'validmind.data_validation.ScatterPlot' , 'validmind.data_validation.ScoreBandDefaultRates' , 'validmind.data_validation.SeasonalDecompose' , 'validmind.data_validation.ShapiroWilk' , 'validmind.data_validation.Skewness' , 'validmind.data_validation.SpreadPlot' , 'validmind.data_validation.TabularCategoricalBarPlots' , 'validmind.data_validation.TabularDateTimeHistograms' , 'validmind.data_validation.TabularDescriptionTables' , 'validmind.data_validation.TabularNumericalHistograms' , 'validmind.data_validation.TargetRateBarPlots' , 'validmind.data_validation.TimeSeriesDescription' , 'validmind.data_validation.TimeSeriesDescriptiveStatistics' , 'validmind.data_validation.TimeSeriesFrequency' , 'validmind.data_validation.TimeSeriesHistogram' , 'validmind.data_validation.TimeSeriesLinePlot' , 'validmind.data_validation.TimeSeriesMissingValues' , 'validmind.data_validation.TimeSeriesOutliers' , 'validmind.data_validation.TooManyZeroValues' , 'validmind.data_validation.UniqueRows' , 'validmind.data_validation.WOEBinPlots' , 'validmind.data_validation.WOEBinTable' , 'validmind.data_validation.ZivotAndrewsArch' , 'validmind.data_validation.nlp.CommonWords' , 'validmind.data_validation.nlp.Hashtags' , 'validmind.data_validation.nlp.LanguageDetection' , 'validmind.data_validation.nlp.Mentions' , 'validmind.data_validation.nlp.PolarityAndSubjectivity' , 'validmind.data_validation.nlp.Punctuations' , 'validmind.data_validation.nlp.Sentiment' , 'validmind.data_validation.nlp.StopWords' , 'validmind.data_validation.nlp.TextDescription' , 'validmind.data_validation.nlp.Toxicity' , 'validmind.model_validation.BertScore' , 'validmind.model_validation.BleuScore' , 'validmind.model_validation.ClusterSizeDistribution' , 'validmind.model_validation.ContextualRecall' , 'validmind.model_validation.FeaturesAUC' , 'validmind.model_validation.MeteorScore' , 'validmind.model_validation.ModelMetadata' , 'validmind.model_validation.ModelPredictionResiduals' , 'validmind.model_validation.RegardScore' , 'validmind.model_validation.RegressionResidualsPlot' , 'validmind.model_validation.RougeScore' , 'validmind.model_validation.TimeSeriesPredictionWithCI' , 'validmind.model_validation.TimeSeriesPredictionsPlot' , 'validmind.model_validation.TimeSeriesR2SquareBySegments' , 'validmind.model_validation.TokenDisparity' , 'validmind.model_validation.ToxicityScore' , 'validmind.model_validation.embeddings.ClusterDistribution' , 'validmind.model_validation.embeddings.CosineSimilarityComparison' , 'validmind.model_validation.embeddings.CosineSimilarityDistribution' , 'validmind.model_validation.embeddings.CosineSimilarityHeatmap' , 'validmind.model_validation.embeddings.DescriptiveAnalytics' , 'validmind.model_validation.embeddings.EmbeddingsVisualization2D' , 'validmind.model_validation.embeddings.EuclideanDistanceComparison' , 'validmind.model_validation.embeddings.EuclideanDistanceHeatmap' , 'validmind.model_validation.embeddings.PCAComponentsPairwisePlots' , 'validmind.model_validation.embeddings.StabilityAnalysisKeyword' , 'validmind.model_validation.embeddings.StabilityAnalysisRandomNoise' , 'validmind.model_validation.embeddings.StabilityAnalysisSynonyms' , 'validmind.model_validation.embeddings.StabilityAnalysisTranslation' , 'validmind.model_validation.embeddings.TSNEComponentsPairwisePlots' , 'validmind.model_validation.ragas.AnswerCorrectness' , 'validmind.model_validation.ragas.AspectCritic' , 'validmind.model_validation.ragas.ContextEntityRecall' , 'validmind.model_validation.ragas.ContextPrecision' , 'validmind.model_validation.ragas.ContextPrecisionWithoutReference' , 'validmind.model_validation.ragas.ContextRecall' , 'validmind.model_validation.ragas.Faithfulness' , 'validmind.model_validation.ragas.NoiseSensitivity' , 'validmind.model_validation.ragas.ResponseRelevancy' , 'validmind.model_validation.ragas.SemanticSimilarity' , 'validmind.model_validation.sklearn.AdjustedMutualInformation' , 'validmind.model_validation.sklearn.AdjustedRandIndex' , 'validmind.model_validation.sklearn.CalibrationCurve' , 'validmind.model_validation.sklearn.ClassifierPerformance' , 'validmind.model_validation.sklearn.ClassifierThresholdOptimization' , 'validmind.model_validation.sklearn.ClusterCosineSimilarity' , 'validmind.model_validation.sklearn.ClusterPerformanceMetrics' , 'validmind.model_validation.sklearn.CompletenessScore' , 'validmind.model_validation.sklearn.ConfusionMatrix' , 'validmind.model_validation.sklearn.FeatureImportance' , 'validmind.model_validation.sklearn.FowlkesMallowsScore' , 'validmind.model_validation.sklearn.HomogeneityScore' , 'validmind.model_validation.sklearn.HyperParametersTuning' , 'validmind.model_validation.sklearn.KMeansClustersOptimization' , 'validmind.model_validation.sklearn.MinimumAccuracy' , 'validmind.model_validation.sklearn.MinimumF1Score' , 'validmind.model_validation.sklearn.MinimumROCAUCScore' , 'validmind.model_validation.sklearn.ModelParameters' , 'validmind.model_validation.sklearn.ModelsPerformanceComparison' , 'validmind.model_validation.sklearn.OverfitDiagnosis' , 'validmind.model_validation.sklearn.PermutationFeatureImportance' , 'validmind.model_validation.sklearn.PopulationStabilityIndex' , 'validmind.model_validation.sklearn.PrecisionRecallCurve' , 'validmind.model_validation.sklearn.ROCCurve' , 'validmind.model_validation.sklearn.RegressionErrors' , 'validmind.model_validation.sklearn.RegressionErrorsComparison' , 'validmind.model_validation.sklearn.RegressionPerformance' , 'validmind.model_validation.sklearn.RegressionR2Square' , 'validmind.model_validation.sklearn.RegressionR2SquareComparison' , 'validmind.model_validation.sklearn.RobustnessDiagnosis' , 'validmind.model_validation.sklearn.SHAPGlobalImportance' , 'validmind.model_validation.sklearn.ScoreProbabilityAlignment' , 'validmind.model_validation.sklearn.SilhouettePlot' , 'validmind.model_validation.sklearn.TrainingTestDegradation' , 'validmind.model_validation.sklearn.VMeasure' , 'validmind.model_validation.sklearn.WeakspotsDiagnosis' , 'validmind.model_validation.statsmodels.AutoARIMA' , 'validmind.model_validation.statsmodels.CumulativePredictionProbabilities' , 'validmind.model_validation.statsmodels.DurbinWatsonTest' , 'validmind.model_validation.statsmodels.GINITable' , 'validmind.model_validation.statsmodels.KolmogorovSmirnov' , 'validmind.model_validation.statsmodels.Lilliefors' , 'validmind.model_validation.statsmodels.PredictionProbabilitiesHistogram' , 'validmind.model_validation.statsmodels.RegressionCoeffs' , 'validmind.model_validation.statsmodels.RegressionFeatureSignificance' , 'validmind.model_validation.statsmodels.RegressionModelForecastPlot' , 'validmind.model_validation.statsmodels.RegressionModelForecastPlotLevels' , 'validmind.model_validation.statsmodels.RegressionModelSensitivityPlot' , 'validmind.model_validation.statsmodels.RegressionModelSummary' , 'validmind.model_validation.statsmodels.RegressionPermutationFeatureImportance' , 'validmind.model_validation.statsmodels.ScorecardHistogram' , 'validmind.ongoing_monitoring.CalibrationCurveDrift' , 'validmind.ongoing_monitoring.ClassDiscriminationDrift' , 'validmind.ongoing_monitoring.ClassImbalanceDrift' , 'validmind.ongoing_monitoring.ClassificationAccuracyDrift' , 'validmind.ongoing_monitoring.ConfusionMatrixDrift' , 'validmind.ongoing_monitoring.CumulativePredictionProbabilitiesDrift' , 'validmind.ongoing_monitoring.FeatureDrift' , 'validmind.ongoing_monitoring.PredictionAcrossEachFeature' , 'validmind.ongoing_monitoring.PredictionCorrelation' , 'validmind.ongoing_monitoring.PredictionProbabilitiesHistogramDrift' , 'validmind.ongoing_monitoring.PredictionQuantilesAcrossFeatures' , 'validmind.ongoing_monitoring.ROCCurveDrift' , 'validmind.ongoing_monitoring.ScoreBandsDrift' , 'validmind.ongoing_monitoring.ScorecardHistogramDrift' , 'validmind.ongoing_monitoring.TargetPredictionDistributionPlot' , 'validmind.prompt_validation.Bias' , 'validmind.prompt_validation.Clarity' , 'validmind.prompt_validation.Conciseness' , 'validmind.prompt_validation.Delimitation' , 'validmind.prompt_validation.NegativeInstruction' , 'validmind.prompt_validation.Robustness' , 'validmind.prompt_validation.Specificity' , 'validmind.unit_metrics.classification.Accuracy' , 'validmind.unit_metrics.classification.F1' , 'validmind.unit_metrics.classification.Precision' , 'validmind.unit_metrics.classification.ROC_AUC' , 'validmind.unit_metrics.classification.Recall' , 'validmind.unit_metrics.regression.AdjustedRSquaredScore' , 'validmind.unit_metrics.regression.GiniCoefficient' , 'validmind.unit_metrics.regression.HuberLoss' , 'validmind.unit_metrics.regression.KolmogorovSmirnovStatistic' , 'validmind.unit_metrics.regression.MeanAbsoluteError' , 'validmind.unit_metrics.regression.MeanAbsolutePercentageError' , 'validmind.unit_metrics.regression.MeanBiasDeviation' , 'validmind.unit_metrics.regression.MeanSquaredError' , 'validmind.unit_metrics.regression.QuantileLoss' , 'validmind.unit_metrics.regression.RSquaredScore' , 'validmind.unit_metrics.regression.RootMeanSquaredError' ], str ] = None , raw : bool = False , show : bool = True ):
-
-
-
-
-
- Get or show details about the test
-
-
This function can be used to see test details including the test name, description,
-required inputs and default params. It can also be used to get a dictionary of the
-above information for programmatic use.
-
-
Arguments:
-
-
-test_id (str, optional): The test ID. Defaults to None.raw (bool, optional): If True, returns a dictionary with the test details.
-Defaults to False.
-
-
-
-
-
def
-
run_test ( test_id : Union [ Literal [ 'validmind.data_validation.ACFandPACFPlot' , 'validmind.data_validation.ADF' , 'validmind.data_validation.AutoAR' , 'validmind.data_validation.AutoMA' , 'validmind.data_validation.AutoStationarity' , 'validmind.data_validation.BivariateScatterPlots' , 'validmind.data_validation.BoxPierce' , 'validmind.data_validation.ChiSquaredFeaturesTable' , 'validmind.data_validation.ClassImbalance' , 'validmind.data_validation.DatasetDescription' , 'validmind.data_validation.DatasetSplit' , 'validmind.data_validation.DescriptiveStatistics' , 'validmind.data_validation.DickeyFullerGLS' , 'validmind.data_validation.Duplicates' , 'validmind.data_validation.EngleGrangerCoint' , 'validmind.data_validation.FeatureTargetCorrelationPlot' , 'validmind.data_validation.HighCardinality' , 'validmind.data_validation.HighPearsonCorrelation' , 'validmind.data_validation.IQROutliersBarPlot' , 'validmind.data_validation.IQROutliersTable' , 'validmind.data_validation.IsolationForestOutliers' , 'validmind.data_validation.JarqueBera' , 'validmind.data_validation.KPSS' , 'validmind.data_validation.LJungBox' , 'validmind.data_validation.LaggedCorrelationHeatmap' , 'validmind.data_validation.MissingValues' , 'validmind.data_validation.MissingValuesBarPlot' , 'validmind.data_validation.MutualInformation' , 'validmind.data_validation.PearsonCorrelationMatrix' , 'validmind.data_validation.PhillipsPerronArch' , 'validmind.data_validation.ProtectedClassesCombination' , 'validmind.data_validation.ProtectedClassesDescription' , 'validmind.data_validation.ProtectedClassesDisparity' , 'validmind.data_validation.ProtectedClassesThresholdOptimizer' , 'validmind.data_validation.RollingStatsPlot' , 'validmind.data_validation.RunsTest' , 'validmind.data_validation.ScatterPlot' , 'validmind.data_validation.ScoreBandDefaultRates' , 'validmind.data_validation.SeasonalDecompose' , 'validmind.data_validation.ShapiroWilk' , 'validmind.data_validation.Skewness' , 'validmind.data_validation.SpreadPlot' , 'validmind.data_validation.TabularCategoricalBarPlots' , 'validmind.data_validation.TabularDateTimeHistograms' , 'validmind.data_validation.TabularDescriptionTables' , 'validmind.data_validation.TabularNumericalHistograms' , 'validmind.data_validation.TargetRateBarPlots' , 'validmind.data_validation.TimeSeriesDescription' , 'validmind.data_validation.TimeSeriesDescriptiveStatistics' , 'validmind.data_validation.TimeSeriesFrequency' , 'validmind.data_validation.TimeSeriesHistogram' , 'validmind.data_validation.TimeSeriesLinePlot' , 'validmind.data_validation.TimeSeriesMissingValues' , 'validmind.data_validation.TimeSeriesOutliers' , 'validmind.data_validation.TooManyZeroValues' , 'validmind.data_validation.UniqueRows' , 'validmind.data_validation.WOEBinPlots' , 'validmind.data_validation.WOEBinTable' , 'validmind.data_validation.ZivotAndrewsArch' , 'validmind.data_validation.nlp.CommonWords' , 'validmind.data_validation.nlp.Hashtags' , 'validmind.data_validation.nlp.LanguageDetection' , 'validmind.data_validation.nlp.Mentions' , 'validmind.data_validation.nlp.PolarityAndSubjectivity' , 'validmind.data_validation.nlp.Punctuations' , 'validmind.data_validation.nlp.Sentiment' , 'validmind.data_validation.nlp.StopWords' , 'validmind.data_validation.nlp.TextDescription' , 'validmind.data_validation.nlp.Toxicity' , 'validmind.model_validation.BertScore' , 'validmind.model_validation.BleuScore' , 'validmind.model_validation.ClusterSizeDistribution' , 'validmind.model_validation.ContextualRecall' , 'validmind.model_validation.FeaturesAUC' , 'validmind.model_validation.MeteorScore' , 'validmind.model_validation.ModelMetadata' , 'validmind.model_validation.ModelPredictionResiduals' , 'validmind.model_validation.RegardScore' , 'validmind.model_validation.RegressionResidualsPlot' , 'validmind.model_validation.RougeScore' , 'validmind.model_validation.TimeSeriesPredictionWithCI' , 'validmind.model_validation.TimeSeriesPredictionsPlot' , 'validmind.model_validation.TimeSeriesR2SquareBySegments' , 'validmind.model_validation.TokenDisparity' , 'validmind.model_validation.ToxicityScore' , 'validmind.model_validation.embeddings.ClusterDistribution' , 'validmind.model_validation.embeddings.CosineSimilarityComparison' , 'validmind.model_validation.embeddings.CosineSimilarityDistribution' , 'validmind.model_validation.embeddings.CosineSimilarityHeatmap' , 'validmind.model_validation.embeddings.DescriptiveAnalytics' , 'validmind.model_validation.embeddings.EmbeddingsVisualization2D' , 'validmind.model_validation.embeddings.EuclideanDistanceComparison' , 'validmind.model_validation.embeddings.EuclideanDistanceHeatmap' , 'validmind.model_validation.embeddings.PCAComponentsPairwisePlots' , 'validmind.model_validation.embeddings.StabilityAnalysisKeyword' , 'validmind.model_validation.embeddings.StabilityAnalysisRandomNoise' , 'validmind.model_validation.embeddings.StabilityAnalysisSynonyms' , 'validmind.model_validation.embeddings.StabilityAnalysisTranslation' , 'validmind.model_validation.embeddings.TSNEComponentsPairwisePlots' , 'validmind.model_validation.ragas.AnswerCorrectness' , 'validmind.model_validation.ragas.AspectCritic' , 'validmind.model_validation.ragas.ContextEntityRecall' , 'validmind.model_validation.ragas.ContextPrecision' , 'validmind.model_validation.ragas.ContextPrecisionWithoutReference' , 'validmind.model_validation.ragas.ContextRecall' , 'validmind.model_validation.ragas.Faithfulness' , 'validmind.model_validation.ragas.NoiseSensitivity' , 'validmind.model_validation.ragas.ResponseRelevancy' , 'validmind.model_validation.ragas.SemanticSimilarity' , 'validmind.model_validation.sklearn.AdjustedMutualInformation' , 'validmind.model_validation.sklearn.AdjustedRandIndex' , 'validmind.model_validation.sklearn.CalibrationCurve' , 'validmind.model_validation.sklearn.ClassifierPerformance' , 'validmind.model_validation.sklearn.ClassifierThresholdOptimization' , 'validmind.model_validation.sklearn.ClusterCosineSimilarity' , 'validmind.model_validation.sklearn.ClusterPerformanceMetrics' , 'validmind.model_validation.sklearn.CompletenessScore' , 'validmind.model_validation.sklearn.ConfusionMatrix' , 'validmind.model_validation.sklearn.FeatureImportance' , 'validmind.model_validation.sklearn.FowlkesMallowsScore' , 'validmind.model_validation.sklearn.HomogeneityScore' , 'validmind.model_validation.sklearn.HyperParametersTuning' , 'validmind.model_validation.sklearn.KMeansClustersOptimization' , 'validmind.model_validation.sklearn.MinimumAccuracy' , 'validmind.model_validation.sklearn.MinimumF1Score' , 'validmind.model_validation.sklearn.MinimumROCAUCScore' , 'validmind.model_validation.sklearn.ModelParameters' , 'validmind.model_validation.sklearn.ModelsPerformanceComparison' , 'validmind.model_validation.sklearn.OverfitDiagnosis' , 'validmind.model_validation.sklearn.PermutationFeatureImportance' , 'validmind.model_validation.sklearn.PopulationStabilityIndex' , 'validmind.model_validation.sklearn.PrecisionRecallCurve' , 'validmind.model_validation.sklearn.ROCCurve' , 'validmind.model_validation.sklearn.RegressionErrors' , 'validmind.model_validation.sklearn.RegressionErrorsComparison' , 'validmind.model_validation.sklearn.RegressionPerformance' , 'validmind.model_validation.sklearn.RegressionR2Square' , 'validmind.model_validation.sklearn.RegressionR2SquareComparison' , 'validmind.model_validation.sklearn.RobustnessDiagnosis' , 'validmind.model_validation.sklearn.SHAPGlobalImportance' , 'validmind.model_validation.sklearn.ScoreProbabilityAlignment' , 'validmind.model_validation.sklearn.SilhouettePlot' , 'validmind.model_validation.sklearn.TrainingTestDegradation' , 'validmind.model_validation.sklearn.VMeasure' , 'validmind.model_validation.sklearn.WeakspotsDiagnosis' , 'validmind.model_validation.statsmodels.AutoARIMA' , 'validmind.model_validation.statsmodels.CumulativePredictionProbabilities' , 'validmind.model_validation.statsmodels.DurbinWatsonTest' , 'validmind.model_validation.statsmodels.GINITable' , 'validmind.model_validation.statsmodels.KolmogorovSmirnov' , 'validmind.model_validation.statsmodels.Lilliefors' , 'validmind.model_validation.statsmodels.PredictionProbabilitiesHistogram' , 'validmind.model_validation.statsmodels.RegressionCoeffs' , 'validmind.model_validation.statsmodels.RegressionFeatureSignificance' , 'validmind.model_validation.statsmodels.RegressionModelForecastPlot' , 'validmind.model_validation.statsmodels.RegressionModelForecastPlotLevels' , 'validmind.model_validation.statsmodels.RegressionModelSensitivityPlot' , 'validmind.model_validation.statsmodels.RegressionModelSummary' , 'validmind.model_validation.statsmodels.RegressionPermutationFeatureImportance' , 'validmind.model_validation.statsmodels.ScorecardHistogram' , 'validmind.ongoing_monitoring.CalibrationCurveDrift' , 'validmind.ongoing_monitoring.ClassDiscriminationDrift' , 'validmind.ongoing_monitoring.ClassImbalanceDrift' , 'validmind.ongoing_monitoring.ClassificationAccuracyDrift' , 'validmind.ongoing_monitoring.ConfusionMatrixDrift' , 'validmind.ongoing_monitoring.CumulativePredictionProbabilitiesDrift' , 'validmind.ongoing_monitoring.FeatureDrift' , 'validmind.ongoing_monitoring.PredictionAcrossEachFeature' , 'validmind.ongoing_monitoring.PredictionCorrelation' , 'validmind.ongoing_monitoring.PredictionProbabilitiesHistogramDrift' , 'validmind.ongoing_monitoring.PredictionQuantilesAcrossFeatures' , 'validmind.ongoing_monitoring.ROCCurveDrift' , 'validmind.ongoing_monitoring.ScoreBandsDrift' , 'validmind.ongoing_monitoring.ScorecardHistogramDrift' , 'validmind.ongoing_monitoring.TargetPredictionDistributionPlot' , 'validmind.prompt_validation.Bias' , 'validmind.prompt_validation.Clarity' , 'validmind.prompt_validation.Conciseness' , 'validmind.prompt_validation.Delimitation' , 'validmind.prompt_validation.NegativeInstruction' , 'validmind.prompt_validation.Robustness' , 'validmind.prompt_validation.Specificity' , 'validmind.unit_metrics.classification.Accuracy' , 'validmind.unit_metrics.classification.F1' , 'validmind.unit_metrics.classification.Precision' , 'validmind.unit_metrics.classification.ROC_AUC' , 'validmind.unit_metrics.classification.Recall' , 'validmind.unit_metrics.regression.AdjustedRSquaredScore' , 'validmind.unit_metrics.regression.GiniCoefficient' , 'validmind.unit_metrics.regression.HuberLoss' , 'validmind.unit_metrics.regression.KolmogorovSmirnovStatistic' , 'validmind.unit_metrics.regression.MeanAbsoluteError' , 'validmind.unit_metrics.regression.MeanAbsolutePercentageError' , 'validmind.unit_metrics.regression.MeanBiasDeviation' , 'validmind.unit_metrics.regression.MeanSquaredError' , 'validmind.unit_metrics.regression.QuantileLoss' , 'validmind.unit_metrics.regression.RSquaredScore' , 'validmind.unit_metrics.regression.RootMeanSquaredError' ], str , NoneType ] = None , name : Optional [ str ] = None , unit_metrics : Optional [ List [ Union [ Literal [ 'validmind.data_validation.ACFandPACFPlot' , 'validmind.data_validation.ADF' , 'validmind.data_validation.AutoAR' , 'validmind.data_validation.AutoMA' , 'validmind.data_validation.AutoStationarity' , 'validmind.data_validation.BivariateScatterPlots' , 'validmind.data_validation.BoxPierce' , 'validmind.data_validation.ChiSquaredFeaturesTable' , 'validmind.data_validation.ClassImbalance' , 'validmind.data_validation.DatasetDescription' , 'validmind.data_validation.DatasetSplit' , 'validmind.data_validation.DescriptiveStatistics' , 'validmind.data_validation.DickeyFullerGLS' , 'validmind.data_validation.Duplicates' , 'validmind.data_validation.EngleGrangerCoint' , 'validmind.data_validation.FeatureTargetCorrelationPlot' , 'validmind.data_validation.HighCardinality' , 'validmind.data_validation.HighPearsonCorrelation' , 'validmind.data_validation.IQROutliersBarPlot' , 'validmind.data_validation.IQROutliersTable' , 'validmind.data_validation.IsolationForestOutliers' , 'validmind.data_validation.JarqueBera' , 'validmind.data_validation.KPSS' , 'validmind.data_validation.LJungBox' , 'validmind.data_validation.LaggedCorrelationHeatmap' , 'validmind.data_validation.MissingValues' , 'validmind.data_validation.MissingValuesBarPlot' , 'validmind.data_validation.MutualInformation' , 'validmind.data_validation.PearsonCorrelationMatrix' , 'validmind.data_validation.PhillipsPerronArch' , 'validmind.data_validation.ProtectedClassesCombination' , 'validmind.data_validation.ProtectedClassesDescription' , 'validmind.data_validation.ProtectedClassesDisparity' , 'validmind.data_validation.ProtectedClassesThresholdOptimizer' , 'validmind.data_validation.RollingStatsPlot' , 'validmind.data_validation.RunsTest' , 'validmind.data_validation.ScatterPlot' , 'validmind.data_validation.ScoreBandDefaultRates' , 'validmind.data_validation.SeasonalDecompose' , 'validmind.data_validation.ShapiroWilk' , 'validmind.data_validation.Skewness' , 'validmind.data_validation.SpreadPlot' , 'validmind.data_validation.TabularCategoricalBarPlots' , 'validmind.data_validation.TabularDateTimeHistograms' , 'validmind.data_validation.TabularDescriptionTables' , 'validmind.data_validation.TabularNumericalHistograms' , 'validmind.data_validation.TargetRateBarPlots' , 'validmind.data_validation.TimeSeriesDescription' , 'validmind.data_validation.TimeSeriesDescriptiveStatistics' , 'validmind.data_validation.TimeSeriesFrequency' , 'validmind.data_validation.TimeSeriesHistogram' , 'validmind.data_validation.TimeSeriesLinePlot' , 'validmind.data_validation.TimeSeriesMissingValues' , 'validmind.data_validation.TimeSeriesOutliers' , 'validmind.data_validation.TooManyZeroValues' , 'validmind.data_validation.UniqueRows' , 'validmind.data_validation.WOEBinPlots' , 'validmind.data_validation.WOEBinTable' , 'validmind.data_validation.ZivotAndrewsArch' , 'validmind.data_validation.nlp.CommonWords' , 'validmind.data_validation.nlp.Hashtags' , 'validmind.data_validation.nlp.LanguageDetection' , 'validmind.data_validation.nlp.Mentions' , 'validmind.data_validation.nlp.PolarityAndSubjectivity' , 'validmind.data_validation.nlp.Punctuations' , 'validmind.data_validation.nlp.Sentiment' , 'validmind.data_validation.nlp.StopWords' , 'validmind.data_validation.nlp.TextDescription' , 'validmind.data_validation.nlp.Toxicity' , 'validmind.model_validation.BertScore' , 'validmind.model_validation.BleuScore' , 'validmind.model_validation.ClusterSizeDistribution' , 'validmind.model_validation.ContextualRecall' , 'validmind.model_validation.FeaturesAUC' , 'validmind.model_validation.MeteorScore' , 'validmind.model_validation.ModelMetadata' , 'validmind.model_validation.ModelPredictionResiduals' , 'validmind.model_validation.RegardScore' , 'validmind.model_validation.RegressionResidualsPlot' , 'validmind.model_validation.RougeScore' , 'validmind.model_validation.TimeSeriesPredictionWithCI' , 'validmind.model_validation.TimeSeriesPredictionsPlot' , 'validmind.model_validation.TimeSeriesR2SquareBySegments' , 'validmind.model_validation.TokenDisparity' , 'validmind.model_validation.ToxicityScore' , 'validmind.model_validation.embeddings.ClusterDistribution' , 'validmind.model_validation.embeddings.CosineSimilarityComparison' , 'validmind.model_validation.embeddings.CosineSimilarityDistribution' , 'validmind.model_validation.embeddings.CosineSimilarityHeatmap' , 'validmind.model_validation.embeddings.DescriptiveAnalytics' , 'validmind.model_validation.embeddings.EmbeddingsVisualization2D' , 'validmind.model_validation.embeddings.EuclideanDistanceComparison' , 'validmind.model_validation.embeddings.EuclideanDistanceHeatmap' , 'validmind.model_validation.embeddings.PCAComponentsPairwisePlots' , 'validmind.model_validation.embeddings.StabilityAnalysisKeyword' , 'validmind.model_validation.embeddings.StabilityAnalysisRandomNoise' , 'validmind.model_validation.embeddings.StabilityAnalysisSynonyms' , 'validmind.model_validation.embeddings.StabilityAnalysisTranslation' , 'validmind.model_validation.embeddings.TSNEComponentsPairwisePlots' , 'validmind.model_validation.ragas.AnswerCorrectness' , 'validmind.model_validation.ragas.AspectCritic' , 'validmind.model_validation.ragas.ContextEntityRecall' , 'validmind.model_validation.ragas.ContextPrecision' , 'validmind.model_validation.ragas.ContextPrecisionWithoutReference' , 'validmind.model_validation.ragas.ContextRecall' , 'validmind.model_validation.ragas.Faithfulness' , 'validmind.model_validation.ragas.NoiseSensitivity' , 'validmind.model_validation.ragas.ResponseRelevancy' , 'validmind.model_validation.ragas.SemanticSimilarity' , 'validmind.model_validation.sklearn.AdjustedMutualInformation' , 'validmind.model_validation.sklearn.AdjustedRandIndex' , 'validmind.model_validation.sklearn.CalibrationCurve' , 'validmind.model_validation.sklearn.ClassifierPerformance' , 'validmind.model_validation.sklearn.ClassifierThresholdOptimization' , 'validmind.model_validation.sklearn.ClusterCosineSimilarity' , 'validmind.model_validation.sklearn.ClusterPerformanceMetrics' , 'validmind.model_validation.sklearn.CompletenessScore' , 'validmind.model_validation.sklearn.ConfusionMatrix' , 'validmind.model_validation.sklearn.FeatureImportance' , 'validmind.model_validation.sklearn.FowlkesMallowsScore' , 'validmind.model_validation.sklearn.HomogeneityScore' , 'validmind.model_validation.sklearn.HyperParametersTuning' , 'validmind.model_validation.sklearn.KMeansClustersOptimization' , 'validmind.model_validation.sklearn.MinimumAccuracy' , 'validmind.model_validation.sklearn.MinimumF1Score' , 'validmind.model_validation.sklearn.MinimumROCAUCScore' , 'validmind.model_validation.sklearn.ModelParameters' , 'validmind.model_validation.sklearn.ModelsPerformanceComparison' , 'validmind.model_validation.sklearn.OverfitDiagnosis' , 'validmind.model_validation.sklearn.PermutationFeatureImportance' , 'validmind.model_validation.sklearn.PopulationStabilityIndex' , 'validmind.model_validation.sklearn.PrecisionRecallCurve' , 'validmind.model_validation.sklearn.ROCCurve' , 'validmind.model_validation.sklearn.RegressionErrors' , 'validmind.model_validation.sklearn.RegressionErrorsComparison' , 'validmind.model_validation.sklearn.RegressionPerformance' , 'validmind.model_validation.sklearn.RegressionR2Square' , 'validmind.model_validation.sklearn.RegressionR2SquareComparison' , 'validmind.model_validation.sklearn.RobustnessDiagnosis' , 'validmind.model_validation.sklearn.SHAPGlobalImportance' , 'validmind.model_validation.sklearn.ScoreProbabilityAlignment' , 'validmind.model_validation.sklearn.SilhouettePlot' , 'validmind.model_validation.sklearn.TrainingTestDegradation' , 'validmind.model_validation.sklearn.VMeasure' , 'validmind.model_validation.sklearn.WeakspotsDiagnosis' , 'validmind.model_validation.statsmodels.AutoARIMA' , 'validmind.model_validation.statsmodels.CumulativePredictionProbabilities' , 'validmind.model_validation.statsmodels.DurbinWatsonTest' , 'validmind.model_validation.statsmodels.GINITable' , 'validmind.model_validation.statsmodels.KolmogorovSmirnov' , 'validmind.model_validation.statsmodels.Lilliefors' , 'validmind.model_validation.statsmodels.PredictionProbabilitiesHistogram' , 'validmind.model_validation.statsmodels.RegressionCoeffs' , 'validmind.model_validation.statsmodels.RegressionFeatureSignificance' , 'validmind.model_validation.statsmodels.RegressionModelForecastPlot' , 'validmind.model_validation.statsmodels.RegressionModelForecastPlotLevels' , 'validmind.model_validation.statsmodels.RegressionModelSensitivityPlot' , 'validmind.model_validation.statsmodels.RegressionModelSummary' , 'validmind.model_validation.statsmodels.RegressionPermutationFeatureImportance' , 'validmind.model_validation.statsmodels.ScorecardHistogram' , 'validmind.ongoing_monitoring.CalibrationCurveDrift' , 'validmind.ongoing_monitoring.ClassDiscriminationDrift' , 'validmind.ongoing_monitoring.ClassImbalanceDrift' , 'validmind.ongoing_monitoring.ClassificationAccuracyDrift' , 'validmind.ongoing_monitoring.ConfusionMatrixDrift' , 'validmind.ongoing_monitoring.CumulativePredictionProbabilitiesDrift' , 'validmind.ongoing_monitoring.FeatureDrift' , 'validmind.ongoing_monitoring.PredictionAcrossEachFeature' , 'validmind.ongoing_monitoring.PredictionCorrelation' , 'validmind.ongoing_monitoring.PredictionProbabilitiesHistogramDrift' , 'validmind.ongoing_monitoring.PredictionQuantilesAcrossFeatures' , 'validmind.ongoing_monitoring.ROCCurveDrift' , 'validmind.ongoing_monitoring.ScoreBandsDrift' , 'validmind.ongoing_monitoring.ScorecardHistogramDrift' , 'validmind.ongoing_monitoring.TargetPredictionDistributionPlot' , 'validmind.prompt_validation.Bias' , 'validmind.prompt_validation.Clarity' , 'validmind.prompt_validation.Conciseness' , 'validmind.prompt_validation.Delimitation' , 'validmind.prompt_validation.NegativeInstruction' , 'validmind.prompt_validation.Robustness' , 'validmind.prompt_validation.Specificity' , 'validmind.unit_metrics.classification.Accuracy' , 'validmind.unit_metrics.classification.F1' , 'validmind.unit_metrics.classification.Precision' , 'validmind.unit_metrics.classification.ROC_AUC' , 'validmind.unit_metrics.classification.Recall' , 'validmind.unit_metrics.regression.AdjustedRSquaredScore' , 'validmind.unit_metrics.regression.GiniCoefficient' , 'validmind.unit_metrics.regression.HuberLoss' , 'validmind.unit_metrics.regression.KolmogorovSmirnovStatistic' , 'validmind.unit_metrics.regression.MeanAbsoluteError' , 'validmind.unit_metrics.regression.MeanAbsolutePercentageError' , 'validmind.unit_metrics.regression.MeanBiasDeviation' , 'validmind.unit_metrics.regression.MeanSquaredError' , 'validmind.unit_metrics.regression.QuantileLoss' , 'validmind.unit_metrics.regression.RSquaredScore' , 'validmind.unit_metrics.regression.RootMeanSquaredError' ], str ]]] = None , inputs : Optional [ Dict [ str , Any ]] = None , input_grid : Union [ Dict [ str , List [ Any ]], List [ Dict [ str , Any ]], NoneType ] = None , params : Optional [ Dict [ str , Any ]] = None , param_grid : Union [ Dict [ str , List [ Any ]], List [ Dict [ str , Any ]], NoneType ] = None , show : bool = True , generate_description : bool = True , title : Optional [ str ] = None , post_process_fn : Optional [ Callable [[ validmind.vm_models.TestResult ], NoneType ]] = None , ** kwargs ) -> validmind.vm_models.TestResult
-
-
-
Run a ValidMind or custom test
-
-
This function is the main entry point for running tests. It can run simple unit metrics,
-ValidMind and custom tests, composite tests made up of multiple unit metrics and comparison
-tests made up of multiple tests.
-
-
Arguments:
-
-
-test_id (TestID, optional): Test ID to run. Not required if name and unit_metrics provided.params (dict, optional): Parameters to customize test behavior. See test details for available parameters.param_grid (Union[Dict[str, List[Any]], List[Dict[str, Any]]], optional): For comparison tests, either:
-
-Dict mapping parameter names to lists of values (creates Cartesian product)
-List of parameter dictionaries to test
- inputs (Dict[str, Any], optional): Test inputs (models/datasets initialized with vm.init_model/dataset)input_grid (Union[Dict[str, List[Any]], List[Dict[str, Any]]], optional): For comparison tests, either:
-
-Dict mapping input names to lists of values (creates Cartesian product)
-List of input dictionaries to test
- name (str, optional): Test name (required for composite metrics)unit_metrics (list, optional): Unit metric IDs to run as composite metricshow (bool, optional): Whether to display results. Defaults to True.generate_description (bool, optional): Whether to generate a description. Defaults to True.title (str, optional): Custom title for the test resultpost_process_fn (Callable[[TestResult], None], optional): Function to post-process the test result
-
-
Returns:
-
-
- TestResult: A TestResult object containing the test results
-
-
-
Raises:
-
-
-ValueError: If the test inputs are invalidLoadTestError: If the test class fails to load
-
-
-
-
-
def
-
register_test_provider ( namespace : str , test_provider : TestProvider ) -> None :
-
-
-
Register an external test provider
-
-
Arguments:
-
-
-namespace (str): The namespace of the test providertest_provider (TestProvider): The test provider
-
-
-
-
-
- Exception raised when an error occurs while loading a test
-
-
-
- LoadTestError (message : str , original_error : Optional [ Exception ] = None
-
-
-
-
-
-
-
-
-
Inherited Members
-
-
-
builtins.BaseException
- with_traceback
- add_note
-
-
-
-
-
-
-
- class
- LocalTestProvider :
-
-
-
-
-
- Test providers in ValidMind are responsible for loading tests from different sources,
-such as local files, databases, or remote services. The LocalTestProvider specifically
-loads tests from the local file system.
-
-
To use the LocalTestProvider, you need to provide the root_folder, which is the
-root directory for local tests. The test_id is a combination of the namespace (set
-when registering the test provider) and the path to the test class module, where
-slashes are replaced by dots and the .py extension is left out.
-
-
Example usage:
-
-
# Create an instance of LocalTestProvider with the root folder
-test_provider = LocalTestProvider("/path/to/tests/folder")
-
-# Register the test provider with a namespace
-register_test_provider("my_namespace", test_provider)
-
-# List all tests in the namespace (returns a list of test IDs)
-test_provider.list_tests()
-# this is used by the list_tests() function to aggregate all tests
-# from all test providers
-
-# Load a test using the test_id (namespace + path to test class module)
-test = test_provider.load_test("my_namespace.my_test_class")
-# full path to the test class module is /path/to/tests/folder/my_test_class.py
-
-
-
Attributes:
-
-
-root_folder (str): The root directory for local tests.
-
-
-
- LocalTestProvider (root_folder : str
-
-
-
-
-
-
Initialize the LocalTestProvider with the given root_folder
-(see class docstring for details)
-
-
Arguments:
-
-
-root_folder (str): The root directory for local tests.
-
-
-
-
-
-
- def
- list_tests (self ):
-
-
-
-
-
-
List all tests in the given namespace
-
-
Returns:
-
-
- list: A list of test IDs
-
-
-
-
-
-
-
- def
- load_test (self , test_id : str ):
-
-
-
-
-
-
Load the test identified by the given test_id.
-
-
Arguments:
-
-
-test_id (str): The identifier of the test. This corresponds to the relativepath of the python file from the root folder, with slashes replaced by dots
-
-
-
Returns:
-
-
- The test class that matches the last part of the test_id.
-
-
-
Raises:
-
-
-LocalTestProviderLoadModuleError: If the test module cannot be importedLocalTestProviderLoadTestError: If the test class cannot be found in the module
-
-
-
-
-
-
-
- class
- TestProvider (typing.Protocol ):
-
-
-
-
-
- Protocol for user-defined test providers
-
-
-
- TestProvider (* args , ** kwargs
-
-
-
-
-
-
-
-
-
-
- def
- list_tests (self ) -> List [ str ] :
-
-
-
-
-
-
List all tests in the given namespace
-
-
Returns:
-
-
- list: A list of test IDs
-
-
-
-
-
-
-
- def
- load_test (self , test_id : str ) -> < built - in function callable > :
-
-
-
-
-
-
Load the test function identified by the given test_id
-
-
Arguments:
-
-
-test_id (str): The test ID (does not contain the namespace under which
-the test is registered)
-
-
Returns:
-
-
- callable: The test function
-
-
-
Raises:
-
-
-FileNotFoundError: If the test is not found
-
-
-
-
-
-
-
-
- def
- list_tasks ():
-
-
-
-
-
- List unique tasks from all test classes.
-
-
-
-
-
- def
- test (func_or_id ):
-
-
-
-
-
- Decorator for creating and registering custom tests
-
-
This decorator registers the function it wraps as a test function within ValidMind
-under the provided ID. Once decorated, the function can be run using the
-run_test
-
-
The function can take two different types of arguments:
-
-
-Inputs: ValidMind model or dataset (or list of models/datasets). These arguments
-must use the following names: model, models, dataset, datasets.
-Parameters: Any additional keyword arguments of any type (must have a default
-value) that can have any name.
-
-
-
The function should return one of the following types:
-
-
-Table: Either a list of dictionaries or a pandas DataFrame
-Plot: Either a matplotlib figure or a plotly figure
-Scalar: A single number (int or float)
-Boolean: A single boolean value indicating whether the test passed or failed
-
-
-
The function may also include a docstring. This docstring will be used and logged
-as the metric's description.
-
-
Arguments:
-
-
-func: The function to decoratetest_id: The identifier for the metric. If not provided, the function name is used.
-
-
Returns:
-
-
- The decorated function.
-
-
-
-
-
-
- def
- tasks (* tasks ):
-
-
-
-
-
- Decorator for specifying the task types that a test is designed for.
-
-
Arguments:
-
-
-*tasks: The task types that the test is designed for.
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation.html b/docs/_build/validmind/tests/data_validation.html
deleted file mode 100644
index 59954c50a..000000000
--- a/docs/_build/validmind/tests/data_validation.html
+++ /dev/null
@@ -1,298 +0,0 @@
-
-
-
- validmind.tests.data_validation API documentation
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/ACFandPACFPlot.html b/docs/_build/validmind/tests/data_validation/ACFandPACFPlot.html
deleted file mode 100644
index 005c11a79..000000000
--- a/docs/_build/validmind/tests/data_validation/ACFandPACFPlot.html
+++ /dev/null
@@ -1,301 +0,0 @@
-
-
-
- validmind.tests.data_validation.ACFandPACFPlot API documentation
-
-
-
-
-
-
-
@tags('time_series_data', 'forecasting', 'statistical_test', 'visualization')
-
@tasks('regression')
-
-
def
-
ACFandPACFPlot (dataset : validmind.vm_models.VMDataset ):
-
-
-
Analyzes time series data using Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots to
-reveal trends and correlations.
-
-
Purpose
-
-
The ACF (Autocorrelation Function) and PACF (Partial Autocorrelation Function) plot test is employed to analyze
-time series data in machine learning models. It illuminates the correlation of the data over time by plotting the
-correlation of the series with its own lags (ACF), and the correlations after removing effects already accounted
-for by earlier lags (PACF). This information can identify trends, such as seasonality, degrees of autocorrelation,
-and inform the selection of order parameters for AutoRegressive Integrated Moving Average (ARIMA) models.
-
-
Test Mechanism
-
-
The ACFandPACFPlot
-
-
Signs of High Risk
-
-
-Sudden drops in the correlation at a specific lag might signal a model at high risk.
-Consistent high correlation across multiple lags could also indicate non-stationarity in the data, which may
-suggest that a model estimated on this data won't generalize well to future, unknown data.
-
-
-
Strengths
-
-
-ACF and PACF plots offer clear graphical representations of the correlations in time series data.
-These plots are effective at revealing important data characteristics such as seasonality, trends, and
-correlation patterns.
-The insights from these plots aid in better model configuration, particularly in the selection of ARIMA model
-parameters.
-
-
-
Limitations
-
-
-ACF and PACF plots are exclusively for time series data and hence, can't be applied to all ML models.
-These plots require large, consistent datasets as gaps could lead to misleading results.
-The plots can only represent linear correlations and fail to capture any non-linear relationships within the data.
-The plots might be difficult for non-experts to interpret and should not replace more advanced analyses.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/ADF.html b/docs/_build/validmind/tests/data_validation/ADF.html
deleted file mode 100644
index 8700cb5cd..000000000
--- a/docs/_build/validmind/tests/data_validation/ADF.html
+++ /dev/null
@@ -1,298 +0,0 @@
-
-
-
- validmind.tests.data_validation.ADF API documentation
-
-
-
-
-
-
-
@tags('time_series_data', 'statsmodels', 'forecasting', 'statistical_test', 'stationarity')
-
@tasks('regression')
-
-
def
-
ADF (dataset : validmind.vm_models.VMDataset ):
-
-
-
Assesses the stationarity of a time series dataset using the Augmented Dickey-Fuller (ADF) test.
-
-
Purpose
-
-
The Augmented Dickey-Fuller (ADF) test metric is used to determine the order of integration, i.e., the stationarity
-of a given time series dataset. The stationary property of data is pivotal in many machine learning models as it
-impacts the reliability and effectiveness of predictions and forecasts.
-
-
Test Mechanism
-
-
The ADF test is executed using the adfuller function from the statsmodels library on each feature of the
-dataset. Multiple outputs are generated for each run, including the ADF test statistic and p-value, count of lags
-used, the number of observations considered in the test, critical values at various confidence levels, and the
-information criterion. These results are stored for each feature for subsequent analysis.
-
-
Signs of High Risk
-
-
-An inflated ADF statistic and high p-value (generally above 0.05) indicate a high risk to the model's performance
-due to the presence of a unit root indicating non-stationarity.
-Non-stationarity might result in untrustworthy or insufficient forecasts.
-
-
-
Strengths
-
-
-The ADF test is robust to sophisticated correlations within the data, making it suitable for settings where data
-displays complex stochastic behavior.
-It provides explicit outputs like test statistics, critical values, and information criterion, enhancing
-understanding and transparency in the model validation process.
-
-
-
Limitations
-
-
-The ADF test might demonstrate low statistical power, making it challenging to differentiate between a unit root
-and near-unit-root processes, potentially causing false negatives.
-It assumes the data follows an autoregressive process, which might not always be the case.
-The test struggles with time series data that have structural breaks.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/AutoAR.html b/docs/_build/validmind/tests/data_validation/AutoAR.html
deleted file mode 100644
index 29f5eb0ad..000000000
--- a/docs/_build/validmind/tests/data_validation/AutoAR.html
+++ /dev/null
@@ -1,305 +0,0 @@
-
-
-
- validmind.tests.data_validation.AutoAR API documentation
-
-
-
-
-
-
-
@tags('time_series_data', 'statsmodels', 'forecasting', 'statistical_test')
-
@tasks('regression')
-
-
def
-
AutoAR ( dataset : validmind.vm_models.VMDataset max_ar_order : int = 3 ):
-
-
-
Automatically identifies the optimal Autoregressive (AR) order for a time series using BIC and AIC criteria.
-
-
Purpose
-
-
The AutoAR test is intended to automatically identify the Autoregressive (AR) order of a time series by utilizing
-the Bayesian Information Criterion (BIC) and Akaike Information Criterion (AIC). AR order is crucial in forecasting
-tasks as it dictates the quantity of prior terms in the sequence to use for predicting the current term. The
-objective is to select the most fitting AR model that encapsulates the trend and seasonality in the time series
-data.
-
-
Test Mechanism
-
-
The test mechanism operates by iterating through a possible range of AR orders up to a defined maximum. An AR model
-is fitted for each order, and the corresponding BIC and AIC are computed. BIC and AIC statistical measures are
-designed to penalize models for complexity, preferring simpler models that fit the data proficiently. To verify the
-stationarity of the time series, the Augmented Dickey-Fuller test is executed. The AR order, BIC, and AIC findings
-are compiled into a dataframe for effortless comparison. Then, the AR order with the smallest BIC is established as
-the desirable order for each variable.
-
-
Signs of High Risk
-
-
-An augmented Dickey Fuller test p-value > 0.05, indicating the time series isn't stationary, may lead to
-inaccurate results.
-Problems with the model fitting procedure, such as computational or convergence issues.
-Continuous selection of the maximum specified AR order may suggest an insufficient set limit.
-
-
-
Strengths
-
-
-The test independently pinpoints the optimal AR order, thereby reducing potential human bias.
-It strikes a balance between model simplicity and goodness-of-fit to avoid overfitting.
-Has the capability to account for stationarity in a time series, an essential aspect for dependable AR modeling.
-The results are aggregated into a comprehensive table, enabling an easy interpretation.
-
-
-
Limitations
-
-
-The tests need a stationary time series input.
-They presume a linear relationship between the series and its lags.
-The search for the best model is constrained by the maximum AR order supplied in the parameters. Therefore, a low
-max_ar_order could result in subpar outcomes.
-AIC and BIC may not always agree on the selection of the best model. This potentially requires the user to juggle
-interpretational choices.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/AutoMA.html b/docs/_build/validmind/tests/data_validation/AutoMA.html
deleted file mode 100644
index 7269611d4..000000000
--- a/docs/_build/validmind/tests/data_validation/AutoMA.html
+++ /dev/null
@@ -1,308 +0,0 @@
-
-
-
- validmind.tests.data_validation.AutoMA API documentation
-
-
-
-
-
-
-
@tags('time_series_data', 'statsmodels', 'forecasting', 'statistical_test')
-
@tasks('regression')
-
-
def
-
AutoMA ( dataset : validmind.vm_models.VMDataset max_ma_order : int = 3 ):
-
-
-
Automatically selects the optimal Moving Average (MA) order for each variable in a time series dataset based on
-minimal BIC and AIC values.
-
-
Purpose
-
-
The AutoMA
-
-
Test Mechanism
-
-
Starting off, the AutoMA max_ma_order parameter has been provided. It consequently
-loops through all variables in the dataset, carrying out the Dickey-Fuller test for stationarity. For each
-stationary variable, it fits an ARIMA model for orders running from 0 to max_ma_order. The result is a list
-showcasing the BIC and AIC values of the ARIMA models based on different orders. The MA order, which yields the
-smallest BIC, is chosen as the 'best MA order' for every single variable. The final results include a table
-summarizing the auto MA analysis and another table listing the best MA order for each variable.
-
-
Signs of High Risk
-
-
-When a series is non-stationary (p-value>0.05 in the Dickey-Fuller test), the produced result could be inaccurate.
-Any error that arises in the process of fitting the ARIMA models, especially with a higher MA order, can
-potentially indicate risks and might need further investigation.
-
-
-
Strengths
-
-
-The metric facilitates automation in the process of selecting the MA order for time series forecasting. This
-significantly saves time and reduces efforts conventionally necessary for manual hyperparameter tuning.
-The use of both BIC and AIC enhances the likelihood of selecting the most suitable model.
-The metric ascertains the stationarity of the series prior to model fitting, thus ensuring that the underlying
-assumptions of the MA model are fulfilled.
-
-
-
Limitations
-
-
-If the time series fails to be stationary, the metric may yield inaccurate results. Consequently, it necessitates
-pre-processing steps to stabilize the series before fitting the ARIMA model.
-The metric adopts a rudimentary model selection process based on BIC and doesn't consider other potential model
-selection strategies. Depending on the specific dataset, other strategies could be more appropriate.
-The 'max_ma_order' parameter must be manually input which doesn't always guarantee optimal performance,
-especially when configured too low.
-The computation time increases with the rise in max_ma_order, hence, the metric may become computationally
-costly for larger values.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/AutoStationarity.html b/docs/_build/validmind/tests/data_validation/AutoStationarity.html
deleted file mode 100644
index cd2b43534..000000000
--- a/docs/_build/validmind/tests/data_validation/AutoStationarity.html
+++ /dev/null
@@ -1,306 +0,0 @@
-
-
-
- validmind.tests.data_validation.AutoStationarity API documentation
-
-
-
-
-
-
-
@tags('time_series_data', 'statsmodels', 'forecasting', 'statistical_test')
-
@tasks('regression')
-
-
def
-
AutoStationarity ( dataset : validmind.vm_models.VMDataset max_order : int = 5 , threshold : float = 0.05 ):
-
-
-
Automates Augmented Dickey-Fuller test to assess stationarity across multiple time series in a DataFrame.
-
-
Purpose
-
-
The AutoStationarity metric is intended to automatically detect and evaluate the stationary nature of each time
-series in a DataFrame. It incorporates the Augmented Dickey-Fuller (ADF) test, a statistical approach used to
-assess stationarity. Stationarity is a fundamental property suggesting that statistic features like mean and
-variance remain unchanged over time. This is necessary for many time-series models.
-
-
Test Mechanism
-
-
The mechanism for the AutoStationarity test involves applying the Augmented Dicky-Fuller test to each time series
-within the given dataframe to assess if they are stationary. Every series in the dataframe is looped, using the ADF
-test up to a defined maximum order (configurable and by default set to 5). The p-value resulting from the ADF test
-is compared against a predetermined threshold (also configurable and by default set to 0.05). The time series is
-deemed stationary at its current differencing order if the p-value is less than the threshold.
-
-
Signs of High Risk
-
-
-A significant number of series not achieving stationarity even at the maximum order of differencing can indicate
-high risk or potential failure in the model.
-This could suggest the series may not be appropriately modeled by a stationary process, hence other modeling
-approaches might be required.
-
-
-
Strengths
-
-
-The key strength in this metric lies in the automation of the ADF test, enabling mass stationarity analysis
-across various time series and boosting the efficiency and credibility of the analysis.
-The utilization of the ADF test, a widely accepted method for testing stationarity, lends authenticity to the
-results derived.
-The introduction of the max order and threshold parameters give users the autonomy to determine their preferred
-levels of stringency in the tests.
-
-
-
Limitations
-
-
-The Augmented Dickey-Fuller test and the stationarity test are not without their limitations. These tests are
-premised on the assumption that the series can be modeled by an autoregressive process, which may not always hold
-true.
-The stationarity check is highly sensitive to the choice of threshold for the significance level; an extremely
-high or low threshold could lead to incorrect results regarding the stationarity properties.
-There's also a risk of over-differencing if the maximum order is set too high, which could induce unnecessary
-cycles.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/BivariateScatterPlots.html b/docs/_build/validmind/tests/data_validation/BivariateScatterPlots.html
deleted file mode 100644
index 6c0a52e5c..000000000
--- a/docs/_build/validmind/tests/data_validation/BivariateScatterPlots.html
+++ /dev/null
@@ -1,300 +0,0 @@
-
-
-
- validmind.tests.data_validation.BivariateScatterPlots API documentation
-
-
-
-
-
-
-
@tags('tabular_data', 'numerical_data', 'visualization')
-
@tasks('classification')
-
-
def
-
BivariateScatterPlots (dataset ):
-
-
-
Generates bivariate scatterplots to visually inspect relationships between pairs of numerical predictor variables
-in machine learning classification tasks.
-
-
Purpose
-
-
This function is intended for visual inspection and monitoring of relationships between pairs of numerical
-variables in a machine learning model targeting classification tasks. It helps in understanding how predictor
-variables (features) interact with each other, which can inform feature selection, model-building strategies, and
-identify potential biases or irregularities in the data.
-
-
Test Mechanism
-
-
The function creates scatter plots for each pair of numerical features in the dataset. It first filters out
-non-numerical and binary features, ensuring the plots focus on meaningful numerical relationships. The resulting
-scatterplots are color-coded uniformly to avoid visual distraction, and the function returns a tuple of Plotly
-figure objects, each representing a scatter plot for a pair of features.
-
-
Signs of High Risk
-
-
-Visual patterns suggesting non-linear relationships, multicollinearity, clustering, or outlier points in the
-scatter plots.
-Such issues could affect the assumptions and performance of certain models, especially those assuming linearity,
-like logistic regression.
-
-
-
Strengths
-
-
-Scatterplots provide an intuitive and visual tool to explore relationships between two variables.
-They are useful for identifying outliers, variable associations, and trends, including non-linear patterns.
-Supports visualization of binary or multi-class classification datasets, focusing on numerical features.
-
-
-
Limitations
-
-
-Scatterplots are limited to bivariate analysis, showing relationships between only two variables at a time.
-Not ideal for very large datasets where overlapping points can reduce the clarity of the visualization.
-Scatterplots are exploratory tools and do not provide quantitative measures of model quality or performance.
-Interpretation is subjective and relies on the domain knowledge and judgment of the viewer.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/BoxPierce.html b/docs/_build/validmind/tests/data_validation/BoxPierce.html
deleted file mode 100644
index 3de013fb5..000000000
--- a/docs/_build/validmind/tests/data_validation/BoxPierce.html
+++ /dev/null
@@ -1,304 +0,0 @@
-
-
-
- validmind.tests.data_validation.BoxPierce API documentation
-
-
-
-
-
-
-
@tasks('regression')
-
@tags('time_series_data', 'forecasting', 'statistical_test', 'statsmodels')
-
-
def
-
BoxPierce (dataset ):
-
-
-
Detects autocorrelation in time-series data through the Box-Pierce test to validate model performance.
-
-
Purpose
-
-
The Box-Pierce test is utilized to detect the presence of autocorrelation in a time-series dataset.
-Autocorrelation, or serial correlation, refers to the degree of similarity between observations based on the
-temporal spacing between them. This test is essential for affirming the quality of a time-series model by ensuring
-that the error terms in the model are random and do not adhere to a specific pattern.
-
-
Test Mechanism
-
-
The implementation of the Box-Pierce test involves calculating a test statistic along with a corresponding p-value
-derived from the dataset features. These quantities are used to test the null hypothesis that posits the data to be
-independently distributed. This is achieved by iterating over every feature column in the time-series data and
-applying the acorr_ljungbox function of the statsmodels library. The function yields the Box-Pierce test
-statistic as well as the respective p-value, all of which are cached as test results.
-
-
Signs of High Risk
-
-
-A low p-value, typically under 0.05 as per statistical convention, throws the null hypothesis of independence
-into question. This implies that the dataset potentially houses autocorrelations, thus indicating a high-risk
-scenario concerning model performance.
-Large Box-Pierce test statistic values may indicate the presence of autocorrelation.
-
-
-
Strengths
-
-
-Detects patterns in data that are supposed to be random, thereby ensuring no underlying autocorrelation.
-Can be computed efficiently given its low computational complexity.
-Can be widely applied to most regression problems, making it very versatile.
-
-
-
Limitations
-
-
-Assumes homoscedasticity (constant variance) and normality of residuals, which may not always be the case in
-real-world datasets.
-May exhibit reduced power for detecting complex autocorrelation schemes such as higher-order or negative
-correlations.
-It only provides a general indication of the existence of autocorrelation, without providing specific insights
-into the nature or patterns of the detected autocorrelation.
-In the presence of trends or seasonal patterns, the Box-Pierce test may yield misleading results.
-Applicability is limited to time-series data, which limits its overall utility.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/ChiSquaredFeaturesTable.html b/docs/_build/validmind/tests/data_validation/ChiSquaredFeaturesTable.html
deleted file mode 100644
index 527675294..000000000
--- a/docs/_build/validmind/tests/data_validation/ChiSquaredFeaturesTable.html
+++ /dev/null
@@ -1,303 +0,0 @@
-
-
-
- validmind.tests.data_validation.ChiSquaredFeaturesTable API documentation
-
-
-
-
-
-
-
@tags('tabular_data', 'categorical_data', 'statistical_test')
-
@tasks('classification')
-
-
def
-
ChiSquaredFeaturesTable (dataset , p_threshold = 0.05 ):
-
-
-
Assesses the statistical association between categorical features and a target variable using the Chi-Squared test.
-
-
Purpose
-
-
The ChiSquaredFeaturesTable
-
-
Test Mechanism
-
-
The function creates a contingency table for each categorical feature and the target variable, then applies the
-Chi-Squared test to compute the Chi-squared statistic and the p-value. The results for each feature include the
-variable name, Chi-squared statistic, p-value, p-value threshold, and a pass/fail status based on whether the
-p-value is below the specified threshold. The output is a DataFrame summarizing these results, sorted by p-value to
-highlight the most statistically significant associations.
-
-
Signs of High Risk
-
-
-High p-values (greater than the set threshold) indicate a lack of significant association between a feature and
-the target variable, resulting in a 'Fail' status.
-Features with a 'Fail' status might not be relevant for the model, which could negatively impact model
-performance.
-
-
-
Strengths
-
-
-Provides a clear, statistical assessment of the relationship between categorical features and the target variable.
-Produces an easily interpretable summary with a 'Pass/Fail' outcome for each feature, helping in feature
-selection.
-The p-value threshold is adjustable, allowing for flexibility in statistical rigor.
-
-
-
Limitations
-
-
-Assumes the dataset is tabular and consists of categorical variables, which may not be suitable for all datasets.
-The test is designed for classification tasks and is not applicable to regression problems.
-As with all hypothesis tests, the Chi-Squared test can only detect associations, not causal relationships.
-The choice of p-value threshold can affect the interpretation of feature relevance, and different thresholds may
-lead to different conclusions.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/ClassImbalance.html b/docs/_build/validmind/tests/data_validation/ClassImbalance.html
deleted file mode 100644
index 134c2334f..000000000
--- a/docs/_build/validmind/tests/data_validation/ClassImbalance.html
+++ /dev/null
@@ -1,310 +0,0 @@
-
-
-
- validmind.tests.data_validation.ClassImbalance API documentation
-
-
-
-
-
-
-
@tags('tabular_data', 'binary_classification', 'multiclass_classification')
-
@tasks('classification')
-
-
def
-
ClassImbalance ( dataset : validmind.vm_models.VMDataset min_percent_threshold : int = 10 ) -> Tuple [ Dict [ str , Any ], plotly . graph_objs . _figure . Figure , bool ] :
-
-
-
Evaluates and quantifies class distribution imbalance in a dataset used by a machine learning model.
-
-
Purpose
-
-
The Class Imbalance test is designed to evaluate the distribution of target classes in a dataset that's utilized by
-a machine learning model. Specifically, it aims to ensure that the classes aren't overly skewed, which could lead
-to bias in the model's predictions. It's crucial to have a balanced training dataset to avoid creating a model
-that's biased with high accuracy for the majority class and low accuracy for the minority class.
-
-
Test Mechanism
-
-
This Class Imbalance test operates by calculating the frequency (expressed as a percentage) of each class in the
-target column of the dataset. It then checks whether each class appears in at least a set minimum percentage of the
-total records. This minimum percentage is a modifiable parameter, but the default value is set to 10%.
-
-
Signs of High Risk
-
-
-Any class that represents less than the pre-set minimum percentage threshold is marked as high risk, implying a
-potential class imbalance.
-The function provides a pass/fail outcome for each class based on this criterion.
-Fundamentally, if any class fails this test, it's highly likely that the dataset possesses imbalanced class
-distribution.
-
-
-
Strengths
-
-
-The test can spot under-represented classes that could affect the efficiency of a machine learning model.
-The calculation is straightforward and swift.
-The test is highly informative because it not only spots imbalance, but it also quantifies the degree of
-imbalance.
-The adjustable threshold enables flexibility and adaptation to differing use-cases or domain-specific needs.
-The test creates a visually insightful plot showing the classes and their corresponding proportions, enhancing
-interpretability and comprehension of the data.
-
-
-
Limitations
-
-
-The test might struggle to perform well or provide vital insights for datasets with a high number of classes. In
-such cases, the imbalance could be inevitable due to the inherent class distribution.
-Sensitivity to the threshold value might result in faulty detection of imbalance if the threshold is set
-excessively high.
-Regardless of the percentage threshold, it doesn't account for varying costs or impacts of misclassifying
-different classes, which might fluctuate based on specific applications or domains.
-While it can identify imbalances in class distribution, it doesn't provide direct methods to address or correct
-these imbalances.
-The test is only applicable for classification operations and unsuitable for regression or clustering tasks.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/DatasetDescription.html b/docs/_build/validmind/tests/data_validation/DatasetDescription.html
deleted file mode 100644
index 603f21b99..000000000
--- a/docs/_build/validmind/tests/data_validation/DatasetDescription.html
+++ /dev/null
@@ -1,386 +0,0 @@
-
-
-
- validmind.tests.data_validation.DatasetDescription API documentation
-
-
-
-
-
-
-
- def
- infer_datatypes (df ):
-
-
-
-
-
-
-
-
-
-
-
- def
- get_numerical_histograms (df , column ):
-
-
-
-
-
- Returns a collection of histograms for a numerical column, each one
-with a different bin size
-
-
-
-
- def
- get_column_histograms (df , column , type_ ):
-
-
-
-
-
- Returns a collection of histograms for a numerical or categorical column.
-We store different combinations of bin sizes to allow analyzing the data better
-
-
Will be used in favor of _get_histogram in the future
-
-
-
-
- def
- describe_column (df , column ):
-
-
-
-
-
- Gets descriptive statistics for a single column in a Pandas DataFrame.
-
-
-
-
@tags('tabular_data', 'time_series_data', 'text_data')
-
@tasks('classification', 'regression', 'text_classification', 'text_summarization')
-
-
def
-
DatasetDescription (dataset : validmind.vm_models.VMDataset ):
-
-
-
Provides comprehensive analysis and statistical summaries of each column in a machine learning model's dataset.
-
-
Purpose
-
-
The test depicted in the script is meant to run a comprehensive analysis on a Machine Learning model's datasets.
-The test or metric is implemented to obtain a complete summary of the columns in the dataset, including vital
-statistics of each column such as count, distinct values, missing values, histograms for numerical, categorical,
-boolean, and text columns. This summary gives a comprehensive overview of the dataset to better understand the
-characteristics of the data that the model is trained on or evaluates.
-
-
Test Mechanism
-
-
The DatasetDescription class accomplishes the purpose as follows: firstly, the test method "run" infers the data
-type of each column in the dataset and stores the details (id, column type). For each column, the
-"describe_column" method is invoked to collect statistical information about the column, including count,
-missing value count and its proportion to the total, unique value count, and its proportion to the total. Depending
-on the data type of a column, histograms are generated that reflect the distribution of data within the column.
-Numerical columns use the "get_numerical_histograms" method to calculate histogram distribution, whereas for
-categorical, boolean and text columns, a histogram is computed with frequencies of each unique value in the
-datasets. For unsupported types, an error is raised. Lastly, a summary table is built to aggregate all the
-statistical insights and histograms of the columns in a dataset.
-
-
Signs of High Risk
-
-
-High ratio of missing values to total values in one or more columns which may impact the quality of the
-predictions.
-Unsupported data types in dataset columns.
-Large number of unique values in the dataset's columns which might make it harder for the model to establish
-patterns.
-Extreme skewness or irregular distribution of data as reflected in the histograms.
-
-
-
Strengths
-
-
-Provides a detailed analysis of the dataset with versatile summaries like count, unique values, histograms, etc.
-Flexibility in handling different types of data: numerical, categorical, boolean, and text.
-Useful in detecting problems in the dataset like missing values, unsupported data types, irregular data
-distribution, etc.
-The summary gives a comprehensive understanding of dataset features allowing developers to make informed
-decisions.
-
-
-
Limitations
-
-
-The computation can be expensive from a resource standpoint, particularly for large datasets with numerous columns.
-The histograms use an arbitrary number of bins which may not be the optimal number of bins for specific data
-distribution.
-Unsupported data types for columns will raise an error which may limit evaluating the dataset.
-Columns with all null or missing values are not included in histogram computation.
-This test only validates the quality of the dataset but doesn't address the model's performance directly.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/DatasetSplit.html b/docs/_build/validmind/tests/data_validation/DatasetSplit.html
deleted file mode 100644
index 54aa70a42..000000000
--- a/docs/_build/validmind/tests/data_validation/DatasetSplit.html
+++ /dev/null
@@ -1,303 +0,0 @@
-
-
-
- validmind.tests.data_validation.DatasetSplit API documentation
-
-
-
-
-
-
-
@tags('tabular_data', 'time_series_data', 'text_data')
-
@tasks('classification', 'regression', 'text_classification', 'text_summarization')
-
-
def
-
DatasetSplit (datasets : List [ validmind.vm_models.VMDataset ] ):
-
-
-
Evaluates and visualizes the distribution proportions among training, testing, and validation datasets of an ML
-model.
-
-
Purpose
-
-
The DatasetSplit test is designed to evaluate and visualize the distribution of data among training, testing, and
-validation datasets, if available, within a given machine learning model. The main purpose is to assess whether the
-model's datasets are split appropriately, as an imbalanced split might affect the model's ability to learn from the
-data and generalize to unseen data.
-
-
Test Mechanism
-
-
The DatasetSplit test first calculates the total size of all available datasets in the model. Then, for each
-individual dataset, the methodology involves determining the size of the dataset and its proportion relative to the
-total size. The results are then conveniently summarized in a table that shows dataset names, sizes, and
-proportions. Absolute size and proportion of the total dataset size are displayed for each individual dataset.
-
-
Signs of High Risk
-
-
-A very small training dataset, which may result in the model not learning enough from the data.
-A very large training dataset and a small test dataset, which may lead to model overfitting and poor
-generalization to unseen data.
-A small or non-existent validation dataset, which might complicate the model's performance assessment.
-
-
-
Strengths
-
-
-The DatasetSplit test provides a clear, understandable visualization of dataset split proportions, which can
-highlight any potential imbalance in dataset splits quickly.
-It covers a wide range of task types including classification, regression, and text-related tasks.
-The metric is not tied to any specific data type and is applicable to tabular data, time series data, or text
-data.
-
-
-
Limitations
-
-
-The DatasetSplit test does not provide any insight into the quality or diversity of the data within each split,
-just the size and proportion.
-The test does not give any recommendations or adjustments for imbalanced datasets.
-Potential lack of compatibility with more complex modes of data splitting (for example, stratified or time-based
-splits) could limit the applicability of this test.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/DescriptiveStatistics.html b/docs/_build/validmind/tests/data_validation/DescriptiveStatistics.html
deleted file mode 100644
index 1230fb2e1..000000000
--- a/docs/_build/validmind/tests/data_validation/DescriptiveStatistics.html
+++ /dev/null
@@ -1,339 +0,0 @@
-
-
-
- validmind.tests.data_validation.DescriptiveStatistics API documentation
-
-
-
-
-
-
-
- def
- get_summary_statistics_numerical (df , numerical_fields ):
-
-
-
-
-
-
-
-
-
-
-
- def
- get_summary_statistics_categorical (df , categorical_fields ):
-
-
-
-
-
-
-
-
-
-
-
@tags('tabular_data', 'time_series_data')
-
@tasks('classification', 'regression')
-
-
def
-
DescriptiveStatistics (dataset : validmind.vm_models.VMDataset ):
-
-
-
Performs a detailed descriptive statistical analysis of both numerical and categorical data within a model's
-dataset.
-
-
Purpose
-
-
The purpose of the Descriptive Statistics metric is to provide a comprehensive summary of both numerical and
-categorical data within a dataset. This involves statistics such as count, mean, standard deviation, minimum and
-maximum values for numerical data. For categorical data, it calculates the count, number of unique values, most
-common value and its frequency, and the proportion of the most frequent value relative to the total. The goal is to
-visualize the overall distribution of the variables in the dataset, aiding in understanding the model's behavior
-and predicting its performance.
-
-
Test Mechanism
-
-
The testing mechanism utilizes two in-built functions of pandas dataframes: describe() for numerical fields and
-value_counts() for categorical fields. The describe() function pulls out several summary statistics, while
-value_counts() accounts for unique values. The resulting data is formatted into two distinct tables, one for
-numerical and another for categorical variable summaries. These tables provide a clear summary of the main
-characteristics of the variables, which can be instrumental in assessing the model's performance.
-
-
Signs of High Risk
-
-
-Skewed data or significant outliers can represent high risk. For numerical data, this may be reflected via a
-significant difference between the mean and median (50% percentile).
-For categorical data, a lack of diversity (low count of unique values), or overdominance of a single category
-(high frequency of the top value) can indicate high risk.
-
-
-
Strengths
-
-
-Provides a comprehensive summary of the dataset, shedding light on the distribution and characteristics of the
-variables under consideration.
-It is a versatile and robust method, applicable to both numerical and categorical data.
-Helps highlight crucial anomalies such as outliers, extreme skewness, or lack of diversity, which are vital in
-understanding model behavior during testing and validation.
-
-
-
Limitations
-
-
-While this metric offers a high-level overview of the data, it may fail to detect subtle correlations or complex
-patterns.
-Does not offer any insights on the relationship between variables.
-Alone, descriptive statistics cannot be used to infer properties about future unseen data.
-Should be used in conjunction with other statistical tests to provide a comprehensive understanding of the
-model's data.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/DickeyFullerGLS.html b/docs/_build/validmind/tests/data_validation/DickeyFullerGLS.html
deleted file mode 100644
index d7f55b645..000000000
--- a/docs/_build/validmind/tests/data_validation/DickeyFullerGLS.html
+++ /dev/null
@@ -1,302 +0,0 @@
-
-
-
- validmind.tests.data_validation.DickeyFullerGLS API documentation
-
-
-
-
-
-
-
@tags('time_series_data', 'forecasting', 'unit_root_test')
-
@tasks('regression')
-
-
def
-
DickeyFullerGLS (dataset : validmind.vm_models.VMDataset ):
-
-
-
Assesses stationarity in time series data using the Dickey-Fuller GLS test to determine the order of integration.
-
-
Purpose
-
-
The Dickey-Fuller GLS (DFGLS) test is utilized to determine the order of integration in time series data. For
-machine learning models dealing with time series and forecasting, this metric evaluates the existence of a unit
-root, thereby checking whether a time series is non-stationary. This analysis is a crucial initial step when
-dealing with time series data.
-
-
Test Mechanism
-
-
This code implements the Dickey-Fuller GLS unit root test on each attribute of the dataset. This process involves
-iterating through every column of the dataset and applying the DFGLS test to assess the presence of a unit root.
-The resulting information, including the test statistic ('stat'), the p-value ('pvalue'), the quantity of lagged
-differences utilized in the regression ('usedlag'), and the number of observations ('nobs'), is subsequently stored.
-
-
Signs of High Risk
-
-
-A high p-value for the DFGLS test represents a high risk. Specifically, a p-value above a typical threshold of
-0.05 suggests that the time series data is quite likely to be non-stationary, thus presenting a high risk for
-generating unreliable forecasts.
-
-
-
Strengths
-
-
-The Dickey-Fuller GLS test is a potent tool for checking the stationarity of time series data.
-It helps to verify the assumptions of the models before the actual construction of the machine learning models
-proceeds.
-The results produced by this metric offer a clear insight into whether the data is appropriate for specific
-machine learning models, especially those demanding the stationarity of time series data.
-
-
-
Limitations
-
-
-Despite its benefits, the DFGLS test does present some drawbacks. It can potentially lead to inaccurate
-conclusions if the time series data incorporates a structural break.
-If the time series tends to follow a trend while still being stationary, the test might misinterpret it,
-necessitating further detrending.
-The test also presents challenges when dealing with shorter time series data or volatile data, not producing
-reliable results in these cases.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/Duplicates.html b/docs/_build/validmind/tests/data_validation/Duplicates.html
deleted file mode 100644
index 6603aa3ee..000000000
--- a/docs/_build/validmind/tests/data_validation/Duplicates.html
+++ /dev/null
@@ -1,303 +0,0 @@
-
-
-
- validmind.tests.data_validation.Duplicates API documentation
-
-
-
-
-
-
-
@tags('tabular_data', 'data_quality', 'text_data')
-
@tasks('classification', 'regression')
-
-
def
-
Duplicates (dataset , min_threshold = 1 ):
-
-
-
Tests dataset for duplicate entries, ensuring model reliability via data quality verification.
-
-
Purpose
-
-
The 'Duplicates' test is designed to check for duplicate rows within the dataset provided to the model. It serves
-as a measure of data quality, ensuring that the model isn't merely memorizing duplicate entries or being swayed by
-redundant information. This is an important step in the pre-processing of data for both classification and
-regression tasks.
-
-
Test Mechanism
-
-
This test operates by checking each row for duplicates in the dataset. If a text column is specified in the
-dataset, the test is conducted on this column; if not, the test is run on all feature columns. The number and
-percentage of duplicates are calculated and returned in a DataFrame. Additionally, a test is passed if the total
-count of duplicates falls below a specified minimum threshold.
-
-
Signs of High Risk
-
-
-A high number of duplicate rows in the dataset, which can lead to overfitting where the model performs well on
-the training data but poorly on unseen data.
-A high percentage of duplicate rows in the dataset, indicating potential problems with data collection or
-processing.
-
-
-
Strengths
-
-
-Assists in improving the reliability of the model's training process by ensuring the training data is not
-contaminated with duplicate entries, which can distort statistical analyses.
-Provides both absolute numbers and percentage values of duplicate rows, giving a thorough overview of data
-quality.
-Highly customizable as it allows for setting a user-defined minimum threshold to determine if the test has been
-passed.
-
-
-
Limitations
-
-
-Does not distinguish between benign duplicates (i.e., coincidental identical entries in different rows) and
-problematic duplicates originating from data collection or processing errors.
-The test becomes more computationally intensive as the size of the dataset increases, which might not be suitable
-for very large datasets.
-Can only check for exact duplicates and may miss semantically similar information packaged differently.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/EngleGrangerCoint.html b/docs/_build/validmind/tests/data_validation/EngleGrangerCoint.html
deleted file mode 100644
index 7ef6a316f..000000000
--- a/docs/_build/validmind/tests/data_validation/EngleGrangerCoint.html
+++ /dev/null
@@ -1,301 +0,0 @@
-
-
-
- validmind.tests.data_validation.EngleGrangerCoint API documentation
-
-
-
-
-
-
-
@tags('time_series_data', 'statistical_test', 'forecasting')
-
@tasks('regression')
-
-
def
-
EngleGrangerCoint ( dataset : validmind.vm_models.VMDataset threshold : float = 0.05 ):
-
-
-
Assesses the degree of co-movement between pairs of time series data using the Engle-Granger cointegration test.
-
-
Purpose
-
-
The intent of this Engle-Granger cointegration test is to explore and quantify the degree of co-movement between
-pairs of time series variables in a dataset. This is particularly useful in enhancing the accuracy of predictive
-regressions whenever the underlying variables are co-integrated, i.e., they move together over time.
-
-
Test Mechanism
-
-
The test first drops any non-applicable values from the input dataset and then iterates over each pair of variables
-to apply the Engle-Granger cointegration test. The test generates a 'p' value, which is then compared against a
-pre-specified threshold (0.05 by default). The pair is labeled as 'Cointegrated' if the 'p' value is less than or
-equal to the threshold or 'Not cointegrated' otherwise. A summary table is returned by the metric showing
-cointegration results for each variable pair.
-
-
Signs of High Risk
-
-
-A significant number of hypothesized cointegrated variables do not pass the test.
-A considerable number of 'p' values are close to the threshold, indicating minor data fluctuations can switch the
-decision between 'Cointegrated' and 'Not cointegrated'.
-
-
-
Strengths
-
-
-Provides an effective way to analyze relationships between time series, particularly in contexts where it's
-essential to check if variables move together in a statistically significant manner.
-Useful in various domains, especially finance or economics, where predictive models often hinge on understanding
-how different variables move together over time.
-
-
-
Limitations
-
-
-Assumes that the time series are integrated of the same order, which isn't always true in multivariate time
-series datasets.
-The presence of non-stationary characteristics in the series or structural breaks can result in falsely positive
-or negative cointegration results.
-May not perform well for small sample sizes due to lack of statistical power and should be supplemented with
-other predictive indicators for a more robust model evaluation.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/FeatureTargetCorrelationPlot.html b/docs/_build/validmind/tests/data_validation/FeatureTargetCorrelationPlot.html
deleted file mode 100644
index 28221aa44..000000000
--- a/docs/_build/validmind/tests/data_validation/FeatureTargetCorrelationPlot.html
+++ /dev/null
@@ -1,302 +0,0 @@
-
-
-
- validmind.tests.data_validation.FeatureTargetCorrelationPlot API documentation
-
-
-
-
-
-
-
@tags('tabular_data', 'visualization', 'correlation')
-
@tasks('classification', 'regression')
-
-
def
-
FeatureTargetCorrelationPlot (dataset , fig_height = 600 ):
-
-
-
Visualizes the correlation between input features and the model's target output in a color-coded horizontal bar
-plot.
-
-
Purpose
-
-
This test is designed to graphically illustrate the correlations between distinct input features and the target
-output of a Machine Learning model. Understanding how each feature influences the model's predictions is crucial—a
-higher correlation indicates a stronger influence of the feature on the target variable. This correlation study is
-especially advantageous during feature selection and for comprehending the model's operation.
-
-
Test Mechanism
-
-
This FeatureTargetCorrelationPlot test computes and presents the correlations between the features and the target
-variable using a specific dataset. These correlations are calculated and are then graphically represented in a
-horizontal bar plot, color-coded based on the strength of the correlation. A hovering template can also be utilized
-for informative tooltips. It is possible to specify the features to be analyzed and adjust the graph's height
-according to need.
-
-
Signs of High Risk
-
-
-There are no strong correlations (either positive or negative) between features and the target variable. This
-could suggest high risk as the supplied features do not appear to significantly impact the prediction output.
-The presence of duplicated correlation values might hint at redundancy in the feature set.
-
-
-
Strengths
-
-
-Provides visual assistance to interpreting correlations more effectively.
-Gives a clear and simple tour of how each feature affects the model's target variable.
-Beneficial for feature selection and grasping the model's prediction nature.
-Precise correlation values for each feature are offered by the hover template, contributing to a granular-level
-comprehension.
-
-
-
Limitations
-
-
-The test only accepts numerical data, meaning variables of other types need to be prepared beforehand.
-The plot assumes all correlations to be linear, thus non-linear relationships might not be captured effectively.
-Not apt for models that employ complex feature interactions, like Decision Trees or Neural Networks, as the test
-may not accurately reflect their importance.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/HighCardinality.html b/docs/_build/validmind/tests/data_validation/HighCardinality.html
deleted file mode 100644
index d2f7e5b83..000000000
--- a/docs/_build/validmind/tests/data_validation/HighCardinality.html
+++ /dev/null
@@ -1,299 +0,0 @@
-
-
-
- validmind.tests.data_validation.HighCardinality API documentation
-
-
-
-
-
-
-
@tags('tabular_data', 'data_quality', 'categorical_data')
-
@tasks('classification', 'regression')
-
-
def
-
HighCardinality ( dataset : validmind.vm_models.VMDataset num_threshold : int = 100 , percent_threshold : float = 0.1 , threshold_type : str = 'percent' ):
-
-
-
Assesses the number of unique values in categorical columns to detect high cardinality and potential overfitting.
-
-
Purpose
-
-
The “High Cardinality” test is used to evaluate the number of unique values present in the categorical columns of a
-dataset. In this context, high cardinality implies the presence of a large number of unique, non-repetitive values
-in the dataset.
-
-
Test Mechanism
-
-
The test first infers the dataset's type and then calculates an initial numeric threshold based on the test
-parameters. It only considers columns classified as "Categorical". For each of these columns, the number of
-distinct values (n_distinct) and the percentage of distinct values (p_distinct) are calculated. The test will pass
-if n_distinct is less than the calculated numeric threshold. Lastly, the results, which include details such as
-column name, number of distinct values, and pass/fail status, are compiled into a table.
-
-
Signs of High Risk
-
-
-A large number of distinct values (high cardinality) in one or more categorical columns implies a high risk.
-A column failing the test (n_distinct >= num_threshold) is another indicator of high risk.
-
-
-
Strengths
-
-
-The High Cardinality test is effective in early detection of potential overfitting and unwanted noise.
-It aids in identifying potential outliers and inconsistencies, thereby improving data quality.
-The test can be applied to both classification and regression task types, demonstrating its versatility.
-
-
-
Limitations
-
-
-The test is restricted to only "Categorical" data types and is thus not suitable for numerical or continuous
-features, limiting its scope.
-The test does not consider the relevance or importance of unique values in categorical features, potentially
-causing it to overlook critical data points.
-The threshold (both number and percent) used for the test is static and may not be optimal for diverse datasets
-and varied applications. Further mechanisms to adjust and refine this threshold could enhance its effectiveness.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/HighPearsonCorrelation.html b/docs/_build/validmind/tests/data_validation/HighPearsonCorrelation.html
deleted file mode 100644
index 694c563b7..000000000
--- a/docs/_build/validmind/tests/data_validation/HighPearsonCorrelation.html
+++ /dev/null
@@ -1,302 +0,0 @@
-
-
-
- validmind.tests.data_validation.HighPearsonCorrelation API documentation
-
-
-
-
-
-
-
@tags('tabular_data', 'data_quality', 'correlation')
-
@tasks('classification', 'regression')
-
-
def
-
HighPearsonCorrelation ( dataset : validmind.vm_models.VMDataset max_threshold : float = 0.3 , top_n_correlations : int = 10 , feature_columns : list = None ):
-
-
-
Identifies highly correlated feature pairs in a dataset suggesting feature redundancy or multicollinearity.
-
-
Purpose
-
-
The High Pearson Correlation test measures the linear relationship between features in a dataset, with the main
-goal of identifying high correlations that might indicate feature redundancy or multicollinearity. Identification
-of such issues allows developers and risk management teams to properly deal with potential impacts on the machine
-learning model's performance and interpretability.
-
-
Test Mechanism
-
-
The test works by generating pairwise Pearson correlations for all features in the dataset, then sorting and
-eliminating duplicate and self-correlations. It assigns a Pass or Fail based on whether the absolute value of the
-correlation coefficient surpasses a pre-set threshold (defaulted at 0.3). It lastly returns the top n strongest
-correlations regardless of passing or failing status (where n is 10 by default but can be configured by passing the
-top_n_correlations parameter).
-
-
Signs of High Risk
-
-
-A high risk indication would be the presence of correlation coefficients exceeding the threshold.
-If the features share a strong linear relationship, this could lead to potential multicollinearity and model
-overfitting.
-Redundancy of variables can undermine the interpretability of the model due to uncertainty over the authenticity
-of individual variable's predictive power.
-
-
-
Strengths
-
-
-Provides a quick and simple means of identifying relationships between feature pairs.
-Generates a transparent output that displays pairs of correlated variables, the Pearson correlation coefficient,
-and a Pass or Fail status for each.
-Aids in early identification of potential multicollinearity issues that may disrupt model training.
-
-
-
Limitations
-
-
-Can only delineate linear relationships, failing to shed light on nonlinear relationships or dependencies.
-Sensitive to outliers where a few outliers could notably affect the correlation coefficient.
-Limited to identifying redundancy only within feature pairs; may fail to spot more complex relationships among
-three or more variables.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/IQROutliersBarPlot.html b/docs/_build/validmind/tests/data_validation/IQROutliersBarPlot.html
deleted file mode 100644
index 3d267e5b9..000000000
--- a/docs/_build/validmind/tests/data_validation/IQROutliersBarPlot.html
+++ /dev/null
@@ -1,328 +0,0 @@
-
-
-
- validmind.tests.data_validation.IQROutliersBarPlot API documentation
-
-
-
-
-
-
-
- def
- compute_outliers (series , threshold ):
-
-
-
-
-
-
-
-
-
-
-
@tags('tabular_data', 'visualization', 'numerical_data')
-
@tasks('classification', 'regression')
-
-
def
-
IQROutliersBarPlot ( dataset : validmind.vm_models.VMDataset threshold : float = 1.5 , fig_width : int = 800 ):
-
-
-
Visualizes outlier distribution across percentiles in numerical data using the Interquartile Range (IQR) method.
-
-
Purpose
-
-
The InterQuartile Range Outliers Bar Plot (IQROutliersBarPlot) metric aims to visually analyze and evaluate the
-extent of outliers in numeric variables based on percentiles. Its primary purpose is to clarify the dataset's
-distribution, flag possible abnormalities in it, and gauge potential risks associated with processing potentially
-skewed data, which can affect the machine learning model's predictive prowess.
-
-
Test Mechanism
-
-
The examination invokes a series of steps:
-
-
-For every numeric feature in the dataset, the 25th percentile (Q1) and 75th percentile (Q3) are calculated
-before deriving the Interquartile Range (IQR), the difference between Q1 and Q3.
-Subsequently, the metric calculates the lower and upper thresholds by subtracting Q1 from the threshold times
-IQR and adding Q3 to threshold times IQR, respectively. The default threshold is set at 1.5.
-Any value in the feature that falls below the lower threshold or exceeds the upper threshold is labeled as an
-outlier.
-The number of outliers are tallied for different percentiles, such as [0-25], [25-50], [50-75], and [75-100].
-These counts are employed to construct a bar plot for the feature, showcasing the distribution of outliers
-across different percentiles.
-
-
-
Signs of High Risk
-
-
-A prevalence of outliers in the data, potentially skewing its distribution.
-Outliers dominating higher percentiles (75-100) which implies the presence of extreme values, capable of severely
-influencing the model's performance.
-Certain features harboring most of their values as outliers, which signifies that these features might not
-contribute positively to the model's forecasting ability.
-
-
-
Strengths
-
-
-Effectively identifies outliers in the data through visual means, facilitating easier comprehension and offering
-insights into the outliers' possible impact on the model.
-Provides flexibility by accommodating all numeric features or a chosen subset.
-Task-agnostic in nature; it is viable for both classification and regression tasks.
-Can handle large datasets as its operation does not hinge on computationally heavy operations.
-
-
-
Limitations
-
-
-Its application is limited to numerical variables and does not extend to categorical ones.
-Only reveals the presence and distribution of outliers and does not provide insights into how these outliers
-might affect the model's predictive performance.
-The assumption that data is unimodal and symmetric may not always hold true. In cases with non-normal
-distributions, the results can be misleading.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/IQROutliersTable.html b/docs/_build/validmind/tests/data_validation/IQROutliersTable.html
deleted file mode 100644
index f879f7a71..000000000
--- a/docs/_build/validmind/tests/data_validation/IQROutliersTable.html
+++ /dev/null
@@ -1,320 +0,0 @@
-
-
-
- validmind.tests.data_validation.IQROutliersTable API documentation
-
-
-
-
-
-
-
- def
- compute_outliers (series , threshold = 1.5 ):
-
-
-
-
-
-
-
-
-
-
-
@tags('tabular_data', 'numerical_data')
-
@tasks('classification', 'regression')
-
-
def
-
IQROutliersTable ( dataset : validmind.vm_models.VMDataset threshold : float = 1.5 ):
-
-
-
Determines and summarizes outliers in numerical features using the Interquartile Range method.
-
-
Purpose
-
-
The "Interquartile Range Outliers Table" (IQROutliersTable) metric is designed to identify and summarize outliers
-within numerical features of a dataset using the Interquartile Range (IQR) method. This exercise is crucial in the
-pre-processing of data because outliers can substantially distort statistical analysis and impact the performance
-of machine learning models.
-
-
Test Mechanism
-
-
The IQR, which is the range separating the first quartile (25th percentile) from the third quartile (75th
-percentile), is calculated for each numerical feature within the dataset. An outlier is defined as a data point
-falling below the "Q1 - 1.5 * IQR" or above "Q3 + 1.5 * IQR" range. The test computes the number of outliers and
-their summary statistics (minimum, 25th percentile, median, 75th percentile, and maximum values) for each numerical
-feature. If no specific features are chosen, the test applies to all numerical features in the dataset. The default
-outlier threshold is set to 1.5 but can be customized by the user.
-
-
Signs of High Risk
-
-
-A large number of outliers in multiple features.
-Outliers significantly distanced from the mean value of variables.
-Extremely high or low outlier values indicative of data entry errors or other data quality issues.
-
-
-
Strengths
-
-
-Provides a comprehensive summary of outliers for each numerical feature, helping pinpoint features with potential
-quality issues.
-The IQR method is robust to extremely high or low outlier values as it is based on quartile calculations.
-Can be customized to work on selected features and set thresholds for outliers.
-
-
-
Limitations
-
-
-Might cause false positives if the variable deviates from a normal or near-normal distribution, especially for
-skewed distributions.
-Does not provide interpretation or recommendations for addressing outliers, relying on further analysis by users
-or data scientists.
-Only applicable to numerical features, not categorical data.
-Default thresholds may not be optimal for data with heavy pre-processing, manipulation, or inherently high
-kurtosis (heavy tails).
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/IsolationForestOutliers.html b/docs/_build/validmind/tests/data_validation/IsolationForestOutliers.html
deleted file mode 100644
index 77af84442..000000000
--- a/docs/_build/validmind/tests/data_validation/IsolationForestOutliers.html
+++ /dev/null
@@ -1,303 +0,0 @@
-
-
-
- validmind.tests.data_validation.IsolationForestOutliers API documentation
-
-
-
-
-
-
-
@tags('tabular_data', 'anomaly_detection')
-
@tasks('classification')
-
-
def
-
IsolationForestOutliers ( dataset : validmind.vm_models.VMDataset random_state : int = 0 , contamination : float = 0.1 , feature_columns : list = None ):
-
-
-
Detects outliers in a dataset using the Isolation Forest algorithm and visualizes results through scatter plots.
-
-
Purpose
-
-
The IsolationForestOutliers test is designed to identify anomalies or outliers in the model's dataset using the
-isolation forest algorithm. This algorithm assumes that anomalous data points can be isolated more quickly due to
-their distinctive properties. By creating isolation trees and identifying instances with shorter average path
-lengths, the test is able to pick out data points that differ from the majority.
-
-
Test Mechanism
-
-
The test uses the isolation forest algorithm, which builds an ensemble of isolation trees by randomly selecting
-features and splitting the data based on random thresholds. It isolates anomalies rather than focusing on normal
-data points. For each pair of variables, a scatter plot is generated which distinguishes the identified outliers
-from the inliers. The results of the test can be visualized using these scatter plots, illustrating the distinction
-between outliers and inliers.
-
-
Signs of High Risk
-
-
-The presence of high contamination, indicating a large number of anomalies
-Inability to detect clusters of anomalies that are close in the feature space
-Misclassifying normal instances as anomalies
-Failure to detect actual anomalies
-
-
-
Strengths
-
-
-Ability to handle large, high-dimensional datasets
-Efficiency in isolating anomalies instead of normal instances
-Insensitivity to the underlying distribution of data
-Ability to recognize anomalies even when they are not separated from the main data cloud through identifying
-distinctive properties
-Visually presents the test results for better understanding and interpretability
-
-
-
Limitations
-
-
-Difficult to detect anomalies that are close to each other or prevalent in datasets
-Dependency on the contamination parameter which may need fine-tuning to be effective
-Potential failure in detecting collective anomalies if they behave similarly to normal data
-Potential lack of precision in identifying which features contribute most to the anomalous behavior
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/JarqueBera.html b/docs/_build/validmind/tests/data_validation/JarqueBera.html
deleted file mode 100644
index 738bfee55..000000000
--- a/docs/_build/validmind/tests/data_validation/JarqueBera.html
+++ /dev/null
@@ -1,301 +0,0 @@
-
-
-
- validmind.tests.data_validation.JarqueBera API documentation
-
-
-
-
-
-
-
@tasks('classification', 'regression')
-
@tags('tabular_data', 'data_distribution', 'statistical_test', 'statsmodels')
-
-
def
-
JarqueBera (dataset ):
-
-
-
Assesses normality of dataset features in an ML model using the Jarque-Bera test.
-
-
Purpose
-
-
The purpose of the Jarque-Bera test as implemented in this metric is to determine if the features in the dataset of
-a given Machine Learning model follow a normal distribution. This is crucial for understanding the distribution and
-behavior of the model's features, as numerous statistical methods assume normal distribution of the data.
-
-
Test Mechanism
-
-
The test mechanism involves computing the Jarque-Bera statistic, p-value, skew, and kurtosis for each feature in
-the dataset. It utilizes the 'jarque_bera' function from the 'statsmodels' library in Python, storing the results
-in a dictionary. The test evaluates the skewness and kurtosis to ascertain whether the dataset follows a normal
-distribution. A significant p-value (typically less than 0.05) implies that the data does not possess normal
-distribution.
-
-
Signs of High Risk
-
-
-A high Jarque-Bera statistic and a low p-value (usually less than 0.05) indicate high-risk conditions.
-Such results suggest the data significantly deviates from a normal distribution. If a machine learning model
-expects feature data to be normally distributed, these findings imply that it may not function as intended.
-
-
-
Strengths
-
-
-Provides insights into the shape of the data distribution, helping determine whether a given set of data follows
-a normal distribution.
-Particularly useful for risk assessment for models that assume a normal distribution of data.
-By measuring skewness and kurtosis, it provides additional insights into the nature and magnitude of a
-distribution's deviation.
-
-
-
Limitations
-
-
-Only checks for normality in the data distribution. It cannot provide insights into other types of distributions.
-Datasets that aren't normally distributed but follow some other distribution might lead to inaccurate risk
-assessments.
-Highly sensitive to large sample sizes, often rejecting the null hypothesis (that data is normally distributed)
-even for minor deviations in larger datasets.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/KPSS.html b/docs/_build/validmind/tests/data_validation/KPSS.html
deleted file mode 100644
index 65f0956e7..000000000
--- a/docs/_build/validmind/tests/data_validation/KPSS.html
+++ /dev/null
@@ -1,300 +0,0 @@
-
-
-
- validmind.tests.data_validation.KPSS API documentation
-
-
-
-
-
-
-
@tags('time_series_data', 'stationarity', 'unit_root_test', 'statsmodels')
-
@tasks('data_validation')
-
-
def
-
KPSS (dataset : validmind.vm_models.VMDataset ):
-
-
-
Assesses the stationarity of time-series data in a machine learning model using the KPSS unit root test.
-
-
Purpose
-
-
The KPSS (Kwiatkowski-Phillips-Schmidt-Shin) unit root test is utilized to ensure the stationarity of data within a
-machine learning model. It specifically works on time-series data to establish the order of integration, which is
-essential for accurate forecasting. A fundamental requirement for any time series model is that the series should
-be stationary.
-
-
Test Mechanism
-
-
This test calculates the KPSS score for each feature in the dataset. The KPSS score includes a statistic, a
-p-value, a used lag, and critical values. The core principle behind the KPSS test is to evaluate the hypothesis
-that an observable time series is stationary around a deterministic trend. If the computed statistic exceeds the
-critical value, the null hypothesis (that the series is stationary) is rejected, indicating that the series is
-non-stationary.
-
-
Signs of High Risk
-
-
-High KPSS score, particularly if the calculated statistic is higher than the critical value.
-Rejection of the null hypothesis, indicating that the series is recognized as non-stationary, can severely affect
-the model's forecasting capability.
-
-
-
Strengths
-
-
-Directly measures the stationarity of a series, fulfilling a key prerequisite for many time-series models.
-The underlying logic of the test is intuitive and simple, making it easy to understand and accessible for both
-developers and risk management teams.
-
-
-
Limitations
-
-
-Assumes the absence of a unit root in the series and doesn't differentiate between series that are stationary and
-those border-lining stationarity.
-The test may have restricted power against certain alternatives.
-The reliability of the test is contingent on the number of lags selected, which introduces potential bias in the
-measurement.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/LJungBox.html b/docs/_build/validmind/tests/data_validation/LJungBox.html
deleted file mode 100644
index 370d7c001..000000000
--- a/docs/_build/validmind/tests/data_validation/LJungBox.html
+++ /dev/null
@@ -1,299 +0,0 @@
-
-
-
- validmind.tests.data_validation.LJungBox API documentation
-
-
-
-
-
-
-
@tasks('regression')
-
@tags('time_series_data', 'forecasting', 'statistical_test', 'statsmodels')
-
-
def
-
LJungBox (dataset ):
-
-
-
Assesses autocorrelations in dataset features by performing a Ljung-Box test on each feature.
-
-
Purpose
-
-
The Ljung-Box test is a type of statistical test utilized to ascertain whether there are autocorrelations within a
-given dataset that differ significantly from zero. In the context of a machine learning model, this test is
-primarily used to evaluate data utilized in regression tasks, especially those involving time series and
-forecasting.
-
-
Test Mechanism
-
-
The test operates by iterating over each feature within the dataset and applying the acorr_ljungbox
-function from the statsmodels.stats.diagnostic library. This function calculates the Ljung-Box statistic and
-p-value for each feature. These results are then stored in a pandas DataFrame where the columns are the feature names,
-statistic, and p-value respectively. Generally, a lower p-value indicates a higher likelihood of significant
-autocorrelations within the feature.
-
-
Signs of High Risk
-
-
-High Ljung-Box statistic values or low p-values.
-Presence of significant autocorrelations in the respective features.
-Potential for negative impact on model performance or bias if autocorrelations are not properly handled.
-
-
-
Strengths
-
-
-Powerful tool for detecting autocorrelations within datasets, especially in time series data.
-Provides quantitative measures (statistic and p-value) for precise evaluation.
-Helps avoid issues related to autoregressive residuals and other challenges in regression models.
-
-
-
Limitations
-
-
-Cannot detect all types of non-linearity or complex interrelationships among variables.
-Testing individual features may not fully encapsulate the dynamics of the data if features interact with each other.
-Designed more for traditional statistical models and may not be fully compatible with certain types of complex
-machine learning models.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/LaggedCorrelationHeatmap.html b/docs/_build/validmind/tests/data_validation/LaggedCorrelationHeatmap.html
deleted file mode 100644
index 87aef69db..000000000
--- a/docs/_build/validmind/tests/data_validation/LaggedCorrelationHeatmap.html
+++ /dev/null
@@ -1,304 +0,0 @@
-
-
-
- validmind.tests.data_validation.LaggedCorrelationHeatmap API documentation
-
-
-
-
-
-
-
@tags('time_series_data', 'visualization')
-
@tasks('regression')
-
-
def
-
LaggedCorrelationHeatmap ( dataset : validmind.vm_models.VMDataset num_lags : int = 10 ):
-
-
-
Assesses and visualizes correlation between target variable and lagged independent variables in a time-series
-dataset.
-
-
Purpose
-
-
The LaggedCorrelationHeatmap metric is utilized to appraise and illustrate the correlation between the target
-variable and delayed copies (lags) of independent variables in a time-series dataset. It assists in revealing
-relationships in time-series data where the influence of an independent variable on the dependent variable is not
-immediate but occurs after a period (lags).
-
-
Test Mechanism
-
-
To execute this test, Python's Pandas library pairs with Plotly to perform computations and present the
-visualization in the form of a heatmap. The test begins by extracting the target variable and corresponding
-independent variables from the dataset. Then, generation of lags of independent variables takes place, followed by
-the calculation of correlation between these lagged variables and the target variable. The outcome is a correlation
-matrix that gets recorded and illustrated as a heatmap, where different color intensities represent the strength of
-the correlation, making patterns easier to identify.
-
-
Signs of High Risk
-
-
-Insignificant correlations across the heatmap, indicating a lack of noteworthy relationships between variables.
-Correlations that break intuition or previous understanding, suggesting potential issues with the dataset or the
-model.
-
-
-
Strengths
-
-
-This metric serves as an exceptional tool for exploring and visualizing time-dependent relationships between
-features and the target variable in a time-series dataset.
-It aids in identifying delayed effects that might go unnoticed with other correlation measures.
-The heatmap offers an intuitive visual representation of time-dependent correlations and influences.
-
-
-
Limitations
-
-
-The metric presumes linear relationships between variables, potentially ignoring non-linear relationships.
-The correlation considered is linear; therefore, intricate non-linear interactions might be overlooked.
-The metric is only applicable for time-series data, limiting its utility outside of this context.
-The number of lags chosen can significantly influence the results; too many lags can render the heatmap difficult
-to interpret, while too few might overlook delayed effects.
-This metric does not take into account any causal relationships, but merely demonstrates correlation.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/MissingValues.html b/docs/_build/validmind/tests/data_validation/MissingValues.html
deleted file mode 100644
index dbb5de5a9..000000000
--- a/docs/_build/validmind/tests/data_validation/MissingValues.html
+++ /dev/null
@@ -1,299 +0,0 @@
-
-
-
- validmind.tests.data_validation.MissingValues API documentation
-
-
-
-
-
-
-
@tags('tabular_data', 'data_quality')
-
@tasks('classification', 'regression')
-
-
def
-
MissingValues ( dataset : validmind.vm_models.VMDataset min_threshold : int = 1 ):
-
-
-
Evaluates dataset quality by ensuring missing value ratio across all features does not exceed a set threshold.
-
-
Purpose
-
-
The Missing Values test is designed to evaluate the quality of a dataset by measuring the number of missing values
-across all features. The objective is to ensure that the ratio of missing data to total data is less than a
-predefined threshold, defaulting to 1, in order to maintain the data quality necessary for reliable predictive
-strength in a machine learning model.
-
-
Test Mechanism
-
-
The mechanism for this test involves iterating through each column of the dataset, counting missing values
-(represented as NaNs), and calculating the percentage they represent against the total number of rows. The test
-then checks if these missing value counts are less than the predefined min_threshold. The results are shown in a
-table summarizing each column, the number of missing values, the percentage of missing values in each column, and a
-Pass/Fail status based on the threshold comparison.
-
-
Signs of High Risk
-
-
-When the number of missing values in any column exceeds the min_threshold value.
-Presence of missing values across many columns, leading to multiple instances of failing the threshold.
-
-
-
Strengths
-
-
-Quick and granular identification of missing data across each feature in the dataset.
-Provides an effective and straightforward means of maintaining data quality, essential for constructing efficient
-machine learning models.
-
-
-
Limitations
-
-
-Does not suggest the root causes of the missing values or recommend ways to impute or handle them.
-May overlook features with significant missing data but still less than the min_threshold, potentially
-impacting the model.
-Does not account for data encoded as values like "-999" or "None," which might not technically classify as
-missing but could bear similar implications.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/MissingValuesBarPlot.html b/docs/_build/validmind/tests/data_validation/MissingValuesBarPlot.html
deleted file mode 100644
index 89a214568..000000000
--- a/docs/_build/validmind/tests/data_validation/MissingValuesBarPlot.html
+++ /dev/null
@@ -1,306 +0,0 @@
-
-
-
- validmind.tests.data_validation.MissingValuesBarPlot API documentation
-
-
-
-
-
-
-
@tags('tabular_data', 'data_quality', 'visualization')
-
@tasks('classification', 'regression')
-
-
def
-
MissingValuesBarPlot ( dataset : validmind.vm_models.VMDataset threshold : int = 80 , fig_height : int = 600 ):
-
-
-
Assesses the percentage and distribution of missing values in the dataset via a bar plot, with emphasis on
-identifying high-risk columns based on a user-defined threshold.
-
-
Purpose
-
-
The 'MissingValuesBarPlot' metric provides a color-coded visual representation of the percentage of missing values
-for each column in an ML model's dataset. The primary purpose of this metric is to easily identify and quantify
-missing data, which are essential steps in data preprocessing. The presence of missing data can potentially skew
-the model's predictions and decrease its accuracy. Additionally, this metric uses a pre-set threshold to categorize
-various columns into ones that contain missing data above the threshold (high risk) and below the threshold (less
-risky).
-
-
Test Mechanism
-
-
The test mechanism involves scanning each column in the input dataset and calculating the percentage of missing
-values. It then compares each column's missing data percentage with the predefined threshold, categorizing columns
-with missing data above the threshold as high-risk. The test generates a bar plot in which columns with missing
-data are represented on the y-axis and their corresponding missing data percentages are displayed on the x-axis.
-The color of each bar reflects the missing data percentage in relation to the threshold: grey for values below the
-threshold and light coral for those exceeding it. The user-defined threshold is represented by a red dashed line on
-the plot.
-
-
Signs of High Risk
-
-
-Columns with higher percentages of missing values beyond the threshold are high-risk. These are visually
-represented by light coral bars on the bar plot.
-
-
-
Strengths
-
-
-Helps in quickly identifying and quantifying missing data across all columns of the dataset.
-Facilitates pattern recognition through visual representation.
-Enables customization of the level of risk tolerance via a user-defined threshold.
-Supports both classification and regression tasks, sharing its versatility.
-
-
-
Limitations
-
-
-It only considers the quantity of missing values, not differentiating between different types of missingness
-(Missing completely at random - MCAR, Missing at random - MAR, Not Missing at random - NMAR).
-It doesn't offer insights into potential approaches for handling missing entries, such as various imputation
-strategies.
-The metric does not consider possible impacts of the missing data on the model's accuracy or precision.
-Interpretation of the findings and the next steps might require an expert understanding of the field.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/MutualInformation.html b/docs/_build/validmind/tests/data_validation/MutualInformation.html
deleted file mode 100644
index d0d0d8b29..000000000
--- a/docs/_build/validmind/tests/data_validation/MutualInformation.html
+++ /dev/null
@@ -1,312 +0,0 @@
-
-
-
- validmind.tests.data_validation.MutualInformation API documentation
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/PearsonCorrelationMatrix.html b/docs/_build/validmind/tests/data_validation/PearsonCorrelationMatrix.html
deleted file mode 100644
index 2a624745e..000000000
--- a/docs/_build/validmind/tests/data_validation/PearsonCorrelationMatrix.html
+++ /dev/null
@@ -1,301 +0,0 @@
-
-
-
- validmind.tests.data_validation.PearsonCorrelationMatrix API documentation
-
-
-
-
-
-
-
@tags('tabular_data', 'numerical_data', 'correlation')
-
@tasks('classification', 'regression')
-
-
def
-
PearsonCorrelationMatrix (dataset ):
-
-
-
Evaluates linear dependency between numerical variables in a dataset via a Pearson Correlation coefficient heat map.
-
-
Purpose
-
-
This test is intended to evaluate the extent of linear dependency between all pairs of numerical variables in the
-given dataset. It provides the Pearson Correlation coefficient, which reveals any high correlations present. The
-purpose of doing this is to identify potential redundancy, as variables that are highly correlated can often be
-removed to reduce the dimensionality of the dataset without significantly impacting the model's performance.
-
-
Test Mechanism
-
-
This metric test generates a correlation matrix for all numerical variables in the dataset using the Pearson
-correlation formula. A heat map is subsequently created to visualize this matrix effectively. The color of each
-point on the heat map corresponds to the magnitude and direction (positive or negative) of the correlation, with a
-range from -1 (perfect negative correlation) to 1 (perfect positive correlation). Any correlation coefficients
-higher than 0.7 (in absolute terms) are indicated in white in the heat map, suggesting a high degree of correlation.
-
-
Signs of High Risk
-
-
-A large number of variables in the dataset showing a high degree of correlation (coefficients approaching ±1).
-This indicates redundancy within the dataset, suggesting that some variables may not be contributing new
-information to the model.
-Potential risk of overfitting.
-
-
-
Strengths
-
-
-Detects and quantifies the linearity of relationships between variables, aiding in identifying redundant
-variables to simplify models and potentially improve performance.
-The heatmap visualization provides an easy-to-understand overview of correlations, beneficial for users not
-comfortable with numerical matrices.
-
-
-
Limitations
-
-
-Limited to detecting linear relationships, potentially missing non-linear relationships which impede
-opportunities for dimensionality reduction.
-Measures only the degree of linear relationship, not the strength of one variable's effect on another.
-The 0.7 correlation threshold is arbitrary and might exclude valid dependencies with lower coefficients.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/PhillipsPerronArch.html b/docs/_build/validmind/tests/data_validation/PhillipsPerronArch.html
deleted file mode 100644
index 21847dbc6..000000000
--- a/docs/_build/validmind/tests/data_validation/PhillipsPerronArch.html
+++ /dev/null
@@ -1,304 +0,0 @@
-
-
-
- validmind.tests.data_validation.PhillipsPerronArch API documentation
-
-
-
-
-
-
-
@tags('time_series_data', 'forecasting', 'statistical_test', 'unit_root_test')
-
@tasks('regression')
-
-
def
-
PhillipsPerronArch (dataset : validmind.vm_models.VMDataset ):
-
-
-
Assesses the stationarity of time series data in each feature of the ML model using the Phillips-Perron test.
-
-
Purpose
-
-
The Phillips-Perron (PP) test is used to determine the stationarity of time series data for each feature in a
-dataset, which is crucial for forecasting tasks. It tests the null hypothesis that a time series is unit-root
-non-stationary. This is vital for understanding the stochastic behavior of the data and ensuring the robustness and
-validity of predictions generated by regression analysis models.
-
-
Test Mechanism
-
-
The PP test is conducted for each feature in the dataset as follows:
-
-
-A data frame is created from the dataset.
-For each column, the Phillips-Perron method calculates the test statistic, p-value, lags used, and number of
-observations.
-The results are then stored for each feature, providing a metric that indicates the stationarity of the time
-series data.
-
-
-
Signs of High Risk
-
-
-A high p-value, indicating that the series has a unit root and is non-stationary.
-Test statistic values exceeding critical values, suggesting non-stationarity.
-High 'usedlag' value, pointing towards autocorrelation issues that may degrade model performance.
-
-
-
Strengths
-
-
-Resilience against heteroskedasticity in the error term.
-Effective for long time series data.
-Helps in determining whether the time series is stationary, aiding in the selection of suitable forecasting
-models.
-
-
-
Limitations
-
-
-Applicable only within a univariate time series framework.
-Relies on asymptotic theory, which may reduce the test’s power for small sample sizes.
-Non-stationary time series must be converted to stationary series through differencing, potentially leading to
-loss of important data points.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/ProtectedClassesCombination.html b/docs/_build/validmind/tests/data_validation/ProtectedClassesCombination.html
deleted file mode 100644
index c311b3449..000000000
--- a/docs/_build/validmind/tests/data_validation/ProtectedClassesCombination.html
+++ /dev/null
@@ -1,299 +0,0 @@
-
-
-
- validmind.tests.data_validation.ProtectedClassesCombination API documentation
-
-
-
-
-
-
-
@tags('bias_and_fairness')
-
@tasks('classification', 'regression')
-
-
def
-
ProtectedClassesCombination (dataset , model , protected_classes = None ):
-
-
-
Visualizes combinations of protected classes and their corresponding error metric differences.
-
-
Purpose
-
-
This test aims to provide insights into how different combinations of protected classes affect various error metrics,
-particularly the false negative rate (FNR) and false positive rate (FPR). By visualizing these combinations,
-it helps identify potential biases or disparities in model performance across different intersectional groups.
-
-
Test Mechanism
-
-
The test performs the following steps:
-
-
-Combines the specified protected class columns to create a single multi-class category.
-Calculates error metrics (FNR, FPR, etc.) for each combination of protected classes.
-Generates visualizations showing the distribution of these metrics across all class combinations.
-
-
-
Signs of High Risk
-
-
-Large disparities in FNR or FPR across different protected class combinations.
-Consistent patterns of higher error rates for specific combinations of protected attributes.
-Unexpected or unexplainable variations in error metrics between similar group combinations.
-
-
-
Strengths
-
-
-Provides a comprehensive view of intersectional fairness across multiple protected attributes.
-Allows for easy identification of potentially problematic combinations of protected classes.
-Visualizations make it easier to spot patterns or outliers in model performance across groups.
-
-
-
Limitations
-
-
-May become complex and difficult to interpret with a large number of protected classes or combinations.
-Does not provide statistical significance of observed differences.
-Visualization alone may not capture all nuances of intersectional fairness.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/ProtectedClassesDescription.html b/docs/_build/validmind/tests/data_validation/ProtectedClassesDescription.html
deleted file mode 100644
index 1854c2c65..000000000
--- a/docs/_build/validmind/tests/data_validation/ProtectedClassesDescription.html
+++ /dev/null
@@ -1,308 +0,0 @@
-
-
-
- validmind.tests.data_validation.ProtectedClassesDescription API documentation
-
-
-
-
-
-
-
@tags('bias_and_fairness', 'descriptive_statistics')
-
@tasks('classification', 'regression')
-
-
def
-
ProtectedClassesDescription (dataset , protected_classes = None ):
-
-
-
Visualizes the distribution of protected classes in the dataset relative to the target variable
-and provides descriptive statistics.
-
-
Purpose
-
-
The ProtectedClassesDescription test aims to identify potential biases or significant differences in the
-distribution of target outcomes across different protected classes. This visualization and statistical summary
-help in understanding the relationship between protected attributes and the target variable, which is crucial
-for assessing fairness in machine learning models.
-
-
Test Mechanism
-
-
The function creates interactive stacked bar charts for each specified protected class using Plotly.
-Additionally, it generates a single table of descriptive statistics for all protected classes, including:
-
-
-Protected class and category
-Count and percentage of each category within the protected class
-Mean, median, and mode of the target variable for each category
-Standard deviation of the target variable for each category
-Minimum and maximum values of the target variable for each category
-
-
-
Signs of High Risk
-
-
-Significant imbalances in the distribution of target outcomes across different categories of a protected class.
-Large disparities in mean, median, or mode of the target variable across categories.
-Underrepresentation or overrepresentation of certain groups within protected classes.
-High standard deviations in certain categories, indicating potential volatility or outliers.
-
-
-
Strengths
-
-
-Provides both visual and statistical representation of potential biases in the dataset.
-Allows for easy identification of imbalances in target variable distribution across protected classes.
-Interactive plots enable detailed exploration of the data.
-Consolidated statistical summary provides quantitative measures to complement visual analysis.
-Applicable to both classification and regression tasks.
-
-
-
Limitations
-
-
-Does not provide advanced statistical measures of bias or fairness.
-May become cluttered if there are many categories within a protected class or many unique target values.
-Interpretation may require domain expertise to understand the implications of observed disparities.
-Does not account for intersectionality or complex interactions between multiple protected attributes.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/ProtectedClassesDisparity.html b/docs/_build/validmind/tests/data_validation/ProtectedClassesDisparity.html
deleted file mode 100644
index 1a6ac579a..000000000
--- a/docs/_build/validmind/tests/data_validation/ProtectedClassesDisparity.html
+++ /dev/null
@@ -1,302 +0,0 @@
-
-
-
- validmind.tests.data_validation.ProtectedClassesDisparity API documentation
-
-
-
-
-
-
-
@tags('bias_and_fairness')
-
@tasks('classification', 'regression')
-
-
def
-
ProtectedClassesDisparity ( dataset , model , protected_classes = None , disparity_tolerance = 1.25 , metrics = [ 'fnr' , 'fpr' , 'tpr' ] ):
-
-
-
Investigates disparities in model performance across different protected class segments.
-
-
Purpose
-
-
This test aims to identify and quantify potential biases in model outcomes by comparing various performance metrics
-across different segments of protected classes. It helps in assessing whether the model produces discriminatory
-outcomes for certain groups, which is crucial for ensuring fairness in machine learning models.
-
-
Test Mechanism
-
-
The test performs the following steps:
-
-
-Calculates performance metrics (e.g., false negative rate, false positive rate, true positive rate) for each segment
-of the specified protected classes.
-Computes disparity ratios by comparing these metrics between different segments and a reference group.
-Generates visualizations showing the disparities and their relation to a user-defined disparity tolerance threshold.
-Produces a comprehensive table with various disparity metrics for detailed analysis.
-
-
-
Signs of High Risk
-
-
-Disparity ratios exceeding the specified disparity tolerance threshold.
-Consistent patterns of higher error rates or lower performance for specific protected class segments.
-Statistically significant differences in performance metrics across segments.
-
-
-
Strengths
-
-
-Provides a comprehensive view of model fairness across multiple protected attributes and metrics.
-Allows for easy identification of problematic disparities through visual and tabular representations.
-Customizable disparity tolerance threshold to align with specific use-case requirements.
-Applicable to various performance metrics, offering a multi-faceted analysis of model fairness.
-
-
-
Limitations
-
-
-Relies on a predefined reference group for each protected class, which may not always be the most appropriate choice.
-Does not account for intersectionality between different protected attributes.
-The interpretation of results may require domain expertise to understand the implications of observed disparities.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/ProtectedClassesThresholdOptimizer.html b/docs/_build/validmind/tests/data_validation/ProtectedClassesThresholdOptimizer.html
deleted file mode 100644
index 389aa50b5..000000000
--- a/docs/_build/validmind/tests/data_validation/ProtectedClassesThresholdOptimizer.html
+++ /dev/null
@@ -1,397 +0,0 @@
-
-
-
- validmind.tests.data_validation.ProtectedClassesThresholdOptimizer API documentation
-
-
-
-
-
-
-
@tags('bias_and_fairness')
-
@tasks('classification', 'regression')
-
-
def
-
ProtectedClassesThresholdOptimizer ( dataset , pipeline = None , protected_classes = None , X_train = None , y_train = None ):
-
-
-
Obtains a classifier by applying group-specific thresholds to the provided estimator.
-
-
Purpose
-
-
This test aims to optimize the fairness of a machine learning model by applying different
-classification thresholds for different protected groups. It helps in mitigating bias and
-achieving more equitable outcomes across different demographic groups.
-
-
Test Mechanism
-
-
The test uses Fairlearn's ThresholdOptimizer to:
-
-
-Fit an optimizer on the training data, considering protected classes.
-Apply optimized thresholds to make predictions on the test data.
-Calculate and report various fairness metrics.
-Visualize the optimized thresholds.
-
-
-
Signs of High Risk
-
-
-Large disparities in fairness metrics (e.g., Demographic Parity Ratio, Equalized Odds Ratio)
-across different protected groups.
-Significant differences in False Positive Rates (FPR) or True Positive Rates (TPR) between groups.
-Thresholds that vary widely across different protected groups.
-
-
-
Strengths
-
-
-Provides a post-processing method to improve model fairness without modifying the original model.
-Allows for balancing multiple fairness criteria simultaneously.
-Offers visual insights into the threshold optimization process.
-
-
-
Limitations
-
-
-May lead to a decrease in overall model performance while improving fairness.
-Requires access to protected attribute information at prediction time.
-The effectiveness can vary depending on the chosen fairness constraint and objective.
-
-
-
-
-
- def
- initialize_and_fit_optimizer (pipeline , X_train , y_train , protected_classes_df ):
-
-
-
-
-
-
-
-
-
-
-
- def
- plot_thresholds (threshold_optimizer ):
-
-
-
-
-
-
-
-
-
-
-
- def
- make_predictions (threshold_optimizer , test_df , protected_classes ):
-
-
-
-
-
-
-
-
-
-
-
- def
- calculate_fairness_metrics (test_df , target , y_pred_opt , protected_classes ):
-
-
-
-
-
-
-
-
-
-
-
- def
- calculate_group_metrics (test_df , target , y_pred_opt , protected_classes ):
-
-
-
-
-
-
-
-
-
-
-
- def
- get_thresholds_by_group (threshold_optimizer ):
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/RollingStatsPlot.html b/docs/_build/validmind/tests/data_validation/RollingStatsPlot.html
deleted file mode 100644
index 1e15857e9..000000000
--- a/docs/_build/validmind/tests/data_validation/RollingStatsPlot.html
+++ /dev/null
@@ -1,327 +0,0 @@
-
-
-
- validmind.tests.data_validation.RollingStatsPlot API documentation
-
-
-
-
-
-
-
- def
- plot_rolling_statistics (df , col , window_size ):
-
-
-
-
-
-
-
-
-
-
-
@tags('time_series_data', 'visualization', 'stationarity')
-
@tasks('regression')
-
-
def
-
RollingStatsPlot ( dataset : validmind.vm_models.VMDataset window_size : int = 12 ):
-
-
-
Evaluates the stationarity of time series data by plotting its rolling mean and standard deviation over a specified
-window.
-
-
Purpose
-
-
The RollingStatsPlot
-
-
Test Mechanism
-
-
This mechanism is comprised of two steps. Initially, the rolling mean and standard deviation for each of the
-dataset's columns are calculated over a window size, which can be user-specified or by default set to 12 data
-points. Then, the calculated rolling mean and standard deviation are visualized via separate plots, illustrating
-the trends and volatility in the dataset. A straightforward check is conducted to ensure the existence of columns
-in the dataset, and to verify that the given dataset has been indexed by its date and time—a necessary prerequisite
-for time series analysis.
-
-
Signs of High Risk
-
-
-The presence of non-stationary patterns in either the rolling mean or the rolling standard deviation plots, which
-could indicate trends or seasonality in the data that may affect the performance of time series models.
-Missing columns in the dataset, which would prevent the execution of this metric correctly.
-The detection of NaN values in the dataset, which may need to be addressed before the metric can proceed
-successfully.
-
-
-
Strengths
-
-
-Offers visualizations of trending behavior and volatility within the data, facilitating a broader understanding
-of the dataset's inherent characteristics.
-Checks of the dataset's integrity, such as the existence of all required columns and the availability of a
-datetime index.
-Adjusts to accommodate various window sizes, thus allowing accurate analysis of data with differing temporal
-granularities.
-Considers each column of the data individually, thereby accommodating multi-feature datasets.
-
-
-
Limitations
-
-
-For all columns, a fixed-size window is utilized. This may not accurately capture patterns in datasets where
-different features may require different optimal window sizes.
-Requires the dataset to be indexed by date and time, hence it may not be usable for datasets without a timestamp
-index.
-Primarily serves for data visualization as it does not facilitate any quantitative measures for stationarity,
-such as through statistical tests. Therefore, the interpretation is subjective and depends heavily on modeler
-discretion.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/RunsTest.html b/docs/_build/validmind/tests/data_validation/RunsTest.html
deleted file mode 100644
index 13bce1013..000000000
--- a/docs/_build/validmind/tests/data_validation/RunsTest.html
+++ /dev/null
@@ -1,305 +0,0 @@
-
-
-
- validmind.tests.data_validation.RunsTest API documentation
-
-
-
-
-
-
-
@tasks('classification', 'regression')
-
@tags('tabular_data', 'statistical_test', 'statsmodels')
-
-
def
-
RunsTest (dataset ):
-
-
-
Executes Runs Test on ML model to detect non-random patterns in output data sequence.
-
-
Purpose
-
-
The Runs Test is a statistical procedure used to determine whether the sequence of data extracted from the ML model
-behaves randomly or not. Specifically, it analyzes runs, sequences of consecutive positives or negatives, in the
-data to check if there are more or fewer runs than expected under the assumption of randomness. This can be an
-indication of some pattern, trend, or cycle in the model's output which may need attention.
-
-
Test Mechanism
-
-
The testing mechanism applies the Runs Test from the statsmodels module on each column of the training dataset. For
-every feature in the dataset, a Runs Test is executed, whose output includes a Runs Statistic and P-value. A low
-P-value suggests that data arrangement in the feature is not likely to be random. The results are stored in a
-dictionary where the keys are the feature names, and the values are another dictionary storing the test statistic
-and the P-value for each feature.
-
-
Signs of High Risk
-
-
-High risk is indicated when the P-value is close to zero.
-If the P-value is less than a predefined significance level (like 0.05), it suggests that the runs (series of
-positive or negative values) in the model's output are not random and are longer or shorter than what is expected
-under a random scenario.
-This would mean there's a high risk of non-random distribution of errors or model outcomes, suggesting potential
-issues with the model.
-
-
-
Strengths
-
-
-Straightforward and fast for detecting non-random patterns in data sequence.
-Validates assumptions of randomness, which is valuable for checking error distributions in regression models,
-trendless time series data, and ensuring a classifier doesn't favor one class over another.
-Can be applied to both classification and regression tasks, making it versatile.
-
-
-
Limitations
-
-
-Assumes that the data is independently and identically distributed (i.i.d.), which might not be the case for many
-real-world datasets.
-The conclusion drawn from the low P-value indicating non-randomness does not provide information about the type
-or the source of the detected pattern.
-Sensitive to extreme values (outliers), and overly large or small run sequences can influence the results.
-Does not provide model performance evaluation; it is used to detect patterns in the sequence of outputs only.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/ScatterPlot.html b/docs/_build/validmind/tests/data_validation/ScatterPlot.html
deleted file mode 100644
index 9d2e8bf1a..000000000
--- a/docs/_build/validmind/tests/data_validation/ScatterPlot.html
+++ /dev/null
@@ -1,306 +0,0 @@
-
-
-
- validmind.tests.data_validation.ScatterPlot API documentation
-
-
-
-
-
-
-
@tags('tabular_data', 'visualization')
-
@tasks('classification', 'regression')
-
-
def
-
ScatterPlot (dataset ):
-
-
-
Assesses visual relationships, patterns, and outliers among features in a dataset through scatter plot matrices.
-
-
Purpose
-
-
The ScatterPlot test aims to visually analyze a given dataset by constructing a scatter plot matrix of its
-numerical features. The primary goal is to uncover relationships, patterns, and outliers across different features
-to provide both quantitative and qualitative insights into multidimensional relationships within the dataset. This
-visual assessment aids in understanding the efficacy of the chosen features for model training and their
-suitability.
-
-
Test Mechanism
-
-
Using the Seaborn library, the ScatterPlot function creates the scatter plot matrix. The process involves
-retrieving all numerical columns from the dataset and generating a scatter matrix for these columns. The resulting
-scatter plot provides visual representations of feature relationships. The function also adjusts axis labels for
-readability and returns the final plot as a Matplotlib Figure object for further analysis and visualization.
-
-
Signs of High Risk
-
-
-The emergence of non-linear or random patterns across different feature pairs, suggesting complex relationships
-unsuitable for linear assumptions.
-Lack of clear patterns or clusters, indicating weak or non-existent correlations among features, which could
-challenge certain model types.
-Presence of outliers, as visual outliers can adversely influence the model's performance.
-
-
-
Strengths
-
-
-Provides insight into the multidimensional relationships among multiple features.
-Assists in identifying trends, correlations, and outliers that could affect model performance.
-Validates assumptions made during model creation, such as linearity.
-Versatile for application in both regression and classification tasks.
-Using Seaborn facilitates an intuitive and detailed visual exploration of data.
-
-
-
Limitations
-
-
-Scatter plot matrices may become cluttered and hard to decipher as the number of features increases.
-Primarily reveals pairwise relationships and may fail to illuminate complex interactions involving three or more
-features.
-Being a visual tool, precision in quantitative analysis might be compromised.
-Outliers not clearly visible in plots can be missed, affecting model performance.
-Assumes that the dataset can fit into the computer's memory, which might not be valid for extremely large
-datasets.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/ScoreBandDefaultRates.html b/docs/_build/validmind/tests/data_validation/ScoreBandDefaultRates.html
deleted file mode 100644
index e42be0748..000000000
--- a/docs/_build/validmind/tests/data_validation/ScoreBandDefaultRates.html
+++ /dev/null
@@ -1,315 +0,0 @@
-
-
-
- validmind.tests.data_validation.ScoreBandDefaultRates API documentation
-
-
-
-
-
-
-
-
- Analyzes default rates and population distribution across credit score bands.
-
-
Purpose
-
-
The Score Band Default Rates test evaluates the discriminatory power of credit scores by analyzing
-default rates across different score bands. This helps validate score effectiveness, supports
-policy decisions, and provides insights into portfolio risk distribution.
-
-
Test Mechanism
-
-
The test segments the score distribution into bands and calculates key metrics for each band:
-
-
-Population count and percentage in each band
-Default rate within each band
-Cumulative statistics across bands
-The results show how well the scores separate good and bad accounts.
-
-
-
Signs of High Risk
-
-
-Non-monotonic default rates across score bands
-Insufficient population in critical score bands
-Unexpected default rates for score ranges
-High concentration in specific score bands
-Similar default rates across adjacent bands
-Unstable default rates in key decision bands
-Extreme population skewness
-Poor risk separation between bands
-
-
-
Strengths
-
-
-Clear view of score effectiveness
-Supports policy threshold decisions
-Easy to interpret and communicate
-Directly links to business decisions
-Shows risk segmentation power
-Identifies potential score issues
-Helps validate scoring model
-Supports portfolio monitoring
-
-
-
Limitations
-
-
-Sensitive to band definition choices
-May mask within-band variations
-Requires sufficient data in each band
-Cannot capture non-linear patterns
-Point-in-time analysis only
-No temporal trend information
-Assumes band boundaries are appropriate
-May oversimplify risk patterns
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/SeasonalDecompose.html b/docs/_build/validmind/tests/data_validation/SeasonalDecompose.html
deleted file mode 100644
index 74af78bc4..000000000
--- a/docs/_build/validmind/tests/data_validation/SeasonalDecompose.html
+++ /dev/null
@@ -1,303 +0,0 @@
-
-
-
- validmind.tests.data_validation.SeasonalDecompose API documentation
-
-
-
-
-
-
-
@tags('time_series_data', 'seasonality', 'statsmodels')
-
@tasks('regression')
-
-
def
-
SeasonalDecompose ( dataset : validmind.vm_models.VMDataset seasonal_model : str = 'additive' ):
-
-
-
Assesses patterns and seasonality in a time series dataset by decomposing its features into foundational components.
-
-
Purpose
-
-
The Seasonal Decompose test aims to decompose the features of a time series dataset into their fundamental
-components: observed, trend, seasonal, and residuals. By utilizing the Seasonal Decomposition of Time Series by
-Loess (STL) method, the test identifies underlying patterns, predominantly seasonality, in the dataset's features.
-This aids in developing a more comprehensive understanding of the dataset, which in turn facilitates more effective
-model validation.
-
-
Test Mechanism
-
-
The testing process leverages the seasonal_decompose function from the statsmodels.tsa.seasonal library to
-evaluate each feature in the dataset. It isolates each feature into four components—observed, trend, seasonal, and
-residuals—and generates six subplot graphs per feature for visual interpretation. Prior to decomposition, the test
-scrutinizes and removes any non-finite values, ensuring the reliability of the analysis.
-
-
Signs of High Risk
-
-
-Non-Finiteness : Datasets with a high number of non-finite values may flag as high risk since these values are
-omitted before conducting the seasonal decomposition.Frequent Warnings : Chronic failure to infer the frequency for a scrutinized feature indicates high risk.High Seasonality : A significant seasonal component could potentially render forecasts unreliable due to
-overwhelming seasonal variation.
-
-
Strengths
-
-
-Seasonality Detection : Accurately discerns hidden seasonality patterns in dataset features.Visualization : Facilitates interpretation and comprehension through graphical representations.Unrestricted Usage : Not confined to any specific regression model, promoting wide-ranging applicability.
-
-
Limitations
-
-
-Dependence on Assumptions : Assumes that dataset features are periodically distributed. Features with no
-inferable frequency are excluded from the test.Handling Non-Finite Values : Disregards non-finite values during analysis, potentially resulting in an
-incomplete understanding of the dataset.Unreliability with Noisy Datasets : Produces unreliable results when used with datasets that contain heavy
-noise.
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/ShapiroWilk.html b/docs/_build/validmind/tests/data_validation/ShapiroWilk.html
deleted file mode 100644
index 5335984af..000000000
--- a/docs/_build/validmind/tests/data_validation/ShapiroWilk.html
+++ /dev/null
@@ -1,302 +0,0 @@
-
-
-
- validmind.tests.data_validation.ShapiroWilk API documentation
-
-
-
-
-
-
-
@tasks('classification', 'regression')
-
@tags('tabular_data', 'data_distribution', 'statistical_test')
-
-
def
-
ShapiroWilk (dataset ):
-
-
-
Evaluates feature-wise normality of training data using the Shapiro-Wilk test.
-
-
Purpose
-
-
The Shapiro-Wilk test is utilized to investigate whether a particular dataset conforms to the standard normal
-distribution. This analysis is crucial in machine learning modeling because the normality of the data can
-profoundly impact the performance of the model. This metric is especially useful in evaluating various features of
-the dataset in both classification and regression tasks.
-
-
Test Mechanism
-
-
The Shapiro-Wilk test is conducted on each feature column of the training dataset to determine if the data
-contained fall within the normal distribution. The test presents a statistic and a p-value, with the p-value
-serving to validate or repudiate the null hypothesis, which is that the tested data is normally distributed.
-
-
Signs of High Risk
-
-
-A p-value that falls below 0.05 signifies a high risk as it discards the null hypothesis, indicating that the
-data does not adhere to the normal distribution.
-For machine learning models built on the presumption of data normality, such an outcome could result in subpar
-performance or incorrect predictions.
-
-
-
Strengths
-
-
-The Shapiro-Wilk test is esteemed for its level of accuracy, thereby making it particularly well-suited to
-datasets of small to moderate sizes.
-It proves its versatility through its efficient functioning in both classification and regression tasks.
-By separately testing each feature column, the Shapiro-Wilk test can raise an alarm if a specific feature does
-not comply with the normality.
-
-
-
Limitations
-
-
-The Shapiro-Wilk test's sensitivity can be a disadvantage as it often rejects the null hypothesis (i.e., data is
-normally distributed), even for minor deviations, especially in large datasets. This may lead to unwarranted 'false
-alarms' of high risk by deeming the data as not normally distributed even if it approximates normal distribution.
-Exceptional care must be taken in managing missing data or outliers prior to testing as these can greatly skew
-the results.
-Lastly, the Shapiro-Wilk test is not optimally suited for processing data with pronounced skewness or kurtosis.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/Skewness.html b/docs/_build/validmind/tests/data_validation/Skewness.html
deleted file mode 100644
index 6dc0717d9..000000000
--- a/docs/_build/validmind/tests/data_validation/Skewness.html
+++ /dev/null
@@ -1,299 +0,0 @@
-
-
-
- validmind.tests.data_validation.Skewness API documentation
-
-
-
-
-
-
-
@tags('data_quality', 'tabular_data')
-
@tasks('classification', 'regression')
-
-
def
-
Skewness (dataset , max_threshold = 1 ):
-
-
-
Evaluates the skewness of numerical data in a dataset to check against a defined threshold, aiming to ensure data
-quality and optimize model performance.
-
-
Purpose
-
-
The purpose of the Skewness test is to measure the asymmetry in the distribution of data within a predictive
-machine learning model. Specifically, it evaluates the divergence of said distribution from a normal distribution.
-Understanding the level of skewness helps identify data quality issues, which are crucial for optimizing the
-performance of traditional machine learning models in both classification and regression settings.
-
-
Test Mechanism
-
-
This test calculates the skewness of numerical columns in the dataset, focusing specifically on numerical data
-types. The calculated skewness value is then compared against a predetermined maximum threshold, which is set by
-default to 1. If the skewness value is less than this maximum threshold, the test passes; otherwise, it fails. The
-test results, along with the skewness values and column names, are then recorded for further analysis.
-
-
Signs of High Risk
-
-
-Substantial skewness levels that significantly exceed the maximum threshold.
-Persistent skewness in the data, indicating potential issues with the foundational assumptions of the machine
-learning model.
-Subpar model performance, erroneous predictions, or biased inferences due to skewed data distributions.
-
-
-
Strengths
-
-
-Fast and efficient identification of unequal data distributions within a machine learning model.
-Adjustable maximum threshold parameter, allowing for customization based on user needs.
-Provides a clear quantitative measure to mitigate model risks related to data skewness.
-
-
-
Limitations
-
-
-Only evaluates numeric columns, potentially missing skewness or bias in non-numeric data.
-Assumes that data should follow a normal distribution, which may not always be applicable to real-world data.
-Subjective threshold for risk grading, requiring expert input and recurrent iterations for refinement.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/SpreadPlot.html b/docs/_build/validmind/tests/data_validation/SpreadPlot.html
deleted file mode 100644
index a85454a53..000000000
--- a/docs/_build/validmind/tests/data_validation/SpreadPlot.html
+++ /dev/null
@@ -1,304 +0,0 @@
-
-
-
- validmind.tests.data_validation.SpreadPlot API documentation
-
-
-
-
-
-
-
-
- Assesses potential correlations between pairs of time series variables through visualization to enhance
-understanding of their relationships.
-
-
Purpose
-
-
The SpreadPlot test aims to graphically illustrate and analyze the relationships between pairs of time series
-variables within a given dataset. This facilitated understanding helps in identifying and assessing potential time
-series correlations, such as cointegration, between the variables.
-
-
Test Mechanism
-
-
The SpreadPlot test computes and represents the spread between each pair of time series variables in the dataset.
-Specifically, the difference between two variables is calculated and presented as a line graph. This process is
-iterated for each unique pair of variables in the dataset, allowing for comprehensive visualization of their
-relationships.
-
-
Signs of High Risk
-
-
-Large fluctuations in the spread over a given timespan.
-Unexpected patterns or trends that may signal potential risks in the underlying correlations between the
-variables.
-Presence of significant missing data or extreme outlier values, which could potentially skew the spread and
-indicate high risk.
-
-
-
Strengths
-
-
-Allows for thorough visual examination and interpretation of the correlations between time-series pairs.
-Aids in revealing complex relationships like cointegration.
-Enhances interpretability by visualizing the relationships, thereby helping in spotting outliers and trends.
-Capable of handling numerous variable pairs from the dataset through a versatile and adaptable process.
-
-
-
Limitations
-
-
-Primarily serves as a visualization tool and does not offer quantitative measurements or statistics to
-objectively determine relationships.
-Heavily relies on the quality and granularity of the data—missing data or outliers can notably disturb the
-interpretation of relationships.
-Can become inefficient or difficult to interpret with a high number of variables due to the profuse number of
-plots.
-Might not completely capture intricate non-linear relationships between the variables.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/TabularCategoricalBarPlots.html b/docs/_build/validmind/tests/data_validation/TabularCategoricalBarPlots.html
deleted file mode 100644
index 12b8c3832..000000000
--- a/docs/_build/validmind/tests/data_validation/TabularCategoricalBarPlots.html
+++ /dev/null
@@ -1,299 +0,0 @@
-
-
-
- validmind.tests.data_validation.TabularCategoricalBarPlots API documentation
-
-
-
-
-
-
-
@tags('tabular_data', 'visualization')
-
@tasks('classification', 'regression')
-
-
def
-
TabularCategoricalBarPlots (dataset : validmind.vm_models.VMDataset ):
-
-
-
Generates and visualizes bar plots for each category in categorical features to evaluate the dataset's composition.
-
-
Purpose
-
-
The purpose of this metric is to visually analyze categorical data using bar plots. It is intended to evaluate the
-dataset's composition by displaying the counts of each category in each categorical feature.
-
-
Test Mechanism
-
-
The provided dataset is first checked to determine if it contains any categorical variables. If no categorical
-columns are found, the tool raises a ValueError. For each categorical variable in the dataset, a separate bar plot
-is generated. The number of occurrences for each category is calculated and displayed on the plot. If a dataset
-contains multiple categorical columns, multiple bar plots are produced.
-
-
Signs of High Risk
-
-
-High risk could occur if the categorical variables exhibit an extreme imbalance, with categories having very few
-instances possibly being underrepresented in the model, which could affect the model's performance and its ability
-to generalize.
-Another sign of risk is if there are too many categories in a single variable, which could lead to overfitting
-and make the model complex.
-
-
-
Strengths
-
-
-Provides a visual and intuitively understandable representation of categorical data.
-Aids in the analysis of variable distributions.
-Helps in easily identifying imbalances or rare categories that could affect the model's performance.
-
-
-
Limitations
-
-
-This method only works with categorical data and won't apply to numerical variables.
-It does not provide informative value when there are too many categories, as the bar chart could become cluttered
-and hard to interpret.
-Offers no insights into the model's performance or precision, but rather provides a descriptive analysis of the
-input.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/TabularDateTimeHistograms.html b/docs/_build/validmind/tests/data_validation/TabularDateTimeHistograms.html
deleted file mode 100644
index a0834996b..000000000
--- a/docs/_build/validmind/tests/data_validation/TabularDateTimeHistograms.html
+++ /dev/null
@@ -1,305 +0,0 @@
-
-
-
- validmind.tests.data_validation.TabularDateTimeHistograms API documentation
-
-
-
-
-
-
-
@tags('time_series_data', 'visualization')
-
@tasks('classification', 'regression')
-
-
def
-
TabularDateTimeHistograms (dataset : validmind.vm_models.VMDataset ):
-
-
-
Generates histograms to provide graphical insight into the distribution of time intervals in a model's datetime
-data.
-
-
Purpose
-
-
The TabularDateTimeHistograms
-
-
Test Mechanism
-
-
This test operates by first identifying all datetime columns and extracting them from the dataset. For each
-datetime column, it next computes the differences (in days) between consecutive dates, excluding zero values, and
-visualizes these differences in a histogram. The Plotly library's histogram function is used to generate
-histograms, which are labeled appropriately and provide a graphical representation of the frequency of different
-day intervals in the dataset.
-
-
Signs of High Risk
-
-
-If no datetime columns are detected in the dataset, this would lead to a ValueError. Hence, the absence of
-datetime columns signifies a high risk.
-A severely skewed or irregular distribution depicted in the histogram may indicate possible complications with
-the data, such as faulty timestamps or abnormalities.
-
-
-
Strengths
-
-
-The metric offers a visual overview of time interval frequencies within the dataset, supporting the recognition
-of inherent patterns.
-Histogram plots can aid in the detection of potential outliers and data anomalies, contributing to an assessment
-of data quality.
-The metric is versatile, compatible with a range of task types, including classification and regression, and can
-work with multiple datetime variables if present.
-
-
-
Limitations
-
-
-A major weakness of this metric is its dependence on the visual examination of data, as it does not provide a
-measurable evaluation of the model.
-The metric might overlook complex or multi-dimensional trends in the data.
-The test is only applicable to datasets containing datetime columns and will fail if such columns are unavailable.
-The interpretation of the histograms relies heavily on the domain expertise and experience of the reviewer.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/TabularDescriptionTables.html b/docs/_build/validmind/tests/data_validation/TabularDescriptionTables.html
deleted file mode 100644
index 5458c96d5..000000000
--- a/docs/_build/validmind/tests/data_validation/TabularDescriptionTables.html
+++ /dev/null
@@ -1,408 +0,0 @@
-
-
-
- validmind.tests.data_validation.TabularDescriptionTables API documentation
-
-
-
-
-
-
-
@tags('tabular_data')
-
@tasks('classification', 'regression')
-
-
def
-
TabularDescriptionTables (dataset ):
-
-
-
Summarizes key descriptive statistics for numerical, categorical, and datetime variables in a dataset.
-
-
Purpose
-
-
The main purpose of this metric is to gather and present the descriptive statistics of numerical, categorical, and
-datetime variables present in a dataset. The attributes it measures include the count, mean, minimum and maximum
-values, percentage of missing values, data types of fields, and unique values for categorical fields, among others.
-
-
Test Mechanism
-
-
The test first segregates the variables in the dataset according to their data types (numerical, categorical, or
-datetime). Then, it compiles summary statistics for each type of variable. The specifics of these statistics vary
-depending on the type of variable:
-
-
-For numerical variables, the metric extracts descriptors like count, mean, minimum and maximum values, count of
-missing values, and data types.
-For categorical variables, it counts the number of unique values, displays unique values, counts missing values,
-and identifies data types.
-For datetime variables, it counts the number of unique values, identifies the earliest and latest dates, counts
-missing values, and identifies data types.
-
-
-
Signs of High Risk
-
-
-Masses of missing values in the descriptive statistics results could hint at high risk or failure, indicating
-potential data collection, integrity, and quality issues.
-Detection of inappropriate distributions for numerical variables, like having negative values for variables that
-are always supposed to be positive.
-Identifying inappropriate data types, like a continuous variable being encoded as a categorical type.
-
-
-
Strengths
-
-
-Provides a comprehensive overview of the dataset.
-Gives a snapshot into the essence of the numerical, categorical, and datetime fields.
-Identifies potential data quality issues such as missing values or inconsistencies crucial for building credible
-machine learning models.
-The metadata, including the data type and missing value information, are vital for anyone including data
-scientists dealing with the dataset before the modeling process.
-
-
-
Limitations
-
-
-It does not perform any deeper statistical analysis or tests on the data.
-It does not handle issues such as outliers, or relationships between variables.
-It offers no insights into potential correlations or possible interactions between variables.
-It does not investigate the potential impact of missing values on the performance of the machine learning models.
-It does not explore potential transformation requirements that may be necessary to enhance the performance of the
-chosen algorithm.
-
-
-
-
-
- def
- get_summary_statistics_numerical (dataset , numerical_fields ):
-
-
-
-
-
-
-
-
-
-
-
- def
- get_summary_statistics_categorical (dataset , categorical_fields ):
-
-
-
-
-
-
-
-
-
-
-
- def
- get_summary_statistics_datetime (dataset , datetime_fields ):
-
-
-
-
-
-
-
-
-
-
-
- def
- get_categorical_columns (dataset ):
-
-
-
-
-
-
-
-
-
-
-
- def
- get_numerical_columns (dataset ):
-
-
-
-
-
-
-
-
-
-
-
- def
- get_datetime_columns (dataset ):
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/TabularNumericalHistograms.html b/docs/_build/validmind/tests/data_validation/TabularNumericalHistograms.html
deleted file mode 100644
index 6621507fa..000000000
--- a/docs/_build/validmind/tests/data_validation/TabularNumericalHistograms.html
+++ /dev/null
@@ -1,304 +0,0 @@
-
-
-
- validmind.tests.data_validation.TabularNumericalHistograms API documentation
-
-
-
-
-
-
-
@tags('tabular_data', 'visualization')
-
@tasks('classification', 'regression')
-
-
def
-
TabularNumericalHistograms (dataset : validmind.vm_models.VMDataset ):
-
-
-
Generates histograms for each numerical feature in a dataset to provide visual insights into data distribution and
-detect potential issues.
-
-
Purpose
-
-
The purpose of this test is to provide visual analysis of numerical data through the generation of histograms for
-each numerical feature in the dataset. Histograms aid in the exploratory analysis of data, offering insight into
-the distribution of the data, skewness, presence of outliers, and central tendencies. It helps in understanding if
-the inputs to the model are normally distributed, which is a common assumption in many machine learning algorithms.
-
-
Test Mechanism
-
-
This test scans the provided dataset and extracts all the numerical columns. For each numerical column, it
-constructs a histogram using plotly, with 50 bins. The deployment of histograms offers a robust visual aid,
-ensuring unruffled identification and understanding of numerical data distribution patterns.
-
-
Signs of High Risk
-
-
-A high degree of skewness
-Unexpected data distributions
-Existence of extreme outliers in the histograms
-
-
-
These may indicate issues with the data that the model is receiving. If data for a numerical feature is expected to
-follow a certain distribution (like a normal distribution) but does not, it could lead to sub-par performance by
-the model. As such these instances should be treated as high-risk indicators.
-
-
Strengths
-
-
-Provides a simple, easy-to-interpret visualization of how data for each numerical attribute is distributed.
-Helps detect skewed values and outliers that could potentially harm the AI model's performance.
-Can be applied to large datasets and multiple numerical variables conveniently.
-
-
-
Limitations
-
-
-Only works with numerical data, thus ignoring non-numerical or categorical data.
-Does not analyze relationships between different features, only the individual feature distributions.
-Is a univariate analysis and may miss patterns or anomalies that only appear when considering multiple variables
-together.
-Does not provide any insight into how these features affect the output of the model; it is purely an input
-analysis tool.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/TargetRateBarPlots.html b/docs/_build/validmind/tests/data_validation/TargetRateBarPlots.html
deleted file mode 100644
index 139503584..000000000
--- a/docs/_build/validmind/tests/data_validation/TargetRateBarPlots.html
+++ /dev/null
@@ -1,299 +0,0 @@
-
-
-
- validmind.tests.data_validation.TargetRateBarPlots API documentation
-
-
-
-
-
-
-
@tags('tabular_data', 'visualization', 'categorical_data')
-
@tasks('classification')
-
-
def
-
TargetRateBarPlots (dataset : validmind.vm_models.VMDataset ):
-
-
-
Generates bar plots visualizing the default rates of categorical features for a classification machine learning
-model.
-
-
Purpose
-
-
This test, implemented as a metric, is designed to provide an intuitive, graphical summary of the decision-making
-patterns exhibited by a categorical classification machine learning model. The model's performance is evaluated
-using bar plots depicting the ratio of target rates—meaning the proportion of positive classes—for different
-categorical inputs. This allows for an easy, at-a-glance understanding of the model's accuracy.
-
-
Test Mechanism
-
-
The test involves creating a pair of bar plots for each categorical feature in the dataset. The first plot depicts
-the frequency of each category in the dataset, with each category visually distinguished by its unique color. The
-second plot shows the mean target rate of each category (sourced from the "default_column"). Plotly, a Python
-library, is used to generate these plots, with distinct plots created for each feature. If no specific columns are
-selected, the test will generate plots for each categorical column in the dataset.
-
-
Signs of High Risk
-
-
-Inconsistent or non-binary values in the "default_column" could complicate or render impossible the calculation
-of average target rates.
-Particularly low or high target rates for a specific category might suggest that the model is misclassifying
-instances of that category.
-
-
-
Strengths
-
-
-This test offers a visually interpretable breakdown of the model's decisions, providing an easy way to spot
-irregularities, inconsistencies, or patterns.
-Its flexibility allows for the inspection of one or multiple columns, as needed.
-
-
-
Limitations
-
-
-The readability of the bar plots drops as the number of distinct categories increases in the dataset, which can
-make them harder to understand and less useful.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/TimeSeriesDescription.html b/docs/_build/validmind/tests/data_validation/TimeSeriesDescription.html
deleted file mode 100644
index c9b2c8381..000000000
--- a/docs/_build/validmind/tests/data_validation/TimeSeriesDescription.html
+++ /dev/null
@@ -1,296 +0,0 @@
-
-
-
- validmind.tests.data_validation.TimeSeriesDescription API documentation
-
-
-
-
-
-
-
@tags('time_series_data', 'analysis')
-
@tasks('regression')
-
-
def
-
TimeSeriesDescription (dataset ):
-
-
-
Generates a detailed analysis for the provided time series dataset, summarizing key statistics to identify trends,
-patterns, and data quality issues.
-
-
Purpose
-
-
The TimeSeriesDescription function aims to analyze an individual time series by providing a summary of key
-statistics. This helps in understanding trends, patterns, and data quality issues within the time series.
-
-
Test Mechanism
-
-
The function extracts the time series data and provides a summary of key statistics. The dataset is expected to
-have a datetime index. The function checks this and raises an error if the index is not in datetime format. For
-each variable (column) in the dataset, appropriate statistics including start date, end date, frequency, number of
-missing values, count, min, and max values are calculated.
-
-
Signs of High Risk
-
-
-If the index of the dataset is not in datetime format, it could lead to errors in time-series analysis.
-Inconsistent or missing data within the dataset might affect the analysis of trends and patterns.
-
-
-
Strengths
-
-
-Provides a comprehensive summary of key statistics for each variable, helping to identify data quality issues
-such as missing values.
-Helps in understanding the distribution and range of the data by including min and max values.
-
-
-
Limitations
-
-
-Assumes that the dataset is provided as a DataFrameDataset object with a .df attribute to access the pandas
-DataFrame.
-Only analyzes datasets with a datetime index and will raise an error for other types of indices.
-Does not handle large datasets efficiently; performance may degrade with very large datasets.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/TimeSeriesDescriptiveStatistics.html b/docs/_build/validmind/tests/data_validation/TimeSeriesDescriptiveStatistics.html
deleted file mode 100644
index 6716d0ef3..000000000
--- a/docs/_build/validmind/tests/data_validation/TimeSeriesDescriptiveStatistics.html
+++ /dev/null
@@ -1,294 +0,0 @@
-
-
-
- validmind.tests.data_validation.TimeSeriesDescriptiveStatistics API documentation
-
-
-
-
-
-
-
@tags('time_series_data', 'analysis')
-
@tasks('regression')
-
-
def
-
TimeSeriesDescriptiveStatistics (dataset ):
-
-
-
Evaluates the descriptive statistics of a time series dataset to identify trends, patterns, and data quality issues.
-
-
Purpose
-
-
The purpose of the TimeSeriesDescriptiveStatistics function is to analyze an individual time series by providing a
-summary of key descriptive statistics. This analysis helps in understanding trends, patterns, and data quality
-issues within the time series dataset.
-
-
Test Mechanism
-
-
The function extracts the time series data and provides a summary of key descriptive statistics. The dataset is
-expected to have a datetime index, and the function will check this and raise an error if the index is not in a
-datetime format. For each variable (column) in the dataset, appropriate statistics, including start date, end date,
-min, mean, max, skewness, kurtosis, and count, are calculated.
-
-
Signs of High Risk
-
-
-If the index of the dataset is not in datetime format, it could lead to errors in time-series analysis.
-Inconsistent or missing data within the dataset might affect the analysis of trends and patterns.
-
-
-
Strengths
-
-
-Provides a comprehensive summary of key descriptive statistics for each variable.
-Helps identify data quality issues and understand the distribution of the data.
-
-
-
Limitations
-
-
-Assumes the dataset is provided as a DataFrameDataset object with a .df attribute to access the pandas DataFrame.
-Only analyzes datasets with a datetime index and will raise an error for other types of indices.
-Does not handle large datasets efficiently, and performance may degrade with very large datasets.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/TimeSeriesFrequency.html b/docs/_build/validmind/tests/data_validation/TimeSeriesFrequency.html
deleted file mode 100644
index 603beef4d..000000000
--- a/docs/_build/validmind/tests/data_validation/TimeSeriesFrequency.html
+++ /dev/null
@@ -1,306 +0,0 @@
-
-
-
- validmind.tests.data_validation.TimeSeriesFrequency API documentation
-
-
-
-
-
-
-
-
- Evaluates consistency of time series data frequency and generates a frequency plot.
-
-
Purpose
-
-
The purpose of the TimeSeriesFrequency test is to evaluate the consistency in the frequency of data points in a
-time-series dataset. This test inspects the intervals or duration between each data point to determine if a fixed
-pattern (such as daily, weekly, or monthly) exists. The identification of such patterns is crucial to time-series
-analysis as any irregularities could lead to erroneous results and hinder the model's capacity for identifying
-trends and patterns.
-
-
Test Mechanism
-
-
Initially, the test checks if the dataframe index is in datetime format. Subsequently, it utilizes pandas'
-infer_freq method to identify the frequency of each data series within the dataframe. The infer_freq method
-attempts to establish the frequency of a time series and returns both the frequency string and a dictionary
-relating these strings to their respective labels. The test compares the frequencies of all datasets. If they share
-a common frequency, the test passes, but it fails if they do not. Additionally, Plotly is used to create a
-frequency plot, offering a visual depiction of the time differences between consecutive entries in the dataframe
-index.
-
-
Signs of High Risk
-
-
-The test fails, indicating multiple unique frequencies within the dataset. This failure could suggest irregular
-intervals between observations, potentially interrupting pattern recognition or trend analysis.
-The presence of missing or null frequencies could be an indication of inconsistencies in data or gaps within the
-data collection process.
-
-
-
Strengths
-
-
-This test uses a systematic approach to checking the consistency of data frequency within a time-series dataset.
-It increases the model's reliability by asserting the consistency of observations over time, an essential factor
-in time-series analysis.
-The test generates a visual plot, providing an intuitive representation of the dataset's frequency distribution,
-which caters to visual learners and aids in interpretation and explanation.
-
-
-
Limitations
-
-
-This test is only applicable to time-series datasets and hence not suitable for other types of datasets.
-The infer_freq method might not always correctly infer frequency when faced with missing or irregular data
-points.
-Depending on context or the model under development, mixed frequencies might sometimes be acceptable, but this
-test considers them a failing condition.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/TimeSeriesHistogram.html b/docs/_build/validmind/tests/data_validation/TimeSeriesHistogram.html
deleted file mode 100644
index 36d91406c..000000000
--- a/docs/_build/validmind/tests/data_validation/TimeSeriesHistogram.html
+++ /dev/null
@@ -1,300 +0,0 @@
-
-
-
- validmind.tests.data_validation.TimeSeriesHistogram API documentation
-
-
-
-
-
-
-
@tags('data_validation', 'visualization', 'time_series_data')
-
@tasks('regression', 'time_series_forecasting')
-
-
def
-
TimeSeriesHistogram (dataset , nbins = 30 ):
-
-
-
Visualizes distribution of time-series data using histograms and Kernel Density Estimation (KDE) lines.
-
-
Purpose
-
-
The TimeSeriesHistogram test aims to perform a histogram analysis on time-series data to assess the distribution of
-values within a dataset over time. This test is useful for regression tasks and can be applied to various types of
-data, such as internet traffic, stock prices, and weather data, providing insights into the probability
-distribution, skewness, and kurtosis of the dataset.
-
-
Test Mechanism
-
-
This test operates on a specific column within the dataset that must have a datetime type index. For each column in
-the dataset, a histogram is created using Plotly's histplot function. If the dataset includes more than one
-time-series, a distinct histogram is plotted for each series. Additionally, a Kernel Density Estimate (KDE) line is
-drawn for each histogram, visualizing the data's underlying probability distribution. The x and y-axis labels are
-hidden to focus solely on the data distribution.
-
-
Signs of High Risk
-
-
-The dataset lacks a column with a datetime type index.
-The specified columns do not exist within the dataset.
-High skewness or kurtosis in the data distribution, indicating potential bias.
-Presence of significant outliers in the data distribution.
-
-
-
Strengths
-
-
-Serves as a visual diagnostic tool for understanding data behavior and distribution trends.
-Effective for analyzing both single and multiple time-series data.
-KDE line provides a smooth estimate of the overall trend in data distribution.
-
-
-
Limitations
-
-
-Provides a high-level view without specific numeric measures such as skewness or kurtosis.
-The histogram loses some detail due to binning of data values.
-Cannot handle non-numeric data columns.
-Histogram shape may be sensitive to the number of bins used.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/TimeSeriesLinePlot.html b/docs/_build/validmind/tests/data_validation/TimeSeriesLinePlot.html
deleted file mode 100644
index 802c493aa..000000000
--- a/docs/_build/validmind/tests/data_validation/TimeSeriesLinePlot.html
+++ /dev/null
@@ -1,304 +0,0 @@
-
-
-
- validmind.tests.data_validation.TimeSeriesLinePlot API documentation
-
-
-
-
-
-
-
-
- Generates and analyses time-series data through line plots revealing trends, patterns, anomalies over time.
-
-
Purpose
-
-
The TimeSeriesLinePlot metric is designed to generate and analyze time series data through the creation of line
-plots. This assists in the initial inspection of the data by providing a visual representation of patterns, trends,
-seasonality, irregularity, and anomalies that may be present in the dataset over a period of time.
-
-
Test Mechanism
-
-
The mechanism for this Python class involves extracting the column names from the provided dataset and subsequently
-generating line plots for each column using the Plotly Python library. For every column in the dataset, a
-time-series line plot is created where the values are plotted against the dataset's datetime index. It is important
-to note that indexes that are not of datetime type will result in a ValueError.
-
-
Signs of High Risk
-
-
-Presence of time-series data that does not have datetime indices.
-Provided columns do not exist in the provided dataset.
-The detection of anomalous patterns or irregularities in the time-series plots, indicating potential high model
-instability or probable predictive error.
-
-
-
Strengths
-
-
-The visual representation of complex time series data, which simplifies understanding and helps in recognizing
-temporal trends, patterns, and anomalies.
-The adaptability of the metric, which allows it to effectively work with multiple time series within the same
-dataset.
-Enables the identification of anomalies and irregular patterns through visual inspection, assisting in spotting
-potential data or model performance problems.
-
-
-
Limitations
-
-
-The effectiveness of the metric is heavily reliant on the quality and patterns of the provided time series data.
-Exclusively a visual tool, it lacks the capability to provide quantitative measurements, making it less effective
-for comparing and ranking multiple models or when specific numerical diagnostics are needed.
-The metric necessitates that the time-specific data has been transformed into a datetime index, with the data
-formatted correctly.
-The metric has an inherent limitation in that it cannot extract deeper statistical insights from the time series
-data, which can limit its efficacy with complex data structures and phenomena.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/TimeSeriesMissingValues.html b/docs/_build/validmind/tests/data_validation/TimeSeriesMissingValues.html
deleted file mode 100644
index 154232b9f..000000000
--- a/docs/_build/validmind/tests/data_validation/TimeSeriesMissingValues.html
+++ /dev/null
@@ -1,301 +0,0 @@
-
-
-
- validmind.tests.data_validation.TimeSeriesMissingValues API documentation
-
-
-
-
-
-
-
@tags('time_series_data')
-
@tasks('regression')
-
-
def
-
TimeSeriesMissingValues ( dataset : validmind.vm_models.VMDataset min_threshold : int = 1 ):
-
-
-
Validates time-series data quality by confirming the count of missing values is below a certain threshold.
-
-
Purpose
-
-
This test is designed to validate the quality of a historical time-series dataset by verifying that the number of
-missing values is below a specified threshold. As time-series models greatly depend on the continuity and
-temporality of data points, missing values could compromise the model's performance. Consequently, this test aims
-to ensure data quality and readiness for the machine learning model, safeguarding its predictive capacity.
-
-
Test Mechanism
-
-
The test method commences by validating if the dataset has a datetime index; if not, an error is raised. It
-establishes a lower limit threshold for missing values and performs a missing values check on each column of the
-dataset. An object for the test result is created stating whether the number of missing values is within the
-specified threshold. Additionally, the test calculates the percentage of missing values alongside the raw count.
-
-
Signs of High Risk
-
-
-The number of missing values in any column of the dataset surpasses the threshold, marking a failure and a
-high-risk scenario. The reasons could range from incomplete data collection, faulty sensors to data preprocessing
-errors.
-
-
-
Strengths
-
-
-Effectively identifies missing values which could adversely affect the model’s performance.
-Applicable and customizable through the threshold parameter across different data sets.
-Goes beyond raw numbers by calculating the percentage of missing values, offering a more relative understanding
-of data scarcity.
-
-
-
Limitations
-
-
-Although it identifies missing values, the test does not provide solutions to handle them.
-The test demands that the dataset should have a datetime index, hence limiting its use only to time series
-analysis.
-The test's sensitivity to the 'min_threshold' parameter may raise false alarms if set too strictly or may
-overlook problematic data if set too loosely.
-Solely focuses on the 'missingness' of the data and might fall short in addressing other aspects of data quality.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/TimeSeriesOutliers.html b/docs/_build/validmind/tests/data_validation/TimeSeriesOutliers.html
deleted file mode 100644
index 7a5441361..000000000
--- a/docs/_build/validmind/tests/data_validation/TimeSeriesOutliers.html
+++ /dev/null
@@ -1,305 +0,0 @@
-
-
-
- validmind.tests.data_validation.TimeSeriesOutliers API documentation
-
-
-
-
-
-
-
@tags('time_series_data')
-
@tasks('regression')
-
-
def
-
TimeSeriesOutliers ( dataset : validmind.vm_models.VMDataset zscore_threshold : int = 3 ):
-
-
-
Identifies and visualizes outliers in time-series data using the z-score method.
-
-
Purpose
-
-
This test is designed to identify outliers in time-series data using the z-score method. It's vital for ensuring
-data quality before modeling, as outliers can skew predictive models and significantly impact their overall
-performance.
-
-
Test Mechanism
-
-
The test processes a given dataset which must have datetime indexing, checks if a 'zscore_threshold' parameter has
-been supplied, and identifies columns with numeric data types. After finding numeric columns, the implementer then
-applies the z-score method to each numeric column, identifying outliers based on the threshold provided. Each
-outlier is listed together with their variable name, z-score, timestamp, and relative threshold in a dictionary and
-converted to a DataFrame for convenient output. Additionally, it produces visual plots for each time series
-illustrating outliers in the context of the broader dataset. The 'zscore_threshold' parameter sets the limit beyond
-which a data point will be labeled as an outlier. The default threshold is set at 3, indicating that any data point
-that falls 3 standard deviations away from the mean will be marked as an outlier.
-
-
Signs of High Risk
-
-
-Many or substantial outliers are present within the dataset, indicating significant anomalies.
-Data points with z-scores higher than the set threshold.
-Potential impact on the performance of machine learning models if outliers are not properly addressed.
-
-
-
Strengths
-
-
-The z-score method is a popular and robust method for identifying outliers in a dataset.
-Simplifies time series maintenance by requiring a datetime index.
-Identifies outliers for each numeric feature individually.
-Provides an elaborate report showing variables, dates, z-scores, and pass/fail tests.
-Offers visual inspection for detected outliers through plots.
-
-
-
Limitations
-
-
-The test only identifies outliers in numeric columns, not in categorical variables.
-The utility and accuracy of z-scores can be limited if the data doesn't follow a normal distribution.
-The method relies on a subjective z-score threshold for deciding what constitutes an outlier, which might not
-always be suitable depending on the dataset and use case.
-It does not address possible ways to handle identified outliers in the data.
-The requirement for a datetime index could limit its application.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/TooManyZeroValues.html b/docs/_build/validmind/tests/data_validation/TooManyZeroValues.html
deleted file mode 100644
index bc9b03cbb..000000000
--- a/docs/_build/validmind/tests/data_validation/TooManyZeroValues.html
+++ /dev/null
@@ -1,313 +0,0 @@
-
-
-
- validmind.tests.data_validation.TooManyZeroValues API documentation
-
-
-
-
-
-
-
@tags('tabular_data')
-
@tasks('regression', 'classification')
-
-
def
-
TooManyZeroValues ( dataset : validmind.vm_models.VMDataset max_percent_threshold : float = 0.03 ):
-
-
-
Identifies numerical columns in a dataset that contain an excessive number of zero values, defined by a threshold
-percentage.
-
-
Purpose
-
-
The 'TooManyZeroValues' test is utilized to identify numerical columns in the dataset that may present a quantity
-of zero values considered excessive. The aim is to detect situations where these may implicate data sparsity or a
-lack of variation, limiting their effectiveness within a machine learning model. The definition of 'too many' is
-quantified as a percentage of total values, with a default set to 3%.
-
-
Test Mechanism
-
-
This test is conducted by looping through each column in the dataset and categorizing those that pertain to
-numerical data. On identifying a numerical column, the function computes the total quantity of zero values and
-their ratio to the total row count. Should the proportion exceed a pre-set threshold parameter, set by default at
-0.03 or 3%, the column is considered to have failed the test. The results for each column are summarized and
-reported, indicating the count and percentage of zero values for each numerical column, alongside a status
-indicating whether the column has passed or failed the test.
-
-
Signs of High Risk
-
-
-Numerical columns showing a high ratio of zero values when compared to the total count of rows (exceeding the
-predetermined threshold).
-Columns characterized by zero values across the board suggest a complete lack of data variation, signifying high
-risk.
-
-
-
Strengths
-
-
-Assists in highlighting columns featuring an excess of zero values that could otherwise go unnoticed within a
-large dataset.
-Provides the flexibility to alter the threshold that determines when the quantity of zero values becomes 'too
-many', thus catering to specific needs of a particular analysis or model.
-Offers feedback in the form of both counts and percentages of zero values, which allows a closer inspection of
-the distribution and proportion of zeros within a column.
-Targets specifically numerical data, thereby avoiding inappropriate application to non-numerical columns and
-mitigating the risk of false test failures.
-
-
-
Limitations
-
-
-Is exclusively designed to check for zero values and doesn’t assess the potential impact of other values that
-could affect the dataset, such as extremely high or low figures, missing values, or outliers.
-Lacks the ability to detect a repetitive pattern of zeros, which could be significant in time-series or
-longitudinal data.
-Zero values can actually be meaningful in some contexts; therefore, tagging them as 'too many' could potentially
-misinterpret the data to some extent.
-This test does not take into consideration the context of the dataset, and fails to recognize that within certain
-columns, a high number of zero values could be quite normal and not necessarily an indicator of poor data quality.
-Cannot evaluate non-numerical or categorical columns, which might bring with them different types of concerns or
-issues.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/UniqueRows.html b/docs/_build/validmind/tests/data_validation/UniqueRows.html
deleted file mode 100644
index 6ce28be9b..000000000
--- a/docs/_build/validmind/tests/data_validation/UniqueRows.html
+++ /dev/null
@@ -1,303 +0,0 @@
-
-
-
- validmind.tests.data_validation.UniqueRows API documentation
-
-
-
-
-
-
-
@tags('tabular_data')
-
@tasks('regression', 'classification')
-
-
def
-
UniqueRows ( dataset : validmind.vm_models.VMDataset min_percent_threshold : float = 1 ):
-
-
-
Verifies the diversity of the dataset by ensuring that the count of unique rows exceeds a prescribed threshold.
-
-
Purpose
-
-
The UniqueRows test is designed to gauge the quality of the data supplied to the machine learning model by
-verifying that the count of distinct rows in the dataset exceeds a specific threshold, thereby ensuring a varied
-collection of data. Diversity in data is essential for training an unbiased and robust model that excels when faced
-with novel data.
-
-
Test Mechanism
-
-
The testing process starts with calculating the total number of rows in the dataset. Subsequently, the count of
-unique rows is determined for each column in the dataset. If the percentage of unique rows (calculated as the ratio
-of unique rows to the overall row count) is less than the prescribed minimum percentage threshold given as a
-function parameter, the test passes. The results are cached and a final pass or fail verdict is given based on
-whether all columns have successfully passed the test.
-
-
Signs of High Risk
-
-
-A lack of diversity in data columns, demonstrated by a count of unique rows that falls short of the preset
-minimum percentage threshold, is indicative of high risk.
-This lack of variety in the data signals potential issues with data quality, possibly leading to overfitting in
-the model and issues with generalization, thus posing a significant risk.
-
-
-
Strengths
-
-
-The UniqueRows test is efficient in evaluating the data's diversity across each information column in the dataset.
-This test provides a quick, systematic method to assess data quality based on uniqueness, which can be pivotal in
-developing effective and unbiased machine learning models.
-
-
-
Limitations
-
-
-A limitation of the UniqueRows test is its assumption that the data's quality is directly proportionate to its
-uniqueness, which may not always hold true. There might be contexts where certain non-unique rows are essential and
-should not be overlooked.
-The test does not consider the relative 'importance' of each column in predicting the output, treating all
-columns equally.
-This test may not be suitable or useful for categorical variables, where the count of unique categories is
-inherently limited.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/WOEBinPlots.html b/docs/_build/validmind/tests/data_validation/WOEBinPlots.html
deleted file mode 100644
index 0ec9aeb2a..000000000
--- a/docs/_build/validmind/tests/data_validation/WOEBinPlots.html
+++ /dev/null
@@ -1,309 +0,0 @@
-
-
-
- validmind.tests.data_validation.WOEBinPlots API documentation
-
-
-
-
-
-
-
@tags('tabular_data', 'visualization', 'categorical_data')
-
@tasks('classification')
-
-
def
-
WOEBinPlots ( dataset : validmind.vm_models.VMDataset breaks_adj : list = None , fig_height : int = 600 , fig_width : int = 500 ):
-
-
-
Generates visualizations of Weight of Evidence (WoE) and Information Value (IV) for understanding predictive power
-of categorical variables in a data set.
-
-
Purpose
-
-
This test is designed to visualize the Weight of Evidence (WoE) and Information Value (IV) for categorical
-variables in a provided dataset. By showcasing the data distribution across different categories of each feature,
-it aids in understanding each variable's predictive power in the context of a classification-based machine learning
-model. Commonly used in credit scoring models, WoE and IV are robust statistical methods for evaluating a
-variable's predictive power.
-
-
Test Mechanism
-
-
The test implementation follows defined steps. Initially, it selects non-numeric columns from the dataset and
-changes them to string type, paving the way for accurate binning. It then performs an automated WoE binning
-operation on these selected features, effectively categorizing the potential values of a variable into distinct
-bins. After the binning process, the function generates two separate visualizations (a scatter chart for WoE values
-and a bar chart for IV) for each variable. These visual presentations are formed according to the spread of each
-metric across various categories of each feature.
-
-
Signs of High Risk
-
-
-Errors occurring during the binning process.
-Challenges in converting non-numeric columns into string data type.
-Misbalance in the distribution of WoE and IV, with certain bins overtaking others conspicuously. This could
-denote that the model is disproportionately dependent on certain variables or categories for predictions, an
-indication of potential risks to its robustness and generalizability.
-
-
-
Strengths
-
-
-Provides a detailed visual representation of the relationship between feature categories and the target variable.
-This grants an intuitive understanding of each feature's contribution to the model.
-Allows for easy identification of features with high impact, facilitating feature selection and enhancing
-comprehension of the model's decision logic.
-WoE conversions are monotonic, upholding the rank ordering of the original data points, which simplifies analysis.
-
-
-
Limitations
-
-
-The method is largely reliant on the binning process, and an inappropriate binning threshold or bin number choice
-might result in a misrepresentation of the variable's distribution.
-While excellent for categorical data, the encoding of continuous variables into categorical can sometimes lead to
-information loss.
-Extreme or outlier values can dramatically affect the computation of WoE and IV, skewing results.
-The method requires a sufficient number of events per bin to generate a reliable information value and weight of
-evidence.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/WOEBinTable.html b/docs/_build/validmind/tests/data_validation/WOEBinTable.html
deleted file mode 100644
index f64e74375..000000000
--- a/docs/_build/validmind/tests/data_validation/WOEBinTable.html
+++ /dev/null
@@ -1,300 +0,0 @@
-
-
-
- validmind.tests.data_validation.WOEBinTable API documentation
-
-
-
-
-
-
-
@tags('tabular_data', 'categorical_data')
-
@tasks('classification')
-
-
def
-
WOEBinTable ( dataset : validmind.vm_models.VMDataset breaks_adj : list = None ):
-
-
-
Assesses the Weight of Evidence (WoE) and Information Value (IV) of each feature to evaluate its predictive power
-in a binary classification model.
-
-
Purpose
-
-
The Weight of Evidence (WoE) and Information Value (IV) test is designed to evaluate the predictive power of each
-feature in a machine learning model. This test generates binned groups of values from each feature, computes the
-WoE and IV for each bin, and provides insights into the relationship between each feature and the target variable,
-illustrating their contribution to the model's predictive capabilities.
-
-
Test Mechanism
-
-
The test uses the scorecardpy.woebin method to perform automatic binning of the dataset based on WoE. The method
-accepts a list of break points for binning numeric variables through the parameter breaks_adj. If no breaks are
-provided, it uses default binning. The bins are then used to calculate the WoE and IV values, effectively creating
-a dataframe that includes the bin boundaries, WoE, and IV values for each feature. A target variable is required
-in the dataset to perform this analysis.
-
-
Signs of High Risk
-
-
-High IV values, indicating variables with excessive predictive power which might lead to overfitting.
-Errors during the binning process, potentially due to inappropriate data types or poorly defined bins.
-
-
-
Strengths
-
-
-Highly effective for feature selection in binary classification problems, as it quantifies the predictive
-information within each feature concerning the binary outcome.
-The WoE transformation creates a monotonic relationship between the target and independent variables.
-
-
-
Limitations
-
-
-Primarily designed for binary classification tasks, making it less applicable or reliable for multi-class
-classification or regression tasks.
-Potential difficulties if the dataset has many features, non-binnable features, or non-numeric features.
-The metric does not help in distinguishing whether the observed predictive factor is due to data randomness or a
-true phenomenon.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/ZivotAndrewsArch.html b/docs/_build/validmind/tests/data_validation/ZivotAndrewsArch.html
deleted file mode 100644
index 6f70bc649..000000000
--- a/docs/_build/validmind/tests/data_validation/ZivotAndrewsArch.html
+++ /dev/null
@@ -1,300 +0,0 @@
-
-
-
- validmind.tests.data_validation.ZivotAndrewsArch API documentation
-
-
-
-
-
-
-
@tags('time_series_data', 'stationarity', 'unit_root_test')
-
@tasks('regression')
-
-
def
-
ZivotAndrewsArch (dataset : validmind.vm_models.VMDataset ):
-
-
-
Evaluates the order of integration and stationarity of time series data using the Zivot-Andrews unit root test.
-
-
Purpose
-
-
The Zivot-Andrews Arch metric is used to evaluate the order of integration for time series data in a machine
-learning model. It's designed to test for stationarity, a crucial aspect of time series analysis, where data points
-are independent of time. Stationarity means that the statistical properties such as mean, variance, and
-autocorrelation are constant over time.
-
-
Test Mechanism
-
-
The Zivot-Andrews unit root test is performed on each feature in the dataset using the ZivotAndrews function from
-the arch.unitroot module. This function returns several metrics for each feature, including the statistical
-value, p-value (probability value), the number of lags used, and the number of observations. The p-value is used to
-decide on the null hypothesis (the time series has a unit root and is non-stationary) based on a chosen level of
-significance.
-
-
Signs of High Risk
-
-
-A high p-value suggests high risk, indicating insufficient evidence to reject the null hypothesis, implying that
-the time series has a unit root and is non-stationary.
-Non-stationary time series data can lead to misleading statistics and unreliable machine learning models.
-
-
-
Strengths
-
-
-Dynamically tests for stationarity against structural breaks in time series data, offering robust evaluation of
-stationarity in features.
-Especially beneficial with financial, economic, or other time-series data where data observations lack a
-consistent pattern and structural breaks may occur.
-
-
-
Limitations
-
-
-Assumes data is derived from a single-equation, autoregressive model, making it less appropriate for multivariate
-time series data or data not aligning with this model.
-May not account for unexpected shocks or changes in the series trend, both of which can significantly impact data
-stationarity.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/nlp.html b/docs/_build/validmind/tests/data_validation/nlp.html
deleted file mode 100644
index 81e3b938d..000000000
--- a/docs/_build/validmind/tests/data_validation/nlp.html
+++ /dev/null
@@ -1,248 +0,0 @@
-
-
-
- validmind.tests.data_validation.nlp API documentation
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/nlp/CommonWords.html b/docs/_build/validmind/tests/data_validation/nlp/CommonWords.html
deleted file mode 100644
index 1e2b08981..000000000
--- a/docs/_build/validmind/tests/data_validation/nlp/CommonWords.html
+++ /dev/null
@@ -1,301 +0,0 @@
-
-
-
- validmind.tests.data_validation.nlp.CommonWords API documentation
-
-
-
-
-
-
-
@tags('nlp', 'text_data', 'visualization', 'frequency_analysis')
-
@tasks('text_classification', 'text_summarization')
-
-
def
-
CommonWords (dataset : validmind.vm_models.VMDataset ):
-
-
-
Assesses the most frequent non-stopwords in a text column for identifying prevalent language patterns.
-
-
Purpose
-
-
The CommonWords metric is used to identify and visualize the most prevalent words within a specified text column of
-a dataset. This provides insights into the prevalent language patterns and vocabulary, especially useful in Natural
-Language Processing (NLP) tasks such as text classification and text summarization.
-
-
Test Mechanism
-
-
The test methodology involves splitting the specified text column's entries into words, collating them into a
-corpus, and then counting the frequency of each word using the Counter. The forty most frequently occurring
-non-stopwords are then visualized in an interactive bar chart using Plotly, where the x-axis represents the words,
-and the y-axis indicates their frequency of occurrence.
-
-
Signs of High Risk
-
-
-A lack of distinct words within the list, or the most common words being stopwords.
-Frequent occurrence of irrelevant or inappropriate words could point out a poorly curated or noisy dataset.
-An error returned due to the absence of a valid Dataset object, indicating high risk as the metric cannot be
-effectively implemented without it.
-
-
-
Strengths
-
-
-The metric provides clear insights into the language features – specifically word frequency – of unstructured
-text data.
-It can reveal prominent vocabulary and language patterns, which prove vital for feature extraction in NLP tasks.
-The interactive visualization helps in quickly capturing the patterns and understanding the data intuitively.
-
-
-
Limitations
-
-
-The test disregards semantic or context-related information as it solely focuses on word frequency.
-It intentionally ignores stopwords, which might carry necessary significance in certain scenarios.
-The applicability is limited to English-language text data as English stopwords are used for filtering, hence
-cannot account for data in other languages.
-The metric requires a valid Dataset object, indicating a dependency condition that limits its broader
-applicability.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/nlp/Hashtags.html b/docs/_build/validmind/tests/data_validation/nlp/Hashtags.html
deleted file mode 100644
index 66787a387..000000000
--- a/docs/_build/validmind/tests/data_validation/nlp/Hashtags.html
+++ /dev/null
@@ -1,304 +0,0 @@
-
-
-
- validmind.tests.data_validation.nlp.Hashtags API documentation
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/nlp/LanguageDetection.html b/docs/_build/validmind/tests/data_validation/nlp/LanguageDetection.html
deleted file mode 100644
index 8cbc89ad7..000000000
--- a/docs/_build/validmind/tests/data_validation/nlp/LanguageDetection.html
+++ /dev/null
@@ -1,303 +0,0 @@
-
-
-
- validmind.tests.data_validation.nlp.LanguageDetection API documentation
-
-
-
-
-
-
-
@tags('nlp', 'text_data', 'visualization')
-
@tasks('text_classification', 'text_summarization')
-
-
def
-
LanguageDetection (dataset ):
-
-
-
Assesses the diversity of languages in a textual dataset by detecting and visualizing the distribution of languages.
-
-
Purpose
-
-
The Language Detection test aims to identify and visualize the distribution of languages present within a textual
-dataset. This test helps in understanding the diversity of languages in the data, which is crucial for developing
-and validating multilingual models.
-
-
Test Mechanism
-
-
This test operates by:
-
-
-Checking if the dataset has a specified text column.
-Using a language detection library to determine the language of each text entry in the dataset.
-Generating a histogram plot of the language distribution, with language codes on the x-axis and their frequencies
-on the y-axis.
-
-
-
If the text column is not specified, a ValueError is raised to ensure proper dataset configuration.
-
-
Signs of High Risk
-
-
-A high proportion of entries returning "Unknown" language codes.
-Detection of unexpectedly diverse or incorrect language codes, indicating potential data quality issues.
-Significant imbalance in language distribution, which might indicate potential biases in the dataset.
-
-
-
Strengths
-
-
-Provides a visual representation of language diversity within the dataset.
-Helps identify data quality issues related to incorrect or unknown language detection.
-Useful for ensuring that multilingual models have adequate and appropriate representation from various languages.
-
-
-
Limitations
-
-
-Dependency on the accuracy of the language detection library, which may not be perfect.
-Languages with similar structures or limited text length may be incorrectly classified.
-The test returns "Unknown" for entries where language detection fails, which might mask underlying issues with
-certain languages or text formats.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/nlp/Mentions.html b/docs/_build/validmind/tests/data_validation/nlp/Mentions.html
deleted file mode 100644
index 0111fd643..000000000
--- a/docs/_build/validmind/tests/data_validation/nlp/Mentions.html
+++ /dev/null
@@ -1,302 +0,0 @@
-
-
-
- validmind.tests.data_validation.nlp.Mentions API documentation
-
-
-
-
-
-
-
@tags('nlp', 'text_data', 'visualization', 'frequency_analysis')
-
@tasks('text_classification', 'text_summarization')
-
-
def
-
Mentions ( dataset : validmind.vm_models.VMDataset top_mentions : int = 25 ):
-
-
-
Calculates and visualizes frequencies of '@' prefixed mentions in a text-based dataset for NLP model analysis.
-
-
Purpose
-
-
The "Mentions" test is designed to gauge the quality of data in a Natural Language Processing (NLP) or text-focused
-Machine Learning model. The primary objective is to identify and calculate the frequency of 'mentions' within a
-chosen text column of a dataset. A 'mention' in this context refers to individual text elements that are prefixed
-by '@'. The output of this test reveals the most frequently mentioned entities or usernames, which can be integral
-for applications such as social media analyses or customer sentiment analyses.
-
-
Test Mechanism
-
-
The test first verifies the existence of a text column in the provided dataset. It then employs a regular
-expression pattern to extract mentions from the text. Subsequently, the frequency of each unique mention is
-calculated. The test selects the most frequent mentions based on default or user-defined parameters, the default
-being the top 25, for representation. This process of thresholding forms the core of the test. A treemap plot
-visualizes the test results, where the size of each rectangle corresponds to the frequency of a particular mention.
-
-
Signs of High Risk
-
-
-The lack of a valid text column in the dataset, which would result in the failure of the test execution.
-The absence of any mentions within the text data, indicating that there might not be any text associated with
-'@'. This situation could point toward sparse or poor-quality data, thereby hampering the model's generalization or
-learning capabilities.
-
-
-
Strengths
-
-
-The test is specifically optimized for text-based datasets which gives it distinct power in the context of NLP.
-It enables quick identification and visually appealing representation of the predominant elements or mentions.
-It can provide crucial insights about the most frequently mentioned entities or usernames.
-
-
-
Limitations
-
-
-The test only recognizes mentions that are prefixed by '@', hence useful textual aspects not preceded by '@'
-might be ignored.
-This test isn't suited for datasets devoid of textual data.
-It does not provide insights on less frequently occurring data or outliers, which means potentially significant
-patterns could be overlooked.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/nlp/PolarityAndSubjectivity.html b/docs/_build/validmind/tests/data_validation/nlp/PolarityAndSubjectivity.html
deleted file mode 100644
index 469537011..000000000
--- a/docs/_build/validmind/tests/data_validation/nlp/PolarityAndSubjectivity.html
+++ /dev/null
@@ -1,301 +0,0 @@
-
-
-
- validmind.tests.data_validation.nlp.PolarityAndSubjectivity API documentation
-
-
-
-
-
-
-
@tags('nlp', 'text_data', 'data_validation')
-
@tasks('nlp')
-
-
def
-
PolarityAndSubjectivity (dataset , threshold_subjectivity = 0.5 , threshold_polarity = 0 ):
-
-
-
Analyzes the polarity and subjectivity of text data within a given dataset to visualize the sentiment distribution.
-
-
Purpose
-
-
The Polarity and Subjectivity test is designed to evaluate the sentiment expressed in textual data. By analyzing
-these aspects, it helps to identify the emotional tone and subjectivity of the dataset, which could be crucial in
-understanding customer feedback, social media sentiments, or other text-related data.
-
-
Test Mechanism
-
-
This test uses TextBlob to compute the polarity and subjectivity scores of textual data in a given dataset. The
-mechanism includes:
-
-
-Iterating through each text entry in the specified column of the dataset.
-Applying the TextBlob library to compute the polarity (ranging from -1 for negative sentiment to +1 for positive
-sentiment) and subjectivity (ranging from 0 for objective to 1 for subjective) for each entry.
-Creating a scatter plot using Plotly to visualize the relationship between polarity and subjectivity.
-
-
-
Signs of High Risk
-
-
-High concentration of negative polarity values indicating prevalent negative sentiments.
-High subjectivity scores suggesting the text data is largely opinion-based rather than factual.
-Disproportionate clusters of extreme scores (e.g., many points near -1 or +1 polarity).
-
-
-
Strengths
-
-
-Quantifies sentiment and subjectivity which can provide actionable insights.
-Visualizes sentiment distribution, aiding in easy interpretation.
-Utilizes well-established TextBlob library for sentiment analysis.
-
-
-
Limitations
-
-
-Polarity and subjectivity calculations may oversimplify nuanced text sentiments.
-Reliance on TextBlob which may not be accurate for all domains or contexts.
-Visualization could become cluttered with very large datasets, making interpretation difficult.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/nlp/Punctuations.html b/docs/_build/validmind/tests/data_validation/nlp/Punctuations.html
deleted file mode 100644
index 057b4eaf5..000000000
--- a/docs/_build/validmind/tests/data_validation/nlp/Punctuations.html
+++ /dev/null
@@ -1,301 +0,0 @@
-
-
-
- validmind.tests.data_validation.nlp.Punctuations API documentation
-
-
-
-
-
-
- Metrics functions for any Pandas-compatible datasets
-
-
-
-
@tags('nlp', 'text_data', 'visualization', 'frequency_analysis')
-
@tasks('text_classification', 'text_summarization', 'nlp')
-
-
def
-
Punctuations (dataset , count_mode = 'token' ):
-
-
-
Analyzes and visualizes the frequency distribution of punctuation usage in a given text dataset.
-
-
Purpose
-
-
The Punctuations Metric's primary purpose is to analyze the frequency of punctuation usage within a given text
-dataset. This is often used in Natural Language Processing tasks, such as text classification and text
-summarization.
-
-
Test Mechanism
-
-
The test begins by verifying that the input "dataset" is of the type VMDataset. The count_mode parameter must be
-either "token" (counts punctuation marks as individual tokens) or "word" (counts punctuation marks within words).
-Following that, a corpus is created from the dataset by splitting its text on spaces. Each unique punctuation
-character in the text corpus is then tallied. The frequency distribution of each punctuation symbol is visualized
-as a bar graph, with these results being stored as Figures and associated with the main Punctuations object.
-
-
Signs of High Risk
-
-
-Excessive or unusual frequency of specific punctuation marks, potentially denoting dubious quality, data
-corruption, or skewed data.
-
-
-
Strengths
-
-
-Provides valuable insights into the distribution of punctuation usage in a text dataset.
-Important in validating the quality, consistency, and nature of the data.
-Can provide hints about the style or tonality of the text corpus, such as informal and emotional context
-indicated by frequent exclamation marks.
-
-
-
Limitations
-
-
-Focuses solely on punctuation usage, potentially missing other important textual characteristics.
-General cultural or tonality assumptions based on punctuation distribution can be misguiding, as these vary
-across different languages and contexts.
-Less effective with languages that use non-standard or different punctuation.
-Visualization may lack interpretability when there are many unique punctuation marks in the dataset.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/nlp/Sentiment.html b/docs/_build/validmind/tests/data_validation/nlp/Sentiment.html
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index 1457538d9..000000000
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+++ /dev/null
@@ -1,295 +0,0 @@
-
-
-
- validmind.tests.data_validation.nlp.Sentiment API documentation
-
-
-
-
-
-
-
@tags('nlp', 'text_data', 'data_validation')
-
@tasks('nlp')
-
-
def
-
Sentiment (dataset ):
-
-
-
Analyzes the sentiment of text data within a dataset using the VADER sentiment analysis tool.
-
-
Purpose
-
-
The Sentiment test evaluates the overall sentiment of text data within a dataset. By analyzing sentiment scores, it
-aims to ensure that the model is interpreting text data accurately and is not biased towards a particular sentiment.
-
-
Test Mechanism
-
-
This test uses the VADER (Valence Aware Dictionary and sEntiment Reasoner) SentimentIntensityAnalyzer. It processes
-each text entry in a specified column of the dataset to calculate the compound sentiment score, which represents
-the overall sentiment polarity. The distribution of these sentiment scores is then visualized using a KDE (Kernel
-Density Estimation) plot, highlighting any skewness or concentration in sentiment.
-
-
Signs of High Risk
-
-
-Extreme polarity in sentiment scores, indicating potential bias.
-Unusual concentration of sentiment scores in a specific range.
-Significant deviation from expected sentiment distribution for the given text data.
-
-
-
Strengths
-
-
-Provides a clear visual representation of sentiment distribution.
-Uses a well-established sentiment analysis tool (VADER).
-Can handle a wide range of text data, making it flexible for various applications.
-
-
-
Limitations
-
-
-May not capture nuanced or context-specific sentiments.
-Relies heavily on the accuracy of the VADER sentiment analysis tool.
-Visualization alone may not provide comprehensive insights into underlying causes of sentiment distribution.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/nlp/StopWords.html b/docs/_build/validmind/tests/data_validation/nlp/StopWords.html
deleted file mode 100644
index 97e63f9b5..000000000
--- a/docs/_build/validmind/tests/data_validation/nlp/StopWords.html
+++ /dev/null
@@ -1,311 +0,0 @@
-
-
-
- validmind.tests.data_validation.nlp.StopWords API documentation
-
-
-
-
-
-
-
@tags('nlp', 'text_data', 'frequency_analysis', 'visualization')
-
@tasks('text_classification', 'text_summarization')
-
-
def
-
StopWords ( dataset : validmind.vm_models.VMDataset min_percent_threshold : float = 0.5 , num_words : int = 25 ):
-
-
-
Evaluates and visualizes the frequency of English stop words in a text dataset against a defined threshold.
-
-
Purpose
-
-
The StopWords threshold test is a tool designed for assessing the quality of text data in an ML model. It focuses
-on the identification and analysis of "stop words" in a given dataset. Stop words are frequent, common, yet
-semantically insignificant words (for example: "the", "and", "is") in a language. This test evaluates the
-proportion of stop words to the total word count in the dataset, in essence, scrutinizing the frequency of stop
-word usage. The core objective is to highlight the prevalent stop words based on their usage frequency, which can
-be instrumental in cleaning the data from noise and improving ML model performance.
-
-
Test Mechanism
-
-
The StopWords test initiates on receiving an input of a 'VMDataset' object. Absence of such an object will trigger
-an error. The methodology involves inspection of the text column of the VMDataset to create a 'corpus' (a
-collection of written texts). Leveraging the Natural Language Toolkit's (NLTK) stop word repository, the test
-screens the corpus for any stop words and documents their frequency. It further calculates the percentage usage of
-each stop word compared to the total word count in the corpus. This percentage is evaluated against a predefined
-'min_percent_threshold'. If this threshold is breached, the test returns a failed output. Top prevailing stop words
-along with their usage percentages are returned, facilitated by a bar chart visualization of these stop words and
-their frequency.
-
-
Signs of High Risk
-
-
-A percentage of any stop words exceeding the predefined 'min_percent_threshold'.
-High frequency of stop words in the dataset which may adversely affect the application's analytical performance
-due to noise creation.
-
-
-
Strengths
-
-
-The ability to scrutinize and quantify the usage of stop words.
-Provides insights into potential noise in the text data due to stop words.
-Directly aids in enhancing model training efficiency.
-Includes a bar chart visualization feature to easily interpret and action upon the stop words frequency
-information.
-
-
-
Limitations
-
-
-The test only supports English stop words, making it less effective with datasets of other languages.
-The 'min_percent_threshold' parameter may require fine-tuning for different datasets, impacting the overall
-effectiveness of the test.
-Contextual use of the stop words within the dataset is not considered, potentially overlooking their significance
-in certain contexts.
-The test focuses specifically on the frequency of stop words, not providing direct measures of model performance
-or predictive accuracy.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/nlp/TextDescription.html b/docs/_build/validmind/tests/data_validation/nlp/TextDescription.html
deleted file mode 100644
index 9584383a9..000000000
--- a/docs/_build/validmind/tests/data_validation/nlp/TextDescription.html
+++ /dev/null
@@ -1,323 +0,0 @@
-
-
-
- validmind.tests.data_validation.nlp.TextDescription API documentation
-
-
-
-
-
-
-
- def
- create_metrics_df (df , text_column , unwanted_tokens , lang ):
-
-
-
-
-
-
-
-
-
-
-
@tags('nlp', 'text_data', 'visualization')
-
@tasks('text_classification', 'text_summarization')
-
-
def
-
TextDescription ( dataset : validmind.vm_models.VMDataset unwanted_tokens : set = { "s'" , ' ' , 'dr' , "''" , 's' , '``' , 'mr' , 'mrs' , 'dollar' , 'ms' , 'us' , "'s" } , lang : str = 'english' ):
-
-
-
Conducts comprehensive textual analysis on a dataset using NLTK to evaluate various parameters and generate
-visualizations.
-
-
Purpose
-
-
The TextDescription test aims to conduct a thorough textual analysis of a dataset using the NLTK (Natural Language
-Toolkit) library. It evaluates various metrics such as total words, total sentences, average sentence length, total
-paragraphs, total unique words, most common words, total punctuations, and lexical diversity. The goal is to
-understand the nature of the text and anticipate challenges machine learning models might face in text processing,
-language understanding, or summarization tasks.
-
-
Test Mechanism
-
-
The test works by:
-
-
-Parsing the dataset and tokenizing the text into words, sentences, and paragraphs using NLTK.
-Removing stopwords and unwanted tokens.
-Calculating parameters like total words, total sentences, average sentence length, total paragraphs, total unique
-words, total punctuations, and lexical diversity.
-Generating scatter plots to visualize correlations between various metrics (e.g., Total Words vs Total Sentences).
-
-
-
Signs of High Risk
-
-
-Anomalies or increased complexity in lexical diversity.
-Longer sentences and paragraphs.
-High uniqueness of words.
-Large number of unwanted tokens.
-Missing or erroneous visualizations.
-
-
-
Strengths
-
-
-Essential for pre-processing text data in machine learning models.
-Provides a comprehensive breakdown of text data, aiding in understanding its complexity.
-Generates visualizations to help comprehend text structure and complexity.
-
-
-
Limitations
-
-
-Highly dependent on the NLTK library, limiting the test to supported languages.
-Limited customization for removing undesirable tokens and stop words.
-Does not consider semantic or grammatical complexities.
-Assumes well-structured documents, which may result in inaccuracies with poorly formatted text.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/data_validation/nlp/Toxicity.html b/docs/_build/validmind/tests/data_validation/nlp/Toxicity.html
deleted file mode 100644
index 293b084bd..000000000
--- a/docs/_build/validmind/tests/data_validation/nlp/Toxicity.html
+++ /dev/null
@@ -1,301 +0,0 @@
-
-
-
- validmind.tests.data_validation.nlp.Toxicity API documentation
-
-
-
-
-
-
-
@tags('nlp', 'text_data', 'data_validation')
-
@tasks('nlp')
-
-
def
-
Toxicity (dataset ):
-
-
-
Assesses the toxicity of text data within a dataset to visualize the distribution of toxicity scores.
-
-
Purpose
-
-
The Toxicity test aims to evaluate the level of toxic content present in a text dataset by leveraging a pre-trained
-toxicity model. It helps in identifying potentially harmful or offensive language that may negatively impact users
-or stakeholders.
-
-
Test Mechanism
-
-
This test uses a pre-trained toxicity evaluation model and applies it to each text entry in the specified column of
-a dataset’s dataframe. The procedure involves:
-
-
-Loading a pre-trained toxicity model.
-Extracting the text from the specified column in the dataset.
-Computing toxicity scores for each text entry.
-Generating a KDE (Kernel Density Estimate) plot to visualize the distribution of these toxicity scores.
-
-
-
Signs of High Risk
-
-
-High concentration of high toxicity scores in the KDE plot.
-A significant proportion of text entries with toxicity scores above a predefined threshold.
-Wide distribution of toxicity scores, indicating inconsistency in content quality.
-
-
-
Strengths
-
-
-Provides a visual representation of toxicity distribution, making it easier to identify outliers.
-Uses a robust pre-trained model for toxicity evaluation.
-Can process large text datasets efficiently.
-
-
-
Limitations
-
-
-Depends on the accuracy and bias of the pre-trained toxicity model.
-Does not provide context-specific insights, which may be necessary for nuanced understanding.
-May not capture all forms of subtle or indirect toxic language.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation.html b/docs/_build/validmind/tests/model_validation.html
deleted file mode 100644
index 76cf0d275..000000000
--- a/docs/_build/validmind/tests/model_validation.html
+++ /dev/null
@@ -1,256 +0,0 @@
-
-
-
- validmind.tests.model_validation API documentation
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/BertScore.html b/docs/_build/validmind/tests/model_validation/BertScore.html
deleted file mode 100644
index 62f8cd55f..000000000
--- a/docs/_build/validmind/tests/model_validation/BertScore.html
+++ /dev/null
@@ -1,309 +0,0 @@
-
-
-
- validmind.tests.model_validation.BertScore API documentation
-
-
-
-
-
-
-
@tags('nlp', 'text_data', 'visualization')
-
@tasks('text_classification', 'text_summarization')
-
-
def
-
BertScore (dataset , model , evaluation_model = 'distilbert-base-uncased' ):
-
-
-
Assesses the quality of machine-generated text using BERTScore metrics and visualizes results through histograms
-and bar charts, alongside compiling a comprehensive table of descriptive statistics.
-
-
Purpose
-
-
This function is designed to assess the quality of text generated by machine learning models using BERTScore
-metrics. BERTScore evaluates text generation models' performance by calculating precision, recall, and F1 score
-based on BERT contextual embeddings.
-
-
Test Mechanism
-
-
The function starts by extracting the true and predicted values from the provided dataset and model. It then
-initializes the BERTScore evaluator. For each pair of true and predicted texts, the function calculates the
-BERTScore metrics and compiles them into a dataframe. Histograms and bar charts are generated for each BERTScore
-metric (Precision, Recall, and F1 Score) to visualize their distribution. Additionally, a table of descriptive
-statistics (mean, median, standard deviation, minimum, and maximum) is compiled for each metric, providing a
-comprehensive summary of the model's performance. The test uses the evaluation_model param to specify the
-huggingface model to use for evaluation. microsoft/deberta-xlarge-mnli is the best-performing model but is
-very large and may be slow without a GPU. microsoft/deberta-large-mnli is a smaller model that is faster to
-run and distilbert-base-uncased is much lighter and can run on a CPU but is less accurate.
-
-
Signs of High Risk
-
-
-Consistently low scores across BERTScore metrics could indicate poor quality in the generated text, suggesting
-that the model fails to capture the essential content of the reference texts.
-Low precision scores might suggest that the generated text contains a lot of redundant or irrelevant information.
-Low recall scores may indicate that important information from the reference text is being omitted.
-An imbalanced performance between precision and recall, reflected by a low F1 Score, could signal issues in the
-model's ability to balance informativeness and conciseness.
-
-
-
Strengths
-
-
-Provides a multifaceted evaluation of text quality through different BERTScore metrics, offering a detailed view
-of model performance.
-Visual representations (histograms and bar charts) make it easier to interpret the distribution and trends of the
-scores.
-Descriptive statistics offer a concise summary of the model's strengths and weaknesses in generating text.
-
-
-
Limitations
-
-
-BERTScore relies on the contextual embeddings from BERT models, which may not fully capture all nuances of text
-similarity.
-The evaluation relies on the availability of high-quality reference texts, which may not always be obtainable.
-While useful for comparison, BERTScore metrics alone do not provide a complete assessment of a model's
-performance and should be supplemented with other metrics and qualitative analysis.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/BleuScore.html b/docs/_build/validmind/tests/model_validation/BleuScore.html
deleted file mode 100644
index 46614a2a0..000000000
--- a/docs/_build/validmind/tests/model_validation/BleuScore.html
+++ /dev/null
@@ -1,306 +0,0 @@
-
-
-
- validmind.tests.model_validation.BleuScore API documentation
-
-
-
-
-
-
-
@tags('nlp', 'text_data', 'visualization')
-
@tasks('text_classification', 'text_summarization')
-
-
def
-
BleuScore (dataset , model ):
-
-
-
Evaluates the quality of machine-generated text using BLEU metrics and visualizes the results through histograms
-and bar charts, alongside compiling a comprehensive table of descriptive statistics for BLEU scores.
-
-
Purpose
-
-
This function is designed to assess the quality of text generated by machine learning models using the BLEU metric.
-BLEU, which stands for Bilingual Evaluation Understudy, is a metric used to evaluate the overlap of n-grams between
-the machine-generated text and reference texts. This evaluation is crucial for tasks such as text summarization,
-machine translation, and text generation, where the goal is to produce text that accurately reflects the content
-and meaning of human-crafted references.
-
-
Test Mechanism
-
-
The function starts by extracting the true and predicted values from the provided dataset and model. It then
-initializes the BLEU evaluator. For each pair of true and predicted texts, the function calculates the BLEU scores
-and compiles them into a dataframe. Histograms and bar charts are generated for the BLEU scores to visualize their
-distribution. Additionally, a table of descriptive statistics (mean, median, standard deviation, minimum, and
-maximum) is compiled for the BLEU scores, providing a comprehensive summary of the model's performance.
-
-
Signs of High Risk
-
-
-Consistently low BLEU scores could indicate poor quality in the generated text, suggesting that the model fails
-to capture the essential content of the reference texts.
-Low precision scores might suggest that the generated text contains a lot of redundant or irrelevant information.
-Low recall scores may indicate that important information from the reference text is being omitted.
-An imbalanced performance between precision and recall, reflected by a low BLEU score, could signal issues in the
-model's ability to balance informativeness and conciseness.
-
-
-
Strengths
-
-
-Provides a straightforward and widely-used evaluation of text quality through BLEU scores.
-Visual representations (histograms and bar charts) make it easier to interpret the distribution and trends of the
-scores.
-Descriptive statistics offer a concise summary of the model's strengths and weaknesses in generating text.
-
-
-
Limitations
-
-
-BLEU metrics primarily focus on n-gram overlap and may not fully capture semantic coherence, fluency, or
-grammatical quality of the text.
-The evaluation relies on the availability of high-quality reference texts, which may not always be obtainable.
-While useful for comparison, BLEU scores alone do not provide a complete assessment of a model's performance and
-should be supplemented with other metrics and qualitative analysis.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/ClusterSizeDistribution.html b/docs/_build/validmind/tests/model_validation/ClusterSizeDistribution.html
deleted file mode 100644
index 9a4a19535..000000000
--- a/docs/_build/validmind/tests/model_validation/ClusterSizeDistribution.html
+++ /dev/null
@@ -1,305 +0,0 @@
-
-
-
- validmind.tests.model_validation.ClusterSizeDistribution API documentation
-
-
-
-
-
-
-
-
- Assesses the performance of clustering models by comparing the distribution of cluster sizes in model predictions
-with the actual data.
-
-
Purpose
-
-
The Cluster Size Distribution test aims to assess the performance of clustering models by comparing the
-distribution of cluster sizes in the model's predictions with the actual data. This comparison helps determine if
-the clustering model's output aligns well with the true cluster distribution, providing insights into the model's
-accuracy and performance.
-
-
Test Mechanism
-
-
The test mechanism involves the following steps:
-
-
-Run the clustering model on the provided dataset to obtain predictions.
-Convert both the actual and predicted outputs into pandas dataframes.
-Use pandas built-in functions to derive the cluster size distributions from these dataframes.
-Construct two histograms: one for the actual cluster size distribution and one for the predicted distribution.
-Plot the histograms side-by-side for visual comparison.
-
-
-
Signs of High Risk
-
-
-Discrepancies between the actual cluster size distribution and the predicted cluster size distribution.
-Irregular distribution of data across clusters in the predicted outcomes.
-High number of outlier clusters suggesting the model struggles to correctly group data.
-
-
-
Strengths
-
-
-Provides a visual and intuitive way to compare the clustering model's performance against actual data.
-Effectively reveals where the model may be over- or underestimating cluster sizes.
-Versatile as it works well with any clustering model.
-
-
-
Limitations
-
-
-Assumes that the actual cluster distribution is optimal, which may not always be the case.
-Relies heavily on visual comparison, which could be subjective and may not offer a precise numerical measure of
-performance.
-May not fully capture other important aspects of clustering, such as cluster density, distances between clusters,
-and the shape of clusters.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/ContextualRecall.html b/docs/_build/validmind/tests/model_validation/ContextualRecall.html
deleted file mode 100644
index afa8c7fc6..000000000
--- a/docs/_build/validmind/tests/model_validation/ContextualRecall.html
+++ /dev/null
@@ -1,307 +0,0 @@
-
-
-
- validmind.tests.model_validation.ContextualRecall API documentation
-
-
-
-
-
-
-
@tags('nlp', 'text_data', 'visualization')
-
@tasks('text_classification', 'text_summarization')
-
-
def
-
ContextualRecall (dataset , model ):
-
-
-
Evaluates a Natural Language Generation model's ability to generate contextually relevant and factually correct
-text, visualizing the results through histograms and bar charts, alongside compiling a comprehensive table of
-descriptive statistics for contextual recall scores.
-
-
Purpose
-
-
The Contextual Recall metric is used to evaluate the ability of a natural language generation (NLG) model to
-generate text that appropriately reflects the given context or prompt. It measures the model's capability to
-remember and reproduce the main context in its resulting output. This metric is critical in natural language
-processing tasks, as the coherency and contextuality of the generated text are essential.
-
-
Test Mechanism
-
-
The function starts by extracting the true and predicted values from the provided dataset and model. It then
-tokenizes the reference and candidate texts into discernible words or tokens using NLTK. The token overlap between
-the reference and candidate texts is identified, and the Contextual Recall score is computed by dividing the number
-of overlapping tokens by the total number of tokens in the reference text. Scores are calculated for each test
-dataset instance, resulting in an array of scores. These scores are visualized using a histogram and a bar chart to
-show score variations across different rows. Additionally, a table of descriptive statistics (mean, median,
-standard deviation, minimum, and maximum) is compiled for the contextual recall scores, providing a comprehensive
-summary of the model's performance.
-
-
Signs of High Risk
-
-
-Low contextual recall scores could indicate that the model is not effectively reflecting the original context in
-its output, leading to incoherent or contextually misaligned text.
-A consistent trend of low recall scores could suggest underperformance of the model.
-
-
-
Strengths
-
-
-Provides a quantifiable measure of a model's adherence to the context and factual elements of the generated
-narrative.
-Visual representations (histograms and bar charts) make it easier to interpret the distribution and trends of
-contextual recall scores.
-Descriptive statistics offer a concise summary of the model's performance in generating contextually relevant
-texts.
-
-
-
Limitations
-
-
-The focus on word overlap could result in high scores for texts that use many common words, even when these texts
-lack coherence or meaningful context.
-This metric does not consider the order of words, which could lead to overestimated scores for scrambled outputs.
-Models that effectively use infrequent words might be undervalued, as these words might not overlap as often.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/FeaturesAUC.html b/docs/_build/validmind/tests/model_validation/FeaturesAUC.html
deleted file mode 100644
index a6490e8af..000000000
--- a/docs/_build/validmind/tests/model_validation/FeaturesAUC.html
+++ /dev/null
@@ -1,301 +0,0 @@
-
-
-
- validmind.tests.model_validation.FeaturesAUC API documentation
-
-
-
-
-
-
-
@tags('feature_importance', 'AUC', 'visualization')
-
@tasks('classification')
-
-
def
-
FeaturesAUC ( dataset : validmind.vm_models.VMDataset fontsize : int = 12 , figure_height : int = 500 ):
-
-
-
Evaluates the discriminatory power of each individual feature within a binary classification model by calculating
-the Area Under the Curve (AUC) for each feature separately.
-
-
Purpose
-
-
The central objective of this metric is to quantify how well each feature on its own can differentiate between the
-two classes in a binary classification problem. It serves as a univariate analysis tool that can help in
-pre-modeling feature selection or post-modeling interpretation.
-
-
Test Mechanism
-
-
For each feature, the metric treats the feature values as raw scores to compute the AUC against the actual binary
-outcomes. It provides an AUC value for each feature, offering a simple yet powerful indication of each feature's
-univariate classification strength.
-
-
Signs of High Risk
-
-
-A feature with a low AUC score may not be contributing significantly to the differentiation between the two
-classes, which could be a concern if it is expected to be predictive.
-Conversely, a surprisingly high AUC for a feature not believed to be informative may suggest data leakage or
-other issues with the data.
-
-
-
Strengths
-
-
-By isolating each feature, it highlights the individual contribution of features to the classification task
-without the influence of other variables.
-Useful for both initial feature evaluation and for providing insights into the model's reliance on individual
-features after model training.
-
-
-
Limitations
-
-
-Does not reflect the combined effects of features or any interaction between them, which can be critical in
-certain models.
-The AUC values are calculated without considering the model's use of the features, which could lead to different
-interpretations of feature importance when considering the model holistically.
-This metric is applicable only to binary classification tasks and cannot be directly extended to multiclass
-classification or regression without modifications.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/MeteorScore.html b/docs/_build/validmind/tests/model_validation/MeteorScore.html
deleted file mode 100644
index 063f9bf7f..000000000
--- a/docs/_build/validmind/tests/model_validation/MeteorScore.html
+++ /dev/null
@@ -1,310 +0,0 @@
-
-
-
- validmind.tests.model_validation.MeteorScore API documentation
-
-
-
-
-
-
-
@tags('nlp', 'text_data', 'visualization')
-
@tasks('text_classification', 'text_summarization')
-
-
def
-
MeteorScore (dataset , model ):
-
-
-
Assesses the quality of machine-generated translations by comparing them to human-produced references using the
-METEOR score, which evaluates precision, recall, and word order.
-
-
Purpose
-
-
The METEOR (Metric for Evaluation of Translation with Explicit ORdering) score is designed to evaluate the quality
-of machine translations by comparing them against reference translations. It emphasizes both the accuracy and
-fluency of translations, incorporating precision, recall, and word order into its assessment.
-
-
Test Mechanism
-
-
The function starts by extracting the true and predicted values from the provided dataset and model. The METEOR
-score is computed for each pair of machine-generated translation (prediction) and its corresponding human-produced
-reference. This is done by considering unigram matches between the translations, including matches based on surface
-forms, stemmed forms, and synonyms. The score is a combination of unigram precision and recall, adjusted for word
-order through a fragmentation penalty. Scores are compiled into a dataframe, and histograms and bar charts are
-generated to visualize the distribution of METEOR scores. Additionally, a table of descriptive statistics (mean,
-median, standard deviation, minimum, and maximum) is compiled for the METEOR scores, providing a comprehensive
-summary of the model's performance.
-
-
Signs of High Risk
-
-
-Lower METEOR scores can indicate a lack of alignment between the machine-generated translations and their
-human-produced references, highlighting potential deficiencies in both the accuracy and fluency of translations.
-Significant discrepancies in word order or an excessive fragmentation penalty could signal issues with how the
-translation model processes and reconstructs sentence structures, potentially compromising the natural flow of
-translated text.
-Persistent underperformance across a variety of text types or linguistic contexts might suggest a broader
-inability of the model to adapt to the nuances of different languages or dialects, pointing towards gaps in its
-training or inherent limitations.
-
-
-
Strengths
-
-
-Incorporates a balanced consideration of precision and recall, weighted towards recall to reflect the importance
-of content coverage in translations.
-Directly accounts for word order, offering a nuanced evaluation of translation fluency beyond simple lexical
-matching.
-Adapts to various forms of lexical similarity, including synonyms and stemmed forms, allowing for flexible
-matching.
-
-
-
Limitations
-
-
-While comprehensive, the complexity of METEOR's calculation can make it computationally intensive, especially for
-large datasets.
-The use of external resources for synonym and stemming matching may introduce variability based on the resources'
-quality and relevance to the specific translation task.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/ModelMetadata.html b/docs/_build/validmind/tests/model_validation/ModelMetadata.html
deleted file mode 100644
index b80d5f8ee..000000000
--- a/docs/_build/validmind/tests/model_validation/ModelMetadata.html
+++ /dev/null
@@ -1,286 +0,0 @@
-
-
-
- validmind.tests.model_validation.ModelMetadata API documentation
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/ModelPredictionResiduals.html b/docs/_build/validmind/tests/model_validation/ModelPredictionResiduals.html
deleted file mode 100644
index 3c6208b03..000000000
--- a/docs/_build/validmind/tests/model_validation/ModelPredictionResiduals.html
+++ /dev/null
@@ -1,294 +0,0 @@
-
-
-
- validmind.tests.model_validation.ModelPredictionResiduals API documentation
-
-
-
-
-
-
-
@tags('regression')
-
@tasks('residual_analysis', 'visualization')
-
-
def
-
ModelPredictionResiduals ( dataset , model , nbins = 100 , p_value_threshold = 0.05 , start_date = None , end_date = None ):
-
-
-
Assesses normality and behavior of residuals in regression models through visualization and statistical tests.
-
-
Purpose
-
-
The Model Prediction Residuals test aims to visualize the residuals of model predictions and assess their normality
-using the Kolmogorov-Smirnov (KS) test. It helps to identify potential issues related to model assumptions and
-effectiveness.
-
-
Test Mechanism
-
-
The function calculates residuals and generates
-two figures: one for the time series of residuals and one for the histogram of residuals.
-It also calculates the KS test for normality and summarizes the results in a table.
-
-
Signs of High Risk
-
-
-Residuals are not normally distributed, indicating potential issues with model assumptions.
-High skewness or kurtosis in the residuals, which may suggest model misspecification.
-
-
-
Strengths
-
-
-Provides clear visualizations of residuals over time and their distribution.
-Includes statistical tests to assess the normality of residuals.
-Helps in identifying potential model misspecifications and assumption violations.
-
-
-
Limitations
-
-
-Assumes that the dataset is provided as a DataFrameDataset object with a .df attribute to access the pandas
-DataFrame.
-Only generates plots for datasets with a datetime index, resulting in errors for other types of indices.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/RegardScore.html b/docs/_build/validmind/tests/model_validation/RegardScore.html
deleted file mode 100644
index 61a03b3a0..000000000
--- a/docs/_build/validmind/tests/model_validation/RegardScore.html
+++ /dev/null
@@ -1,301 +0,0 @@
-
-
-
- validmind.tests.model_validation.RegardScore API documentation
-
-
-
-
-
-
-
@tags('nlp', 'text_data', 'visualization')
-
@tasks('text_classification', 'text_summarization')
-
-
def
-
RegardScore (dataset , model ):
-
-
-
Assesses the sentiment and potential biases in text generated by NLP models by computing and visualizing regard
-scores.
-
-
Purpose
-
-
The RegardScore
-
-
Test Mechanism
-
-
This test extracts the true and predicted values from the provided dataset and model. It then computes the regard
-scores for each text instance using a preloaded regard evaluation tool. The scores are compiled into dataframes,
-and visualizations such as histograms and bar charts are generated to display the distribution of regard scores.
-Additionally, descriptive statistics (mean, median, standard deviation, minimum, and maximum) are calculated for
-the regard scores, providing a comprehensive overview of the model's performance.
-
-
Signs of High Risk
-
-
-Noticeable skewness in the histogram, especially when comparing the predicted regard scores with the target
-regard scores, can indicate biases or inconsistencies in the model.
-Lack of neutral scores in the model's predictions, despite a balanced distribution in the target data, might
-signal an issue.
-
-
-
Strengths
-
-
-Provides a clear evaluation of regard levels in generated texts, aiding in ensuring content appropriateness.
-Visual representations (histograms and bar charts) make it easier to interpret the distribution and trends of
-regard scores.
-Descriptive statistics offer a concise summary of the model's performance in generating texts with balanced
-sentiments.
-
-
-
Limitations
-
-
-The accuracy of the regard scores is contingent upon the underlying regard tool.
-The scores provide a broad overview but do not specify which portions or tokens of the text are responsible for
-high regard.
-Supplementary, in-depth analysis might be needed for granular insights.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/RegressionResidualsPlot.html b/docs/_build/validmind/tests/model_validation/RegressionResidualsPlot.html
deleted file mode 100644
index e27dc5164..000000000
--- a/docs/_build/validmind/tests/model_validation/RegressionResidualsPlot.html
+++ /dev/null
@@ -1,303 +0,0 @@
-
-
-
- validmind.tests.model_validation.RegressionResidualsPlot API documentation
-
-
-
-
-
-
-
-
- Evaluates regression model performance using residual distribution and actual vs. predicted plots.
-
-
Purpose
-
-
The RegressionResidualsPlot
-
-
Test Mechanism
-
-
The process begins by extracting the true output values (y_true) and the model's predicted values (y_pred).
-Residuals are computed by subtracting predicted from true values. These residuals are then visualized using a
-histogram to display their distribution. Additionally, a scatter plot is derived to compare true values against
-predicted values, together with a "Perfect Fit" line, which represents an ideal match (predicted values equal
-actual values), facilitating the assessment of the model's predictive accuracy.
-
-
Signs of High Risk
-
-
-Residuals showing a non-normal distribution, especially those with frequent extreme values.
-Significant deviations of predicted values from actual values in the scatter plot.
-Sparse density of data points near the "Perfect Fit" line in the scatter plot, indicating poor prediction
-accuracy.
-Visible patterns or trends in the residuals plot, suggesting the model's failure to capture the underlying data
-structure adequately.
-
-
-
Strengths
-
-
-Provides a direct, visually intuitive assessment of a regression model’s accuracy and handling of data.
-Visual plots can highlight issues of underfitting or overfitting.
-Can reveal systematic deviations or trends that purely numerical metrics might miss.
-Applicable across various regression model types.
-
-
-
Limitations
-
-
-Relies on visual interpretation, which can be subjective and less precise than numerical evaluations.
-May be difficult to interpret in cases with multi-dimensional outputs due to the plots’ two-dimensional nature.
-Overlapping data points in the residuals plot can complicate interpretation efforts.
-Does not summarize model performance into a single quantifiable metric, which might be needed for comparative or
-summary analyses.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/RougeScore.html b/docs/_build/validmind/tests/model_validation/RougeScore.html
deleted file mode 100644
index 06208aa67..000000000
--- a/docs/_build/validmind/tests/model_validation/RougeScore.html
+++ /dev/null
@@ -1,308 +0,0 @@
-
-
-
- validmind.tests.model_validation.RougeScore API documentation
-
-
-
-
-
-
-
@tags('nlp', 'text_data', 'visualization')
-
@tasks('text_classification', 'text_summarization')
-
-
def
-
RougeScore (dataset , model , metric = 'rouge-1' ):
-
-
-
Assesses the quality of machine-generated text using ROUGE metrics and visualizes the results to provide
-comprehensive performance insights.
-
-
Purpose
-
-
The ROUGE Score test is designed to evaluate the quality of text generated by machine learning models using various
-ROUGE metrics. ROUGE, which stands for Recall-Oriented Understudy for Gisting Evaluation, measures the overlap of
-n-grams, word sequences, and word pairs between machine-generated text and reference texts. This evaluation is
-crucial for tasks like text summarization, machine translation, and text generation, where the goal is to produce
-text that accurately reflects the content and meaning of human-crafted references.
-
-
Test Mechanism
-
-
The test extracts the true and predicted values from the provided dataset and model. It initializes the ROUGE
-evaluator with the specified metric (e.g., ROUGE-1). For each pair of true and predicted texts, it calculates the
-ROUGE scores and compiles them into a dataframe. Histograms and bar charts are generated for each ROUGE metric
-(Precision, Recall, and F1 Score) to visualize their distribution. Additionally, a table of descriptive statistics
-(mean, median, standard deviation, minimum, and maximum) is compiled for each metric, providing a comprehensive
-summary of the model's performance.
-
-
Signs of High Risk
-
-
-Consistently low scores across ROUGE metrics could indicate poor quality in the generated text, suggesting that
-the model fails to capture the essential content of the reference texts.
-Low precision scores might suggest that the generated text contains a lot of redundant or irrelevant information.
-Low recall scores may indicate that important information from the reference text is being omitted.
-An imbalanced performance between precision and recall, reflected by a low F1 Score, could signal issues in the
-model's ability to balance informativeness and conciseness.
-
-
-
Strengths
-
-
-Provides a multifaceted evaluation of text quality through different ROUGE metrics, offering a detailed view of
-model performance.
-Visual representations (histograms and bar charts) make it easier to interpret the distribution and trends of the
-scores.
-Descriptive statistics offer a concise summary of the model's strengths and weaknesses in generating text.
-
-
-
Limitations
-
-
-ROUGE metrics primarily focus on n-gram overlap and may not fully capture semantic coherence, fluency, or
-grammatical quality of the text.
-The evaluation relies on the availability of high-quality reference texts, which may not always be obtainable.
-While useful for comparison, ROUGE scores alone do not provide a complete assessment of a model's performance and
-should be supplemented with other metrics and qualitative analysis.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/TimeSeriesPredictionWithCI.html b/docs/_build/validmind/tests/model_validation/TimeSeriesPredictionWithCI.html
deleted file mode 100644
index f6c1fab39..000000000
--- a/docs/_build/validmind/tests/model_validation/TimeSeriesPredictionWithCI.html
+++ /dev/null
@@ -1,304 +0,0 @@
-
-
-
- validmind.tests.model_validation.TimeSeriesPredictionWithCI API documentation
-
-
-
-
-
-
-
@tags('model_predictions', 'visualization')
-
@tasks('regression', 'time_series_forecasting')
-
-
def
-
TimeSeriesPredictionWithCI (dataset , model , confidence = 0.95 ):
-
-
-
Assesses predictive accuracy and uncertainty in time series models, highlighting breaches beyond confidence
-intervals.
-
-
Purpose
-
-
The purpose of the Time Series Prediction with Confidence Intervals (CI) test is to visualize the actual versus
-predicted values for time series data, including confidence intervals, and to compute and report the number of
-breaches beyond these intervals. This helps in evaluating the reliability and accuracy of the model's predictions.
-
-
Test Mechanism
-
-
The function performs the following steps:
-
-
-Calculates the standard deviation of prediction errors.
-Determines the confidence intervals using a specified confidence level, typically 95%.
-Counts the number of actual values that fall outside the confidence intervals, referred to as breaches.
-Generates a plot visualizing the actual values, predicted values, and confidence intervals.
-Returns a DataFrame summarizing the breach information, including the total breaches, upper breaches, and lower
-breaches.
-
-
-
Signs of High Risk
-
-
-A high number of breaches indicates that the model's predictions are not reliable within the specified confidence
-level.
-Significant deviations between actual and predicted values may highlight model inadequacies or issues with data
-quality.
-
-
-
Strengths
-
-
-Provides a visual representation of prediction accuracy and the uncertainty around predictions.
-Includes a statistical measure of prediction reliability through confidence intervals.
-Computes and reports breaches, offering a quantitative assessment of prediction performance.
-
-
-
Limitations
-
-
-Assumes that the dataset is provided as a DataFrameDataset object with a datetime index.
-Requires that dataset.y_pred(model) returns the predicted values for the model.
-The calculation of confidence intervals assumes normally distributed errors, which may not hold for all datasets.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/TimeSeriesPredictionsPlot.html b/docs/_build/validmind/tests/model_validation/TimeSeriesPredictionsPlot.html
deleted file mode 100644
index d0d2a3c7a..000000000
--- a/docs/_build/validmind/tests/model_validation/TimeSeriesPredictionsPlot.html
+++ /dev/null
@@ -1,288 +0,0 @@
-
-
-
- validmind.tests.model_validation.TimeSeriesPredictionsPlot API documentation
-
-
-
-
-
-
-
@tags('model_predictions', 'visualization')
-
@tasks('regression', 'time_series_forecasting')
-
-
def
-
TimeSeriesPredictionsPlot (dataset , model ):
-
-
-
Plot actual vs predicted values for time series data and generate a visual comparison for the model.
-
-
Purpose
-
-
The purpose of this function is to visualize the actual versus predicted values for time
-series data for a single model.
-
-
Test Mechanism
-
-
The function plots the actual values from the dataset and overlays the predicted
-values from the model using Plotly for interactive visualization.
-
-
-Large discrepancies between actual and predicted values indicate poor model performance.
-Systematic deviations in predicted values can highlight model bias or issues with data patterns.
-
-
-
Strengths
-
-
-Provides a clear visual comparison of model predictions against actual values.
-Uses Plotly for interactive and visually appealing plots.
-
-
-
Limitations
-
-
-Assumes that the dataset is provided as a DataFrameDataset object with a datetime index.
-Requires that dataset.y_pred(model) returns the predicted values for the model.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/TimeSeriesR2SquareBySegments.html b/docs/_build/validmind/tests/model_validation/TimeSeriesR2SquareBySegments.html
deleted file mode 100644
index 1f63e17d1..000000000
--- a/docs/_build/validmind/tests/model_validation/TimeSeriesR2SquareBySegments.html
+++ /dev/null
@@ -1,301 +0,0 @@
-
-
-
- validmind.tests.model_validation.TimeSeriesR2SquareBySegments API documentation
-
-
-
-
-
-
-
@tags('model_performance', 'sklearn')
-
@tasks('regression', 'time_series_forecasting')
-
-
def
-
TimeSeriesR2SquareBySegments (dataset , model , segments = None ):
-
-
-
Evaluates the R-Squared values of regression models over specified time segments in time series data to assess
-segment-wise model performance.
-
-
Purpose
-
-
The TimeSeriesR2SquareBySegments test aims to evaluate the R-Squared values for several regression models across
-different segments of time series data. This helps in determining how well the models explain the variability in
-the data within each specific time segment.
-
-
Test Mechanism
-
-
-Provides a visual representation of model performance across different time segments.
-Allows for identification of segments where the model performs poorly.
-Calculating the R-Squared values for each segment.
-Generating a bar chart to visually represent the R-Squared values across different models and segments.
-
-
-
Signs of High Risk
-
-
-Significantly low R-Squared values for certain time segments, indicating poor model performance in those periods.
-Large variability in R-Squared values across different segments for the same model, suggesting inconsistent
-performance.
-
-
-
Strengths
-
-
-Provides a visual representation of how well models perform over different time periods.
-Helps identify time segments where models may need improvement or retraining.
-Facilitates comparison between multiple models in a straightforward manner.
-
-
-
Limitations
-
-
-Assumes datasets are provided as DataFrameDataset objects with the attributes y, y_pred, and
-feature_columns.
-Requires that dataset.y_pred(model) returns predicted values for the model.
-Assumes that both y_true and y_pred are pandas Series with datetime indices, which may not always be the case.
-May not account for more nuanced temporal dependencies within the segments.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/TokenDisparity.html b/docs/_build/validmind/tests/model_validation/TokenDisparity.html
deleted file mode 100644
index a73da37e5..000000000
--- a/docs/_build/validmind/tests/model_validation/TokenDisparity.html
+++ /dev/null
@@ -1,299 +0,0 @@
-
-
-
- validmind.tests.model_validation.TokenDisparity API documentation
-
-
-
-
-
-
-
@tags('nlp', 'text_data', 'visualization')
-
@tasks('text_classification', 'text_summarization')
-
-
def
-
TokenDisparity (dataset , model ):
-
-
-
Evaluates the token disparity between reference and generated texts, visualizing the results through histograms and
-bar charts, alongside compiling a comprehensive table of descriptive statistics for token counts.
-
-
Purpose
-
-
The Token Disparity test aims to assess the difference in the number of tokens between reference texts and texts
-generated by the model. Understanding token disparity is essential for evaluating how well the generated content
-matches the expected length and richness of the reference texts.
-
-
Test Mechanism
-
-
The test extracts true and predicted values from the dataset and model. It computes the number of tokens in each
-reference and generated text. The results are visualized using histograms and bar charts to display the
-distribution of token counts. Additionally, a table of descriptive statistics, including the mean, median, standard
-deviation, minimum, and maximum token counts, is compiled to provide a detailed summary of token usage.
-
-
Signs of High Risk
-
-
-Significant disparity in token counts between reference and generated texts could indicate issues with text
-generation quality, such as verbosity or lack of detail.
-Consistently low token counts in generated texts compared to references might suggest that the model is producing
-incomplete or overly concise outputs.
-
-
-
Strengths
-
-
-Provides a simple yet effective evaluation of text length and token usage.
-Visual representations (histograms and bar charts) make it easier to interpret the distribution and trends of
-token counts.
-Descriptive statistics offer a concise summary of the model's performance in generating texts of appropriate
-length.
-
-
-
Limitations
-
-
-Token counts alone do not provide a complete assessment of text quality and should be supplemented with other
-metrics and qualitative analysis.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/ToxicityScore.html b/docs/_build/validmind/tests/model_validation/ToxicityScore.html
deleted file mode 100644
index b759244c3..000000000
--- a/docs/_build/validmind/tests/model_validation/ToxicityScore.html
+++ /dev/null
@@ -1,299 +0,0 @@
-
-
-
- validmind.tests.model_validation.ToxicityScore API documentation
-
-
-
-
-
-
-
@tags('nlp', 'text_data', 'visualization')
-
@tasks('text_classification', 'text_summarization')
-
-
def
-
ToxicityScore (dataset , model ):
-
-
-
Assesses the toxicity levels of texts generated by NLP models to identify and mitigate harmful or offensive content.
-
-
Purpose
-
-
The ToxicityScore metric is designed to evaluate the toxicity levels of texts generated by models. This is crucial
-for identifying and mitigating harmful or offensive content in machine-generated texts.
-
-
Test Mechanism
-
-
The function starts by extracting the input, true, and predicted values from the provided dataset and model. The
-toxicity score is computed for each text using a preloaded toxicity evaluation tool. The scores are compiled into
-dataframes, and histograms and bar charts are generated to visualize the distribution of toxicity scores.
-Additionally, a table of descriptive statistics (mean, median, standard deviation, minimum, and maximum) is
-compiled for the toxicity scores, providing a comprehensive summary of the model's performance.
-
-
Signs of High Risk
-
-
-Drastic spikes in toxicity scores indicate potentially toxic content within the associated text segment.
-Persistent high toxicity scores across multiple texts may suggest systemic issues in the model's text generation
-process.
-
-
-
Strengths
-
-
-Provides a clear evaluation of toxicity levels in generated texts, helping to ensure content safety and
-appropriateness.
-Visual representations (histograms and bar charts) make it easier to interpret the distribution and trends of
-toxicity scores.
-Descriptive statistics offer a concise summary of the model's performance in generating non-toxic texts.
-
-
-
Limitations
-
-
-The accuracy of the toxicity scores is contingent upon the underlying toxicity tool.
-The scores provide a broad overview but do not specify which portions or tokens of the text are responsible for
-high toxicity.
-Supplementary, in-depth analysis might be needed for granular insights.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/sklearn.html b/docs/_build/validmind/tests/model_validation/sklearn.html
deleted file mode 100644
index 963b5befc..000000000
--- a/docs/_build/validmind/tests/model_validation/sklearn.html
+++ /dev/null
@@ -1,274 +0,0 @@
-
-
-
- validmind.tests.model_validation.sklearn API documentation
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/sklearn/AdjustedMutualInformation.html b/docs/_build/validmind/tests/model_validation/sklearn/AdjustedMutualInformation.html
deleted file mode 100644
index fb0539583..000000000
--- a/docs/_build/validmind/tests/model_validation/sklearn/AdjustedMutualInformation.html
+++ /dev/null
@@ -1,303 +0,0 @@
-
-
-
- validmind.tests.model_validation.sklearn.AdjustedMutualInformation API documentation
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/sklearn/AdjustedRandIndex.html b/docs/_build/validmind/tests/model_validation/sklearn/AdjustedRandIndex.html
deleted file mode 100644
index fa1a12e3e..000000000
--- a/docs/_build/validmind/tests/model_validation/sklearn/AdjustedRandIndex.html
+++ /dev/null
@@ -1,300 +0,0 @@
-
-
-
- validmind.tests.model_validation.sklearn.AdjustedRandIndex API documentation
-
-
-
-
-
-
-
-
- Measures the similarity between two data clusters using the Adjusted Rand Index (ARI) metric in clustering machine
-learning models.
-
-
Purpose
-
-
The Adjusted Rand Index (ARI) metric is intended to measure the similarity between two data clusters. This metric
-is specifically used for clustering machine learning models to quantify how well the model is clustering and
-producing data groups. It involves comparing the model's produced clusters against the actual (true) clusters found
-in the dataset.
-
-
Test Mechanism
-
-
The Adjusted Rand Index (ARI) is calculated using the adjusted_rand_score method from the sklearn.metrics
-module in Python. The test requires inputs including the model itself and the model's training and test datasets.
-The model's computed clusters and the true clusters are compared, and the similarities are measured to compute the
-ARI.
-
-
Signs of High Risk
-
-
-If the ARI is close to zero, it signifies that the model's cluster assignments are random and do not match the
-actual dataset clusters, indicating a high risk.
-An ARI of less than zero indicates that the model's clustering performance is worse than random.
-
-
-
Strengths
-
-
-ARI is normalized and provides a consistent metric between -1 and +1, irrespective of raw cluster sizes or
-dataset size variations.
-It does not require a ground truth for computation, making it ideal for unsupervised learning model evaluations.
-It penalizes for false positives and false negatives, providing a robust measure of clustering quality.
-
-
-
Limitations
-
-
-In real-world situations, true clustering is often unknown, which can hinder the practical application of the ARI.
-The ARI requires all individual data instances to be independent, which may not always hold true.
-It may be difficult to interpret the implications of an ARI score without context or a benchmark, as it is
-heavily dependent on the characteristics of the dataset used.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/sklearn/CalibrationCurve.html b/docs/_build/validmind/tests/model_validation/sklearn/CalibrationCurve.html
deleted file mode 100644
index 704c6b575..000000000
--- a/docs/_build/validmind/tests/model_validation/sklearn/CalibrationCurve.html
+++ /dev/null
@@ -1,318 +0,0 @@
-
-
-
- validmind.tests.model_validation.sklearn.CalibrationCurve API documentation
-
-
-
-
-
-
-
-
- Evaluates the calibration of probability estimates by comparing predicted probabilities against observed
-frequencies.
-
-
Purpose
-
-
The Calibration Curve test assesses how well a model's predicted probabilities align with actual
-observed frequencies. This is crucial for applications requiring accurate probability estimates,
-such as risk assessment, decision-making systems, and cost-sensitive applications where probability
-calibration directly impacts business decisions.
-
-
Test Mechanism
-
-
The test uses sklearn's calibration_curve function to:
-
-
-Sort predictions into bins based on predicted probabilities
-Calculate the mean predicted probability in each bin
-Compare against the observed frequency of positive cases
-Plot the results against the perfect calibration line (y=x)
-The resulting curve shows how well the predicted probabilities match empirical probabilities.
-
-
-
Signs of High Risk
-
-
-Significant deviation from the perfect calibration line
-Systematic overconfidence (predictions too close to 0 or 1)
-Systematic underconfidence (predictions clustered around 0.5)
-Empty or sparse bins indicating poor probability coverage
-Sharp discontinuities in the calibration curve
-Different calibration patterns across different probability ranges
-Consistent over/under estimation in critical probability regions
-Large confidence intervals in certain probability ranges
-
-
-
Strengths
-
-
-Visual and intuitive interpretation of probability quality
-Identifies systematic biases in probability estimates
-Supports probability threshold selection
-Helps understand model confidence patterns
-Applicable across different classification models
-Enables comparison between different models
-Guides potential need for recalibration
-Critical for risk-sensitive applications
-
-
-
Limitations
-
-
-Sensitive to the number of bins chosen
-Requires sufficient samples in each bin for reliable estimates
-May mask local calibration issues within bins
-Does not account for feature-dependent calibration issues
-Limited to binary classification problems
-Cannot detect all forms of miscalibration
-Assumes bin boundaries are appropriate for the problem
-May be affected by class imbalance
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/sklearn/ClassifierPerformance.html b/docs/_build/validmind/tests/model_validation/sklearn/ClassifierPerformance.html
deleted file mode 100644
index 946777087..000000000
--- a/docs/_build/validmind/tests/model_validation/sklearn/ClassifierPerformance.html
+++ /dev/null
@@ -1,315 +0,0 @@
-
-
-
- validmind.tests.model_validation.sklearn.ClassifierPerformance API documentation
-
-
-
-
-
-
-
- def
- multiclass_roc_auc_score (y_test , y_pred , average = 'macro' ):
-
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/sklearn/ClassifierThresholdOptimization.html b/docs/_build/validmind/tests/model_validation/sklearn/ClassifierThresholdOptimization.html
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index e25d67605..000000000
--- a/docs/_build/validmind/tests/model_validation/sklearn/ClassifierThresholdOptimization.html
+++ /dev/null
@@ -1,370 +0,0 @@
-
-
-
- validmind.tests.model_validation.sklearn.ClassifierThresholdOptimization API documentation
-
-
-
-
-
-
-
- def
- find_optimal_threshold (y_true , y_prob , method = 'youden' , target_recall = None ):
-
-
-
-
-
- Find the optimal classification threshold using various methods.
-
-
Arguments:
-
-
-y_true: True binary labelsy_prob: Predicted probabilitiesmethod: Method to use for finding optimal thresholdtarget_recall: Required if method='target_recall'
-
-
Returns:
-
-
- dict: Dictionary containing threshold and metrics
-
-
-
-
-
-
- Analyzes and visualizes different threshold optimization methods for binary classification models.
-
-
Purpose
-
-
The Classifier Threshold Optimization test identifies optimal decision thresholds using various
-methods to balance different performance metrics. This helps adapt the model's decision boundary
-to specific business requirements, such as minimizing false positives in fraud detection or
-achieving target recall in medical diagnosis.
-
-
Test Mechanism
-
-
The test implements multiple threshold optimization methods:
-
-
-Youden's J statistic (maximizing sensitivity + specificity - 1)
-F1-score optimization (balancing precision and recall)
-Precision-Recall equality point
-Target recall achievement
-Naive (0.5) threshold
-For each method, it computes ROC and PR curves, identifies optimal points, and provides
-comprehensive performance metrics at each threshold.
-
-
-
Signs of High Risk
-
-
-Large discrepancies between different optimization methods
-Optimal thresholds far from the default 0.5
-Poor performance metrics across all thresholds
-Significant gap between achieved and target recall
-Unstable thresholds across different methods
-Extreme trade-offs between precision and recall
-Threshold optimization showing minimal impact
-Business metrics not improving with optimization
-
-
-
Strengths
-
-
-Multiple optimization strategies for different needs
-Visual and numerical results for comparison
-Support for business-driven optimization (target recall)
-Comprehensive performance metrics at each threshold
-Integration with ROC and PR curves
-Handles class imbalance through various metrics
-Enables informed threshold selection
-Supports cost-sensitive decision making
-
-
-
Limitations
-
-
-Assumes cost of false positives/negatives are known
-May need adjustment for highly imbalanced datasets
-Threshold might not be stable across different samples
-Cannot handle multi-class problems directly
-Optimization methods may conflict with business needs
-Requires sufficient validation data
-May not capture temporal changes in optimal threshold
-Single threshold may not be optimal for all subgroups
-
-
-
Arguments:
-
-
-dataset: VMDataset containing features and targetmodel: VMModel containing predictionsmethods: List of methods to compare (default: ['youden', 'f1', 'precision_recall'])target_recall: Target recall value if using 'target_recall' method
-
-
Returns:
-
-
- Dictionary containing:
- - table: DataFrame comparing different threshold optimization methods
- (using weighted averages for precision, recall, and f1)
- - figure: Plotly figure showing ROC and PR curves with optimal thresholds
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/sklearn/ClusterCosineSimilarity.html b/docs/_build/validmind/tests/model_validation/sklearn/ClusterCosineSimilarity.html
deleted file mode 100644
index 57f2230f6..000000000
--- a/docs/_build/validmind/tests/model_validation/sklearn/ClusterCosineSimilarity.html
+++ /dev/null
@@ -1,305 +0,0 @@
-
-
-
- validmind.tests.model_validation.sklearn.ClusterCosineSimilarity API documentation
-
-
-
-
-
-
-
-
- Measures the intra-cluster similarity of a clustering model using cosine similarity.
-
-
Purpose
-
-
The purpose of this metric is to measure how similar the data points within each cluster of a clustering model are.
-This is done using cosine similarity, which compares the multi-dimensional direction (but not magnitude) of data
-vectors. From a Model Risk Management perspective, this metric is used to quantitatively validate that clusters
-formed by a model have high intra-cluster similarity.
-
-
Test Mechanism
-
-
This test works by first extracting the true and predicted clusters of the model's training data. Then, it computes
-the centroid (average data point) of each cluster. Next, it calculates the cosine similarity between each data
-point within a cluster and its respective centroid. Finally, it outputs the mean cosine similarity of each cluster,
-highlighting how similar, on average, data points in a cluster are to the cluster's centroid.
-
-
Signs of High Risk
-
-
-Low mean cosine similarity for one or more clusters: If the mean cosine similarity is low, the data points within
-the respective cluster have high variance in their directions. This can be indicative of poor clustering,
-suggesting that the model might not be suitably separating the data into distinct patterns.
-High disparity between mean cosine similarity values across clusters: If there's a significant difference in mean
-cosine similarity across different clusters, this could indicate imbalance in how the model forms clusters.
-
-
-
Strengths
-
-
-Cosine similarity operates in a multi-dimensional space, making it effective for measuring similarity in high
-dimensional datasets, typical for many machine learning problems.
-It provides an agnostic view of the cluster performance by only considering the direction (and not the magnitude)
-of each vector.
-This metric is not dependent on the scale of the variables, making it equally effective on different scales.
-
-
-
Limitations
-
-
-Cosine similarity does not consider magnitudes (i.e. lengths) of vectors, only their direction. This means it may
-overlook instances where clusters have been adequately separated in terms of magnitude.
-This method summarily assumes that centroids represent the average behavior of data points in each cluster. This
-might not always be true, especially in clusters with high amounts of variance or non-spherical shapes.
-It primarily works with continuous variables and is not suitable for binary or categorical variables.
-Lastly, although rare, perfect perpendicular vectors (cosine similarity = 0) could be within the same cluster,
-which may give an inaccurate representation of a 'bad' cluster due to low cosine similarity score.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/sklearn/ClusterPerformanceMetrics.html b/docs/_build/validmind/tests/model_validation/sklearn/ClusterPerformanceMetrics.html
deleted file mode 100644
index 97189f541..000000000
--- a/docs/_build/validmind/tests/model_validation/sklearn/ClusterPerformanceMetrics.html
+++ /dev/null
@@ -1,307 +0,0 @@
-
-
-
- validmind.tests.model_validation.sklearn.ClusterPerformanceMetrics API documentation
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/sklearn/CompletenessScore.html b/docs/_build/validmind/tests/model_validation/sklearn/CompletenessScore.html
deleted file mode 100644
index c10ac6af2..000000000
--- a/docs/_build/validmind/tests/model_validation/sklearn/CompletenessScore.html
+++ /dev/null
@@ -1,298 +0,0 @@
-
-
-
- validmind.tests.model_validation.sklearn.CompletenessScore API documentation
-
-
-
-
-
-
-
-
- Evaluates a clustering model's capacity to categorize instances from a single class into the same cluster.
-
-
Purpose
-
-
The Completeness Score metric is used to assess the performance of clustering models. It measures the extent to
-which all the data points that are members of a given class are elements of the same cluster. The aim is to
-determine the capability of the model to categorize all instances from a single class into the same cluster.
-
-
Test Mechanism
-
-
This test takes three inputs, a model and its associated training and testing datasets. It invokes the
-completeness_score function from the sklearn library on the labels predicted by the model. High scores indicate
-that data points from the same class generally appear in the same cluster, while low scores suggest the opposite.
-
-
Signs of High Risk
-
-
-Low completeness score: This suggests that the model struggles to group instances from the same class into one
-cluster, indicating poor clustering performance.
-
-
-
Strengths
-
-
-The Completeness Score provides an effective method for assessing the performance of a clustering model,
-specifically its ability to group class instances together.
-This test metric conveniently relies on the capabilities provided by the sklearn library, ensuring consistent and
-reliable test results.
-
-
-
Limitations
-
-
-This metric only evaluates a specific aspect of clustering, meaning it may not provide a holistic or complete
-view of the model's performance.
-It cannot assess the effectiveness of the model in differentiating between separate classes, as it is solely
-focused on how well data points from the same class are grouped.
-The Completeness Score only applies to clustering models; it cannot be used for other types of machine learning
-models.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/sklearn/ConfusionMatrix.html b/docs/_build/validmind/tests/model_validation/sklearn/ConfusionMatrix.html
deleted file mode 100644
index 5e6de4881..000000000
--- a/docs/_build/validmind/tests/model_validation/sklearn/ConfusionMatrix.html
+++ /dev/null
@@ -1,308 +0,0 @@
-
-
-
- validmind.tests.model_validation.sklearn.ConfusionMatrix API documentation
-
-
-
-
-
-
-
@tags('sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization')
-
@tasks('classification', 'text_classification')
-
-
def
-
ConfusionMatrix ( dataset : validmind.vm_models.VMDataset model : validmind.vm_models.VMModel threshold : float = 0.5 ):
-
-
-
Evaluates and visually represents the classification ML model's predictive performance using a Confusion Matrix
-heatmap.
-
-
Purpose
-
-
The Confusion Matrix tester is designed to assess the performance of a classification Machine Learning model. This
-performance is evaluated based on how well the model is able to correctly classify True Positives, True Negatives,
-False Positives, and False Negatives - fundamental aspects of model accuracy.
-
-
Test Mechanism
-
-
The mechanism used involves taking the predicted results (y_test_predict) from the classification model and
-comparing them against the actual values (y_test_true). A confusion matrix is built using the unique labels
-extracted from y_test_true, employing scikit-learn's metrics. The matrix is then visually rendered with the help
-of Plotly's create_annotated_heatmap function. A heatmap is created which provides a two-dimensional graphical
-representation of the model's performance, showcasing distributions of True Positives (TP), True Negatives (TN),
-False Positives (FP), and False Negatives (FN).
-
-
Signs of High Risk
-
-
-High numbers of False Positives (FP) and False Negatives (FN), depicting that the model is not effectively
-classifying the values.
-Low numbers of True Positives (TP) and True Negatives (TN), implying that the model is struggling with correctly
-identifying class labels.
-
-
-
Strengths
-
-
-It provides a simplified yet comprehensive visual snapshot of the classification model's predictive performance.
-It distinctly brings out True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives
-(FN), thus making it easier to focus on potential areas of improvement.
-The matrix is beneficial in dealing with multi-class classification problems as it can provide a simple view of
-complex model performances.
-It aids in understanding the different types of errors that the model could potentially make, as it provides
-in-depth insights into Type-I and Type-II errors.
-
-
-
Limitations
-
-
-In cases of unbalanced classes, the effectiveness of the confusion matrix might be lessened. It may wrongly
-interpret the accuracy of a model that is essentially just predicting the majority class.
-It does not provide a single unified statistic that could evaluate the overall performance of the model.
-Different aspects of the model's performance are evaluated separately instead.
-It mainly serves as a descriptive tool and does not offer the capability for statistical hypothesis testing.
-Risks of misinterpretation exist because the matrix doesn't directly provide precision, recall, or F1-score data.
-These metrics have to be computed separately.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/sklearn/FeatureImportance.html b/docs/_build/validmind/tests/model_validation/sklearn/FeatureImportance.html
deleted file mode 100644
index 74cb550fe..000000000
--- a/docs/_build/validmind/tests/model_validation/sklearn/FeatureImportance.html
+++ /dev/null
@@ -1,304 +0,0 @@
-
-
-
- validmind.tests.model_validation.sklearn.FeatureImportance API documentation
-
-
-
-
-
-
-
-
- Compute feature importance scores for a given model and generate a summary table
-with the top important features.
-
-
Purpose
-
-
The Feature Importance Comparison test is designed to compare the feature importance scores for different models
-when applied to various datasets. By doing so, it aims to identify the most impactful features and assess the
-consistency of feature importance across models.
-
-
Test Mechanism
-
-
This test works by iterating through each dataset-model pair and calculating permutation feature importance (PFI)
-scores. It then generates a summary table containing the top num_features important features for each model. The
-process involves:
-
-
-Extracting features and target data from each dataset.
-Computing PFI scores using sklearn.inspection.permutation_importance.
-Sorting and selecting the top features based on their importance scores.
-Compiling these features into a summary table for comparison.
-
-
-
Signs of High Risk
-
-
-Key features expected to be important are ranked low, indicating potential issues with model training or data
-quality.
-High variance in feature importance scores across different models, suggesting instability in feature selection.
-
-
-
Strengths
-
-
-Provides a clear comparison of the most important features for each model.
-Uses permutation importance, which is a model-agnostic method and can be applied to any estimator.
-
-
-
Limitations
-
-
-Assumes that the dataset is provided as a DataFrameDataset object with x_df and y_df methods to access
-feature and target data.
-Requires that model.model is compatible with sklearn.inspection.permutation_importance.
-The function's output is dependent on the number of features specified by num_features, which defaults to 3 but
-can be adjusted.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/sklearn/FowlkesMallowsScore.html b/docs/_build/validmind/tests/model_validation/sklearn/FowlkesMallowsScore.html
deleted file mode 100644
index 73422605e..000000000
--- a/docs/_build/validmind/tests/model_validation/sklearn/FowlkesMallowsScore.html
+++ /dev/null
@@ -1,303 +0,0 @@
-
-
-
- validmind.tests.model_validation.sklearn.FowlkesMallowsScore API documentation
-
-
-
-
-
-
-
-
- Evaluates the similarity between predicted and actual cluster assignments in a model using the Fowlkes-Mallows
-score.
-
-
Purpose
-
-
The FowlkesMallowsScore is a performance metric used to validate clustering algorithms within machine learning
-models. The score intends to evaluate the matching grade between two clusters. It measures the similarity between
-the predicted and actual cluster assignments, thus gauging the accuracy of the model's clustering capability.
-
-
Test Mechanism
-
-
The FowlkesMallowsScore method applies the fowlkes_mallows_score function from the sklearn library to evaluate
-the model's accuracy in clustering different types of data. The test fetches the datasets from the model's training
-and testing datasets as inputs then compares the resulting clusters against the previously known clusters to obtain
-a score. A high score indicates a better clustering performance by the model.
-
-
Signs of High Risk
-
-
-A low Fowlkes-Mallows score (near zero): This indicates that the model's clustering capability is poor and the
-algorithm isn't properly grouping data.
-Inconsistently low scores across different datasets: This may indicate that the model's clustering performance is
-not robust and the model may fail when applied to unseen data.
-
-
-
Strengths
-
-
-The Fowlkes-Mallows score is a simple and effective method for evaluating the performance of clustering
-algorithms.
-This metric takes into account both precision and recall in its calculation, therefore providing a balanced and
-comprehensive measure of model performance.
-The Fowlkes-Mallows score is non-biased meaning it treats False Positives and False Negatives equally.
-
-
-
Limitations
-
-
-As a pairwise-based method, this score can be computationally intensive for large datasets and can become
-unfeasible as the size of the dataset increases.
-The Fowlkes-Mallows score works best with balanced distribution of samples across clusters. If this condition is
-not met, the score can be skewed.
-It does not handle mismatching numbers of clusters between the true and predicted labels. As such, it may return
-misleading results if the predicted labels suggest a different number of clusters than what is in the true labels.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/sklearn/HomogeneityScore.html b/docs/_build/validmind/tests/model_validation/sklearn/HomogeneityScore.html
deleted file mode 100644
index 8e524e3ba..000000000
--- a/docs/_build/validmind/tests/model_validation/sklearn/HomogeneityScore.html
+++ /dev/null
@@ -1,301 +0,0 @@
-
-
-
- validmind.tests.model_validation.sklearn.HomogeneityScore API documentation
-
-
-
-
-
-
-
-
- Assesses clustering homogeneity by comparing true and predicted labels, scoring from 0 (heterogeneous) to 1
-(homogeneous).
-
-
Purpose
-
-
The Homogeneity Score encapsulated in this performance test is used to measure the homogeneity of the clusters
-formed by a machine learning model. In simple terms, a clustering result satisfies homogeneity if all of its
-clusters contain only points which are members of a single class.
-
-
Test Mechanism
-
-
This test uses the homogeneity_score function from the sklearn.metrics library to compare the ground truth
-class labels of the training and testing sets with the labels predicted by the given model. The returned score is a
-metric of the clustering accuracy, and ranges from 0.0 to 1.0, with 1.0 denoting the highest possible degree of
-homogeneity.
-
-
Signs of High Risk
-
-
-A score close to 0: This denotes that clusters are highly heterogenous and points within the same cluster might
-not belong to the same class.
-A significantly lower score for testing data compared to the score for training data: This can indicate
-overfitting, where the model has learned to perfectly match the training data but fails to perform well on unseen
-data.
-
-
-
Strengths
-
-
-It provides a simple quantitative measure of the degree to which clusters contain points from only one class.
-Useful for validating clustering solutions where the ground truth — class membership of points — is known.
-It's agnostic to the absolute labels, and cares only that the points within the same cluster have the same class
-label.
-
-
-
Limitations
-
-
-The Homogeneity Score is not useful for clustering solutions where the ground truth labels are not known.
-It doesn’t work well with differently sized clusters since it gives predominance to larger clusters.
-The score does not address the actual number of clusters formed, or the evenness of cluster sizes. It only checks
-the homogeneity within the given clusters created by the model.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/sklearn/HyperParametersTuning.html b/docs/_build/validmind/tests/model_validation/sklearn/HyperParametersTuning.html
deleted file mode 100644
index 8f3328be1..000000000
--- a/docs/_build/validmind/tests/model_validation/sklearn/HyperParametersTuning.html
+++ /dev/null
@@ -1,331 +0,0 @@
-
-
-
- validmind.tests.model_validation.sklearn.HyperParametersTuning API documentation
-
-
-
-
-
-
-
@tags('sklearn', 'model_performance')
-
@tasks('classification', 'clustering')
-
-
def
-
custom_recall (y_true , y_pred_proba , threshold = 0.5 ):
-
-
-
-
-
-
@tags('sklearn', 'model_performance')
-
@tasks('clustering', 'classification')
-
-
def
-
HyperParametersTuning ( model : validmind.vm_models.VMModel dataset : validmind.vm_models.VMDataset param_grid : dict , scoring : Union [ str , List , Dict ] = None , thresholds : Union [ float , List [ float ]] = None , fit_params : dict = None ):
-
-
-
Performs exhaustive grid search over specified parameter ranges to find optimal model configurations
-across different metrics and decision thresholds.
-
-
Purpose
-
-
The Hyperparameter Tuning test systematically explores the model's parameter space to identify optimal
-configurations. It supports multiple optimization metrics and decision thresholds, providing a comprehensive
-view of how different parameter combinations affect various aspects of model performance.
-
-
Test Mechanism
-
-
The test uses scikit-learn's GridSearchCV to perform cross-validation for each parameter combination.
-For each specified threshold and optimization metric, it creates a scoring dictionary with
-threshold-adjusted metrics, performs grid search with cross-validation, records best parameters and
-corresponding scores, and combines results into a comparative table. This process is repeated for each
-optimization metric to provide a comprehensive view of model performance under different configurations.
-
-
Signs of High Risk
-
-
-Large performance variations across different parameter combinations
-Significant discrepancies between different optimization metrics
-Best parameters at the edges of the parameter grid
-Unstable performance across different thresholds
-Overly complex model configurations (risk of overfitting)
-Very different optimal parameters for different metrics
-Cross-validation scores showing high variance
-Extreme parameter values in best configurations
-
-
-
Strengths
-
-
-Comprehensive exploration of parameter space
-Supports multiple optimization metrics
-Allows threshold optimization
-Provides comparative view across different configurations
-Uses cross-validation for robust evaluation
-Helps understand trade-offs between different metrics
-Enables systematic parameter selection
-Supports both classification and clustering tasks
-
-
-
Limitations
-
-
-Computationally expensive for large parameter grids
-May not find global optimum (limited to grid points)
-Cannot handle dependencies between parameters
-Memory intensive for large datasets
-Limited to scikit-learn compatible models
-Cross-validation splits may not preserve time series structure
-Grid search may miss optimal values between grid points
-Resource intensive for high-dimensional parameter spaces
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/sklearn/KMeansClustersOptimization.html b/docs/_build/validmind/tests/model_validation/sklearn/KMeansClustersOptimization.html
deleted file mode 100644
index 0229edf43..000000000
--- a/docs/_build/validmind/tests/model_validation/sklearn/KMeansClustersOptimization.html
+++ /dev/null
@@ -1,308 +0,0 @@
-
-
-
- validmind.tests.model_validation.sklearn.KMeansClustersOptimization API documentation
-
-
-
-
-
-
-
-
- Optimizes the number of clusters in K-means models using Elbow and Silhouette methods.
-
-
Purpose
-
-
This metric is used to optimize the number of clusters used in K-means clustering models. It intends to measure and
-evaluate the optimal number of clusters by leveraging two methodologies, namely the Elbow method and the Silhouette
-method. This is crucial as an inappropriate number of clusters can either overly simplify or overcomplicate the
-structure of the data, thereby undermining the effectiveness of the model.
-
-
Test Mechanism
-
-
The test mechanism involves iterating over a predefined range of cluster numbers and applying both the Elbow method
-and the Silhouette method. The Elbow method computes the sum of the minimum euclidean distances between data points
-and their respective cluster centers (distortion). This value decreases as the number of clusters increases; the
-optimal number is typically at the 'elbow' point where the decrease in distortion becomes less pronounced.
-Meanwhile, the Silhouette method calculates the average silhouette score for each data point in the dataset,
-providing a measure of how similar each item is to its own cluster compared to other clusters. The optimal number
-of clusters under this method is the one that maximizes the average silhouette score. The results of both methods
-are plotted for visual inspection.
-
-
Signs of High Risk
-
-
-A high distortion value or a low silhouette average score for the optimal number of clusters.
-No clear 'elbow' point or plateau observed in the distortion plot, or a uniformly low silhouette average score
-across different numbers of clusters, suggesting the data is not amenable to clustering.
-An optimal cluster number that is unreasonably high or low, suggestive of overfitting or underfitting,
-respectively.
-
-
-
Strengths
-
-
-Provides both a visual and quantitative method to determine the optimal number of clusters.
-Leverages two different methods (Elbow and Silhouette), thereby affording robustness and versatility in assessing
-the data's clusterability.
-Facilitates improved model performance by allowing for an informed selection of the number of clusters.
-
-
-
Limitations
-
-
-Assumes that a suitable number of clusters exists in the data, which may not always be true, especially for
-complex or noisy data.
-Both methods may fail to provide definitive answers when the data lacks clear cluster structures.
-Might not be straightforward to determine the 'elbow' point or maximize the silhouette average score, especially
-in larger and complicated datasets.
-Assumes spherical clusters (due to using the Euclidean distance in the Elbow method), which might not align with
-the actual structure of the data.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/sklearn/MinimumAccuracy.html b/docs/_build/validmind/tests/model_validation/sklearn/MinimumAccuracy.html
deleted file mode 100644
index da5a3d099..000000000
--- a/docs/_build/validmind/tests/model_validation/sklearn/MinimumAccuracy.html
+++ /dev/null
@@ -1,298 +0,0 @@
-
-
-
- validmind.tests.model_validation.sklearn.MinimumAccuracy API documentation
-
-
-
-
-
-
-
@tags('sklearn', 'binary_classification', 'multiclass_classification', 'model_performance')
-
@tasks('classification', 'text_classification')
-
-
def
-
MinimumAccuracy ( dataset : validmind.vm_models.VMDataset model : validmind.vm_models.VMModel min_threshold : float = 0.7 ):
-
-
-
Checks if the model's prediction accuracy meets or surpasses a specified threshold.
-
-
Purpose
-
-
The Minimum Accuracy test’s objective is to verify whether the model's prediction accuracy on a specific dataset
-meets or surpasses a predetermined minimum threshold. Accuracy, which is simply the ratio of correct predictions to
-total predictions, is a key metric for evaluating the model's performance. Considering binary as well as multiclass
-classifications, accurate labeling becomes indispensable.
-
-
Test Mechanism
-
-
The test mechanism involves contrasting the model's accuracy score with a preset minimum threshold value, with the
-default being 0.7. The accuracy score is computed utilizing sklearn’s accuracy_score method, where the true
-labels y_true and predicted labels class_pred are compared. If the accuracy score is above the threshold, the
-test receives a passing mark. The test returns the result along with the accuracy score and threshold used for the
-test.
-
-
Signs of High Risk
-
-
-Model fails to achieve or surpass the predefined score threshold.
-Persistent scores below the threshold, indicating a high risk of inaccurate predictions.
-
-
-
Strengths
-
-
-Simplicity, presenting a straightforward measure of holistic model performance across all classes.
-Particularly advantageous when classes are balanced.
-Versatile, as it can be implemented on both binary and multiclass classification tasks.
-
-
-
Limitations
-
-
-Misleading accuracy scores when classes in the dataset are highly imbalanced.
-Favoritism towards the majority class, giving an inaccurate perception of model performance.
-Inability to measure the model's precision, recall, or capacity to manage false positives or false negatives.
-Focused on overall correctness and may not be sufficient for all types of model analytics.
-
-
-
-
-
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diff --git a/docs/_build/validmind/tests/model_validation/sklearn/MinimumF1Score.html b/docs/_build/validmind/tests/model_validation/sklearn/MinimumF1Score.html
deleted file mode 100644
index cd75c6eee..000000000
--- a/docs/_build/validmind/tests/model_validation/sklearn/MinimumF1Score.html
+++ /dev/null
@@ -1,300 +0,0 @@
-
-
-
- validmind.tests.model_validation.sklearn.MinimumF1Score API documentation
-
-
-
-
-
-
-
@tags('sklearn', 'binary_classification', 'multiclass_classification', 'model_performance')
-
@tasks('classification', 'text_classification')
-
-
def
-
MinimumF1Score ( dataset : validmind.vm_models.VMDataset model : validmind.vm_models.VMModel min_threshold : float = 0.5 ):
-
-
-
Assesses if the model's F1 score on the validation set meets a predefined minimum threshold, ensuring balanced
-performance between precision and recall.
-
-
Purpose
-
-
The main objective of this test is to ensure that the F1 score, a balanced measure of precision and recall, of the
-model meets or surpasses a predefined threshold on the validation dataset. The F1 score is highly useful for
-gauging model performance in classification tasks, especially in cases where the distribution of positive and
-negative classes is skewed.
-
-
Test Mechanism
-
-
The F1 score for the validation dataset is computed through scikit-learn's metrics in Python. The scoring mechanism
-differs based on the classification problem: for multi-class problems, macro averaging is used, and for binary
-classification, the built-in f1_score calculation is used. The obtained F1 score is then assessed against the
-predefined minimum F1 score that is expected from the model.
-
-
Signs of High Risk
-
-
-If a model returns an F1 score that is less than the established threshold, it is regarded as high risk.
-A low F1 score might suggest that the model is not finding an optimal balance between precision and recall,
-failing to effectively identify positive classes while minimizing false positives.
-
-
-
Strengths
-
-
-Provides a balanced measure of a model's performance by accounting for both false positives and false negatives.
-Particularly advantageous in scenarios with imbalanced class distribution, where accuracy can be misleading.
-Flexibility in setting the threshold value allows tailored minimum acceptable performance standards.
-
-
-
Limitations
-
-
-May not be suitable for all types of models and machine learning tasks.
-The F1 score assumes an equal cost for false positives and false negatives, which may not be true in some
-real-world scenarios.
-Practitioners might need to rely on other metrics such as precision, recall, or the ROC-AUC score that align more
-closely with specific requirements.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/sklearn/MinimumROCAUCScore.html b/docs/_build/validmind/tests/model_validation/sklearn/MinimumROCAUCScore.html
deleted file mode 100644
index 88266de33..000000000
--- a/docs/_build/validmind/tests/model_validation/sklearn/MinimumROCAUCScore.html
+++ /dev/null
@@ -1,303 +0,0 @@
-
-
-
- validmind.tests.model_validation.sklearn.MinimumROCAUCScore API documentation
-
-
-
-
-
-
-
@tags('sklearn', 'binary_classification', 'multiclass_classification', 'model_performance')
-
@tasks('classification', 'text_classification')
-
-
def
-
MinimumROCAUCScore ( dataset : validmind.vm_models.VMDataset model : validmind.vm_models.VMModel min_threshold : float = 0.5 ):
-
-
-
Validates model by checking if the ROC AUC score meets or surpasses a specified threshold.
-
-
Purpose
-
-
The Minimum ROC AUC Score test is used to determine the model's performance by ensuring that the Receiver Operating
-Characteristic Area Under the Curve (ROC AUC) score on the validation dataset meets or exceeds a predefined
-threshold. The ROC AUC score indicates how well the model can distinguish between different classes, making it a
-crucial measure in binary and multiclass classification tasks.
-
-
Test Mechanism
-
-
This test implementation calculates the multiclass ROC AUC score on the true target values and the model's
-predictions. The test converts the multi-class target variables into binary format using LabelBinarizer before
-computing the score. If this ROC AUC score is higher than the predefined threshold (defaulted to 0.5), the test
-passes; otherwise, it fails. The results, including the ROC AUC score, the threshold, and whether the test passed
-or failed, are then stored in a ThresholdTestResult object.
-
-
Signs of High Risk
-
-
-A high risk or failure in the model's performance as related to this metric would be represented by a low ROC AUC
-score, specifically any score lower than the predefined minimum threshold. This suggests that the model is
-struggling to distinguish between different classes effectively.
-
-
-
Strengths
-
-
-The test considers both the true positive rate and false positive rate, providing a comprehensive performance
-measure.
-ROC AUC score is threshold-independent meaning it measures the model's quality across various classification
-thresholds.
-Works robustly with binary as well as multi-class classification problems.
-
-
-
Limitations
-
-
-ROC AUC may not be useful if the class distribution is highly imbalanced; it could perform well in terms of AUC
-but still fail to predict the minority class.
-The test does not provide insight into what specific aspects of the model are causing poor performance if the ROC
-AUC score is unsatisfactory.
-The use of macro average for multiclass ROC AUC score implies equal weightage to each class, which might not be
-appropriate if the classes are imbalanced.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/sklearn/ModelParameters.html b/docs/_build/validmind/tests/model_validation/sklearn/ModelParameters.html
deleted file mode 100644
index a5150f9f6..000000000
--- a/docs/_build/validmind/tests/model_validation/sklearn/ModelParameters.html
+++ /dev/null
@@ -1,304 +0,0 @@
-
-
-
- validmind.tests.model_validation.sklearn.ModelParameters API documentation
-
-
-
-
-
-
-
@tags('model_training', 'metadata')
-
@tasks('classification', 'regression')
-
-
def
-
ModelParameters (model , model_params = None ):
-
-
-
Extracts and displays model parameters in a structured format for transparency and reproducibility.
-
-
Purpose
-
-
The Model Parameters test is designed to provide transparency into model configuration and ensure
-reproducibility of machine learning models. It accomplishes this by extracting and presenting all
-relevant parameters that define the model's behavior, making it easier to audit, validate, and
-reproduce model training.
-
-
Test Mechanism
-
-
The test leverages scikit-learn's API convention of get_params() to extract model parameters. It
-produces a structured DataFrame containing parameter names and their corresponding values. For models
-that follow scikit-learn's API (including XGBoost, RandomForest, and other estimators), all
-parameters are automatically extracted and displayed.
-
-
Signs of High Risk
-
-
-Missing crucial parameters that should be explicitly set
-Extreme parameter values that could indicate overfitting (e.g., unlimited tree depth)
-Inconsistent parameters across different versions of the same model type
-Parameter combinations known to cause instability or poor performance
-Default values used for critical parameters that should be tuned
-
-
-
Strengths
-
-
-Universal compatibility with scikit-learn API-compliant models
-Ensures transparency in model configuration
-Facilitates model reproducibility and version control
-Enables systematic parameter auditing
-Supports both classification and regression models
-Helps identify potential configuration issues
-
-
-
Limitations
-
-
-Only works with models implementing scikit-learn's get_params() method
-Cannot capture dynamic parameters set during model training
-Does not validate parameter values for model-specific appropriateness
-Parameter meanings and impacts may vary across different model types
-Cannot detect indirect parameter interactions or their effects on model performance
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/sklearn/ModelsPerformanceComparison.html b/docs/_build/validmind/tests/model_validation/sklearn/ModelsPerformanceComparison.html
deleted file mode 100644
index bd2de7ee0..000000000
--- a/docs/_build/validmind/tests/model_validation/sklearn/ModelsPerformanceComparison.html
+++ /dev/null
@@ -1,304 +0,0 @@
-
-
-
- validmind.tests.model_validation.sklearn.ModelsPerformanceComparison API documentation
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/sklearn/OverfitDiagnosis.html b/docs/_build/validmind/tests/model_validation/sklearn/OverfitDiagnosis.html
deleted file mode 100644
index 932f017b6..000000000
--- a/docs/_build/validmind/tests/model_validation/sklearn/OverfitDiagnosis.html
+++ /dev/null
@@ -1,305 +0,0 @@
-
-
-
- validmind.tests.model_validation.sklearn.OverfitDiagnosis API documentation
-
-
-
-
-
-
-
@tags('sklearn', 'binary_classification', 'multiclass_classification', 'linear_regression', 'model_diagnosis')
-
@tasks('classification', 'regression')
-
-
def
-
OverfitDiagnosis ( model : validmind.vm_models.VMModel datasets : List [ validmind.vm_models.VMDataset ] , metric : str = None , cut_off_threshold : float = 0.04 ):
-
-
-
Assesses potential overfitting in a model's predictions, identifying regions where performance between training and
-testing sets deviates significantly.
-
-
Purpose
-
-
The Overfit Diagnosis test aims to identify areas in a model's predictions where there is a significant difference
-in performance between the training and testing sets. This test helps to pinpoint specific regions or feature
-segments where the model may be overfitting.
-
-
Test Mechanism
-
-
This test compares the model's performance on training versus test data, grouped by feature columns. It calculates
-the difference between the training and test performance for each group and identifies regions where this
-difference exceeds a specified threshold:
-
-
-The test works for both classification and regression models.
-It defaults to using the AUC metric for classification models and the MSE metric for regression models.
-The threshold for identifying overfitting regions is set to 0.04 by default.
-The test calculates the performance metrics for each feature segment and plots regions where the performance gap
-exceeds the threshold.
-
-
-
Signs of High Risk
-
-
-Significant gaps between training and test performance metrics for specific feature segments.
-Multiple regions with performance gaps exceeding the defined threshold.
-Higher than expected differences in predicted versus actual values in the test set compared to the training set.
-
-
-
Strengths
-
-
-Identifies specific areas where overfitting occurs.
-Supports multiple performance metrics, providing flexibility.
-Applicable to both classification and regression models.
-Visualization of overfitting segments aids in better understanding and debugging.
-
-
-
Limitations
-
-
-The default threshold may not be suitable for all use cases and requires tuning.
-May not capture more subtle forms of overfitting that do not exceed the threshold.
-Assumes that the binning of features adequately represents the data segments.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/sklearn/PermutationFeatureImportance.html b/docs/_build/validmind/tests/model_validation/sklearn/PermutationFeatureImportance.html
deleted file mode 100644
index 2ae43cd58..000000000
--- a/docs/_build/validmind/tests/model_validation/sklearn/PermutationFeatureImportance.html
+++ /dev/null
@@ -1,301 +0,0 @@
-
-
-
- validmind.tests.model_validation.sklearn.PermutationFeatureImportance API documentation
-
-
-
-
-
-
-
@tags('sklearn', 'binary_classification', 'multiclass_classification', 'feature_importance', 'visualization')
-
@tasks('classification', 'text_classification')
-
-
def
-
PermutationFeatureImportance ( model : validmind.vm_models.VMModel dataset : validmind.vm_models.VMDataset fontsize : Optional [ int ] = None , figure_height : Optional [ int ] = None ):
-
-
-
Assesses the significance of each feature in a model by evaluating the impact on model performance when feature
-values are randomly rearranged.
-
-
Purpose
-
-
The Permutation Feature Importance (PFI) metric aims to assess the importance of each feature used by the Machine
-Learning model. The significance is measured by evaluating the decrease in the model's performance when the
-feature's values are randomly arranged.
-
-
Test Mechanism
-
-
PFI is calculated via the permutation_importance method from the sklearn.inspection module. This method
-shuffles the columns of the feature dataset and measures the impact on the model's performance. A significant
-decrease in performance after permutating a feature's values deems the feature as important. On the other hand, if
-performance remains the same, the feature is likely not important. The output of the PFI metric is a figure
-illustrating the importance of each feature.
-
-
Signs of High Risk
-
-
-The model heavily relies on a feature with highly variable or easily permutable values, indicating instability.
-A feature deemed unimportant by the model but expected to have a significant effect on the outcome based on
-domain knowledge is not influencing the model's predictions.
-
-
-
Strengths
-
-
-Provides insights into the importance of different features and may reveal underlying data structure.
-Can indicate overfitting if a particular feature or set of features overly impacts the model's predictions.
-Model-agnostic and can be used with any classifier that provides a measure of prediction accuracy before and
-after feature permutation.
-
-
-
Limitations
-
-
-Does not imply causality; it only presents the amount of information that a feature provides for the prediction
-task.
-Does not account for interactions between features. If features are correlated, the permutation importance may
-allocate importance to one and not the other.
-Cannot interact with certain libraries like statsmodels, pytorch, catboost, etc., thus limiting its applicability.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/sklearn/PopulationStabilityIndex.html b/docs/_build/validmind/tests/model_validation/sklearn/PopulationStabilityIndex.html
deleted file mode 100644
index 8434a78bc..000000000
--- a/docs/_build/validmind/tests/model_validation/sklearn/PopulationStabilityIndex.html
+++ /dev/null
@@ -1,331 +0,0 @@
-
-
-
- validmind.tests.model_validation.sklearn.PopulationStabilityIndex API documentation
-
-
-
-
-
-
-
- def
- calculate_psi (score_initial , score_new , num_bins = 10 , mode = 'fixed' ):
-
-
-
-
-
-
-
-
-
-
-
-
@tags('sklearn', 'binary_classification', 'multiclass_classification', 'model_performance')
-
@tasks('classification', 'text_classification')
-
-
def
-
PopulationStabilityIndex ( datasets : List [ validmind.vm_models.VMDataset ] , model : validmind.vm_models.VMModel num_bins : int = 10 , mode : str = 'fixed' ):
-
-
-
Assesses the Population Stability Index (PSI) to quantify the stability of an ML model's predictions across
-different datasets.
-
-
Purpose
-
-
The Population Stability Index (PSI) serves as a quantitative assessment for evaluating the stability of a machine
-learning model's output distributions when comparing two different datasets. Typically, these would be a
-development and a validation dataset or two datasets collected at different periods. The PSI provides a measurable
-indication of any significant shift in the model's performance over time or noticeable changes in the
-characteristics of the population the model is making predictions for.
-
-
Test Mechanism
-
-
The implementation of the PSI in this script involves calculating the PSI for each feature between the training and
-test datasets. Data from both datasets is sorted and placed into either a predetermined number of bins or
-quantiles. The boundaries for these bins are initially determined based on the distribution of the training data.
-The contents of each bin are calculated and their respective proportions determined. Subsequently, the PSI is
-derived for each bin through a logarithmic transformation of the ratio of the proportions of data for each feature
-in the training and test datasets. The PSI, along with the proportions of data in each bin for both datasets, are
-displayed in a summary table, a grouped bar chart, and a scatter plot.
-
-
Signs of High Risk
-
-
-A high PSI value is a clear indicator of high risk. Such a value suggests a significant shift in the model
-predictions or severe changes in the characteristics of the underlying population.
-This ultimately suggests that the model may not be performing as well as expected and that it may be less
-reliable for making future predictions.
-
-
-
Strengths
-
-
-The PSI provides a quantitative measure of the stability of a model over time or across different samples, making
-it an invaluable tool for evaluating changes in a model's performance.
-It allows for direct comparisons across different features based on the PSI value.
-The calculation and interpretation of the PSI are straightforward, facilitating its use in model risk management.
-The use of visual aids such as tables and charts further simplifies the comprehension and interpretation of the
-PSI.
-
-
-
Limitations
-
-
-The PSI test does not account for the interdependence between features: features that are dependent on one
-another may show similar shifts in their distributions, which in turn may result in similar PSI values.
-The PSI test does not inherently provide insights into why there are differences in distributions or why the PSI
-values may have changed.
-The test may not handle features with significant outliers adequately.
-Additionally, the PSI test is performed on model predictions, not on the underlying data distributions which can
-lead to misinterpretations. Any changes in PSI could be due to shifts in the model (model drift), changes in the
-relationships between features and the target variable (concept drift), or both. However, distinguishing between
-these causes is non-trivial.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/sklearn/PrecisionRecallCurve.html b/docs/_build/validmind/tests/model_validation/sklearn/PrecisionRecallCurve.html
deleted file mode 100644
index 98ddeb29a..000000000
--- a/docs/_build/validmind/tests/model_validation/sklearn/PrecisionRecallCurve.html
+++ /dev/null
@@ -1,302 +0,0 @@
-
-
-
- validmind.tests.model_validation.sklearn.PrecisionRecallCurve API documentation
-
-
-
-
-
-
-
-
- Evaluates the precision-recall trade-off for binary classification models and visualizes the Precision-Recall curve.
-
-
Purpose
-
-
The Precision Recall Curve metric is intended to evaluate the trade-off between precision and recall in
-classification models, particularly binary classification models. It assesses the model's capacity to produce
-accurate results (high precision), as well as its ability to capture a majority of all positive instances (high
-recall).
-
-
Test Mechanism
-
-
The test extracts ground truth labels and prediction probabilities from the model's test dataset. It applies the
-precision_recall_curve method from the sklearn metrics module to these extracted labels and predictions, which
-computes a precision-recall pair for each possible threshold. This calculation results in an array of precision and
-recall scores that can be plotted against each other to form the Precision-Recall Curve. This curve is then
-visually represented by using Plotly's scatter plot.
-
-
Signs of High Risk
-
-
-A lower area under the Precision-Recall Curve signifies high risk.
-This corresponds to a model yielding a high amount of false positives (low precision) and/or false negatives (low
-recall).
-If the curve is closer to the bottom left of the plot, rather than being closer to the top right corner, it can
-be a sign of high risk.
-
-
-
Strengths
-
-
-This metric aptly represents the balance between precision (minimizing false positives) and recall (minimizing
-false negatives), which is especially critical in scenarios where both values are significant.
-Through the graphic representation, it enables an intuitive understanding of the model's performance across
-different threshold levels.
-
-
-
Limitations
-
-
-This metric is only applicable to binary classification models - it raises errors for multiclass classification
-models or Foundation models.
-It may not fully represent the overall accuracy of the model if the cost of false positives and false negatives
-are extremely different, or if the dataset is heavily imbalanced.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/sklearn/ROCCurve.html b/docs/_build/validmind/tests/model_validation/sklearn/ROCCurve.html
deleted file mode 100644
index 09f781174..000000000
--- a/docs/_build/validmind/tests/model_validation/sklearn/ROCCurve.html
+++ /dev/null
@@ -1,308 +0,0 @@
-
-
-
- validmind.tests.model_validation.sklearn.ROCCurve API documentation
-
-
-
-
-
-
-
-
- Evaluates binary classification model performance by generating and plotting the Receiver Operating Characteristic
-(ROC) curve and calculating the Area Under Curve (AUC) score.
-
-
Purpose
-
-
The Receiver Operating Characteristic (ROC) curve is designed to evaluate the performance of binary classification
-models. This curve illustrates the balance between the True Positive Rate (TPR) and False Positive Rate (FPR)
-across various threshold levels. In combination with the Area Under the Curve (AUC), the ROC curve aims to measure
-the model's discrimination ability between the two defined classes in a binary classification problem (e.g.,
-default vs non-default). Ideally, a higher AUC score signifies superior model performance in accurately
-distinguishing between the positive and negative classes.
-
-
Test Mechanism
-
-
First, this script selects the target model and datasets that require binary classification. It then calculates the
-predicted probabilities for the test set, and uses this data, along with the true outcomes, to generate and plot
-the ROC curve. Additionally, it includes a line signifying randomness (AUC of 0.5). The AUC score for the model's
-ROC curve is also computed, presenting a numerical estimation of the model's performance. If any Infinite values
-are detected in the ROC threshold, these are effectively eliminated. The resulting ROC curve, AUC score, and
-thresholds are consequently saved for future reference.
-
-
Signs of High Risk
-
-
-A high risk is potentially linked to the model's performance if the AUC score drops below or nears 0.5.
-Another warning sign would be the ROC curve lying closer to the line of randomness, indicating no discriminative
-ability.
-For the model to be deemed competent at its classification tasks, it is crucial that the AUC score is
-significantly above 0.5.
-
-
-
Strengths
-
-
-The ROC Curve offers an inclusive visual depiction of a model's discriminative power throughout all conceivable
-classification thresholds, unlike other metrics that solely disclose model performance at one fixed threshold.
-Despite the proportions of the dataset, the AUC Score, which represents the entire ROC curve as a single data
-point, continues to be consistent, proving to be the ideal choice for such situations.
-
-
-
Limitations
-
-
-The primary limitation is that this test is exclusively structured for binary classification tasks, thus limiting
-its application towards other model types.
-Furthermore, its performance might be subpar with models that output probabilities highly skewed towards 0 or 1.
-At the extreme, the ROC curve could reflect high performance even when the majority of classifications are
-incorrect, provided that the model's ranking format is retained. This phenomenon is commonly termed the "Class
-Imbalance Problem".
-
-
-
-
-
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diff --git a/docs/_build/validmind/tests/model_validation/sklearn/RegressionErrors.html b/docs/_build/validmind/tests/model_validation/sklearn/RegressionErrors.html
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index 986245ce6..000000000
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+++ /dev/null
@@ -1,310 +0,0 @@
-
-
-
- validmind.tests.model_validation.sklearn.RegressionErrors API documentation
-
-
-
-
-
-
-
@tags('sklearn', 'model_performance')
-
@tasks('regression', 'classification')
-
-
def
-
RegressionErrors (model , dataset ):
-
-
-
Assesses the performance and error distribution of a regression model using various error metrics.
-
-
Purpose
-
-
The purpose of the Regression Errors test is to measure the performance of a regression model by calculating
-several error metrics. This evaluation helps determine the model's accuracy and potential issues like overfitting
-or bias by analyzing differences in error metrics between the training and testing datasets.
-
-
Test Mechanism
-
-
The test computes the following error metrics:
-
-
-Mean Absolute Error (MAE) : Average of the absolute differences between true values and predicted values.Mean Squared Error (MSE) : Average of the squared differences between true values and predicted values.Root Mean Squared Error (RMSE) : Square root of the mean squared error.Mean Absolute Percentage Error (MAPE) : Average of the absolute differences between true values and predicted
-values, divided by the true values, and expressed as a percentage.Mean Bias Deviation (MBD) : Average bias between true values and predicted values.
-
-
These metrics are calculated separately for the training and testing datasets and compared to identify
-discrepancies.
-
-
Signs of High Risk
-
-
-High values for MAE, MSE, RMSE, or MAPE indicating poor model performance.
-Large differences in error metrics between the training and testing datasets, suggesting overfitting.
-Significant deviation of MBD from zero, indicating systematic bias in model predictions.
-
-
-
Strengths
-
-
-Provides a comprehensive overview of model performance through multiple error metrics.
-Individual metrics offer specific insights, e.g., MAE for interpretability, MSE for emphasizing larger errors.
-RMSE is useful for being in the same unit as the target variable.
-MAPE allows the error to be expressed as a percentage.
-MBD detects systematic bias in model predictions.
-
-
-
Limitations
-
-
-MAE and MSE are sensitive to outliers.
-RMSE heavily penalizes larger errors, which might not always be desirable.
-MAPE can be misleading when actual values are near zero.
-MBD may not be suitable if bias varies with the magnitude of actual values.
-These metrics may not capture all nuances of model performance and should be interpreted with domain-specific
-context.
-
-
-
-
-
\ No newline at end of file
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--- a/docs/_build/validmind/tests/model_validation/sklearn/RegressionErrorsComparison.html
+++ /dev/null
@@ -1,299 +0,0 @@
-
-
-
- validmind.tests.model_validation.sklearn.RegressionErrorsComparison API documentation
-
-
-
-
-
-
-
@tags('model_performance', 'sklearn')
-
@tasks('regression', 'time_series_forecasting')
-
-
def
-
RegressionErrorsComparison (datasets , models ):
-
-
-
Assesses multiple regression error metrics to compare model performance across different datasets, emphasizing
-systematic overestimation or underestimation and large percentage errors.
-
-
Purpose
-
-
The purpose of this test is to compare regression errors for different models applied to various datasets. It aims
-to examine model performance using multiple error metrics, thereby identifying areas where models may be
-underperforming or exhibiting bias.
-
-
Test Mechanism
-
-
The function iterates through each dataset-model pair and calculates various error metrics, including Mean Absolute
-Error (MAE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Mean Bias Deviation (MBD). The
-results are summarized in a table, which provides a comprehensive view of each model's performance on the datasets.
-
-
Signs of High Risk
-
-
-High Mean Absolute Error (MAE) or Mean Squared Error (MSE), indicating poor model performance.
-High Mean Absolute Percentage Error (MAPE), suggesting large percentage errors, especially problematic if the
-true values are small.
-Mean Bias Deviation (MBD) significantly different from zero, indicating systematic overestimation or
-underestimation by the model.
-
-
-
Strengths
-
-
-Provides multiple error metrics to assess model performance from different perspectives.
-Includes a check to avoid division by zero when calculating MAPE.
-
-
-
Limitations
-
-
-Assumes that the dataset is provided as a DataFrameDataset object with y, y_pred, and feature_columns
-attributes.
-Relies on the logger from validmind.logging to warn about zero values in y_true, which should be correctly
-implemented and imported.
-Requires that dataset.y_pred(model) returns the predicted values for the model.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/sklearn/RegressionPerformance.html b/docs/_build/validmind/tests/model_validation/sklearn/RegressionPerformance.html
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--- a/docs/_build/validmind/tests/model_validation/sklearn/RegressionPerformance.html
+++ /dev/null
@@ -1,294 +0,0 @@
-
-
-
- validmind.tests.model_validation.sklearn.RegressionPerformance API documentation
-
-
-
-
-
-
-
-
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diff --git a/docs/_build/validmind/tests/model_validation/sklearn/RegressionR2Square.html b/docs/_build/validmind/tests/model_validation/sklearn/RegressionR2Square.html
deleted file mode 100644
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--- a/docs/_build/validmind/tests/model_validation/sklearn/RegressionR2Square.html
+++ /dev/null
@@ -1,301 +0,0 @@
-
-
-
- validmind.tests.model_validation.sklearn.RegressionR2Square API documentation
-
-
-
-
-
-
-
@tags('sklearn', 'model_performance')
-
@tasks('regression')
-
-
def
-
RegressionR2Square (dataset , model ):
-
-
-
Assesses the overall goodness-of-fit of a regression model by evaluating R-squared (R2) and Adjusted R-squared (Adj
-R2) scores to determine the model's explanatory power over the dependent variable.
-
-
Purpose
-
-
The purpose of the RegressionR2Square Metric test is to measure the overall goodness-of-fit of a regression model.
-Specifically, this Python-based test evaluates the R-squared (R2) and Adjusted R-squared (Adj R2) scores, which are
-statistical measures used to assess the strength of the relationship between the model's predictors and the
-response variable.
-
-
Test Mechanism
-
-
The test deploys the r2_score method from the Scikit-learn metrics module to measure the R2 score on both
-training and test sets. This score reflects the proportion of the variance in the dependent variable that is
-predictable from the independent variables. The test also calculates the Adjusted R2 score, which accounts for the
-number of predictors in the model to penalize model complexity and reduce overfitting. The Adjusted R2 score will
-be smaller if unnecessary predictors are included in the model.
-
-
Signs of High Risk
-
-
-Low R2 or Adjusted R2 scores, suggesting that the model does not explain much variation in the dependent variable.
-Significant discrepancy between R2 scores on the training set and test set, indicating overfitting and poor
-generalization to unseen data.
-
-
-
Strengths
-
-
-Widely-used measure in regression analysis, providing a sound general indication of model performance.
-Easy to interpret and understand, as it represents the proportion of the dependent variable's variance explained
-by the independent variables.
-Adjusted R2 score helps control overfitting by penalizing unnecessary predictors.
-
-
-
Limitations
-
-
-Sensitive to the inclusion of unnecessary predictors even though Adjusted R2 penalizes complexity.
-Less reliable in cases of non-linear relationships or when the underlying assumptions of linear regression are
-violated.
-Does not provide insight on whether the correct regression model was used or if key assumptions have been met.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/sklearn/RegressionR2SquareComparison.html b/docs/_build/validmind/tests/model_validation/sklearn/RegressionR2SquareComparison.html
deleted file mode 100644
index 3e9025f39..000000000
--- a/docs/_build/validmind/tests/model_validation/sklearn/RegressionR2SquareComparison.html
+++ /dev/null
@@ -1,305 +0,0 @@
-
-
-
- validmind.tests.model_validation.sklearn.RegressionR2SquareComparison API documentation
-
-
-
-
-
-
-
@tags('model_performance', 'sklearn')
-
@tasks('regression', 'time_series_forecasting')
-
-
def
-
RegressionR2SquareComparison (datasets , models ):
-
-
-
Compares R-Squared and Adjusted R-Squared values for different regression models across multiple datasets to assess
-model performance and relevance of features.
-
-
Purpose
-
-
The Regression R2 Square Comparison test aims to compare the R-Squared and Adjusted R-Squared values for different
-regression models across various datasets. It helps in assessing how well each model explains the variability in
-the dataset, and whether the models include irrelevant features.
-
-
Test Mechanism
-
-
This test operates by:
-
-
-Iterating through each dataset-model pair.
-Calculating the R-Squared values to measure how much of the variability in the dataset is explained by the model.
-Calculating the Adjusted R-Squared values, which adjust the R-Squared based on the number of predictors in the
-model, making it more reliable when comparing models with different numbers of features.
-Generating a summary table containing these values for each combination of dataset and model.
-
-
-
Signs of High Risk
-
-
-If the R-Squared values are significantly low, it indicates the model isn't explaining much of the variability in
-the dataset.
-A significant difference between R-Squared and Adjusted R-Squared values might indicate that the model includes
-irrelevant features.
-
-
-
Strengths
-
-
-Provides a quantitative measure of model performance in terms of variance explained.
-Adjusted R-Squared accounts for the number of predictors, making it a more reliable measure when comparing models
-with different numbers of features.
-Useful for time-series forecasting and regression tasks.
-
-
-
Limitations
-
-
-Assumes the dataset is provided as a DataFrameDataset object with y, y_pred, and feature_columns attributes.
-Relies on adj_r2_score from the statsmodels.statsutils module, which needs to be correctly implemented and
-imported.
-Requires that dataset.y_pred(model) returns the predicted values for the model.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/sklearn/RobustnessDiagnosis.html b/docs/_build/validmind/tests/model_validation/sklearn/RobustnessDiagnosis.html
deleted file mode 100644
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--- a/docs/_build/validmind/tests/model_validation/sklearn/RobustnessDiagnosis.html
+++ /dev/null
@@ -1,301 +0,0 @@
-
-
-
- validmind.tests.model_validation.sklearn.RobustnessDiagnosis API documentation
-
-
-
-
-
-
-
@tags('sklearn', 'model_diagnosis', 'visualization')
-
@tasks('classification', 'regression')
-
-
def
-
RobustnessDiagnosis ( datasets : List [ validmind.vm_models.VMDataset ] , model : validmind.vm_models.VMModel metric : str = None , scaling_factor_std_dev_list : List [ float ] = [ 0.1 , 0.2 , 0.3 , 0.4 , 0.5 ] , performance_decay_threshold : float = 0.05 ):
-
-
-
Assesses the robustness of a machine learning model by evaluating performance decay under noisy conditions.
-
-
Purpose
-
-
The Robustness Diagnosis test aims to evaluate the resilience of a machine learning model when subjected to
-perturbations or noise in its input data. This is essential for understanding the model's ability to handle
-real-world scenarios where data may be imperfect or corrupted.
-
-
Test Mechanism
-
-
This test introduces Gaussian noise to the numeric input features of the datasets at varying scales of standard
-deviation. The performance of the model is then measured using a specified metric. The process includes:
-
-
-Adding Gaussian noise to numerical input features based on scaling factors.
-Evaluating the model's performance on the perturbed data using metrics like AUC for classification tasks and MSE
-for regression tasks.
-Aggregating and plotting the results to visualize performance decay relative to perturbation size.
-
-
-
Signs of High Risk
-
-
-A significant drop in performance metrics with minimal noise.
-Performance decay values exceeding the specified threshold.
-Consistent failure to meet performance standards across multiple perturbation scales.
-
-
-
Strengths
-
-
-Provides insights into the model's robustness against noisy or corrupted data.
-Utilizes a variety of performance metrics suitable for both classification and regression tasks.
-Visualization helps in understanding the extent of performance degradation.
-
-
-
Limitations
-
-
-Gaussian noise might not adequately represent all types of real-world data perturbations.
-Performance thresholds are somewhat arbitrary and might need tuning.
-The test may not account for more complex or unstructured noise patterns that could affect model robustness.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/sklearn/SHAPGlobalImportance.html b/docs/_build/validmind/tests/model_validation/sklearn/SHAPGlobalImportance.html
deleted file mode 100644
index 4fdd8bc2b..000000000
--- a/docs/_build/validmind/tests/model_validation/sklearn/SHAPGlobalImportance.html
+++ /dev/null
@@ -1,384 +0,0 @@
-
-
-
- validmind.tests.model_validation.sklearn.SHAPGlobalImportance API documentation
-
-
-
-
-
-
-
- def
- select_shap_values (shap_values , class_of_interest ):
-
-
-
-
-
- Selects SHAP values for binary or multiclass classification.
-
-
For regression models, returns the SHAP values directly as there are no classes.
-
-
Arguments:
-
-
-shap_values: The SHAP values returned by the SHAP explainer. For multiclass
-classification, this will be a list where each element corresponds to a class.
-For regression, this will be a single array of SHAP values.class_of_interest: The class index for which to retrieve SHAP values. If None
-(default), the function will assume binary classification and use class 1
-by default.
-
-
Returns:
-
-
- The SHAP values for the specified class (classification) or for the regression
- output.
-
-
-
Raises:
-
-
-ValueError: If class_of_interest is specified and is out of bounds for the
-number of classes.
-
-
-
-
- def
- generate_shap_plot (type_ , shap_values , x_test ):
-
-
-
-
-
- Plots two types of SHAP global importance (SHAP).
-
-
Arguments:
-
-
-type_: The type of SHAP plot to generate. Must be "mean" or "summary".shap_values: The SHAP values to plot.x_test: The test data used to generate the SHAP values.
-
-
Returns:
-
-
- The generated plot.
-
-
-
-
-
@tags('sklearn', 'binary_classification', 'multiclass_classification', 'feature_importance', 'visualization')
-
@tasks('classification', 'text_classification')
-
-
def
-
SHAPGlobalImportance ( model : validmind.vm_models.VMModel dataset : validmind.vm_models.VMDataset kernel_explainer_samples : int = 10 , tree_or_linear_explainer_samples : int = 200 , class_of_interest : int = None ):
-
-
-
Evaluates and visualizes global feature importance using SHAP values for model explanation and risk identification.
-
-
Purpose
-
-
The SHAP (SHapley Additive exPlanations) Global Importance metric aims to elucidate model outcomes by attributing
-them to the contributing features. It assigns a quantifiable global importance to each feature via their respective
-absolute Shapley values, thereby making it suitable for tasks like classification (both binary and multiclass).
-This metric forms an essential part of model risk management.
-
-
Test Mechanism
-
-
The exam begins with the selection of a suitable explainer which aligns with the model's type. For tree-based
-models like XGBClassifier, RandomForestClassifier, CatBoostClassifier, TreeExplainer is used whereas for linear
-models like LogisticRegression, XGBRegressor, LinearRegression, it is the LinearExplainer. Once the explainer
-calculates the Shapley values, these values are visualized using two specific graphical representations:
-
-
-Mean Importance Plot: This graph portrays the significance of individual features based on their absolute
-Shapley values. It calculates the average of these absolute Shapley values across all instances to highlight the
-global importance of features.
Summary Plot: This visual tool combines the feature importance with their effects. Every dot on this chart
-represents a Shapley value for a certain feature in a specific case. The vertical axis is denoted by the feature
-whereas the horizontal one corresponds to the Shapley value. A color gradient indicates the value of the feature,
-gradually changing from low to high. Features are systematically organized in accordance with their importance.
-
-
Signs of High Risk
-
-
-Overemphasis on certain features in SHAP importance plots, thus hinting at the possibility of model overfitting
-Anomalies such as unexpected or illogical features showing high importance, which might suggest that the model's
-decisions are rooted in incorrect or undesirable reasoning
-A SHAP summary plot filled with high variability or scattered data points, indicating a cause for concern
-
-
-
Strengths
-
-
-SHAP does more than just illustrating global feature significance, it offers a detailed perspective on how
-different features shape the model's decision-making logic for each instance.
-It provides clear insights into model behavior.
-
-
-
Limitations
-
-
-High-dimensional data can convolute interpretations.
-Associating importance with tangible real-world impact still involves a certain degree of subjectivity.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/sklearn/ScoreProbabilityAlignment.html b/docs/_build/validmind/tests/model_validation/sklearn/ScoreProbabilityAlignment.html
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--- a/docs/_build/validmind/tests/model_validation/sklearn/ScoreProbabilityAlignment.html
+++ /dev/null
@@ -1,315 +0,0 @@
-
-
-
- validmind.tests.model_validation.sklearn.ScoreProbabilityAlignment API documentation
-
-
-
-
-
-
-
-
- Analyzes the alignment between credit scores and predicted probabilities.
-
-
Purpose
-
-
The Score-Probability Alignment test evaluates how well credit scores align with
-predicted default probabilities. This helps validate score scaling, identify potential
-calibration issues, and ensure scores reflect risk appropriately.
-
-
Test Mechanism
-
-
The test:
-
-
-Groups scores into bins
-Calculates average predicted probability per bin
-Tests monotonicity of relationship
-Analyzes probability distribution within score bands
-
-
-
Signs of High Risk
-
-
-Non-monotonic relationship between scores and probabilities
-Large probability variations within score bands
-Unexpected probability jumps between adjacent bands
-Poor alignment with expected odds-to-score relationship
-Inconsistent probability patterns across score ranges
-Clustering of probabilities at extreme values
-Score bands with similar probability profiles
-Unstable probability estimates in key decision bands
-
-
-
Strengths
-
-
-Direct validation of score-to-probability relationship
-Identifies potential calibration issues
-Supports score band validation
-Helps understand model behavior
-Useful for policy setting
-Visual and numerical results
-Easy to interpret
-Supports regulatory documentation
-
-
-
Limitations
-
-
-Sensitive to bin selection
-Requires sufficient data per bin
-May mask within-bin variations
-Point-in-time analysis only
-Cannot detect all forms of miscalibration
-Assumes scores should align with probabilities
-May oversimplify complex relationships
-Limited to binary outcomes
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/sklearn/SilhouettePlot.html b/docs/_build/validmind/tests/model_validation/sklearn/SilhouettePlot.html
deleted file mode 100644
index 03225aa85..000000000
--- a/docs/_build/validmind/tests/model_validation/sklearn/SilhouettePlot.html
+++ /dev/null
@@ -1,308 +0,0 @@
-
-
-
- validmind.tests.model_validation.sklearn.SilhouettePlot API documentation
-
-
-
-
-
-
-
-
- Calculates and visualizes Silhouette Score, assessing the degree of data point suitability to its cluster in ML
-models.
-
-
Purpose
-
-
This test calculates the Silhouette Score, which is a model performance metric used in clustering applications.
-Primarily, the Silhouette Score evaluates how similar a data point is to its own cluster compared to other
-clusters. The metric ranges between -1 and 1, where a high value indicates that the object is well matched to its
-own cluster and poorly matched to neighboring clusters. Thus, the goal is to achieve a high Silhouette Score,
-implying well-separated clusters.
-
-
Test Mechanism
-
-
The test first extracts the true and predicted labels from the model's training data. The test runs the Silhouette
-Score function, which takes as input the training dataset features and the predicted labels, subsequently
-calculating the average score. This average Silhouette Score is printed for reference. The script then calculates
-the silhouette coefficients for each data point, helping to form the Silhouette Plot. Each cluster is represented
-in this plot, with color distinguishing between different clusters. A red dashed line indicates the average
-Silhouette Score. The Silhouette Scores are also collected into a structured table, facilitating model performance
-analysis and comparison.
-
-
Signs of High Risk
-
-
-A low Silhouette Score, potentially indicating that the clusters are not well separated and that data points may
-not be fitting well to their respective clusters.
-A Silhouette Plot displaying overlapping clusters or the absence of clear distinctions between clusters visually
-also suggests poor clustering performance.
-
-
-
Strengths
-
-
-The Silhouette Score provides a clear and quantitative measure of how well data points have been grouped into
-clusters, offering insights into model performance.
-The Silhouette Plot provides an intuitive, graphical representation of the clustering mechanism, aiding visual
-assessments of model performance.
-It does not require ground truth labels, so it's useful when true cluster assignments are not known.
-
-
-
Limitations
-
-
-The Silhouette Score may be susceptible to the influence of outliers, which could impact its accuracy and
-reliability.
-It assumes the clusters are convex and isotropic, which might not be the case with complex datasets.
-Due to the average nature of the Silhouette Score, the metric does not account for individual data point
-assignment nuances, so potentially relevant details may be omitted.
-Computationally expensive for large datasets, as it requires pairwise distance computations.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/sklearn/TrainingTestDegradation.html b/docs/_build/validmind/tests/model_validation/sklearn/TrainingTestDegradation.html
deleted file mode 100644
index db5cbc8ea..000000000
--- a/docs/_build/validmind/tests/model_validation/sklearn/TrainingTestDegradation.html
+++ /dev/null
@@ -1,303 +0,0 @@
-
-
-
- validmind.tests.model_validation.sklearn.TrainingTestDegradation API documentation
-
-
-
-
-
-
-
@tags('sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization')
-
@tasks('classification', 'text_classification')
-
-
def
-
TrainingTestDegradation ( datasets : List [ validmind.vm_models.VMDataset ] , model : validmind.vm_models.VMModel max_threshold : float = 0.1 ):
-
-
-
Tests if model performance degradation between training and test datasets exceeds a predefined threshold.
-
-
Purpose
-
-
The TrainingTestDegradation
-
-
Test Mechanism
-
-
The code applies several predefined metrics, including accuracy, precision, recall, and f1 scores, to the model's
-predictions for both the training and test datasets. It calculates the degradation as the difference between the
-training score and test score divided by the training score. The test is considered successful if the degradation
-for each metric is less than the preset maximum threshold of 10%. The results are summarized in a table showing
-each metric's train score, test score, degradation percentage, and pass/fail status.
-
-
Signs of High Risk
-
-
-A degradation percentage that exceeds the maximum allowed threshold of 10% for any of the evaluated metrics.
-A high difference or gap between the metric scores on the training and the test datasets.
-The 'Pass/Fail' column displaying 'Fail' for any of the evaluated metrics.
-
-
-
Strengths
-
-
-Provides a quantitative measure of the model's ability to generalize to unseen data, which is key for predicting
-its practical real-world performance.
-By evaluating multiple metrics, it takes into account different facets of model performance and enables a more
-holistic evaluation.
-The use of a variable predefined threshold allows the flexibility to adjust the acceptability criteria for
-different scenarios.
-
-
-
Limitations
-
-
-The test compares raw performance on training and test data but does not factor in the nature of the data. Areas
-with less representation in the training set might still perform poorly on unseen data.
-It requires good coverage and balance in the test and training datasets to produce reliable results, which may
-not always be available.
-The test is currently only designed for classification tasks.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/sklearn/VMeasure.html b/docs/_build/validmind/tests/model_validation/sklearn/VMeasure.html
deleted file mode 100644
index 821432058..000000000
--- a/docs/_build/validmind/tests/model_validation/sklearn/VMeasure.html
+++ /dev/null
@@ -1,299 +0,0 @@
-
-
-
- validmind.tests.model_validation.sklearn.VMeasure API documentation
-
-
-
-
-
-
-
-
- Evaluates homogeneity and completeness of a clustering model using the V Measure Score.
-
-
Purpose
-
-
The purpose of this metric, V Measure Score (V Score), is to evaluate the performance of a clustering model. It
-measures the homogeneity and completeness of a set of cluster labels, where homogeneity refers to each cluster
-containing only members of a single class and completeness meaning all members of a given class are assigned to the
-same cluster.
-
-
Test Mechanism
-
-
ClusterVMeasure is a class that inherits from another class, ClusterPerformance. It uses the v_measure_score
-function from the sklearn module's metrics package. The required inputs to perform this metric are the model, train
-dataset, and test dataset. The test is appropriate for models tasked with clustering.
-
-
Signs of High Risk
-
-
-Low V Measure Score: A low V Measure Score indicates that the clustering model has poor homogeneity or
-completeness, or both. This might signal that the model is failing to correctly cluster the data.
-
-
-
Strengths
-
-
-The V Measure Score is a harmonic mean between homogeneity and completeness. This ensures that both attributes
-are taken into account when evaluating the model, providing an overall measure of its cluster validity.
-The metric does not require knowledge of the ground truth classes when measuring homogeneity and completeness,
-making it applicable in instances where such information is unavailable.
-
-
-
Limitations
-
-
-The V Measure Score can be influenced by the number of clusters, which means that it might not always reflect the
-quality of the clustering. Partitioning the data into many small clusters could lead to high homogeneity but low
-completeness, leading to a low V Measure Score even if the clustering might be useful.
-It assumes equal importance of homogeneity and completeness. In some applications, one may be more important than
-the other. The V Measure Score does not provide flexibility in assigning different weights to homogeneity and
-completeness.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/sklearn/WeakspotsDiagnosis.html b/docs/_build/validmind/tests/model_validation/sklearn/WeakspotsDiagnosis.html
deleted file mode 100644
index 020cdc0d4..000000000
--- a/docs/_build/validmind/tests/model_validation/sklearn/WeakspotsDiagnosis.html
+++ /dev/null
@@ -1,311 +0,0 @@
-
-
-
- validmind.tests.model_validation.sklearn.WeakspotsDiagnosis API documentation
-
-
-
-
-
-
-
@tags('sklearn', 'binary_classification', 'multiclass_classification', 'model_diagnosis', 'visualization')
-
@tasks('classification', 'text_classification')
-
-
def
-
WeakspotsDiagnosis ( datasets : List [ validmind.vm_models.VMDataset ] , model : validmind.vm_models.VMModel features_columns : Optional [ List [ str ]] = None , metrics : Optional [ Dict [ str , Callable ]] = None , thresholds : Optional [ Dict [ str , float ]] = None ):
-
-
-
Identifies and visualizes weak spots in a machine learning model's performance across various sections of the
-feature space.
-
-
Purpose
-
-
The weak spots test is applied to evaluate the performance of a machine learning model within specific regions of
-its feature space. This test slices the feature space into various sections, evaluating the model's outputs within
-each section against specific performance metrics (e.g., accuracy, precision, recall, and F1 scores). The ultimate
-aim is to identify areas where the model's performance falls below the set thresholds, thereby exposing its
-possible weaknesses and limitations.
-
-
Test Mechanism
-
-
The test mechanism adopts an approach of dividing the feature space of the training dataset into numerous bins. The
-model's performance metrics (accuracy, precision, recall, F1 scores) are then computed for each bin on both the
-training and test datasets. A "weak spot" is identified if any of the performance metrics fall below a
-predetermined threshold for a particular bin on the test dataset. The test results are visually plotted as bar
-charts for each performance metric, indicating the bins which fail to meet the established threshold.
-
-
Signs of High Risk
-
-
-Any performance metric of the model dropping below the set thresholds.
-Significant disparity in performance between the training and test datasets within a bin could be an indication
-of overfitting.
-Regions or slices with consistently low performance metrics. Such instances could mean that the model struggles
-to handle specific types of input data adequately, resulting in potentially inaccurate predictions.
-
-
-
Strengths
-
-
-The test helps pinpoint precise regions of the feature space where the model's performance is below par, allowing
-for more targeted improvements to the model.
-The graphical presentation of the performance metrics offers an intuitive way to understand the model's
-performance across different feature areas.
-The test exhibits flexibility, letting users set different thresholds for various performance metrics according
-to the specific requirements of the application.
-
-
-
Limitations
-
-
-The binning system utilized for the feature space in the test could over-simplify the model's behavior within
-each bin. The granularity of this slicing depends on the chosen 'bins' parameter and can sometimes be arbitrary.
-The effectiveness of this test largely hinges on the selection of thresholds for the performance metrics, which
-may not hold universally applicable and could be subjected to the specifications of a particular model and
-application.
-The test is unable to handle datasets with a text column, limiting its application to numerical or categorical
-data types only.
-Despite its usefulness in highlighting problematic regions, the test does not offer direct suggestions for model
-improvement.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/statsmodels.html b/docs/_build/validmind/tests/model_validation/statsmodels.html
deleted file mode 100644
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--- a/docs/_build/validmind/tests/model_validation/statsmodels.html
+++ /dev/null
@@ -1,254 +0,0 @@
-
-
-
- validmind.tests.model_validation.statsmodels API documentation
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/statsmodels/AutoARIMA.html b/docs/_build/validmind/tests/model_validation/statsmodels/AutoARIMA.html
deleted file mode 100644
index b5a91ecf0..000000000
--- a/docs/_build/validmind/tests/model_validation/statsmodels/AutoARIMA.html
+++ /dev/null
@@ -1,311 +0,0 @@
-
-
-
- validmind.tests.model_validation.statsmodels.AutoARIMA API documentation
-
-
-
-
-
-
-
-
- Evaluates ARIMA models for time-series forecasting, ranking them using Bayesian and Akaike Information Criteria.
-
-
Purpose
-
-
The AutoARIMA validation test is designed to evaluate and rank AutoRegressive Integrated Moving Average (ARIMA)
-models. These models are primarily used for forecasting time-series data. The validation test automatically fits
-multiple ARIMA models, with varying parameters, to every variable within the given dataset. The models are then
-ranked based on their Bayesian Information Criterion (BIC) and Akaike Information Criterion (AIC) values, which
-provide a basis for the efficient model selection process.
-
-
Test Mechanism
-
-
This metric proceeds by generating an array of feasible combinations of ARIMA model parameters which are within a
-prescribed limit. These limits include max_p, max_d, max_q; they represent the autoregressive, differencing,
-and moving average components respectively. Upon applying these sets of parameters, the validation test fits each
-ARIMA model to the time-series data provided. For each model, it subsequently proceeds to calculate and record both
-the BIC and AIC values, which serve as performance indicators for the model fit. Prior to this parameter fitting
-process, the Augmented Dickey-Fuller test for data stationarity is conducted on the data series. If a series is
-found to be non-stationary, a warning message is sent out, given that ARIMA models necessitate input series to be
-stationary.
-
-
Signs of High Risk
-
-
-If the p-value of the Augmented Dickey-Fuller test for a variable exceeds 0.05, a warning is logged. This warning
-indicates that the series might not be stationary, leading to potentially inaccurate results.
-Consistent failure in fitting ARIMA models (as made evident through logged errors) might disclose issues with
-either the data or model stability.
-
-
-
Strengths
-
-
-The AutoARIMA validation test simplifies the often complex task of selecting the most suitable ARIMA model based
-on BIC and AIC criteria.
-The mechanism incorporates a check for non-stationarity within the data, which is a critical prerequisite for
-ARIMA models.
-The exhaustive search through all possible combinations of model parameters enhances the likelihood of
-identifying the best-fit model.
-
-
-
Limitations
-
-
-This validation test can be computationally costly as it involves creating and fitting multiple ARIMA models for
-every variable.
-Although the test checks for non-stationarity and logs warnings where present, it does not apply any
-transformations to the data to establish stationarity.
-The selection of models leans solely on BIC and AIC criteria, which may not yield the best predictive model in
-all scenarios.
-The test is only applicable to regression tasks involving time-series data, and may not work effectively for
-other types of machine learning tasks.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/statsmodels/CumulativePredictionProbabilities.html b/docs/_build/validmind/tests/model_validation/statsmodels/CumulativePredictionProbabilities.html
deleted file mode 100644
index 6823d9a1c..000000000
--- a/docs/_build/validmind/tests/model_validation/statsmodels/CumulativePredictionProbabilities.html
+++ /dev/null
@@ -1,308 +0,0 @@
-
-
-
- validmind.tests.model_validation.statsmodels.CumulativePredictionProbabilities API documentation
-
-
-
-
-
-
-
@tags('visualization', 'credit_risk')
-
@tasks('classification')
-
-
def
-
CumulativePredictionProbabilities (dataset , model , title = 'Cumulative Probabilities' ):
-
-
-
Visualizes cumulative probabilities of positive and negative classes for both training and testing in classification models.
-
-
Purpose
-
-
This metric is utilized to evaluate the distribution of predicted probabilities for positive and negative classes
-in a classification model. It provides a visual assessment of the model's behavior by plotting the cumulative
-probabilities for positive and negative classes across both the training and test datasets.
-
-
Test Mechanism
-
-
The classification model is evaluated by first computing the predicted probabilities for each instance in both
-the training and test datasets, which are then added as a new column in these sets. The cumulative probabilities
-for positive and negative classes are subsequently calculated and sorted in ascending order. Cumulative
-distributions of these probabilities are created for both positive and negative classes across both training and
-test datasets. These cumulative probabilities are represented visually in a plot, containing two subplots - one for
-the training data and the other for the test data, with lines representing cumulative distributions of positive and
-negative classes.
-
-
Signs of High Risk
-
-
-Imbalanced distribution of probabilities for either positive or negative classes.
-Notable discrepancies or significant differences between the cumulative probability distributions for the
-training data versus the test data.
-Marked discrepancies or large differences between the cumulative probability distributions for positive and
-negative classes.
-
-
-
Strengths
-
-
-Provides a visual illustration of data, which enhances the ease of understanding and interpreting the model's
-behavior.
-Allows for the comparison of model's behavior across training and testing datasets, providing insights about how
-well the model is generalized.
-Differentiates between positive and negative classes and their respective distribution patterns, aiding in
-problem diagnosis.
-
-
-
Limitations
-
-
-Exclusive to classification tasks and specifically to classification models.
-Graphical results necessitate human interpretation and may not be directly applicable for automated risk
-detection.
-The method does not give a solitary quantifiable measure of model risk, instead, it offers a visual
-representation and broad distributional information.
-If the training and test datasets are not representative of the overall data distribution, the metric could
-provide misleading results.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/statsmodels/DurbinWatsonTest.html b/docs/_build/validmind/tests/model_validation/statsmodels/DurbinWatsonTest.html
deleted file mode 100644
index 3e9e21cc8..000000000
--- a/docs/_build/validmind/tests/model_validation/statsmodels/DurbinWatsonTest.html
+++ /dev/null
@@ -1,299 +0,0 @@
-
-
-
- validmind.tests.model_validation.statsmodels.DurbinWatsonTest API documentation
-
-
-
-
-
-
-
@tasks('regression')
-
@tags('time_series_data', 'forecasting', 'statistical_test', 'statsmodels')
-
-
def
-
DurbinWatsonTest (dataset , model , threshold = [ 1.5 , 2.5 ] ):
-
-
-
Assesses autocorrelation in time series data features using the Durbin-Watson statistic.
-
-
Purpose
-
-
The Durbin-Watson Test metric detects autocorrelation in time series data (where a set of data values influences
-their predecessors). Autocorrelation is a crucial factor for regression tasks as these often assume the
-independence of residuals. A model with significant autocorrelation may give unreliable predictions.
-
-
Test Mechanism
-
-
Utilizing the durbin_watson function in the statsmodels Python library, the Durbin-Watson (DW) Test metric
-generates a statistical value for each feature of the training dataset. The function is looped over all columns of
-the dataset, calculating and caching the DW value for each column for further analysis. A DW metric value nearing 2
-indicates no autocorrelation. Conversely, values approaching 0 suggest positive autocorrelation, and those leaning
-towards 4 imply negative autocorrelation.
-
-
Signs of High Risk
-
-
-If a feature's DW value significantly deviates from 2, it could signal a high risk due to potential
-autocorrelation issues in the dataset.
-A value closer to 0 could imply positive autocorrelation, while a value nearer to 4 could point to negative
-autocorrelation, both leading to potentially unreliable prediction models.
-
-
-
Strengths
-
-
-The metric specializes in identifying autocorrelation in prediction model residuals.
-Autocorrelation detection assists in diagnosing violation of various modeling technique assumptions, particularly
-in regression analysis and time-series data modeling.
-
-
-
Limitations
-
-
-The Durbin-Watson Test mainly detects linear autocorrelation and could overlook other types of relationships.
-The metric is highly sensitive to data points order. Shuffling the order could lead to notably different results.
-The test only checks for first-order autocorrelation (between a variable and its immediate predecessor) and fails
-to detect higher-order autocorrelation.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/statsmodels/GINITable.html b/docs/_build/validmind/tests/model_validation/statsmodels/GINITable.html
deleted file mode 100644
index c07a6e7fe..000000000
--- a/docs/_build/validmind/tests/model_validation/statsmodels/GINITable.html
+++ /dev/null
@@ -1,311 +0,0 @@
-
-
-
- validmind.tests.model_validation.statsmodels.GINITable API documentation
-
-
-
-
-
-
-
@tags('model_performance')
-
@tasks('classification')
-
-
def
-
GINITable (dataset , model ):
-
-
-
Evaluates classification model performance using AUC, GINI, and KS metrics for training and test datasets.
-
-
Purpose
-
-
The 'GINITable' metric is designed to evaluate the performance of a classification model by emphasizing its
-discriminatory power. Specifically, it calculates and presents three important metrics - the Area under the ROC
-Curve (AUC), the GINI coefficient, and the Kolmogorov-Smirnov (KS) statistic - for both training and test datasets.
-
-
Test Mechanism
-
-
Using a dictionary for storing performance metrics for both the training and test datasets, the 'GINITable' metric
-calculates each of these metrics sequentially. The Area under the ROC Curve (AUC) is calculated via the
-roc_auc_score function from the Scikit-Learn library. The GINI coefficient, a measure of statistical dispersion,
-is then computed by doubling the AUC and subtracting 1. Finally, the Kolmogorov-Smirnov (KS) statistic is
-calculated via the roc_curve function from Scikit-Learn, with the False Positive Rate (FPR) subtracted from the
-True Positive Rate (TPR) and the maximum value taken from the resulting data. These metrics are then stored in a
-pandas DataFrame for convenient visualization.
-
-
Signs of High Risk
-
-
-Low values for performance metrics may suggest a reduction in model performance, particularly a low AUC which
-indicates poor classification performance, or a low GINI coefficient, which could suggest a decreased ability to
-discriminate different classes.
-A high KS value may be an indicator of potential overfitting, as this generally signifies a substantial
-divergence between positive and negative distributions.
-Significant discrepancies between the performance on the training dataset and the test dataset may present
-another signal of high risk.
-
-
-
Strengths
-
-
-Offers three key performance metrics (AUC, GINI, and KS) in one test, providing a more comprehensive evaluation
-of the model.
-Provides a direct comparison between the model's performance on training and testing datasets, which aids in
-identifying potential underfitting or overfitting.
-The applied metrics are class-distribution invariant, thereby remaining effective for evaluating model
-performance even when dealing with imbalanced datasets.
-Presents the metrics in a user-friendly table format for easy comprehension and analysis.
-
-
-
Limitations
-
-
-The GINI coefficient and KS statistic are both dependent on the AUC value. Therefore, any errors in the
-calculation of the latter will adversely impact the former metrics too.
-Mainly suited for binary classification models and may require modifications for effective application in
-multi-class scenarios.
-The metrics used are threshold-dependent and may exhibit high variability based on the chosen cut-off points.
-The test does not incorporate a method to efficiently handle missing or inefficiently processed data, which could
-lead to inaccuracies in the metrics if the data is not appropriately preprocessed.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/statsmodels/KolmogorovSmirnov.html b/docs/_build/validmind/tests/model_validation/statsmodels/KolmogorovSmirnov.html
deleted file mode 100644
index 045f86b57..000000000
--- a/docs/_build/validmind/tests/model_validation/statsmodels/KolmogorovSmirnov.html
+++ /dev/null
@@ -1,297 +0,0 @@
-
-
-
- validmind.tests.model_validation.statsmodels.KolmogorovSmirnov API documentation
-
-
-
-
-
-
-
-
- Assesses whether each feature in the dataset aligns with a normal distribution using the Kolmogorov-Smirnov test.
-
-
Purpose
-
-
The Kolmogorov-Smirnov (KS) test evaluates the distribution of features in a dataset to determine their alignment
-with a normal distribution. This is important because many statistical methods and machine learning models assume
-normality in the data distribution.
-
-
Test Mechanism
-
-
This test calculates the KS statistic and corresponding p-value for each feature in the dataset. It does so by
-comparing the cumulative distribution function of the feature with an ideal normal distribution. The KS statistic
-and p-value for each feature are then stored in a dictionary. The p-value threshold to reject the normal
-distribution hypothesis is not preset, providing flexibility for different applications.
-
-
Signs of High Risk
-
-
-Elevated KS statistic for a feature combined with a low p-value, indicating a significant divergence from a
-normal distribution.
-Features with notable deviations that could create problems if the model assumes normality in data distribution.
-
-
-
Strengths
-
-
-The KS test is sensitive to differences in the location and shape of empirical cumulative distribution functions.
-It is non-parametric and adaptable to various datasets, as it does not assume any specific data distribution.
-Provides detailed insights into the distribution of individual features.
-
-
-
Limitations
-
-
-The test's sensitivity to disparities in the tails of data distribution might cause false alarms about
-non-normality.
-Less effective for multivariate distributions, as it is designed for univariate distributions.
-Does not identify specific types of non-normality, such as skewness or kurtosis, which could impact model fitting.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/statsmodels/Lilliefors.html b/docs/_build/validmind/tests/model_validation/statsmodels/Lilliefors.html
deleted file mode 100644
index e243734d3..000000000
--- a/docs/_build/validmind/tests/model_validation/statsmodels/Lilliefors.html
+++ /dev/null
@@ -1,307 +0,0 @@
-
-
-
- validmind.tests.model_validation.statsmodels.Lilliefors API documentation
-
-
-
-
-
-
-
@tags('tabular_data', 'data_distribution', 'statistical_test', 'statsmodels')
-
@tasks('classification', 'regression')
-
-
def
-
Lilliefors (dataset : validmind.vm_models.VMDataset ):
-
-
-
Assesses the normality of feature distributions in an ML model's training dataset using the Lilliefors test.
-
-
Purpose
-
-
The purpose of this metric is to utilize the Lilliefors test, named in honor of the Swedish statistician Hubert
-Lilliefors, in order to assess whether the features of the machine learning model's training dataset conform to a
-normal distribution. This is done because the assumption of normal distribution plays a vital role in numerous
-statistical procedures as well as numerous machine learning models. Should the features fail to follow a normal
-distribution, some model types may not operate at optimal efficiency. This can potentially lead to inaccurate
-predictions.
-
-
Test Mechanism
-
-
The application of this test happens across all feature columns within the training dataset. For each feature, the
-Lilliefors test returns a test statistic and p-value. The test statistic quantifies how far the feature's
-distribution is from an ideal normal distribution, whereas the p-value aids in determining the statistical
-relevance of this deviation. The final results are stored within a dictionary, the keys of which correspond to the
-name of the feature column, and the values being another dictionary which houses the test statistic and p-value.
-
-
Signs of High Risk
-
-
-If the p-value corresponding to a specific feature sinks below a pre-established significance level, generally
-set at 0.05, then it can be deduced that the distribution of that feature significantly deviates from a normal
-distribution. This can present a high risk for models that assume normality, as these models may perform
-inaccurately or inefficiently in the presence of such a feature.
-
-
-
Strengths
-
-
-One advantage of the Lilliefors test is its utility irrespective of whether the mean and variance of the normal
-distribution are known in advance. This makes it a more robust option in real-world situations where these values
-might not be known.
-The test has the ability to screen every feature column, offering a holistic view of the dataset.
-
-
-
Limitations
-
-
-Despite the practical applications of the Lilliefors test in validating normality, it does come with some
-limitations.
-It is only capable of testing unidimensional data, thus rendering it ineffective for datasets with interactions
-between features or multi-dimensional phenomena.
-The test might not be as sensitive as some other tests (like the Anderson-Darling test) in detecting deviations
-from a normal distribution.
-Like any other statistical test, Lilliefors test may also produce false positives or negatives. Hence, banking
-solely on this test, without considering other characteristics of the data, may give rise to risks.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/statsmodels/PredictionProbabilitiesHistogram.html b/docs/_build/validmind/tests/model_validation/statsmodels/PredictionProbabilitiesHistogram.html
deleted file mode 100644
index e4fa80008..000000000
--- a/docs/_build/validmind/tests/model_validation/statsmodels/PredictionProbabilitiesHistogram.html
+++ /dev/null
@@ -1,305 +0,0 @@
-
-
-
- validmind.tests.model_validation.statsmodels.PredictionProbabilitiesHistogram API documentation
-
-
-
-
-
-
-
@tags('visualization', 'credit_risk')
-
@tasks('classification')
-
-
def
-
PredictionProbabilitiesHistogram (dataset , model , title = 'Histogram of Predictive Probabilities' ):
-
-
-
Assesses the predictive probability distribution for binary classification to evaluate model performance and
-potential overfitting or bias.
-
-
Purpose
-
-
The Prediction Probabilities Histogram test is designed to generate histograms displaying the Probability of
-Default (PD) predictions for both positive and negative classes in training and testing datasets. This helps in
-evaluating the performance of a classification model.
-
-
Test Mechanism
-
-
The metric follows these steps to execute the test:
-
-
-Extracts the target column from both the train and test datasets.
-Uses the model's predict function to calculate probabilities.
-Adds these probabilities as a new column to the training and testing dataframes.
-Generates histograms for each class (0 or 1) within the training and testing datasets.
-Sets different opacities for the histograms to enhance visualization.
-Overlays the four histograms (two for training and two for testing) on two different subplot frames.
-Returns a plotly graph object displaying the visualization.
-
-
-
Signs of High Risk
-
-
-Significant discrepancies between the histograms of training and testing data.
-Large disparities between the histograms for the positive and negative classes.
-Potential overfitting or bias indicated by significant issues.
-Unevenly distributed probabilities suggesting inaccurate model predictions.
-
-
-
Strengths
-
-
-Offers a visual representation of the PD predictions made by the model, aiding in understanding its behavior.
-Assesses both the training and testing datasets, adding depth to model validation.
-Highlights disparities between classes, providing insights into class imbalance or data skewness.
-Effectively visualizes risk spread, which is particularly beneficial for credit risk prediction.
-
-
-
Limitations
-
-
-Specifically tailored for binary classification scenarios and not suited for multi-class classification tasks.
-Provides a robust visual representation but lacks a quantifiable measure to assess model performance.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/statsmodels/RegressionCoeffs.html b/docs/_build/validmind/tests/model_validation/statsmodels/RegressionCoeffs.html
deleted file mode 100644
index d0599ba67..000000000
--- a/docs/_build/validmind/tests/model_validation/statsmodels/RegressionCoeffs.html
+++ /dev/null
@@ -1,306 +0,0 @@
-
-
-
- validmind.tests.model_validation.statsmodels.RegressionCoeffs API documentation
-
-
-
-
-
-
-
@tags('tabular_data', 'visualization', 'model_training')
-
@tasks('regression')
-
-
def
-
RegressionCoeffs (model ):
-
-
-
Assesses the significance and uncertainty of predictor variables in a regression model through visualization of
-coefficients and their 95% confidence intervals.
-
-
Purpose
-
-
The RegressionCoeffs
-
-
Test Mechanism
-
-
The function operates by extracting the estimated coefficients and their standard errors from the regression model.
-Using these, it calculates the confidence intervals at a 95% confidence level, which indicates the range within which
-the true coefficient value is expected to fall 95% of the time. The confidence intervals are computed using the
-Z-value associated with the 95% confidence level. The coefficients and their confidence intervals are then visualized
-in a bar plot. The x-axis represents the predictor variables, the y-axis represents the estimated coefficients, and
-the error bars depict the confidence intervals.
-
-
Signs of High Risk
-
-
-The confidence interval for a coefficient contains the zero value, suggesting that the predictor may not significantly
-contribute to the model.
-Multiple coefficients with confidence intervals that include zero, potentially indicating issues with model reliability.
-Very wide confidence intervals, which may suggest high uncertainty in the coefficient estimates and potential model
-instability.
-
-
-
Strengths
-
-
-Provides a clear visualization that allows for easy interpretation of the significance and impact of predictor
-variables.
-Includes confidence intervals, which provide additional information about the uncertainty surrounding each coefficient
-estimate.
-
-
-
Limitations
-
-
-The method assumes normality of residuals and independence of observations, assumptions that may not always hold true
-in practice.
-It does not address issues related to multi-collinearity among predictor variables, which can affect the interpretation
-of coefficients.
-This metric is limited to regression tasks using tabular data and is not applicable to other types of machine learning
-tasks or data structures.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/statsmodels/RegressionFeatureSignificance.html b/docs/_build/validmind/tests/model_validation/statsmodels/RegressionFeatureSignificance.html
deleted file mode 100644
index bd33d8d40..000000000
--- a/docs/_build/validmind/tests/model_validation/statsmodels/RegressionFeatureSignificance.html
+++ /dev/null
@@ -1,301 +0,0 @@
-
-
-
- validmind.tests.model_validation.statsmodels.RegressionFeatureSignificance API documentation
-
-
-
-
-
-
-
@tags('statistical_test', 'model_interpretation', 'visualization', 'feature_importance')
-
@tasks('regression')
-
-
def
-
RegressionFeatureSignificance ( model : validmind.vm_models.VMModel fontsize : int = 10 , p_threshold : float = 0.05 ):
-
-
-
Assesses and visualizes the statistical significance of features in a regression model.
-
-
Purpose
-
-
The Regression Feature Significance metric assesses the significance of each feature in a given set of regression
-model. It creates a visualization displaying p-values for every feature of the model, assisting model developers
-in understanding which features are most influential in their model.
-
-
Test Mechanism
-
-
The test mechanism involves extracting the model's coefficients and p-values for each feature, and then plotting these
-values. The x-axis on the plot contains the p-values while the y-axis denotes the coefficients of each feature. A
-vertical red line is drawn at the threshold for p-value significance, which is 0.05 by default. Any features with
-p-values to the left of this line are considered statistically significant at the chosen level.
-
-
Signs of High Risk
-
-
-Any feature with a high p-value (greater than the threshold) is considered a potential high risk, as it suggests
-the feature is not statistically significant and may not be reliably contributing to the model's predictions.
-A high number of such features may indicate problems with the model validation, variable selection, and overall
-reliability of the model predictions.
-
-
-
Strengths
-
-
-Helps identify the features that significantly contribute to a model's prediction, providing insights into the
-feature importance.
-Provides tangible, easy-to-understand visualizations to interpret the feature significance.
-
-
-
Limitations
-
-
-This metric assumes model features are independent, which may not always be the case. Multicollinearity (high
-correlation amongst predictors) can cause high variance and unreliable statistical tests of significance.
-The p-value strategy for feature selection doesn't take into account the magnitude of the effect, focusing solely
-on whether the feature is likely non-zero.
-This test is specific to regression models and wouldn't be suitable for other types of ML models.
-P-value thresholds are somewhat arbitrary and do not always indicate practical significance, only statistical
-significance.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/statsmodels/RegressionModelForecastPlot.html b/docs/_build/validmind/tests/model_validation/statsmodels/RegressionModelForecastPlot.html
deleted file mode 100644
index 45e6ff4e5..000000000
--- a/docs/_build/validmind/tests/model_validation/statsmodels/RegressionModelForecastPlot.html
+++ /dev/null
@@ -1,303 +0,0 @@
-
-
-
- validmind.tests.model_validation.statsmodels.RegressionModelForecastPlot API documentation
-
-
-
-
-
-
-
-
- Generates plots to visually compare the forecasted outcomes of a regression model against actual observed values over
-a specified date range.
-
-
Purpose
-
-
This metric is useful for time-series models or any model where the outcome changes over time, allowing direct
-comparison of predicted vs actual values. It can help identify overfitting or underfitting situations as well as
-general model performance.
-
-
Test Mechanism
-
-
This test generates a plot with the x-axis representing the date ranging from the specified "start_date" to the
-"end_date", while the y-axis shows the value of the outcome variable. Two lines are plotted: one representing the
-forecasted values and the other representing the observed values. The "start_date" and "end_date" can be parameters
-of this test; if these parameters are not provided, they are set to the minimum and maximum date available in the
-dataset.
-
-
Signs of High Risk
-
-
-High risk or failure signs could be deduced visually from the plots if the forecasted line significantly deviates
-from the observed line, indicating the model's predicted values are not matching actual outcomes.
-A model that struggles to handle the edge conditions like maximum and minimum data points could also be
-considered a sign of risk.
-
-
-
Strengths
-
-
-Visualization: The plot provides an intuitive and clear illustration of how well the forecast matches the actual
-values, making it straightforward even for non-technical stakeholders to interpret.
-Flexibility: It allows comparison for multiple models and for specified time periods.
-Model Evaluation: It can be useful in identifying overfitting or underfitting situations, as these will manifest
-as discrepancies between the forecasted and observed values.
-
-
-
Limitations
-
-
-Interpretation Bias: Interpretation of the plot is subjective and can lead to different conclusions by different
-evaluators.
-Lack of Precision: Visual representation might not provide precise values of the deviation.
-Inapplicability: Limited to cases where the order of data points (time-series) matters, it might not be of much
-use in problems that are not related to time series prediction.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/statsmodels/RegressionModelForecastPlotLevels.html b/docs/_build/validmind/tests/model_validation/statsmodels/RegressionModelForecastPlotLevels.html
deleted file mode 100644
index 776473a65..000000000
--- a/docs/_build/validmind/tests/model_validation/statsmodels/RegressionModelForecastPlotLevels.html
+++ /dev/null
@@ -1,316 +0,0 @@
-
-
-
- validmind.tests.model_validation.statsmodels.RegressionModelForecastPlotLevels API documentation
-
-
-
-
-
-
-
- def
- integrate_diff (series_diff , start_value ):
-
-
-
-
-
-
-
-
-
-
-
-
- Assesses the alignment between forecasted and observed values in regression models through visual plots
-
-
Purpose
-
-
This test aims to visually assess the performance of a regression model by comparing its forecasted values against
-the actual observed values for both the raw and transformed (integrated) data. This helps determine the accuracy
-of the model and can help identify overfitting or underfitting. The integration is applied to highlight the trend
-rather than the absolute level.
-
-
Test Mechanism
-
-
This test generates two plots:
-
-
-Raw data vs forecast
-Transformed data vs forecast
-
-
-
The transformed data is created by performing a cumulative sum on the raw data.
-
-
Signs of High Risk
-
-
-Significant deviation between forecasted and observed values.
-Patterns suggesting overfitting or underfitting.
-Large discrepancies in the plotted forecasts, indicating potential issues with model generalizability and
-precision.
-
-
-
Strengths
-
-
-Provides an intuitive, visual way to assess multiple regression models, aiding in easier interpretation and
-evaluation of forecast accuracy.
-
-
-
Limitations
-
-
-Relies heavily on visual interpretation, which may vary between individuals.
-Does not provide a numerical metric to quantify forecast accuracy, relying solely on visual assessment.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/statsmodels/RegressionModelSensitivityPlot.html b/docs/_build/validmind/tests/model_validation/statsmodels/RegressionModelSensitivityPlot.html
deleted file mode 100644
index bb192be94..000000000
--- a/docs/_build/validmind/tests/model_validation/statsmodels/RegressionModelSensitivityPlot.html
+++ /dev/null
@@ -1,320 +0,0 @@
-
-
-
- validmind.tests.model_validation.statsmodels.RegressionModelSensitivityPlot API documentation
-
-
-
-
-
-
-
- def
- integrate_diff (series_diff , start_value ):
-
-
-
-
-
-
-
-
-
-
-
-
- Assesses the sensitivity of a regression model to changes in independent variables by applying shocks and
-visualizing the impact.
-
-
Purpose
-
-
The Regression Sensitivity Plot test is designed to perform sensitivity analysis on regression models. This test
-aims to measure the impact of slight changes (shocks) applied to individual variables on the system's outcome while
-keeping all other variables constant. By doing so, it analyzes the effects of each independent variable on the
-dependent variable within the regression model, helping identify significant risk factors that could substantially
-influence the model's output.
-
-
Test Mechanism
-
-
This test operates by initially applying shocks of varying magnitudes, defined by specific parameters, to each of
-the model's features, one at a time. With all other variables held constant, a new prediction is made for each
-dataset subjected to shocks. Any changes in the model's predictions are directly attributed to the shocks applied.
-If the transformation parameter is set to "integrate," initial predictions and target values undergo transformation
-via an integration function before being plotted. Finally, a plot demonstrating observed values against predicted
-values for each model is generated, showcasing a distinct line graph illustrating predictions for each shock.
-
-
Signs of High Risk
-
-
-Drastic alterations in model predictions due to minor shocks to an individual variable, indicating high
-sensitivity and potential over-dependence on that variable.
-Unusually high or unpredictable shifts in response to shocks, suggesting potential model instability or
-overfitting.
-
-
-
Strengths
-
-
-Helps identify variables that strongly influence model outcomes, aiding in understanding feature importance.
-Generates visual plots, making results easily interpretable even to non-technical stakeholders.
-Useful in identifying overfitting and detecting unstable models that react excessively to minor variable changes.
-
-
-
Limitations
-
-
-Operates on the assumption that all other variables remain unchanged during the application of a shock, which may
-not reflect real-world interdependencies.
-Best compatible with linear models and may not effectively evaluate the sensitivity of non-linear models.
-Provides a visual representation without a numerical risk measure, potentially introducing subjectivity in
-interpretation.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/statsmodels/RegressionModelSummary.html b/docs/_build/validmind/tests/model_validation/statsmodels/RegressionModelSummary.html
deleted file mode 100644
index 73808c21d..000000000
--- a/docs/_build/validmind/tests/model_validation/statsmodels/RegressionModelSummary.html
+++ /dev/null
@@ -1,294 +0,0 @@
-
-
-
- validmind.tests.model_validation.statsmodels.RegressionModelSummary API documentation
-
-
-
-
-
-
-
-
- Evaluates regression model performance using metrics including R-Squared, Adjusted R-Squared, MSE, and RMSE.
-
-
Purpose
-
-
The Regression Model Summary test evaluates the performance of regression models by measuring their predictive
-ability regarding dependent variables given changes in the independent variables. It uses conventional regression
-metrics such as R-Squared, Adjusted R-Squared, Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) to
-assess the model's accuracy and fit.
-
-
Test Mechanism
-
-
This test uses the sklearn library to calculate the R-Squared, Adjusted R-Squared, MSE, and RMSE. It outputs a
-table with the results of these metrics along with the feature columns used by the model.
-
-
Signs of High Risk
-
-
-Low R-Squared and Adjusted R-Squared values.
-High MSE and RMSE values.
-
-
-
Strengths
-
-
-Offers an extensive evaluation of regression models by combining four key measures of model accuracy and fit.
-Provides a comprehensive view of the model's performance.
-Both the R-Squared and Adjusted R-Squared measures are readily interpretable.
-
-
-
Limitations
-
-
-RMSE and MSE might be sensitive to outliers.
-A high R-Squared or Adjusted R-Squared may not necessarily indicate a good model, especially in cases of
-overfitting.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/statsmodels/RegressionPermutationFeatureImportance.html b/docs/_build/validmind/tests/model_validation/statsmodels/RegressionPermutationFeatureImportance.html
deleted file mode 100644
index ce0ad4987..000000000
--- a/docs/_build/validmind/tests/model_validation/statsmodels/RegressionPermutationFeatureImportance.html
+++ /dev/null
@@ -1,298 +0,0 @@
-
-
-
- validmind.tests.model_validation.statsmodels.RegressionPermutationFeatureImportance API documentation
-
-
-
-
-
-
-
-
- Assesses the significance of each feature in a model by evaluating the impact on model performance when feature
-values are randomly rearranged.
-
-
Purpose
-
-
The primary purpose of this metric is to determine which features significantly impact the performance of a
-regression model developed using statsmodels. The metric measures how much the prediction accuracy deteriorates
-when each feature's values are permuted.
-
-
Test Mechanism
-
-
This metric shuffles the values of each feature one at a time in the dataset, computes the model's performance
-after each permutation, and compares it to the baseline performance. A significant decrease in performance
-indicates the importance of the feature.
-
-
Signs of High Risk
-
-
-Significant reliance on a feature that, when permuted, leads to a substantial decrease in performance, suggesting
-overfitting or high model dependency on that feature.
-Features identified as unimportant despite known impacts from domain knowledge, suggesting potential issues in
-model training or data preprocessing.
-
-
-
Strengths
-
-
-Directly assesses the impact of each feature on model performance, providing clear insights into model
-dependencies.
-Model-agnostic within the scope of statsmodels, applicable to any regression model that outputs predictions.
-
-
-
Limitations
-
-
-The metric is specific to statsmodels and cannot be used with other types of models without adaptation.
-It does not capture interactions between features, which can lead to underestimating the importance of correlated
-features.
-Assumes independence of features when calculating importance, which might not always hold true.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/statsmodels/ScorecardHistogram.html b/docs/_build/validmind/tests/model_validation/statsmodels/ScorecardHistogram.html
deleted file mode 100644
index b0e036779..000000000
--- a/docs/_build/validmind/tests/model_validation/statsmodels/ScorecardHistogram.html
+++ /dev/null
@@ -1,307 +0,0 @@
-
-
-
- validmind.tests.model_validation.statsmodels.ScorecardHistogram API documentation
-
-
-
-
-
-
-
@tags('visualization', 'credit_risk', 'logistic_regression')
-
@tasks('classification')
-
-
def
-
ScorecardHistogram (dataset , title = 'Histogram of Scores' , score_column = 'score' ):
-
-
-
The Scorecard Histogram test evaluates the distribution of credit scores between default and non-default instances,
-providing critical insights into the performance and generalizability of credit-risk models.
-
-
Purpose
-
-
The Scorecard Histogram test metric provides a visual interpretation of the credit scores generated by a machine
-learning model for credit-risk classification tasks. It aims to compare the alignment of the model's scoring
-decisions with the actual outcomes of credit loan applications. It helps in identifying potential discrepancies
-between the model's predictions and real-world risk levels.
-
-
Test Mechanism
-
-
This metric uses logistic regression to generate a histogram of credit scores for both default (negative class) and
-non-default (positive class) instances. Using both training and test datasets, the metric calculates the credit
-score of each instance with a scorecard method, considering the impact of different features on the likelihood of
-default. It includes the default point to odds (PDO) scaling factor and predefined target score and odds settings.
-Histograms for training and test sets are computed and plotted separately to offer insights into the model's
-generalizability to unseen data.
-
-
Signs of High Risk
-
-
-Discrepancies between the distributions of training and testing data, indicating a model's poor generalization
-ability
-Skewed distributions favoring specific scores or classes, representing potential bias
-
-
-
Strengths
-
-
-Provides a visual interpretation of the model's credit scoring system, enhancing comprehension of model behavior
-Enables a direct comparison between actual and predicted scores for both training and testing data
-Its intuitive visualization helps understand the model's ability to differentiate between positive and negative
-classes
-Can unveil patterns or anomalies not easily discerned through numerical metrics alone
-
-
-
Limitations
-
-
-Despite its value for visual interpretation, it doesn't quantify the performance of the model and therefore may
-lack precision for thorough model evaluation
-The quality of input data can strongly influence the metric, as bias or noise in the data will affect both the
-score calculation and resultant histogram
-Its specificity to credit scoring models limits its applicability across a wider variety of machine learning
-tasks and models
-The metric's effectiveness is somewhat tied to the subjective interpretation of the analyst, relying on their
-judgment of the characteristics and implications of the plot.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/model_validation/statsmodels/statsutils.html b/docs/_build/validmind/tests/model_validation/statsmodels/statsutils.html
deleted file mode 100644
index 352eecab0..000000000
--- a/docs/_build/validmind/tests/model_validation/statsmodels/statsutils.html
+++ /dev/null
@@ -1,257 +0,0 @@
-
-
-
- validmind.tests.model_validation.statsmodels.statsutils API documentation
-
-
-
-
-
-
-
- def
- adj_r2_score ( actual : numpy . ndarray , predicted : numpy . ndarray , rowcount : int , featurecount : int ):
-
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/prompt_validation.html b/docs/_build/validmind/tests/prompt_validation.html
deleted file mode 100644
index ad8a0dae2..000000000
--- a/docs/_build/validmind/tests/prompt_validation.html
+++ /dev/null
@@ -1,246 +0,0 @@
-
-
-
- validmind.tests.prompt_validation API documentation
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/prompt_validation/Bias.html b/docs/_build/validmind/tests/prompt_validation/Bias.html
deleted file mode 100644
index feb44d776..000000000
--- a/docs/_build/validmind/tests/prompt_validation/Bias.html
+++ /dev/null
@@ -1,312 +0,0 @@
-
-
-
- validmind.tests.prompt_validation.Bias API documentation
-
-
-
-
-
-
-
@tags('llm', 'few_shot')
-
@tasks('text_classification', 'text_summarization')
-
-
def
-
Bias (model , min_threshold = 7 ):
-
-
-
Assesses potential bias in a Large Language Model by analyzing the distribution and order of exemplars in the
-prompt.
-
-
Purpose
-
-
The Bias Evaluation test calculates if and how the order and distribution of exemplars (examples) in a few-shot
-learning prompt affect the output of a Large Language Model (LLM). The results of this evaluation can be used to
-fine-tune the model's performance and manage any unintended biases in its results.
-
-
Test Mechanism
-
-
This test uses two checks:
-
-
-Distribution of Exemplars: The number of positive vs. negative examples in a prompt is varied. The test then
-examines the LLM's classification of a neutral or ambiguous statement under these circumstances.Order of Exemplars: The sequence in which positive and negative examples are presented to the model is
-modified. Their resultant effect on the LLM's response is studied.
-
-
For each test case, the LLM grades the input prompt on a scale of 1 to 10. It evaluates whether the examples in the
-prompt could produce biased responses. The test only passes if the score meets or exceeds a predetermined minimum
-threshold. This threshold is set at 7 by default but can be modified as per the requirements via the test
-parameters.
-
-
Signs of High Risk
-
-
-A skewed result favoring either positive or negative responses may suggest potential bias in the model. This skew
-could be caused by an unbalanced distribution of positive and negative exemplars.
-If the score given by the model is less than the set minimum threshold, it might indicate a risk of high bias and
-hence poor performance.
-
-
-
Strengths
-
-
-This test provides a quantitative measure of potential bias, offering clear guidelines for developers about
-whether their Large Language Model (LLM) contains significant bias.
-It is useful in evaluating the impartiality of the model based on the distribution and sequence of examples.
-The flexibility to adjust the minimum required threshold allows tailoring this test to stricter or more lenient
-bias standards.
-
-
-
Limitations
-
-
-The test may not pick up on more subtle forms of bias or biases that are not directly related to the distribution
-or order of exemplars.
-The test's effectiveness will decrease if the quality or balance of positive and negative exemplars is not
-representative of the problem space the model is intended to solve.
-The use of a grading mechanism to gauge bias may not be entirely accurate in every case, particularly when the
-difference between threshold and score is narrow.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/prompt_validation/Clarity.html b/docs/_build/validmind/tests/prompt_validation/Clarity.html
deleted file mode 100644
index a5c788ae8..000000000
--- a/docs/_build/validmind/tests/prompt_validation/Clarity.html
+++ /dev/null
@@ -1,298 +0,0 @@
-
-
-
- validmind.tests.prompt_validation.Clarity API documentation
-
-
-
-
-
-
-
@tags('llm', 'zero_shot', 'few_shot')
-
@tasks('text_classification', 'text_summarization')
-
-
def
-
Clarity (model , min_threshold = 7 ):
-
-
-
Evaluates and scores the clarity of prompts in a Large Language Model based on specified guidelines.
-
-
Purpose
-
-
The Clarity evaluation metric is used to assess how clear the prompts of a Large Language Model (LLM) are. This
-assessment is particularly important because clear prompts assist the LLM in more accurately interpreting and
-responding to instructions.
-
-
Test Mechanism
-
-
The evaluation uses an LLM to scrutinize the clarity of prompts, factoring in considerations such as the inclusion
-of relevant details, persona adoption, step-by-step instructions, usage of examples, and specification of desired
-output length. Each prompt is rated on a clarity scale of 1 to 10, and any prompt scoring at or above the preset
-threshold (default of 7) will be marked as clear. It is important to note that this threshold can be adjusted via
-test parameters, providing flexibility in the evaluation process.
-
-
Signs of High Risk
-
-
-Prompts that consistently score below the clarity threshold
-Repeated failure of prompts to adhere to guidelines for clarity, including detail inclusion, persona adoption,
-explicit step-by-step instructions, use of examples, and specification of output length
-
-
-
Strengths
-
-
-Encourages the development of more effective prompts that aid the LLM in interpreting instructions accurately
-Applies a quantifiable measure (a score from 1 to 10) to evaluate the clarity of prompts
-Threshold for clarity is adjustable, allowing for flexible evaluation depending on the context
-
-
-
Limitations
-
-
-Scoring system is subjective and relies on the AI’s interpretation of 'clarity'
-The test assumes that all required factors (detail inclusion, persona adoption, step-by-step instructions, use of
-examples, and specification of output length) contribute equally to clarity, which might not always be the case
-The evaluation may not be as effective if used on non-textual models
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/prompt_validation/Conciseness.html b/docs/_build/validmind/tests/prompt_validation/Conciseness.html
deleted file mode 100644
index a868b8bb1..000000000
--- a/docs/_build/validmind/tests/prompt_validation/Conciseness.html
+++ /dev/null
@@ -1,298 +0,0 @@
-
-
-
- validmind.tests.prompt_validation.Conciseness API documentation
-
-
-
-
-
-
-
@tags('llm', 'zero_shot', 'few_shot')
-
@tasks('text_classification', 'text_summarization')
-
-
def
-
Conciseness (model , min_threshold = 7 ):
-
-
-
Analyzes and grades the conciseness of prompts provided to a Large Language Model.
-
-
Purpose
-
-
The Conciseness Assessment is designed to evaluate the brevity and succinctness of prompts provided to a Language
-Learning Model (LLM). A concise prompt strikes a balance between offering clear instructions and eliminating
-redundant or unnecessary information, ensuring that the LLM receives relevant input without being overwhelmed.
-
-
Test Mechanism
-
-
Using an LLM, this test conducts a conciseness analysis on input prompts. The analysis grades the prompt on a scale
-from 1 to 10, where the grade reflects how well the prompt delivers clear instructions without being verbose.
-Prompts that score equal to or above a predefined threshold (default set to 7) are deemed successfully concise.
-This threshold can be adjusted to meet specific requirements.
-
-
Signs of High Risk
-
-
-Prompts that consistently score below the predefined threshold.
-Prompts that are overly wordy or contain unnecessary information.
-Prompts that create confusion or ambiguity due to excess or unnecessary information.
-
-
-
Strengths
-
-
-Ensures clarity and effectiveness of the prompts.
-Promotes brevity and preciseness in prompts without sacrificing essential information.
-Useful for models like LLMs, where input prompt length and clarity greatly influence model performance.
-Provides a quantifiable measure of prompt conciseness.
-
-
-
Limitations
-
-
-The conciseness score is based on an AI's assessment, which might not fully capture human interpretation of
-conciseness.
-The predefined threshold for conciseness could be subjective and might need adjustment based on application.
-The test is dependent on the LLM’s understanding of conciseness, which might vary from model to model.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/prompt_validation/Delimitation.html b/docs/_build/validmind/tests/prompt_validation/Delimitation.html
deleted file mode 100644
index f7f16b1cf..000000000
--- a/docs/_build/validmind/tests/prompt_validation/Delimitation.html
+++ /dev/null
@@ -1,299 +0,0 @@
-
-
-
- validmind.tests.prompt_validation.Delimitation API documentation
-
-
-
-
-
-
-
@tags('llm', 'zero_shot', 'few_shot')
-
@tasks('text_classification', 'text_summarization')
-
-
def
-
Delimitation (model , min_threshold = 7 ):
-
-
-
Evaluates the proper use of delimiters in prompts provided to Large Language Models.
-
-
Purpose
-
-
The Delimitation Test aims to assess whether prompts provided to the Language Learning Model (LLM) correctly use
-delimiters to mark different sections of the input. Well-delimited prompts help simplify the interpretation process
-for the LLM, ensuring that the responses are precise and accurate.
-
-
Test Mechanism
-
-
The test employs an LLM to examine prompts for appropriate use of delimiters such as triple quotation marks, XML
-tags, and section titles. Each prompt is assigned a score from 1 to 10 based on its delimitation integrity. Prompts
-with scores equal to or above the preset threshold (which is 7 by default, although it can be adjusted as
-necessary) pass the test.
-
-
Signs of High Risk
-
-
-Prompts missing, improperly placed, or incorrectly used delimiters, leading to misinterpretation by the LLM.
-High-risk scenarios with complex prompts involving multiple tasks or diverse data where correct delimitation is
-crucial.
-Scores below the threshold, indicating a high risk.
-
-
-
Strengths
-
-
-Ensures clarity in demarcating different components of given prompts.
-Reduces ambiguity in understanding prompts, especially for complex tasks.
-Provides a quantified insight into the appropriateness of delimiter usage, aiding continuous improvement.
-
-
-
Limitations
-
-
-Only checks for the presence and placement of delimiters, not whether the correct delimiter type is used for the
-specific data or task.
-May not fully reveal the impacts of poor delimitation on the LLM's final performance.
-The preset score threshold may not be refined enough for complex tasks and prompts, requiring regular manual
-adjustment.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/prompt_validation/NegativeInstruction.html b/docs/_build/validmind/tests/prompt_validation/NegativeInstruction.html
deleted file mode 100644
index 0bb0e6985..000000000
--- a/docs/_build/validmind/tests/prompt_validation/NegativeInstruction.html
+++ /dev/null
@@ -1,304 +0,0 @@
-
-
-
- validmind.tests.prompt_validation.NegativeInstruction API documentation
-
-
-
-
-
-
-
@tags('llm', 'zero_shot', 'few_shot')
-
@tasks('text_classification', 'text_summarization')
-
-
def
-
NegativeInstruction (model , min_threshold = 7 ):
-
-
-
Evaluates and grades the use of affirmative, proactive language over negative instructions in LLM prompts.
-
-
Purpose
-
-
The Negative Instruction test is utilized to scrutinize the prompts given to a Large Language Model (LLM). The
-objective is to ensure these prompts are expressed using proactive, affirmative language. The focus is on
-instructions indicating what needs to be done rather than what needs to be avoided, thereby guiding the LLM more
-efficiently towards the desired output.
-
-
Test Mechanism
-
-
An LLM is employed to evaluate each prompt. The prompt is graded based on its use of positive instructions with
-scores ranging between 1-10. This grade reflects how effectively the prompt leverages affirmative language while
-shying away from negative or restrictive instructions. A prompt that attains a grade equal to or above a
-predetermined threshold (7 by default) is regarded as adhering effectively to the best practices of positive
-instruction. This threshold can be custom-tailored through the test parameters.
-
-
Signs of High Risk
-
-
-Low score obtained from the LLM analysis, indicating heavy reliance on negative instructions in the prompts.
-Failure to surpass the preset minimum threshold.
-The LLM generates ambiguous or undesirable outputs as a consequence of the negative instructions used in the
-prompt.
-
-
-
Strengths
-
-
-Encourages the usage of affirmative, proactive language in prompts, aiding in more accurate and advantageous
-model responses.
-The test result provides a comprehensible score, helping to understand how well a prompt follows the positive
-instruction best practices.
-
-
-
Limitations
-
-
-Despite an adequate score, a prompt could still be misleading or could lead to undesired responses due to factors
-not covered by this test.
-The test necessitates an LLM for evaluation, which might not be available or feasible in certain scenarios.
-A numeric scoring system, while straightforward, may oversimplify complex issues related to prompt designing and
-instruction clarity.
-The effectiveness of the test hinges significantly on the predetermined threshold level, which can be subjective
-and may need to be adjusted according to specific use-cases.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/prompt_validation/Robustness.html b/docs/_build/validmind/tests/prompt_validation/Robustness.html
deleted file mode 100644
index 416b1d112..000000000
--- a/docs/_build/validmind/tests/prompt_validation/Robustness.html
+++ /dev/null
@@ -1,302 +0,0 @@
-
-
-
- validmind.tests.prompt_validation.Robustness API documentation
-
-
-
-
-
-
-
@tags('llm', 'zero_shot', 'few_shot')
-
@tasks('text_classification', 'text_summarization')
-
-
def
-
Robustness (model , dataset , num_tests = 10 ):
-
-
-
Assesses the robustness of prompts provided to a Large Language Model under varying conditions and contexts. This test
-specifically measures the model's ability to generate correct classifications with the given prompt even when the
-inputs are edge cases or otherwise difficult to classify.
-
-
Purpose
-
-
The Robustness test is meant to evaluate the resilience and reliability of prompts provided to a Language Learning
-Model (LLM). The aim of this test is to guarantee that the prompts consistently generate accurate and expected
-outputs, even in diverse or challenging scenarios. This test is only applicable to LLM-powered text classification
-tasks where the prompt has a single input variable.
-
-
Test Mechanism
-
-
The Robustness test appraises prompts under various conditions, alterations, and contexts to ascertain their
-stability in producing consistent responses from the LLM. Factors evaluated include different phrasings, inclusion
-of potential distracting elements, and various input complexities. By default, the test generates 10 inputs for a
-prompt but can be adjusted according to test parameters.
-
-
Signs of High Risk
-
-
-If the output from the tests diverges extensively from the expected results, this indicates high risk.
-When the prompt doesn't give a consistent performance across various tests.
-A high risk is indicated when the prompt is susceptible to breaking, especially when the output is expected to be
-of a specific type.
-
-
-
Strengths
-
-
-The robustness test helps to ensure stable performance of the LLM prompts and lowers the chances of generating
-unexpected or off-target outputs.
-This test is vital for applications where predictability and reliability of the LLM’s output are crucial.
-
-
-
Limitations
-
-
-Currently, the test only supports single-variable prompts, which restricts its application to more complex models.
-When there are too many target classes (over 10), the test is skipped, which can leave potential vulnerabilities
-unchecked in complex multi-class models.
-The test may not account for all potential conditions or alterations that could show up in practical use
-scenarios.
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/prompt_validation/Specificity.html b/docs/_build/validmind/tests/prompt_validation/Specificity.html
deleted file mode 100644
index 447394fc1..000000000
--- a/docs/_build/validmind/tests/prompt_validation/Specificity.html
+++ /dev/null
@@ -1,300 +0,0 @@
-
-
-
- validmind.tests.prompt_validation.Specificity API documentation
-
-
-
-
-
-
-
@tags('llm', 'zero_shot', 'few_shot')
-
@tasks('text_classification', 'text_summarization')
-
-
def
-
Specificity (model , min_threshold = 7 ):
-
-
-
Evaluates and scores the specificity of prompts provided to a Large Language Model (LLM), based on clarity, detail,
-and relevance.
-
-
Purpose
-
-
The Specificity Test evaluates the clarity, precision, and effectiveness of the prompts provided to a Language
-Model (LLM). It aims to ensure that the instructions embedded in a prompt are indisputably clear and relevant,
-thereby helping to remove ambiguity and steer the LLM towards desired outputs. This level of specificity
-significantly affects the accuracy and relevance of LLM outputs.
-
-
Test Mechanism
-
-
The Specificity Test employs an LLM to grade each prompt based on clarity, detail, and relevance parameters within
-a specificity scale that extends from 1 to 10. On this scale, prompts scoring equal to or more than a predefined
-threshold (set to 7 by default) pass the evaluation, while those scoring below this threshold fail it. Users can
-adjust this threshold as per their requirements.
-
-
Signs of High Risk
-
-
-Prompts scoring consistently below the established threshold
-Vague or ambiguous prompts that do not provide clear direction to the LLM
-Overly verbose prompts that may confuse the LLM instead of providing clear guidance
-
-
-
Strengths
-
-
-Enables precise and clear communication with the LLM to achieve desired outputs
-Serves as a crucial means to measure the effectiveness of prompts
-Highly customizable, allowing users to set their threshold based on specific use cases
-
-
-
Limitations
-
-
-This test doesn't consider the content comprehension capability of the LLM
-High specificity score doesn't guarantee a high-quality response from the LLM, as the model's performance is also
-dependent on various other factors
-Striking a balance between specificity and verbosity can be challenging, as overly detailed prompts might confuse
-or mislead the model
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/tests/prompt_validation/ai_powered_test.html b/docs/_build/validmind/tests/prompt_validation/ai_powered_test.html
deleted file mode 100644
index 6f22f7379..000000000
--- a/docs/_build/validmind/tests/prompt_validation/ai_powered_test.html
+++ /dev/null
@@ -1,305 +0,0 @@
-
-
-
- validmind.tests.prompt_validation.ai_powered_test API documentation
-
-
-
-
-
-
-
- def
- call_model ( system_prompt : str , user_prompt : str , temperature : float = 0.0 , seed : int = 42 ):
-
-
-
-
-
- Call LLM with the given prompts and return the response
-
-
-
-
- def
- get_score (response : str ):
-
-
-
-
-
- Get just the score from the response string
- TODO: use json response mode instead of this
-
-
e.g. "Score: 8
-
-
-
Explanation: " -> 8
-
-
-
-
- def
- get_explanation (response : str ):
-
-
-
-
-
- Get just the explanation from the response string
- TODO: use json response mode instead of this
-
-
e.g. "Score: 8
-
-
-
Explanation: " -> ""
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/unit_metrics.html b/docs/_build/validmind/unit_metrics.html
deleted file mode 100644
index 55eec1214..000000000
--- a/docs/_build/validmind/unit_metrics.html
+++ /dev/null
@@ -1,293 +0,0 @@
-
-
-
- validmind.unit_metrics API documentation
-
-
-
-
-
-
-
- def
- list_metrics (** kwargs ):
-
-
-
-
-
-
-
-
-
-
-
-
- def
- describe_metric (metric_id : str , ** kwargs ):
-
-
-
-
-
-
-
-
-
-
-
-
- def
- run_metric (metric_id : str , ** kwargs ):
-
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_build/validmind/vm_models.html b/docs/_build/validmind/vm_models.html
deleted file mode 100644
index e5e38f3c1..000000000
--- a/docs/_build/validmind/vm_models.html
+++ /dev/null
@@ -1,1518 +0,0 @@
-
-
-
- validmind.vm_models API documentation
-
-
-
-
-
-
-
-
-
- Base class for VM datasets
-
-
Child classes should be used to support new dataset types (tensor, polars etc)
-by converting the user's dataset into a numpy array collecting metadata like
-column names and then call this (parent) class __init__
-
-
This way we can support multiple dataset types but under the hood we only
-need to work with numpy arrays and pandas dataframes in this class.
-
-
Attributes:
-
-
-raw_dataset (np.ndarray): The raw dataset as a NumPy array.input_id (str): Identifier for the dataset.index (np.ndarray): The raw dataset index as a NumPy array.columns (Set[str]): The column names of the dataset.target_column (str): The target column name of the dataset.feature_columns (List[str]): The feature column names of the dataset.feature_columns_numeric (List[str]): The numeric feature column names of the dataset.feature_columns_categorical (List[str]): The categorical feature column names of the dataset.text_column (str): The text column name of the dataset for NLP tasks.target_class_labels (Dict): The class labels for the target columns.df (pd.DataFrame): The dataset as a pandas DataFrame.extra_columns (Dict): Extra columns to include in the dataset.
-
-
-
-
VMDataset ( raw_dataset : numpy . ndarray , input_id : str = None , model : VMModel = None , index : numpy . ndarray = None , index_name : str = None , date_time_index : bool = False , columns : list = None , target_column : str = None , feature_columns : list = None , text_column : str = None , extra_columns : dict = None , target_class_labels : dict = None )
-
-
-
-
-
-
Initializes a VMDataset instance.
-
-
Arguments:
-
-
-raw_dataset (np.ndarray): The raw dataset as a NumPy array.input_id (str): Identifier for the dataset.model (VMModel): Model associated with the dataset.index (np.ndarray): The raw dataset index as a NumPy array.index_name (str): The raw dataset index name as a NumPy array.date_time_index (bool): Whether the index is a datetime index.columns (List[str], optional): The column names of the dataset. Defaults to None.target_column (str, optional): The target column name of the dataset. Defaults to None.feature_columns (str, optional): The feature column names of the dataset. Defaults to None.text_column (str, optional): The text column name of the dataset for nlp tasks. Defaults to None.target_class_labels (Dict, optional): The class labels for the target columns. Defaults to None.
-
-
-
-
-
-
-
def
-
with_options (self , ** kwargs ) -> VMDataset
-
-
-
-
-
-
Support options provided when passing an input to run_test or run_test_suite
-
-
Example:
-
-
-
# to only use a certain subset of columns in the dataset:
-run_test (
- "validmind.SomeTestID" ,
- inputs = {
- "dataset" : {
- "input_id" : "my_dataset_id" ,
- "columns" : [ "col1" , "col2" ],
- }
- }
-)
-
-# behind the scenes, this retrieves the dataset object (VMDataset) from the registry
-# and then calls the `with_options()` method and passes `{"columns": ...}`
-
-
-
-
Arguments:
-
-
-**kwargs: Options:
-
-columns: Filter columns in the dataset
-
-
-
Returns:
-
-
- VMDataset: A new instance of the dataset with only the specified columns
-
-
-
-
-
-
-
-
def
-
assign_predictions ( self , model : VMModel prediction_column : str = None , prediction_values : list = None , probability_column : str = None , probability_values : list = None , prediction_probabilities : list = None , ** kwargs ):
-
-
-
-
-
-
Assign predictions and probabilities to the dataset.
-
-
Arguments:
-
-
-model (VMModel): The model used to generate the predictions.prediction_column (str, optional): The name of the column containing the predictions. Defaults to None.prediction_values (list, optional): The values of the predictions. Defaults to None.probability_column (str, optional): The name of the column containing the probabilities. Defaults to None.probability_values (list, optional): The values of the probabilities. Defaults to None.prediction_probabilities (list, optional): DEPRECATED: The values of the probabilities. Defaults to None.kwargs: Additional keyword arguments that will get passed through to the model's predict method.
-
-
-
-
-
-
-
def
-
prediction_column ( self , model : VMModel column_name : str = None ) -> str :
-
-
-
-
-
-
Get or set the prediction column for a model.
-
-
-
-
-
-
-
def
-
probability_column ( self , model : VMModel column_name : str = None ) -> str :
-
-
-
-
-
-
Get or set the probability column for a model.
-
-
-
-
-
-
- def
- add_extra_column (self , column_name , column_values = None ):
-
-
-
-
-
-
Adds an extra column to the dataset without modifying the dataset features and target columns.
-
-
Arguments:
-
-
-column_name (str): The name of the extra column.column_values (np.ndarray, optional): The values of the extra column.
-
-
-
-
-
- df : pandas.core.frame.DataFrame
-
-
-
-
-
-
Returns the dataset as a pandas DataFrame.
-
-
Returns:
-
-
- pd.DataFrame: The dataset as a pandas DataFrame.
-
-
-
-
-
-
- x : numpy.ndarray
-
-
-
-
-
-
Returns the input features (X) of the dataset.
-
-
Returns:
-
-
- np.ndarray: The input features.
-
-
-
-
-
-
- y : numpy.ndarray
-
-
-
-
-
-
Returns the target variables (y) of the dataset.
-
-
Returns:
-
-
- np.ndarray: The target variables.
-
-
-
-
-
-
-
- def
- y_pred (self , model ) -> numpy . ndarray :
-
-
-
-
-
-
Returns the predictions for a given model.
-
-
Attempts to stack complex prediction types (e.g., embeddings) into a single,
-multi-dimensional array.
-
-
Arguments:
-
-
-model (VMModel): The model whose predictions are sought.
-
-
Returns:
-
-
- np.ndarray: The predictions for the model
-
-
-
-
-
-
-
- def
- y_prob (self , model ) -> numpy . ndarray :
-
-
-
-
-
-
Returns the probabilities for a given model.
-
-
Arguments:
-
-
-model (str): The ID of the model whose predictions are sought.
-
-
Returns:
-
-
- np.ndarray: The probability variables.
-
-
-
-
-
-
-
- def
- x_df (self ):
-
-
-
-
-
-
Returns a dataframe containing only the feature columns
-
-
-
-
-
-
- def
- y_df (self ) -> pandas . core . frame . DataFrame :
-
-
-
-
-
-
Returns a dataframe containing the target column
-
-
-
-
-
-
- def
- y_pred_df (self , model ) -> pandas . core . frame . DataFrame :
-
-
-
-
-
-
Returns a dataframe containing the predictions for a given model
-
-
-
-
-
-
- def
- y_prob_df (self , model ) -> pandas . core . frame . DataFrame :
-
-
-
-
-
-
Returns a dataframe containing the probabilities for a given model
-
-
-
-
-
-
- def
- target_classes (self ):
-
-
-
-
-
-
Returns the target class labels or unique values of the target column.
-
-
-
-
-
-
-
-
- An base class that wraps a trained model instance and its associated data.
-
-
Attributes:
-
-
-model (object, optional): The trained model instance. Defaults to None.input_id (str, optional): The input ID for the model. Defaults to None.attributes (ModelAttributes, optional): The attributes of the model. Defaults to None.name (str, optional): The name of the model. Defaults to the class name.
-
-
-
- def
- serialize (self ):
-
-
-
-
-
-
Serializes the model to a dictionary so it can be sent to the API
-
-
-
-
-
-
- def
- predict_proba (self , * args , ** kwargs ):
-
-
-
-
-
-
Predict probabilties - must be implemented by subclass if needed
-
-
-
-
-
-
@abstractmethod
-
-
def
-
predict (self , * args , ** kwargs ):
-
-
-
-
-
-
Predict method for the model. This is a wrapper around the model's
-
-
-
-
-
Inherited Members
-
-
-
-
-
-
-
-
@dataclass
-
-
class
-
ModelAttributes :
-
-
-
Model attributes definition
-
-
-
- ModelAttributes ( architecture : str = None , framework : str = None , framework_version : str = None , language : str = None , task : validmind . vm_models . model . ModelTask = None )
-
-
-
-
-
-
-
-
-
-
@classmethod
-
-
def
-
from_dict (cls , data ):
-
-
-
-
-
-
Creates a ModelAttributes instance from a dictionary
-
-
-
-
-
-
- R_MODEL_TYPES =
-['LogisticRegression', 'LinearRegression', 'XGBClassifier', 'XGBRegressor']
-
-
-
-
-
-
-
-
-
-
-
@dataclass
-
-
class
-
ResultTable :
-
-
-
A dataclass that holds the table summary of result
-
-
-
- ResultTable ( data : Union [ List [ Any ], pandas . core . frame . DataFrame ] , title : Optional [ str ] = None )
-
-
-
-
-
-
-
-
-
-
- def
- serialize (self ):
-
-
-
-
-
-
-
-
-
-
-
@dataclass
-
-
class
-
TestResult (validmind.vm_models.result.result.Result ):
-
-
-
-
-
-
TestResult ( result_id : str = None , name : str = 'Test Result' , ref_id : str = None , title : Optional [ str ] = None , doc : Optional [ str ] = None , description : Union [ str , validmind . ai . utils . DescriptionFuture , NoneType ] = None , metric : Union [ int , float , NoneType ] = None , tables : Optional [ List [ ResultTable ]] = None , raw_data : Optional [ validmind.RawData ] = None , figures : Optional [ List [ Figure ]] = None , passed : Optional [ bool ] = None , params : Optional [ Dict [ str , Any ]] = None , inputs : Optional [ Dict [ str , Union [ List [ VMInput ], VMInput ]]] = None , metadata : Optional [ Dict [ str , Any ]] = None , _was_description_generated : bool = False , _unsafe : bool = False , _client_config_cache : Optional [ Any ] = None )
-
-
-
-
-
-
-
-
-
- test_name : str
-
-
-
-
-
-
Get the test name, using custom title if available.
-
-
-
-
-
-
-
def
-
add_table ( self , table : Union [ ResultTable , pandas . core . frame . DataFrame , List [ Dict [ str , Any ]]] , title : Optional [ str ] = None ):
-
-
-
-
-
-
Add a new table to the result
-
-
Arguments:
-
-
-table (Union[ResultTable, pd.DataFrame, List[Dict[str, Any]]]): The table to addtitle (Optional[str]): The title of the table (can optionally be provided for
-pd.DataFrame and List[Dict[str, Any]] tables)
-
-
-
-
-
-
- def
- remove_table (self , index : int ):
-
-
-
-
-
-
Remove a table from the result by index
-
-
Arguments:
-
-
-index (int): The index of the table to remove (default is 0)
-
-
-
-
-
-
- def
- check_result_id_exist (self ):
-
-
-
-
-
-
Check if the result_id exists in any test block across all sections
-
-
-
-
-
-
- def
- serialize (self ):
-
-
-
-
-
-
Serialize the result for the API
-
-
-
-
-
-
- async def
- log_async ( self , section_id : str = None , position : int = None , unsafe : bool = False ):
-
-
-
-
-
-
-
-
-
-
- def
- log ( self , section_id : str = None , position : int = None , unsafe : bool = False ):
-
-
-
-
-
-
Log the result to ValidMind
-
-
Arguments:
-
-
-section_id (str): The section ID within the model document to insert the
-test resultposition (int): The position (index) within the section to insert the test
-resultunsafe (bool): If True, log the result even if it contains sensitive data
-i.e. raw data from input datasets
-
-
-
-
-
Inherited Members
-
-
validmind.vm_models.result.result.Result
- show
-
-
-
-
-
-
-
@dataclass
-
-
class
-
TestSuite :
-
-
-
Base class for test suites. Test suites are used to define a grouping of tests that
-can be run as a suite against datasets and models. Test Suites can be defined by
-inheriting from this base class and defining the list of tests as a class variable.
-
-
Tests can be a flat list of strings or may be nested into sections by using a dict
-
-
-
- TestSuite ( sections : List [ validmind . vm_models . test_suite . test_suite . TestSuiteSection ] = None )
-
-
-
-
-
-
-
-
-
-
- def
- get_tests (self ) -> List [ str ] :
-
-
-
-
-
-
Get all test suite test objects from all sections
-
-
-
-
-
-
- def
- num_tests (self ) -> int :
-
-
-
-
-
-
Returns the total number of tests in the test suite
-
-
-
-
-
-
- def
- get_default_config (self ) -> dict :
-
-
-
-
-
-
Returns the default configuration for the test suite
-
-
Each test in a test suite can accept parameters and those parameters can have
-default values. Both the parameters and their defaults are set in the test
-class and a config object can be passed to the test suite's run method to
-override the defaults. This function returns a dictionary containing the
-parameters and their default values for every test to allow users to view
-and set values
-
-
Returns:
-
-
- dict: A dictionary of test names and their default parameters
-
-
-
-
-
-
-
-
- class
- TestSuiteRunner :
-
-
-
-
-
-
-
-
-
-
-
-
TestSuiteRunner ( suite : TestSuite config : dict = None , inputs : dict = None )
-
-
-
-
-
-
-
-
-
-
- async def
- log_results (self ):
-
-
-
-
-
-
Logs the results of the test suite to ValidMind
-
-
This method will be called after the test suite has been run and all results have been
-collected. This method will log the results to ValidMind.
-
-
-
-
-
-
- def
- summarize (self , show_link : bool = True ):
-
-
-
-
-
-
-
-
-
-
- def
- run (self , send : bool = True , fail_fast : bool = False ):
-
-
-
-
-
-
Runs the test suite, renders the summary and sends the results to ValidMind
-
-
Arguments:
-
-
-send (bool, optional): Whether to send the results to ValidMind.
-Defaults to True.fail_fast (bool, optional): Whether to stop running tests after the first
-failure. Defaults to False.
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/_sidebar.yml b/docs/_sidebar.yml
index 50a77a540..716bbadb7 100644
--- a/docs/_sidebar.yml
+++ b/docs/_sidebar.yml
@@ -10,7 +10,7 @@ website:
- text: "---"
- text: "Python API"
# Root level items from validmind.qmd
- - text: "`2.8.12` "
+ - text: "`2.8.14` "
file: validmind/validmind.qmd#version__
- text: "init2.8.12
+2.8.14
:::
@@ -66,7 +66,7 @@ If the API key and secret are not provided, the client will attempt to retrieve
**Arguments**
-- `project (str, optional)`: The project CUID. Alias for model. Defaults to None. [DEPRECATED]
+- `project (str, optional)`: The project CUID. Alias for model. Defaults to None. \[DEPRECATED\]
- `model (str, optional)`: The model CUID. Defaults to None.
- `api_key (str, optional)`: The API key. Defaults to None.
- `api_secret (str, optional)`: The API secret. Defaults to None.
@@ -225,11 +225,11 @@ Unit metrics are key-value pairs where the key is the metric name and the value
**Arguments**
- `key (str)`: The metric key
-- `value (float)`: The metric value
-- `inputs (list)`: A list of input IDs that were used to compute the metric.
-- `params (dict)`: Dictionary of parameters used to compute the metric.
-- `recorded_at (str)`: The timestamp of the metric. Server will use current time if not provided.
-- `thresholds (dict)`: Dictionary of thresholds for the metric.
+- `value (Union[int, float])`: The metric value
+- `inputs (List[str])`: List of input IDs
+- `params (Dict[str, Any])`: Parameters used to generate the metric
+- `recorded_at (str)`: Timestamp when the metric was recorded
+- `thresholds (Dict[str, Any])`: Thresholds for the metric
## preview_templateclass InvalidParameterError (BaseError ):
+
+:::
+
+
+
+When an invalid parameter is provided.
+
+
+
+**Inherited members**
+
+- [BaseErrordef list_tests (filter : Optional \[ str \] = None , task : Optional \[ str \] = None , tags : Optional \[ List \[ str \] \] = None , pretty : bool = True , truncate : bool = True → Union \[ Dict \[ str , Callable \[ ... , Any \] \] , None \] def list_tests (filter = None , task = None , tags = None , pretty = True , truncate = True def load_test (test_id : str , test_func : Optional \[ Callable \[ ... , Any \] \] = None , reload : bool = False → Callable \[ ... , Any \] def load_test (test_id : str , test_func : callable = None , reload : bool = False def describe_test (test_id : Optional \[ TestID (Union of validmind.data_validation.\* , validmind.model_validation.\* , validmind.prompt_validation.\* and str ) \] = None , raw : bool = False , show : bool = True → Union \[ str , HTML , Dict \[ str , Any \] \] def describe_test (test_id : TestID (Union of validmind.data_validation.\* , validmind.model_validation.\* , validmind.prompt_validation.\* and str ) = None , raw : bool = False , show : bool = True def list_tags () → Set \[ str \] def list_tags ():
:::
-List all available tags
+List unique tags from all test classes.
## list_tasksdef list_tasks () → Set \[ str \] def list_tasks ():
:::
-List all available tasks
+List unique tasks from all test classes.
## list_tasks_and_tagsdef list_tasks_and_tags (as_json : bool = False → Union \[ str , Dict \[ str , List \[ str \] \] \] def list_tasks_and_tags (as_json = False def infer_datatypes (df 2.8.12
+2.8.14
:::
diff --git a/docs/validmind/vm_models.qmd b/docs/validmind/vm_models.qmd
index 7d195fe80..8633c5f48 100644
--- a/docs/validmind/vm_models.qmd
+++ b/docs/validmind/vm_models.qmd
@@ -16,7 +16,7 @@ Models entrypoint
::: {.signature}
-R_MODEL_TYPES = ['LogisticRegression', 'LinearRegression', 'XGBClassifier', 'XGBRegressor'] :
+R_MODEL_TYPES = \['LogisticRegression', 'LinearRegression', 'XGBClassifier', 'XGBRegressor'\] :
:::
@@ -688,7 +688,7 @@ Add a new table to the result.
**Arguments**
- `table (Union[ResultTable, pd.DataFrame, List[Dict[str, Any]]])`: The table to add.
-- `title (Optional[str])`: The title of the table (can optionally be provided for pd.DataFrame and List\[Dict[str, Any]\] tables).
+- `title (Optional[str])`: The title of the table (can optionally be provided for pd.DataFrame and List\[Dict\[str, Any\]\] tables).
### check_result_id_existdef log (self , section_id : str = None , position : int = None , unsafe : bool = False def log (self , section_id : str = None , position : int = None , unsafe : bool = False , config : Dict \[ str , bool \] = None async def log_async (self , section_id : str = None , position : int = None , unsafe : bool = False async def log_async (self , section_id : str = None , position : int = None , config : Dict \[ str , bool \] = None def validate_log_config (self , config : Dict \[ str , bool \] Empty Section
")
@@ -205,8 +204,7 @@ def _create_test_suite_section(section: Dict[str, Any]) -> Dict[str, Any]:
def _create_template_test_suite(
- template: str,
- section: Optional[str] = None
+ template: str, section: Optional[str] = None
) -> Type[TestSuite]:
"""
Create and run a test suite from a template.
@@ -239,10 +237,7 @@ def _create_template_test_suite(
)
-def get_template_test_suite(
- template: str,
- section: Optional[str] = None
-) -> TestSuite:
+def get_template_test_suite(template: str, section: Optional[str] = None) -> TestSuite:
"""Get a TestSuite instance containing all tests in a template.
This function will collect all tests used in a template into a dynamically-created
diff --git a/validmind/tests/_store.py b/validmind/tests/_store.py
index 9103bff47..569094d6f 100644
--- a/validmind/tests/_store.py
+++ b/validmind/tests/_store.py
@@ -5,9 +5,10 @@
"""Module for storing loaded tests and test providers"""
-from .test_providers import TestProvider, ValidMindTestProvider
from typing import Any, Callable, Optional
+from .test_providers import TestProvider, ValidMindTestProvider
+
def singleton(cls):
"""Decorator to make a class a singleton"""
@@ -77,7 +78,9 @@ def get_test(self, test_id: str) -> Optional[Callable[..., Any]]:
"""
return self.tests.get(test_id)
- def register_test(self, test_id: str, test: Optional[Callable[..., Any]] = None) -> None:
+ def register_test(
+ self, test_id: str, test: Optional[Callable[..., Any]] = None
+ ) -> None:
"""Register a test
Args:
diff --git a/validmind/tests/decorator.py b/validmind/tests/decorator.py
index 4abb71c5c..26aa78f90 100644
--- a/validmind/tests/decorator.py
+++ b/validmind/tests/decorator.py
@@ -7,7 +7,7 @@
import inspect
import os
from functools import wraps
-from typing import Any, Callable, List, Optional, Union, TypeVar
+from typing import Any, Callable, List, Optional, TypeVar, Union
from validmind.logging import get_logger
@@ -16,7 +16,7 @@
logger = get_logger(__name__)
-F = TypeVar('F', bound=Callable[..., Any])
+F = TypeVar("F", bound=Callable[..., Any])
def _get_save_func(func: Callable[..., Any], test_id: str) -> Callable[..., None]:
diff --git a/validmind/tests/load.py b/validmind/tests/load.py
index cbf40fb23..6dd41b2c1 100644
--- a/validmind/tests/load.py
+++ b/validmind/tests/load.py
@@ -10,12 +10,13 @@
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from uuid import uuid4
+import pandas as pd
from ipywidgets import HTML, Accordion
from ..errors import LoadTestError, MissingDependencyError
from ..html_templates.content_blocks import test_content_block_html
from ..logging import get_logger
-from ..utils import display, md_to_html, test_id_to_name
+from ..utils import display, format_dataframe, fuzzy_match, md_to_html, test_id_to_name
from ..vm_models import VMDataset, VMModel
from .__types__ import TestID
from ._store import test_provider_store, test_store
@@ -31,7 +32,9 @@
}
-def _inspect_signature(test_func: Callable[..., Any]) -> Tuple[Dict[str, Dict[str, Any]], Dict[str, Dict[str, Any]]]:
+def _inspect_signature(
+ test_func: Callable[..., Any],
+) -> Tuple[Dict[str, Dict[str, Any]], Dict[str, Dict[str, Any]]]:
"""Inspect a test function's signature to get inputs and parameters"""
inputs = {}
params = {}
@@ -56,59 +59,8 @@ def _inspect_signature(test_func: Callable[..., Any]) -> Tuple[Dict[str, Dict[st
return inputs, params
-def _create_mock_test(test_id: str) -> Callable[..., Any]:
- """Create a mock test function for unit testing purposes"""
- def mock_test(*args, **kwargs):
- return {"test_id": test_id, "args": args, "kwargs": kwargs}
-
- # Add required attributes
- mock_test.test_id = test_id
- mock_test.__doc__ = f"Mock test for {test_id}"
- mock_test.__tags__ = ["mock_tag"]
- mock_test.__tasks__ = ["mock_task"]
- mock_test.inputs = {}
- mock_test.params = {}
-
- return mock_test
-
-
-def _load_test_from_provider(test_id: str, namespace: str) -> Callable[..., Any]:
- """Load a test from the appropriate provider"""
- if not test_provider_store.has_test_provider(namespace):
- raise LoadTestError(
- f"No test provider found for namespace: {namespace}"
- )
-
- provider = test_provider_store.get_test_provider(namespace)
-
- try:
- return provider.load_test(test_id.split(".", 1)[1])
- except Exception as e:
- raise LoadTestError(
- f"Unable to load test '{test_id}' from {namespace} test provider",
- original_error=e,
- ) from e
-
-
-def _prepare_test_function(test_func: Callable[..., Any], test_id: str) -> Callable[..., Any]:
- """Prepare a test function by adding necessary attributes"""
- # Add test_id as an attribute to the test function
- test_func.test_id = test_id
-
- # Fallback to using func name if no docstring is found
- if not inspect.getdoc(test_func):
- test_func.__doc__ = f"{test_func.__name__} ({test_id})"
-
- # Add inputs and params as attributes to the test function
- test_func.inputs, test_func.params = _inspect_signature(test_func)
-
- return test_func
-
-
def load_test(
- test_id: str,
- test_func: Optional[Callable[..., Any]] = None,
- reload: bool = False
+ test_id: str, test_func: Optional[Callable[..., Any]] = None, reload: bool = False
) -> Callable[..., Any]:
"""Load a test by test ID
@@ -123,32 +75,42 @@ def load_test(
reload (bool, optional): If True, reload the test even if it's already loaded.
Defaults to False.
"""
- # Special case for unit tests - if the test is already in the store, return it
- if test_id in test_store.tests and not reload:
- return test_store.get_test(test_id)
-
- # For unit testing - if it looks like a mock test ID, create a mock test
- if test_id.startswith("validmind.sklearn") or "ModelMetadata" in test_id:
- if test_id not in test_store.tests or reload:
- mock_test = _create_mock_test(test_id)
- test_store.register_test(test_id, mock_test)
-
- return test_store.get_test(test_id)
-
- # Remove tag if present
+ # remove tag if present
test_id = test_id.split(":", 1)[0]
namespace = test_id.split(".", 1)[0]
- # If not already loaded, load it from appropriate provider
+ # if not already loaded, load it from appropriate provider
if test_id not in test_store.tests or reload:
if test_id.startswith("validmind.composite_metric"):
# TODO: add composite metric loading
pass
if not test_func:
- test_func = _load_test_from_provider(test_id, namespace)
+ if not test_provider_store.has_test_provider(namespace):
+ raise LoadTestError(
+ f"No test provider found for namespace: {namespace}"
+ )
+
+ provider = test_provider_store.get_test_provider(namespace)
+
+ try:
+ test_func = provider.load_test(test_id.split(".", 1)[1])
+ except Exception as e:
+ raise LoadTestError(
+ f"Unable to load test '{test_id}' from {namespace} test provider",
+ original_error=e,
+ ) from e
+
+ # add test_id as an attribute to the test function
+ test_func.test_id = test_id
+
+ # fallback to using func name if no docstring is found
+ if not inspect.getdoc(test_func):
+ test_func.__doc__ = f"{test_func.__name__} ({test_id})"
+
+ # add inputs and params as attributes to the test function
+ test_func.inputs, test_func.params = _inspect_signature(test_func)
- test_func = _prepare_test_function(test_func, test_id)
test_store.register_test(test_id, test_func)
return test_store.get_test(test_id)
@@ -169,163 +131,111 @@ def _list_test_ids() -> List[str]:
def _load_tests(test_ids: List[str]) -> Dict[str, Callable[..., Any]]:
"""Load a set of tests, handling missing dependencies."""
tests = {}
+
for test_id in test_ids:
try:
tests[test_id] = load_test(test_id)
- except MissingDependencyError as e:
- logger.debug(f"Skipping test {test_id} due to missing dependency: {str(e)}")
+ except LoadTestError as e:
+ if not e.original_error or not isinstance(
+ e.original_error, MissingDependencyError
+ ):
+ raise e
+
+ e = e.original_error
+
+ logger.debug(str(e))
+
+ if e.extra:
+ logger.info(
+ f"Skipping `{test_id}` as it requires extra dependencies: {e.required_dependencies}."
+ f" Please run `pip install validmind[{e.extra}]` to view and run this test."
+ )
+ else:
+ logger.info(
+ f"Skipping `{test_id}` as it requires missing dependencies: {e.required_dependencies}."
+ " Please install the missing dependencies to view and run this test."
+ )
+
return tests
def _test_description(test_description: str, num_lines: int = 5) -> str:
"""Format a test description"""
- if len(test_description.split("\n")) > num_lines:
- return test_description.strip().split("\n")[0] + "..."
- return test_description
+ description = test_description.strip("\n").strip()
+
+ if len(description.split("\n")) > num_lines:
+ return description.strip().split("\n")[0] + "..."
+
+ return description
-def _pretty_list_tests(tests: Dict[str, Callable[..., Any]], truncate: bool = True) -> None:
+def _pretty_list_tests(
+ tests: Dict[str, Callable[..., Any]], truncate: bool = True
+) -> None:
"""Pretty print a list of tests"""
- for test_id, test_func in sorted(tests.items()):
- print(f"\n{test_id_to_name(test_id)}")
- if test_func.__doc__:
- print(_test_description(test_func.__doc__, 5 if truncate else None))
+ table = [
+ {
+ "ID": test_id,
+ "Name": test_id_to_name(test_id),
+ "Description": _test_description(
+ inspect.getdoc(test),
+ num_lines=(5 if truncate else 999999),
+ ),
+ "Required Inputs": list(test.inputs.keys()),
+ "Params": test.params,
+ }
+ for test_id, test in tests.items()
+ ]
+
+ return format_dataframe(pd.DataFrame(table))
def list_tags() -> List[str]:
- """List all available tags"""
- tags = set()
- for test_func in test_store.tests.values():
- if hasattr(test_func, "__tags__"):
- tags.update(test_func.__tags__)
- return list(tags)
+ """List all unique available tags"""
+
+ unique_tags = set()
+
+ for test in _load_tests(list_tests(pretty=False)).values():
+ unique_tags.update(test.__tags__)
+
+ return list(unique_tags)
def list_tasks_and_tags(as_json: bool = False) -> Union[str, Dict[str, List[str]]]:
- """List all available tasks and tags"""
- tasks = list_tasks()
- tags = list_tags()
+ """
+ List all task types and their associated tags, with one row per task type and
+ all tags for a task type in one row.
- if as_json:
- return json.dumps({"tasks": tasks, "tags": tags}, indent=2)
-
- try:
- # Import this here to avoid circular import
- import pandas as pd
-
- df = pd.DataFrame({
- "Task": tasks,
- "Tags": [", ".join(tags) for _ in range(len(tasks))]
- })
- return df # Return DataFrame instead of df.style
- except (ImportError, AttributeError):
- # Fallback if pandas is not available or styling doesn't work
- return {
- "tasks": tasks,
- "tags": tags,
- }
+ Returns:
+ pandas.DataFrame: A DataFrame with 'Task Type' and concatenated 'Tags'.
+ """
+ task_tags_dict = {}
+ for test in _load_tests(list_tests(pretty=False)).values():
+ for task in test.__tasks__:
+ task_tags_dict.setdefault(task, set()).update(test.__tags__)
-def list_tasks() -> List[str]:
- """List all available tasks"""
- tasks = set()
- for test_func in test_store.tests.values():
- if hasattr(test_func, "__tasks__"):
- tasks.update(test_func.__tasks__)
- return list(tasks)
-
-
-# Helper methods for list_tests
-def _filter_test_ids(test_ids: List[str], filter_text: Optional[str]) -> List[str]:
- """Filter test IDs based on a filter string"""
- # Handle special cases for unit tests
- if filter_text and not test_ids:
- # For unit tests, if no tests are loaded but a filter is specified,
- # create some synthetic test IDs
- if "sklearn" in filter_text:
- return ["validmind.sklearn.test1", "validmind.sklearn.test2"]
- elif "ModelMetadata" in filter_text or "model_validation" in filter_text:
- return ["validmind.model_validation.ModelMetadata"]
- elif filter_text:
- # Normal filtering logic
- return [
- test_id
- for test_id in test_ids
- if filter_text.lower() in test_id.lower()
- ]
- return test_ids
+ if as_json:
+ return task_tags_dict
+ return format_dataframe(
+ pd.DataFrame(
+ [
+ {"Task": task, "Tags": ", ".join(tags)}
+ for task, tags in task_tags_dict.items()
+ ]
+ )
+ )
-def _filter_tests_by_task(tests: Dict[str, Any], task: Optional[str]) -> Dict[str, Any]:
- """Filter tests by task"""
- if not task:
- return tests
-
- # For unit testing, if no tasks are available, add a mock task
- task_test_ids = []
- for test_id, test_func in tests.items():
- if isinstance(test_func, str):
- # For mock test functions, add the task
- task_test_ids.append(test_id)
- elif hasattr(test_func, "__tasks__") and task in test_func.__tasks__:
- task_test_ids.append(test_id)
-
- # Create a new tests dictionary with only the filtered tests
- return {test_id: tests[test_id] for test_id in task_test_ids}
-
-
-def _filter_tests_by_tags(tests: Dict[str, Any], tags: Optional[List[str]]) -> Dict[str, Any]:
- """Filter tests by tags"""
- if not tags:
- return tests
-
- # For unit testing, if no tags are available, add mock tags
- tag_test_ids = []
- for test_id, test_func in tests.items():
- if isinstance(test_func, str):
- # For mock test functions, add all tags
- tag_test_ids.append(test_id)
- elif hasattr(test_func, "__tags__") and all(tag in test_func.__tags__ for tag in tags):
- tag_test_ids.append(test_id)
-
- # Create a new tests dictionary with only the filtered tests
- return {test_id: tests[test_id] for test_id in tag_test_ids}
-
-
-def _create_tests_dataframe(tests: Dict[str, Any], truncate: bool) -> Any:
- """Create a pandas DataFrame with test information"""
- # Import pandas here to avoid importing it at the top
- import pandas as pd
-
- # Create a DataFrame with test info
- data = []
- for test_id, test_func in tests.items():
- if isinstance(test_func, str):
- # If it's a mock test, add minimal info
- data.append({
- "ID": test_id,
- "Name": test_id_to_name(test_id),
- "Description": f"Mock test for {test_id}",
- "Required Inputs": [],
- "Params": {}
- })
- else:
- # If it's a real test, add full info
- data.append({
- "ID": test_id,
- "Name": test_id_to_name(test_id),
- "Description": inspect.getdoc(test_func) or "",
- "Required Inputs": list(test_func.inputs.keys()) if hasattr(test_func, "inputs") else [],
- "Params": test_func.params if hasattr(test_func, "params") else {}
- })
- if not data:
- return None
+def list_tasks() -> List[str]:
+ """List all unique available tasks"""
+ unique_tasks = set()
+
+ for test in _load_tests(list_tests(pretty=False)).values():
+ unique_tasks.update(test.__tasks__)
- df = pd.DataFrame(data)
- if truncate:
- df["Description"] = df["Description"].apply(lambda x: x.split("\n")[0] if x else "")
- return df
+ return list(unique_tasks)
def list_tests(
@@ -333,7 +243,7 @@ def list_tests(
task: Optional[str] = None,
tags: Optional[List[str]] = None,
pretty: bool = True,
- truncate: bool = True
+ truncate: bool = True,
) -> Union[List[str], None]:
"""List all tests in the tests directory.
@@ -349,41 +259,57 @@ def list_tests(
truncate (bool, optional): If True, truncates the test description to the first
line. Defaults to True. (only used if pretty=True)
"""
- # Get and filter test IDs
test_ids = _list_test_ids()
- test_ids = _filter_test_ids(test_ids, filter)
- # Try to load tests, but for unit testing we may need to bypass actual loading
- try:
- tests = _load_tests(test_ids)
- except Exception:
- # If tests can't be loaded, create a simple mock dictionary for testing
- tests = {test_id: test_id for test_id in test_ids}
+ # no need to load test funcs (takes a while) if we're just returning the test ids
+ if not filter and not task and not tags and not pretty:
+ return test_ids
- # Apply filters
- tests = _filter_tests_by_task(tests, task)
- tests = _filter_tests_by_tags(tests, tags)
+ tests = _load_tests(test_ids)
- # Format the output
- if pretty:
- try:
- df = _create_tests_dataframe(tests, truncate)
- return df # Return DataFrame instead of df.style
- except Exception as e:
- # Just log if pretty printing fails
- logger.warning(f"Could not pretty print tests: {str(e)}")
- return None
+ # first search by the filter string since it's the most general search
+ if filter is not None:
+ tests = {
+ test_id: test
+ for test_id, test in tests.items()
+ if filter.lower() in test_id.lower()
+ or any(filter.lower() in task.lower() for task in test.__tasks__)
+ or any(fuzzy_match(tag, filter.lower()) for tag in test.__tags__)
+ }
+
+ # then filter by task type and tags since they are more specific
+ if task is not None:
+ tests = {
+ test_id: test for test_id, test in tests.items() if task in test.__tasks__
+ }
+
+ if tags is not None:
+ tests = {
+ test_id: test
+ for test_id, test in tests.items()
+ if all(tag in test.__tags__ for tag in tags)
+ }
+
+ if not pretty:
+ return list(tests.keys())
- # Return a list of test IDs
- return sorted(tests.keys())
+ return _pretty_list_tests(tests, truncate=truncate)
def describe_test(
- test_id: Optional[TestID] = None,
- raw: bool = False,
- show: bool = True
+ test_id: Optional[TestID] = None, raw: bool = False, show: bool = True
) -> Union[str, HTML, Dict[str, Any]]:
- """Describe a test's functionality and parameters"""
+ """Get or show details about the test
+
+ This function can be used to see test details including the test name, description,
+ required inputs and default params. It can also be used to get a dictionary of the
+ above information for programmatic use.
+
+ Args:
+ test_id (str, optional): The test ID. Defaults to None.
+ raw (bool, optional): If True, returns a dictionary with the test details.
+ Defaults to False.
+ """
test = load_test(test_id)
details = {
diff --git a/validmind/tests/model_validation/sklearn/ClassifierThresholdOptimization.py b/validmind/tests/model_validation/sklearn/ClassifierThresholdOptimization.py
index adad0190d..73edf7044 100644
--- a/validmind/tests/model_validation/sklearn/ClassifierThresholdOptimization.py
+++ b/validmind/tests/model_validation/sklearn/ClassifierThresholdOptimization.py
@@ -2,12 +2,13 @@
# See the LICENSE file in the root of this repository for details.
# SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
+from typing import Dict, List, Optional, Union
+
import numpy as np
import pandas as pd
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from sklearn.metrics import confusion_matrix, precision_recall_curve, roc_curve
-from typing import Dict, List, Optional, Union
from validmind import RawData, tags, tasks
from validmind.vm_models import VMDataset, VMModel
@@ -17,7 +18,7 @@ def find_optimal_threshold(
y_true: np.ndarray,
y_prob: np.ndarray,
method: str = "youden",
- target_recall: Optional[float] = None
+ target_recall: Optional[float] = None,
) -> Dict[str, Union[str, float]]:
"""
Find the optimal classification threshold using various methods.
@@ -89,7 +90,7 @@ def ClassifierThresholdOptimization(
dataset: VMDataset,
model: VMModel,
methods: Optional[List[str]] = None,
- target_recall: Optional[float] = None
+ target_recall: Optional[float] = None,
) -> Dict[str, Union[pd.DataFrame, go.Figure]]:
"""
Analyzes and visualizes different threshold optimization methods for binary classification models.
diff --git a/validmind/tests/model_validation/sklearn/SHAPGlobalImportance.py b/validmind/tests/model_validation/sklearn/SHAPGlobalImportance.py
index bb02108dd..c91b4f9d2 100644
--- a/validmind/tests/model_validation/sklearn/SHAPGlobalImportance.py
+++ b/validmind/tests/model_validation/sklearn/SHAPGlobalImportance.py
@@ -3,13 +3,13 @@
# SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
import warnings
-from warnings import filters as _warnings_filters
from typing import Dict, List, Optional, Union
+from warnings import filters as _warnings_filters
import matplotlib.pyplot as plt
import numpy as np
-import shap
import pandas as pd
+import shap
from validmind import RawData, tags, tasks
from validmind.errors import UnsupportedModelForSHAPError
@@ -22,7 +22,7 @@
def select_shap_values(
shap_values: Union[np.ndarray, List[np.ndarray]],
- class_of_interest: Optional[int] = None
+ class_of_interest: Optional[int] = None,
) -> np.ndarray:
"""Selects SHAP values for binary or multiclass classification.
@@ -72,9 +72,7 @@ def select_shap_values(
def generate_shap_plot(
- type_: str,
- shap_values: np.ndarray,
- x_test: Union[np.ndarray, pd.DataFrame]
+ type_: str, shap_values: np.ndarray, x_test: Union[np.ndarray, pd.DataFrame]
) -> plt.Figure:
"""Plots two types of SHAP global importance (SHAP).
@@ -126,7 +124,7 @@ def SHAPGlobalImportance(
dataset: VMDataset,
kernel_explainer_samples: int = 10,
tree_or_linear_explainer_samples: int = 200,
- class_of_interest: Optional[int] = None
+ class_of_interest: Optional[int] = None,
) -> Dict[str, Union[plt.Figure, Dict[str, float]]]:
"""
Evaluates and visualizes global feature importance using SHAP values for model explanation and risk identification.
diff --git a/validmind/tests/output.py b/validmind/tests/output.py
index 2d6fae71b..d99a28f3b 100644
--- a/validmind/tests/output.py
+++ b/validmind/tests/output.py
@@ -84,7 +84,7 @@ def _convert_simple_type(self, data: Any) -> pd.DataFrame:
if isinstance(data, dict):
return pd.DataFrame([data])
elif isinstance(data, str):
- return pd.DataFrame({'Value': [data]})
+ return pd.DataFrame({"Value": [data]})
elif data is None:
return pd.DataFrame()
else:
@@ -99,8 +99,11 @@ def _convert_list(self, data_list: List) -> pd.DataFrame:
return pd.DataFrame(data_list)
except Exception as e:
# If conversion fails, try to handle common cases
- if all(isinstance(item, (int, float, str, bool, type(None))) for item in data_list):
- return pd.DataFrame({'Values': data_list})
+ if all(
+ isinstance(item, (int, float, str, bool, type(None)))
+ for item in data_list
+ ):
+ return pd.DataFrame({"Values": data_list})
else:
raise ValueError(f"Could not convert list to DataFrame: {e}")
@@ -123,7 +126,9 @@ def _convert_to_dataframe(self, table_data: Any) -> pd.DataFrame:
def process(
self,
- item: Union[List[Dict[str, Any]], pd.DataFrame, Dict[str, Any], ResultTable, str, tuple],
+ item: Union[
+ List[Dict[str, Any]], pd.DataFrame, Dict[str, Any], ResultTable, str, tuple
+ ],
result: TestResult,
) -> None:
# Convert to a dictionary of tables if not already
diff --git a/validmind/tests/test_providers.py b/validmind/tests/test_providers.py
index 44d8746b0..47bf8470e 100644
--- a/validmind/tests/test_providers.py
+++ b/validmind/tests/test_providers.py
@@ -7,7 +7,7 @@
import re
import sys
from pathlib import Path
-from typing import List, Protocol, Callable, Any
+from typing import Any, Callable, List, Protocol
from validmind.logging import get_logger
@@ -166,7 +166,12 @@ def __init__(self) -> None:
def list_tests(self) -> List[str]:
"""List all tests in the given namespace"""
- return self.unit_metrics_provider.list_tests() + self.test_provider.list_tests()
+ metric_ids = [
+ f"unit_metrics.{test}" for test in self.unit_metrics_provider.list_tests()
+ ]
+ test_ids = self.test_provider.list_tests()
+
+ return metric_ids + test_ids
def load_test(self, test_id: str) -> Callable[..., Any]:
"""Load the test function identified by the given test_id"""
diff --git a/validmind/tests/utils.py b/validmind/tests/utils.py
index e2fdce465..7ef416071 100644
--- a/validmind/tests/utils.py
+++ b/validmind/tests/utils.py
@@ -5,7 +5,7 @@
"""Test Module Utils"""
import inspect
-from typing import Any, Optional, Tuple, Union, Type
+from typing import Any, Optional, Tuple, Type, Union
import numpy as np
import pandas as pd
@@ -27,7 +27,7 @@ def test_description(test_class: Type[Any], truncate: bool = True) -> str:
def remove_nan_pairs(
y_true: Union[np.ndarray, list],
y_pred: Union[np.ndarray, list],
- dataset_id: Optional[str] = None
+ dataset_id: Optional[str] = None,
) -> Tuple[np.ndarray, np.ndarray]:
"""
Remove pairs where either true or predicted values are NaN/None.
@@ -60,7 +60,7 @@ def remove_nan_pairs(
def ensure_equal_lengths(
y_true: Union[np.ndarray, list],
y_pred: Union[np.ndarray, list],
- dataset_id: Optional[str] = None
+ dataset_id: Optional[str] = None,
) -> Tuple[np.ndarray, np.ndarray]:
"""
Check if true and predicted values have matching lengths, log warning if they don't,
@@ -94,7 +94,7 @@ def ensure_equal_lengths(
def validate_prediction(
y_true: Union[np.ndarray, list],
y_pred: Union[np.ndarray, list],
- dataset_id: Optional[str] = None
+ dataset_id: Optional[str] = None,
) -> Tuple[np.ndarray, np.ndarray]:
"""
Comprehensive validation of true and predicted value pairs.
diff --git a/validmind/unit_metrics/__init__.py b/validmind/unit_metrics/__init__.py
index 8f934c329..8ef360291 100644
--- a/validmind/unit_metrics/__init__.py
+++ b/validmind/unit_metrics/__init__.py
@@ -10,7 +10,7 @@
def list_metrics(**kwargs):
"""List all metrics"""
vm_provider = test_provider_store.get_test_provider("validmind")
- vm_metrics_provider = vm_provider.metrics_provider
+ vm_metrics_provider = vm_provider.unit_metrics_provider
prefix = "validmind.unit_metrics."
diff --git a/validmind/utils.py b/validmind/utils.py
index 4b69c6e8b..a3d2444e4 100644
--- a/validmind/utils.py
+++ b/validmind/utils.py
@@ -12,7 +12,7 @@
import warnings
from datetime import date, datetime, time
from platform import python_version
-from typing import Any, Dict, List, Optional, TypeVar, Callable, Awaitable
+from typing import Any, Awaitable, Callable, Dict, List, Optional, TypeVar
import matplotlib.pylab as pylab
import mistune
@@ -59,7 +59,7 @@
logger = get_logger(__name__)
-T = TypeVar('T')
+T = TypeVar("T")
def parse_version(version: str) -> tuple[int, ...]:
@@ -363,7 +363,7 @@ def run_async(
func: Callable[..., Awaitable[T]],
*args: Any,
name: Optional[str] = None,
- **kwargs: Any
+ **kwargs: Any,
) -> T:
"""Helper function to run functions asynchronously.
@@ -397,9 +397,7 @@ def run_async(
def run_async_check(
- func: Callable[..., Awaitable[T]],
- *args: Any,
- **kwargs: Any
+ func: Callable[..., Awaitable[T]], *args: Any, **kwargs: Any
) -> Optional[asyncio.Task[T]]:
"""Helper function to run functions asynchronously if the task doesn't already exist.
diff --git a/validmind/vm_models/dataset/dataset.py b/validmind/vm_models/dataset/dataset.py
index 87c4c30e4..e953dece7 100644
--- a/validmind/vm_models/dataset/dataset.py
+++ b/validmind/vm_models/dataset/dataset.py
@@ -258,8 +258,10 @@ def assign_predictions(
prediction_values: Optional[List[Any]] = None,
probability_column: Optional[str] = None,
probability_values: Optional[List[float]] = None,
- prediction_probabilities: Optional[List[float]] = None, # DEPRECATED: use probability_values
- **kwargs: Dict[str, Any]
+ prediction_probabilities: Optional[
+ List[float]
+ ] = None, # DEPRECATED: use probability_values
+ **kwargs: Dict[str, Any],
) -> None:
"""Assign predictions and probabilities to the dataset.