From 93025bc9a30250f74b5b9a9352151f7177c343a4 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Tue, 13 May 2025 15:33:50 -0700 Subject: [PATCH 01/26] New documentation quickstart file --- .../quickstart_model_documentation.ipynb | 276 ++++++++++++++++++ 1 file changed, 276 insertions(+) create mode 100644 notebooks/code_sharing/quickstart_model_documentation.ipynb diff --git a/notebooks/code_sharing/quickstart_model_documentation.ipynb b/notebooks/code_sharing/quickstart_model_documentation.ipynb new file mode 100644 index 000000000..bca5a2dd2 --- /dev/null +++ b/notebooks/code_sharing/quickstart_model_documentation.ipynb @@ -0,0 +1,276 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "21a28d0f", + "metadata": {}, + "source": [ + "# Quickstart for model documentation" + ] + }, + { + "cell_type": "markdown", + "id": "about-intro-00609311-15b1-4baa-a507-b0a9945928eb", + "metadata": {}, + "source": [ + "## About ValidMind\n", + "\n", + "ValidMind is a suite of tools for managing model risk, including risk associated with AI and statistical models. \n", + "\n", + "You use the ValidMind Library to automate documentation and validation tests, and then use the ValidMind Platform to collaborate on model documentation. Together, these products simplify model risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and model validators." + ] + }, + { + "cell_type": "markdown", + "id": "about-begin-08a29872-53e7-414f-ba12-f46adf588e7a", + "metadata": {}, + "source": [ + "### Before you begin\n", + "\n", + "This notebook assumes you have basic familiarity with Python, including an understanding of how functions work. If you are new to Python, you can still run the notebook but we recommend further familiarizing yourself with the language. \n", + "\n", + "If you encounter errors due to missing modules in your Python environment, install the modules with `pip install`, and then re-run the notebook. For more help, refer to [Installing Python Modules](https://docs.python.org/3/installing/index.html)." + ] + }, + { + "cell_type": "markdown", + "id": "about-signup-a52b1b8f-723c-40f6-9e43-cee7bfda8ba6", + "metadata": {}, + "source": [ + "### New to ValidMind?\n", + "\n", + "If you haven't already seen our documentation on the [ValidMind Library](https://docs.validmind.ai/developer/validmind-library.html), we recommend you begin by exploring the available resources in this section. There, you can learn more about documenting models and running tests, as well as find code samples and our Python Library API reference.\n", + "\n", + "
For access to all features available in this notebook, create a free ValidMind account.\n", + "

\n", + "Signing up is FREE — Register with ValidMind
" + ] + }, + { + "cell_type": "markdown", + "id": "about-concepts-ab2cff73-e26c-44d6-82ee-cd32519cfe8b", + "metadata": {}, + "source": [ + "### Key concepts\n", + "\n", + "**Model documentation**: A structured and detailed record pertaining to a model, encompassing key components such as its underlying assumptions, methodologies, data sources, inputs, performance metrics, evaluations, limitations, and intended uses. It serves to ensure transparency, adherence to regulatory requirements, and a clear understanding of potential risks associated with the model’s application.\n", + "\n", + "**Documentation template**: Functions as a test suite and lays out the structure of model documentation, segmented into various sections and sub-sections. Documentation templates define the structure of your model documentation, specifying the tests that should be run, and how the results should be displayed.\n", + "\n", + "**Tests**: A function contained in the ValidMind Library, designed to run a specific quantitative test on the dataset or model. Tests are the building blocks of ValidMind, used to evaluate and document models and datasets, and can be run individually or as part of a suite defined by your model documentation template.\n", + "\n", + "**Metrics**: A subset of tests that do not have thresholds. In the context of this notebook, metrics and tests can be thought of as interchangeable concepts.\n", + "\n", + "**Custom metrics**: Custom metrics are functions that you define to evaluate your model or dataset. These functions can be registered with the ValidMind Library to be used in the ValidMind Platform.\n", + "\n", + "**Inputs**: Objects to be evaluated and documented in the ValidMind Library. They can be any of the following:\n", + "\n", + " - **model**: A single model that has been initialized in ValidMind with [`vm.init_model()`](https://docs.validmind.ai/validmind/validmind.html#init_model).\n", + " - **dataset**: Single dataset that has been initialized in ValidMind with [`vm.init_dataset()`](https://docs.validmind.ai/validmind/validmind.html#init_dataset).\n", + " - **models**: A list of ValidMind models - usually this is used when you want to compare multiple models in your custom metric.\n", + " - **datasets**: A list of ValidMind datasets - usually this is used when you want to compare multiple datasets in your custom metric. (Learn more: [Run tests with multiple datasets](https://docs.validmind.ai/notebooks/how_to/run_tests_that_require_multiple_datasets.html))\n", + "\n", + "**Parameters**: Additional arguments that can be passed when running a ValidMind test, used to pass additional information to a metric, customize its behavior, or provide additional context.\n", + "\n", + "**Outputs**: Custom metrics can return elements like tables or plots. Tables may be a list of dictionaries (each representing a row) or a pandas DataFrame. Plots may be matplotlib or plotly figures.\n", + "\n", + "**Test suites**: Collections of tests designed to run together to automate and generate model documentation end-to-end for specific use-cases.\n", + "\n", + "Example: the [`classifier_full_suite`](https://docs.validmind.ai/validmind/validmind/test_suites/classifier.html#ClassifierFullSuite) test suite runs tests from the [`tabular_dataset`](https://docs.validmind.ai/validmind/validmind/test_suites/tabular_datasets.html) and [`classifier`](https://docs.validmind.ai/validmind/validmind/test_suites/classifier.html) test suites to fully document the data and model sections for binary classification model use-cases." + ] + }, + { + "cell_type": "markdown", + "id": "install-library-bc77eb94-9f35-4fb0-8a7c-3cae29acd64a", + "metadata": {}, + "source": [ + "## Install the ValidMind Library\n", + "\n", + "
Recommended Python versions\n", + "

\n", + "Python 3.8 <= x <= 3.11
\n", + "\n", + "To install the library:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "install-python-ebd70fee-b6be-462e-a43a-fbe14f18fb62", + "metadata": {}, + "outputs": [], + "source": [ + "%pip install -q validmind" + ] + }, + { + "cell_type": "markdown", + "id": "install-initialize-3db79826-578b-48d3-8cca-3f3e69bd0ada", + "metadata": {}, + "source": [ + "## Initialize the ValidMind Library\n", + "\n", + "ValidMind generates a unique _code snippet_ for each registered model to connect with your developer environment. You initialize the ValidMind Library with this code snippet, which ensures that your documentation and tests are uploaded to the correct model when you run the notebook." + ] + }, + { + "cell_type": "markdown", + "id": "install-snippet-6eafc0c3-27cc-4e1e-8179-7dff1686d8d4", + "metadata": {}, + "source": [ + "### Get your code snippet\n", + "\n", + "1. In a browser, [log in to ValidMind](https://docs.validmind.ai/guide/configuration/log-in-to-validmind.html).\n", + "\n", + "2. In the left sidebar, navigate to **Inventory** and click **+ Register Model**.\n", + "\n", + "3. Enter the model details and click **Continue**. ([Need more help?](https://docs.validmind.ai/guide/model-inventory/register-models-in-inventory.html))\n", + "\n", + " For example, to register a model for use with this notebook, select:" + ] + }, + { + "cell_type": "markdown", + "id": "install-variables-114d7dd5-09f4-49a4-9a00-0c11d4fdefe3", + "metadata": {}, + "source": [ + " - Documentation template: `Binary classification`\n", + " - Use case: `Marketing/Sales - Attrition/Churn Management`" + ] + }, + { + "cell_type": "markdown", + "id": "install-credentials-4c404843-3474-4618-8898-f3bcce33fadd", + "metadata": {}, + "source": [ + " You can fill in other options according to your preference.\n", + " \n", + "4. Go to **Getting Started** and click **Copy snippet to clipboard**.\n", + "\n", + "Next, [load your model identifier credentials from an `.env` file](https://docs.validmind.ai/developer/model-documentation/store-credentials-in-env-file.html) or replace the placeholder with your own code snippet:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "install-init-a90debbe-251c-46cc-b9ea-5c8588467df6", + "metadata": {}, + "outputs": [], + "source": [ + "# Load your model identifier credentials from an `.env` file\n", + "\n", + "%load_ext dotenv\n", + "%dotenv .env\n", + "\n", + "# Or replace with your code snippet\n", + "\n", + "import validmind as vm\n", + "\n", + "vm.init(\n", + " # api_host=\"...\",\n", + " # api_key=\"...\",\n", + " # api_secret=\"...\",\n", + " # model=\"...\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "next-steps-9782d94b-b8dd-4894-b7ca-c3d92ec548fd", + "metadata": {}, + "source": [ + "## Next steps\n", + "\n", + "You can look at the output produced by the ValidMind Library right in the notebook where you ran the code, as you would expect. But there is a better way: use the ValidMind Platform to work with your model documentation." + ] + }, + { + "cell_type": "markdown", + "id": "next-docs-bdd1e7e2-32f7-4a95-b39f-e8b4aa2e1214", + "metadata": {}, + "source": [ + "### Work with your model documentation\n", + "\n", + "1. From the **Inventory** in the ValidMind Platform, go to the model you registered earlier. ([Need more help?](https://docs.validmind.ai/guide/model-inventory/working-with-model-inventory.html))\n", + "\n", + "2. In the left sidebar that appears for your model, click **Documentation**.\n", + "\n", + "What you see is the full draft of your model documentation in a more easily consumable version. From here, you can make qualitative edits to model documentation, view guidelines, collaborate with validators, and submit your model documentation for approval when it's ready. [Learn more ...](https://docs.validmind.ai/guide/working-with-model-documentation.html)" + ] + }, + { + "cell_type": "markdown", + "id": "next-resources-9214c11f-23b8-48d3-8c56-e96c675dea8c", + "metadata": {}, + "source": [ + "### Discover more learning resources\n", + "\n", + "We offer many interactive notebooks to help you document models:\n", + "\n", + "- [Run tests & test suites](https://docs.validmind.ai/guide/testing-overview.html)\n", + "- [Code samples](https://docs.validmind.ai/guide/samples-jupyter-notebooks.html)\n", + "\n", + "Or, visit our [documentation](https://docs.validmind.ai/) to learn more about ValidMind." + ] + }, + { + "cell_type": "markdown", + "id": "upgrade-vm-5193df48-8e35-43df-9f30-76c1156707a2", + "metadata": {}, + "source": [ + "## Upgrade ValidMind\n", + "\n", + "
After installing ValidMind, you’ll want to periodically make sure you are on the latest version to access any new features and other enhancements.
\n", + "\n", + "Retrieve the information for the currently installed version of ValidMind:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "upgrade-show-5f530fed-178d-4c73-adad-0bc191b0aee3", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "%pip show validmind" + ] + }, + { + "cell_type": "markdown", + "id": "upgrade-version-ab5a531d-4334-4c5b-b6aa-754e336f127f", + "metadata": {}, + "source": [ + "If the version returned is lower than the version indicated in our [production open-source code](https://github.com/validmind/validmind-library/blob/prod/validmind/__version__.py), restart your notebook and run:\n", + "\n", + "```bash\n", + "%pip install --upgrade validmind\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "upgrade-restart-faf9c324-3332-4eaf-aef5-2c0ca650d5ca", + "metadata": {}, + "source": [ + "You may need to restart your kernel after running the upgrade package for changes to be applied." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 878e3ab18adcf7c2574a6a46d9e2170e50eb2c48 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Tue, 13 May 2025 15:41:17 -0700 Subject: [PATCH 02/26] Making a copy of old notebook to reference --- ...start_customer_churn_full_suite_COPY.ipynb | 567 ++++++++++++++++++ .../quickstart_model_documentation.ipynb | 19 +- 2 files changed, 585 insertions(+), 1 deletion(-) create mode 100644 notebooks/code_samples/quickstart_customer_churn_full_suite_COPY.ipynb diff --git a/notebooks/code_samples/quickstart_customer_churn_full_suite_COPY.ipynb b/notebooks/code_samples/quickstart_customer_churn_full_suite_COPY.ipynb new file mode 100644 index 000000000..d7a9c6b87 --- /dev/null +++ b/notebooks/code_samples/quickstart_customer_churn_full_suite_COPY.ipynb @@ -0,0 +1,567 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Quickstart for model documentation\n", + "\n", + "Welcome! Let's get you started with the basic process of documenting models with ValidMind.\n", + "\n", + "You will learn how to initialize the ValidMind Library, load a sample dataset to train a simple classification model, and then run a ValidMind test suite to quickly generate documentation about the data and model.\n", + "\n", + "This notebook uses the [Bank Customer Churn Prediction](https://www.kaggle.com/code/kmalit/bank-customer-churn-prediction/data) sample dataset from Kaggle to train the classification model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "## Contents \n", + "- [About ValidMind](#toc1_) \n", + " - [Before you begin](#toc1_1_) \n", + " - [New to ValidMind?](#toc1_2_) \n", + " - [Key concepts](#toc1_3_) \n", + "- [Install the ValidMind Library](#toc2_)\n", + "- [Initialize the client library](#toc3_) \n", + " - [Get your code snippet](#toc3_1_)\n", + "- [Initialize the Python environment](#toc4_) \n", + " - [Preview the documentation template](#toc4_1_) \n", + "- [Load the sample dataset](#toc5_) \n", + "- [Document the model](#toc6_) \n", + " - [Prepocess the raw dataset](#toc6_1_) \n", + " - [Initialize the ValidMind datasets](#toc6_2_) \n", + " - [Initialize a model object](#toc6_3_) \n", + " - [Assign predictions to the datasets](#toc6_4_) \n", + " - [Run the full suite of tests](#toc6_5_) \n", + "- [Next steps](#toc7_) \n", + " - [Work with your model documentation](#toc7_1_) \n", + " - [Discover more learning resources](#toc7_2_) \n", + "- [Upgrade ValidMind](#toc8_) \n", + "\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "## About ValidMind\n", + "\n", + "ValidMind is a suite of tools for managing model risk, including risk associated with AI and statistical models.\n", + "\n", + "You use the ValidMind Library to automate documentation and validation tests, and then use the ValidMind Platform to collaborate on model documentation. Together, these products simplify model risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and model validators.\n", + "\n", + "\n", + "\n", + "### Before you begin\n", + "\n", + "This notebook assumes you have basic familiarity with Python, including an understanding of how functions work. If you are new to Python, you can still run the notebook but we recommend further familiarizing yourself with the language. \n", + "\n", + "If you encounter errors due to missing modules in your Python environment, install the modules with `pip install`, and then re-run the notebook. For more help, refer to [Installing Python Modules](https://docs.python.org/3/installing/index.html).\n", + "\n", + "\n", + "\n", + "### New to ValidMind?\n", + "\n", + "If you haven't already seen our documentation on the [ValidMind Library](https://docs.validmind.ai/developer/validmind-library.html), we recommend you begin by exploring the available resources in this section. There, you can learn more about documenting models and running tests, as well as find code samples and our Python Library API reference.\n", + "\n", + "
For access to all features available in this notebook, create a free ValidMind account.\n", + "

\n", + "Signing up is FREE — Register with ValidMind
\n", + "\n", + "\n", + "\n", + "### Key concepts\n", + "\n", + "**Model documentation**: A structured and detailed record pertaining to a model, encompassing key components such as its underlying assumptions, methodologies, data sources, inputs, performance metrics, evaluations, limitations, and intended uses. It serves to ensure transparency, adherence to regulatory requirements, and a clear understanding of potential risks associated with the model’s application.\n", + "\n", + "**Documentation template**: Functions as a test suite and lays out the structure of model documentation, segmented into various sections and sub-sections. Documentation templates define the structure of your model documentation, specifying the tests that should be run, and how the results should be displayed.\n", + "\n", + "**Tests**: A function contained in the ValidMind Library, designed to run a specific quantitative test on the dataset or model. Tests are the building blocks of ValidMind, used to evaluate and document models and datasets, and can be run individually or as part of a suite defined by your model documentation template.\n", + "\n", + "**Custom tests**: Custom tests are functions that you define to evaluate your model or dataset. These functions can be registered via the ValidMind Library to be used with the ValidMind Platform.\n", + "\n", + "**Inputs**: Objects to be evaluated and documented in the ValidMind Library. They can be any of the following:\n", + "\n", + " - **model**: A single model that has been initialized in ValidMind with [`vm.init_model()`](https://docs.validmind.ai/validmind/validmind.html#init_model).\n", + " - **dataset**: Single dataset that has been initialized in ValidMind with [`vm.init_dataset()`](https://docs.validmind.ai/validmind/validmind.html#init_dataset).\n", + " - **models**: A list of ValidMind models - usually this is used when you want to compare multiple models in your custom test.\n", + " - **datasets**: A list of ValidMind datasets - usually this is used when you want to compare multiple datasets in your custom test. See this [example](https://docs.validmind.ai/notebooks/how_to/run_tests_that_require_multiple_datasets.html) for more information.\n", + "\n", + "**Parameters**: Additional arguments that can be passed when running a ValidMind test, used to pass additional information to a test, customize its behavior, or provide additional context.\n", + "\n", + "**Outputs**: Custom tests can return elements like tables or plots. Tables may be a list of dictionaries (each representing a row) or a pandas DataFrame. Plots may be matplotlib or plotly figures.\n", + "\n", + "**Test suites**: Collections of tests designed to run together to automate and generate model documentation end-to-end for specific use-cases.\n", + "\n", + "Example: the [`classifier_full_suite`](https://docs.validmind.ai/validmind/validmind/test_suites/classifier.html#ClassifierFullSuite) test suite runs tests from the [`tabular_dataset`](https://docs.validmind.ai/validmind/validmind/test_suites/tabular_datasets.html) and [`classifier`](https://docs.validmind.ai/validmind/validmind/test_suites/classifier.html) test suites to fully document the data and model sections for binary classification model use-cases.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "## Install the ValidMind Library\n", + "\n", + "To install the library:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install -q validmind" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "## Initialize the ValidMind Library\n", + "\n", + "ValidMind generates a unique _code snippet_ for each registered model to connect with your developer environment. You initialize the ValidMind Library with this code snippet, which ensures that your documentation and tests are uploaded to the correct model when you run the notebook.\n", + "\n", + "\n", + "\n", + "### Get your code snippet\n", + "\n", + "1. In a browser, [log in to ValidMind](https://docs.validmind.ai/guide/configuration/log-in-to-validmind.html).\n", + "\n", + "2. In the left sidebar, navigate to **Model Inventory** and click **+ Register Model**.\n", + "\n", + "3. Enter the model details and click **Continue**. ([Need more help?](https://docs.validmind.ai/guide/model-inventory/register-models-in-inventory.html))\n", + "\n", + " For example, to register a model for use with this notebook, select:\n", + "\n", + " - Documentation template: `Binary classification`\n", + " - Use case: `Marketing/Sales - Attrition/Churn Management`\n", + "\n", + " You can fill in other options according to your preference.\n", + "\n", + "4. Go to **Getting Started** and click **Copy snippet to clipboard**.\n", + "\n", + "Next, [load your model identifier credentials from an `.env` file](https://docs.validmind.ai/developer/model-documentation/store-credentials-in-env-file.html) or replace the placeholder with your own code snippet:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load your model identifier credentials from an `.env` file\n", + "\n", + "%load_ext dotenv\n", + "%dotenv .env\n", + "\n", + "# Or replace with your code snippet\n", + "\n", + "import validmind as vm\n", + "\n", + "vm.init(\n", + " # api_host=\"...\",\n", + " # api_key=\"...\",\n", + " # api_secret=\"...\",\n", + " # model=\"...\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "## Initialize the Python environment\n", + "\n", + "Next, let's import the necessary libraries and set up your Python environment for data analysis:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import xgboost as xgb\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "### Preview the documentation template\n", + "\n", + "A template predefines sections for your model documentation and provides a general outline to follow, making the documentation process much easier.\n", + "\n", + "You will upload documentation and test results into this template later on. For now, take a look at the structure that the template provides with the `vm.preview_template()` function from the ValidMind library and note the empty sections:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vm.preview_template()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "## Load the sample dataset\n", + "\n", + "The sample dataset used here is provided by the ValidMind library. To be able to use it, you need to import the dataset and load it into a pandas [DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html), a two-dimensional tabular data structure that makes use of rows and columns:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import the sample dataset from the library\n", + "\n", + "from validmind.datasets.classification import customer_churn\n", + "\n", + "print(\n", + " f\"Loaded demo dataset with: \\n\\n\\t• Target column: '{customer_churn.target_column}' \\n\\t• Class labels: {customer_churn.class_labels}\"\n", + ")\n", + "\n", + "raw_df = customer_churn.load_data()\n", + "raw_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "## Document the model\n", + "\n", + "As part of documenting the model with the ValidMind Library, you need to preprocess the raw dataset, initialize some training and test datasets, initialize a model object you can use for testing, and then run the full suite of tests.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "### Prepocess the raw dataset\n", + "\n", + "Preprocessing performs a number of operations to get ready for the subsequent steps:\n", + "\n", + "- Preprocess the data: Splits the DataFrame (`df`) into multiple datasets (`train_df`, `validation_df`, and `test_df`) using `demo_dataset.preprocess` to simplify preprocessing.\n", + "- Separate features and targets: Drops the target column to create feature sets (`x_train`, `x_val`) and target sets (`y_train`, `y_val`).\n", + "- Initialize XGBoost classifier: Creates an `XGBClassifier` object with early stopping rounds set to 10.\n", + "- Set evaluation metrics: Specifies metrics for model evaluation as \"error,\" \"logloss,\" and \"auc.\"\n", + "- Fit the model: Trains the model on `x_train` and `y_train` using the validation set `(x_val, y_val)`. Verbose output is disabled.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train_df, validation_df, test_df = customer_churn.preprocess(raw_df)\n", + "\n", + "x_train = train_df.drop(customer_churn.target_column, axis=1)\n", + "y_train = train_df[customer_churn.target_column]\n", + "x_val = validation_df.drop(customer_churn.target_column, axis=1)\n", + "y_val = validation_df[customer_churn.target_column]\n", + "\n", + "model = xgb.XGBClassifier(early_stopping_rounds=10)\n", + "model.set_params(\n", + " eval_metric=[\"error\", \"logloss\", \"auc\"],\n", + ")\n", + "model.fit(\n", + " x_train,\n", + " y_train,\n", + " eval_set=[(x_val, y_val)],\n", + " verbose=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "### Initialize the ValidMind datasets\n", + "\n", + "Before you can run tests, you must first initialize a ValidMind dataset object using the [`init_dataset`](https://docs.validmind.ai/validmind/validmind.html#init_dataset) function from the ValidMind (`vm`) module.\n", + "\n", + "This function takes a number of arguments:\n", + "\n", + "- `dataset` — the raw dataset that you want to provide as input to tests\n", + "- `input_id` - a unique identifier that allows tracking what inputs are used when running each individual test\n", + "- `target_column` — a required argument if tests require access to true values. This is the name of the target column in the dataset\n", + "- `class_labels` — an optional value to map predicted classes to class labels\n", + "\n", + "With all datasets ready, you can now initialize the raw, training and test datasets (`raw_df`, `train_df` and `test_df`) created earlier into their own dataset objects using [`vm.init_dataset()`](https://docs.validmind.ai/validmind/validmind.html#init_dataset):\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vm_raw_dataset = vm.init_dataset(\n", + " dataset=raw_df,\n", + " input_id=\"raw_dataset\",\n", + " target_column=customer_churn.target_column,\n", + " class_labels=customer_churn.class_labels,\n", + ")\n", + "\n", + "vm_train_ds = vm.init_dataset(\n", + " dataset=train_df,\n", + " input_id=\"train_dataset\",\n", + " target_column=customer_churn.target_column,\n", + ")\n", + "\n", + "vm_test_ds = vm.init_dataset(\n", + " dataset=test_df, input_id=\"test_dataset\", target_column=customer_churn.target_column\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "### Initialize a model object\n", + "\n", + "Additionally, you need to initialize a ValidMind model object (`vm_model`) that can be passed to other functions for analysis and tests on the data. You simply intialize this model object with [`vm.init_model()`](https://docs.validmind.ai/validmind/validmind.html#init_model):\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vm_model = vm.init_model(\n", + " model,\n", + " input_id=\"model\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "### Assign predictions to the datasets\n", + "\n", + "We can now use the assign_predictions() method from the Dataset object to link existing predictions to any model. If no prediction values are passed, the method will compute predictions automatically:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vm_train_ds.assign_predictions(\n", + " model=vm_model,\n", + ")\n", + "\n", + "vm_test_ds.assign_predictions(\n", + " model=vm_model,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "### Run the full suite of tests\n", + "\n", + "This is where it all comes together: you are now ready to run the documentation tests for the model as defined by the documentation template you looked at earlier.\n", + "\n", + "The [`vm.run_documentation_tests`](https://docs.validmind.ai/validmind/validmind.html#run_documentation_tests) function finds and runs every test specified in the template and then uploads all the documentation and test artifacts that get generated to the ValidMind Platform.\n", + "\n", + "The function requires information about the inputs to use on every test. These inputs can be passed as an `inputs` argument if we want to use the same inputs for all tests. It's also possible to pass a `config` argument that has information about the `params` and `inputs` that each test requires. The `config` parameter is a dictionary with the following structure:\n", + "\n", + "```python\n", + "config = {\n", + " \"\": {\n", + " \"params\": {\n", + " \"param1\": \"value1\",\n", + " \"param2\": \"value2\",\n", + " ...\n", + " },\n", + " \"inputs\": {\n", + " \"input1\": \"value1\",\n", + " \"input2\": \"value2\",\n", + " ...\n", + " }\n", + " },\n", + " ...\n", + "}\n", + "```\n", + "\n", + "Each `` above corresponds to the test driven block identifiers shown by `vm.preview_template()`. For this model, we will use the default parameters for all tests, but we'll need to specify the input configuration for each one. The method `get_demo_test_config()` below constructs the default input configuration for our demo.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from validmind.utils import preview_test_config\n", + "\n", + "test_config = customer_churn.get_demo_test_config()\n", + "preview_test_config(test_config)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can pass the input configuration to `vm.run_documentation_tests()` and run the full suite of tests. The variable `full_suite` then holds the result of these tests.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "full_suite = vm.run_documentation_tests(config=test_config)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "## Next steps\n", + "\n", + "You can look at the results of this test suite right in the notebook where you ran the code, as you would expect. But there is a better way — use the ValidMind Platform to work with your model documentation.\n", + "\n", + "\n", + "\n", + "### Work with your model documentation\n", + "\n", + "1. From the **Model Inventory** in the ValidMind Platform, go to the model you registered earlier. ([Need more help?](https://docs.validmind.ai/guide/model-inventory/working-with-model-inventory.html))\n", + "\n", + "2. Click and expand the **Model Development** section.\n", + "\n", + "What you see is the full draft of your model documentation in a more easily consumable version. From here, you can make qualitative edits to model documentation, view guidelines, collaborate with validators, and submit your model documentation for approval when it's ready. [Learn more ...](https://docs.validmind.ai/guide/model-documentation/working-with-model-documentation.html)\n", + "\n", + "\n", + "\n", + "### Discover more learning resources\n", + "\n", + "We offer many interactive notebooks to help you document models:\n", + "\n", + "- [Run tests & test suites](https://docs.validmind.ai/developer/model-testing/testing-overview.html)\n", + "- [Code samples](https://docs.validmind.ai/developer/samples-jupyter-notebooks.html)\n", + "\n", + "Or, visit our [documentation](https://docs.validmind.ai/) to learn more about ValidMind." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "## Upgrade ValidMind\n", + "\n", + "
After installing ValidMind, you’ll want to periodically make sure you are on the latest version to access any new features and other enhancements.
\n", + "\n", + "Retrieve the information for the currently installed version of ValidMind:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip show validmind" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If the version returned is lower than the version indicated in our [production open-source code](https://github.com/validmind/validmind-library/blob/prod/validmind/__version__.py), restart your notebook and run:\n", + "\n", + "```bash\n", + "%pip install --upgrade validmind\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You may need to restart your kernel after running the upgrade package for changes to be applied." + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "gpuClass": "standard", + "kernelspec": { + "display_name": "ValidMind Library", + "language": "python", + "name": "validmind" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/code_sharing/quickstart_model_documentation.ipynb b/notebooks/code_sharing/quickstart_model_documentation.ipynb index bca5a2dd2..4d877eb6e 100644 --- a/notebooks/code_sharing/quickstart_model_documentation.ipynb +++ b/notebooks/code_sharing/quickstart_model_documentation.ipynb @@ -5,7 +5,24 @@ "id": "21a28d0f", "metadata": {}, "source": [ - "# Quickstart for model documentation" + "# Quickstart for model documentation\n", + "\n", + "Learn the basics of using ValidMind to document a simple binary classification model for customer churn as part of a sample model development workflow." + ] + }, + { + "cell_type": "markdown", + "id": "5928bca4", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "Model development aims to produce a fit-for-purpose *champion model* by conducting thorough testing and analysis, supporting the capabilities of the model with evidence in the form of documentation and test results. Model documentation should be clear and comprehensive, ideally following a structure or template covering all aspects of compliance with model risk regulation.\n", + "\n", + "A *binary classification model* is a type of predictive model used in churn analysis to identify customers who are likely to leave a service or subscription by analyzing various behavioral, transactional, and demographic factors.\n", + "\n", + "- This model helps businesses take proactive measures to retain at-risk customers by offering personalized incentives, improving customer service, or adjusting pricing strategies.\n", + "- Effective validation of a churn prediction model ensures that businesses can accurately identify potential churners, optimize retention efforts, and enhance overall customer satisfaction while minimizing revenue loss." ] }, { From 3bd5d105cf5c711e39c892bbad707db24613ab4e Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Tue, 13 May 2025 16:04:10 -0700 Subject: [PATCH 03/26] Save point --- ...start_customer_churn_full_suite_COPY.ipynb | 211 ------------------ .../quickstart_model_documentation.ipynb | 125 ++++++++++- .../2-start_development_process.ipynb | 2 +- .../2-start_validation_process.ipynb | 2 +- 4 files changed, 123 insertions(+), 217 deletions(-) diff --git a/notebooks/code_samples/quickstart_customer_churn_full_suite_COPY.ipynb b/notebooks/code_samples/quickstart_customer_churn_full_suite_COPY.ipynb index d7a9c6b87..1179a21ed 100644 --- a/notebooks/code_samples/quickstart_customer_churn_full_suite_COPY.ipynb +++ b/notebooks/code_samples/quickstart_customer_churn_full_suite_COPY.ipynb @@ -4,11 +4,6 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Quickstart for model documentation\n", - "\n", - "Welcome! Let's get you started with the basic process of documenting models with ValidMind.\n", - "\n", - "You will learn how to initialize the ValidMind Library, load a sample dataset to train a simple classification model, and then run a ValidMind test suite to quickly generate documentation about the data and model.\n", "\n", "This notebook uses the [Bank Customer Churn Prediction](https://www.kaggle.com/code/kmalit/bank-customer-churn-prediction/data) sample dataset from Kaggle to train the classification model." ] @@ -51,212 +46,6 @@ "" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "## About ValidMind\n", - "\n", - "ValidMind is a suite of tools for managing model risk, including risk associated with AI and statistical models.\n", - "\n", - "You use the ValidMind Library to automate documentation and validation tests, and then use the ValidMind Platform to collaborate on model documentation. Together, these products simplify model risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and model validators.\n", - "\n", - "\n", - "\n", - "### Before you begin\n", - "\n", - "This notebook assumes you have basic familiarity with Python, including an understanding of how functions work. If you are new to Python, you can still run the notebook but we recommend further familiarizing yourself with the language. \n", - "\n", - "If you encounter errors due to missing modules in your Python environment, install the modules with `pip install`, and then re-run the notebook. For more help, refer to [Installing Python Modules](https://docs.python.org/3/installing/index.html).\n", - "\n", - "\n", - "\n", - "### New to ValidMind?\n", - "\n", - "If you haven't already seen our documentation on the [ValidMind Library](https://docs.validmind.ai/developer/validmind-library.html), we recommend you begin by exploring the available resources in this section. There, you can learn more about documenting models and running tests, as well as find code samples and our Python Library API reference.\n", - "\n", - "
For access to all features available in this notebook, create a free ValidMind account.\n", - "

\n", - "Signing up is FREE — Register with ValidMind
\n", - "\n", - "\n", - "\n", - "### Key concepts\n", - "\n", - "**Model documentation**: A structured and detailed record pertaining to a model, encompassing key components such as its underlying assumptions, methodologies, data sources, inputs, performance metrics, evaluations, limitations, and intended uses. It serves to ensure transparency, adherence to regulatory requirements, and a clear understanding of potential risks associated with the model’s application.\n", - "\n", - "**Documentation template**: Functions as a test suite and lays out the structure of model documentation, segmented into various sections and sub-sections. Documentation templates define the structure of your model documentation, specifying the tests that should be run, and how the results should be displayed.\n", - "\n", - "**Tests**: A function contained in the ValidMind Library, designed to run a specific quantitative test on the dataset or model. Tests are the building blocks of ValidMind, used to evaluate and document models and datasets, and can be run individually or as part of a suite defined by your model documentation template.\n", - "\n", - "**Custom tests**: Custom tests are functions that you define to evaluate your model or dataset. These functions can be registered via the ValidMind Library to be used with the ValidMind Platform.\n", - "\n", - "**Inputs**: Objects to be evaluated and documented in the ValidMind Library. They can be any of the following:\n", - "\n", - " - **model**: A single model that has been initialized in ValidMind with [`vm.init_model()`](https://docs.validmind.ai/validmind/validmind.html#init_model).\n", - " - **dataset**: Single dataset that has been initialized in ValidMind with [`vm.init_dataset()`](https://docs.validmind.ai/validmind/validmind.html#init_dataset).\n", - " - **models**: A list of ValidMind models - usually this is used when you want to compare multiple models in your custom test.\n", - " - **datasets**: A list of ValidMind datasets - usually this is used when you want to compare multiple datasets in your custom test. See this [example](https://docs.validmind.ai/notebooks/how_to/run_tests_that_require_multiple_datasets.html) for more information.\n", - "\n", - "**Parameters**: Additional arguments that can be passed when running a ValidMind test, used to pass additional information to a test, customize its behavior, or provide additional context.\n", - "\n", - "**Outputs**: Custom tests can return elements like tables or plots. Tables may be a list of dictionaries (each representing a row) or a pandas DataFrame. Plots may be matplotlib or plotly figures.\n", - "\n", - "**Test suites**: Collections of tests designed to run together to automate and generate model documentation end-to-end for specific use-cases.\n", - "\n", - "Example: the [`classifier_full_suite`](https://docs.validmind.ai/validmind/validmind/test_suites/classifier.html#ClassifierFullSuite) test suite runs tests from the [`tabular_dataset`](https://docs.validmind.ai/validmind/validmind/test_suites/tabular_datasets.html) and [`classifier`](https://docs.validmind.ai/validmind/validmind/test_suites/classifier.html) test suites to fully document the data and model sections for binary classification model use-cases.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "## Install the ValidMind Library\n", - "\n", - "To install the library:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%pip install -q validmind" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "## Initialize the ValidMind Library\n", - "\n", - "ValidMind generates a unique _code snippet_ for each registered model to connect with your developer environment. You initialize the ValidMind Library with this code snippet, which ensures that your documentation and tests are uploaded to the correct model when you run the notebook.\n", - "\n", - "\n", - "\n", - "### Get your code snippet\n", - "\n", - "1. In a browser, [log in to ValidMind](https://docs.validmind.ai/guide/configuration/log-in-to-validmind.html).\n", - "\n", - "2. In the left sidebar, navigate to **Model Inventory** and click **+ Register Model**.\n", - "\n", - "3. Enter the model details and click **Continue**. ([Need more help?](https://docs.validmind.ai/guide/model-inventory/register-models-in-inventory.html))\n", - "\n", - " For example, to register a model for use with this notebook, select:\n", - "\n", - " - Documentation template: `Binary classification`\n", - " - Use case: `Marketing/Sales - Attrition/Churn Management`\n", - "\n", - " You can fill in other options according to your preference.\n", - "\n", - "4. Go to **Getting Started** and click **Copy snippet to clipboard**.\n", - "\n", - "Next, [load your model identifier credentials from an `.env` file](https://docs.validmind.ai/developer/model-documentation/store-credentials-in-env-file.html) or replace the placeholder with your own code snippet:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Load your model identifier credentials from an `.env` file\n", - "\n", - "%load_ext dotenv\n", - "%dotenv .env\n", - "\n", - "# Or replace with your code snippet\n", - "\n", - "import validmind as vm\n", - "\n", - "vm.init(\n", - " # api_host=\"...\",\n", - " # api_key=\"...\",\n", - " # api_secret=\"...\",\n", - " # model=\"...\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "## Initialize the Python environment\n", - "\n", - "Next, let's import the necessary libraries and set up your Python environment for data analysis:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import xgboost as xgb\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "### Preview the documentation template\n", - "\n", - "A template predefines sections for your model documentation and provides a general outline to follow, making the documentation process much easier.\n", - "\n", - "You will upload documentation and test results into this template later on. For now, take a look at the structure that the template provides with the `vm.preview_template()` function from the ValidMind library and note the empty sections:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vm.preview_template()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "## Load the sample dataset\n", - "\n", - "The sample dataset used here is provided by the ValidMind library. To be able to use it, you need to import the dataset and load it into a pandas [DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html), a two-dimensional tabular data structure that makes use of rows and columns:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import the sample dataset from the library\n", - "\n", - "from validmind.datasets.classification import customer_churn\n", - "\n", - "print(\n", - " f\"Loaded demo dataset with: \\n\\n\\t• Target column: '{customer_churn.target_column}' \\n\\t• Class labels: {customer_churn.class_labels}\"\n", - ")\n", - "\n", - "raw_df = customer_churn.load_data()\n", - "raw_df.head()" - ] - }, { "cell_type": "markdown", "metadata": {}, diff --git a/notebooks/code_sharing/quickstart_model_documentation.ipynb b/notebooks/code_sharing/quickstart_model_documentation.ipynb index 4d877eb6e..5bca86bc7 100644 --- a/notebooks/code_sharing/quickstart_model_documentation.ipynb +++ b/notebooks/code_sharing/quickstart_model_documentation.ipynb @@ -7,7 +7,7 @@ "source": [ "# Quickstart for model documentation\n", "\n", - "Learn the basics of using ValidMind to document a simple binary classification model for customer churn as part of a sample model development workflow." + "Learn the basics of using ValidMind to document models as part of a model development workflow. Set up the ValidMind Library in your environment, and generate a draft of documentation using ValidMind tests for a binary classification model." ] }, { @@ -96,12 +96,20 @@ "Example: the [`classifier_full_suite`](https://docs.validmind.ai/validmind/validmind/test_suites/classifier.html#ClassifierFullSuite) test suite runs tests from the [`tabular_dataset`](https://docs.validmind.ai/validmind/validmind/test_suites/tabular_datasets.html) and [`classifier`](https://docs.validmind.ai/validmind/validmind/test_suites/classifier.html) test suites to fully document the data and model sections for binary classification model use-cases." ] }, + { + "cell_type": "markdown", + "id": "63023166", + "metadata": {}, + "source": [ + "## Setting up" + ] + }, { "cell_type": "markdown", "id": "install-library-bc77eb94-9f35-4fb0-8a7c-3cae29acd64a", "metadata": {}, "source": [ - "## Install the ValidMind Library\n", + "### Install the ValidMind Library\n", "\n", "
Recommended Python versions\n", "

\n", @@ -125,7 +133,7 @@ "id": "install-initialize-3db79826-578b-48d3-8cca-3f3e69bd0ada", "metadata": {}, "source": [ - "## Initialize the ValidMind Library\n", + "### Initialize the ValidMind Library\n", "\n", "ValidMind generates a unique _code snippet_ for each registered model to connect with your developer environment. You initialize the ValidMind Library with this code snippet, which ensures that your documentation and tests are uploaded to the correct model when you run the notebook." ] @@ -135,7 +143,7 @@ "id": "install-snippet-6eafc0c3-27cc-4e1e-8179-7dff1686d8d4", "metadata": {}, "source": [ - "### Get your code snippet\n", + "#### Get your code snippet\n", "\n", "1. In a browser, [log in to ValidMind](https://docs.validmind.ai/guide/configuration/log-in-to-validmind.html).\n", "\n", @@ -191,6 +199,115 @@ ")" ] }, + { + "cell_type": "markdown", + "id": "aa61686d", + "metadata": {}, + "source": [ + "### Initialize the Python environment\n", + "\n", + "Next, let's import the necessary libraries and set up your Python environment for data analysis:\n", + "\n", + "- Import **Extreme Gradient Boosting** (XGBoost) with an alias so that we can reference its functions in later calls. XGBoost is a powerful machine learning library designed for speed and performance, especially in handling structured or tabular data.\n", + "- Enable **`matplotlib`**, a plotting library used for visualizing data. Ensures that any plots you generate will render inline in our notebook output rather than opening in a separate window." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "881f26af", + "metadata": {}, + "outputs": [], + "source": [ + "import xgboost as xgb\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "7fc8ea4c", + "metadata": {}, + "source": [ + "## Getting to know ValidMind" + ] + }, + { + "cell_type": "markdown", + "id": "af99b2cb", + "metadata": {}, + "source": [ + "### Preview the documentation template\n", + "\n", + "Let's verify that you have connected the ValidMind Library to the ValidMind Platform and that the appropriate *template* is selected for your model. A template predefines sections for your model documentation and provides a general outline to follow, making the documentation process much easier.\n", + "\n", + "You will upload documentation and test results unique to your model based on this template later on. For now, **take a look at the default structure that the template provides with [the `vm.preview_template()` function](https://docs.validmind.ai/validmind/validmind.html#preview_template)** from the ValidMind library and note the empty sections:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a14b9091", + "metadata": {}, + "outputs": [], + "source": [ + "vm.preview_template()" + ] + }, + { + "cell_type": "markdown", + "id": "d155b6a4", + "metadata": {}, + "source": [ + "### View model documentation in the ValidMind Platform\n", + "\n", + "Next, let's head to the ValidMind Platform to see the template in action:\n", + "\n", + "1. In a browser, [log in to ValidMind](https://docs.validmind.ai/guide/configuration/log-in-to-validmind.html).\n", + "\n", + "2. In the left sidebar, navigate to **Inventory** and select the model you registered for this notebook.\n", + "\n", + "3. Click on the **Documentation** for your model and note how the structure of the documentation matches our preview above." + ] + }, + { + "cell_type": "markdown", + "id": "a2c5b527", + "metadata": {}, + "source": [ + "## Import sample dataset\n", + "\n", + "Then, let's import the public [Bank Customer Churn Prediction](https://www.kaggle.com/datasets/shantanudhakadd/bank-customer-churn-prediction) dataset from Kaggle. \n", + "\n", + "In our below example, note that: \n", + "\n", + "- The target column, `Exited` has a value of `1` when a customer has churned and `0` otherwise.\n", + "- The ValidMind Library provides a wrapper to automatically load the dataset as a [Pandas DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) object. A Pandas Dataframe is a two-dimensional tabular data structure that makes use of rows and columns." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5050b3a7", + "metadata": {}, + "outputs": [], + "source": [ + "from validmind.datasets.classification import customer_churn\n", + "\n", + "print(\n", + " f\"Loaded demo dataset with: \\n\\n\\t• Target column: '{customer_churn.target_column}' \\n\\t• Class labels: {customer_churn.class_labels}\"\n", + ")\n", + "\n", + "raw_df = customer_churn.load_data()\n", + "raw_df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "fdae3a7b", + "metadata": {}, + "source": [] + }, { "cell_type": "markdown", "id": "next-steps-9782d94b-b8dd-4894-b7ca-c3d92ec548fd", diff --git a/notebooks/tutorials/model_development/2-start_development_process.ipynb b/notebooks/tutorials/model_development/2-start_development_process.ipynb index ead67c31b..4711399be 100644 --- a/notebooks/tutorials/model_development/2-start_development_process.ipynb +++ b/notebooks/tutorials/model_development/2-start_development_process.ipynb @@ -138,7 +138,7 @@ "In our below example, note that: \n", "\n", "- The target column, `Exited` has a value of `1` when a customer has churned and `0` otherwise.\n", - "- The ValidMind Library provides a wrapper to automatically load the dataset as a Pandas DataFrame object." + "- The ValidMind Library provides a wrapper to automatically load the dataset as a [Pandas DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) object. A Pandas Dataframe is a two-dimensional tabular data structure that makes use of rows and columns." ] }, { diff --git a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb index 3ebbade72..820aaa866 100644 --- a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb +++ b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb @@ -143,7 +143,7 @@ "In our below example, note that:\n", "\n", "- The target column, `Exited` has a value of `1` when a customer has churned and `0` otherwise.\n", - "- The ValidMind Library provides a wrapper to automatically load the dataset as a Pandas [DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) object." + "- The ValidMind Library provides a wrapper to automatically load the dataset as a [Pandas DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) object. A Pandas Dataframe is a two-dimensional tabular data structure that makes use of rows and columns." ] }, { From d1fc376f3b2c7a7257e30bbfb0d105702f09ab20 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Tue, 13 May 2025 16:39:37 -0700 Subject: [PATCH 04/26] wip --- ...start_customer_churn_full_suite_COPY.ipynb | 356 ------------------ .../quickstart_model_documentation.ipynb | 228 ++++++++++- .../2-start_development_process.ipynb | 2 +- 3 files changed, 226 insertions(+), 360 deletions(-) delete mode 100644 notebooks/code_samples/quickstart_customer_churn_full_suite_COPY.ipynb diff --git a/notebooks/code_samples/quickstart_customer_churn_full_suite_COPY.ipynb b/notebooks/code_samples/quickstart_customer_churn_full_suite_COPY.ipynb deleted file mode 100644 index 1179a21ed..000000000 --- a/notebooks/code_samples/quickstart_customer_churn_full_suite_COPY.ipynb +++ /dev/null @@ -1,356 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "This notebook uses the [Bank Customer Churn Prediction](https://www.kaggle.com/code/kmalit/bank-customer-churn-prediction/data) sample dataset from Kaggle to train the classification model." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "## Contents \n", - "- [About ValidMind](#toc1_) \n", - " - [Before you begin](#toc1_1_) \n", - " - [New to ValidMind?](#toc1_2_) \n", - " - [Key concepts](#toc1_3_) \n", - "- [Install the ValidMind Library](#toc2_)\n", - "- [Initialize the client library](#toc3_) \n", - " - [Get your code snippet](#toc3_1_)\n", - "- [Initialize the Python environment](#toc4_) \n", - " - [Preview the documentation template](#toc4_1_) \n", - "- [Load the sample dataset](#toc5_) \n", - "- [Document the model](#toc6_) \n", - " - [Prepocess the raw dataset](#toc6_1_) \n", - " - [Initialize the ValidMind datasets](#toc6_2_) \n", - " - [Initialize a model object](#toc6_3_) \n", - " - [Assign predictions to the datasets](#toc6_4_) \n", - " - [Run the full suite of tests](#toc6_5_) \n", - "- [Next steps](#toc7_) \n", - " - [Work with your model documentation](#toc7_1_) \n", - " - [Discover more learning resources](#toc7_2_) \n", - "- [Upgrade ValidMind](#toc8_) \n", - "\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "## Document the model\n", - "\n", - "As part of documenting the model with the ValidMind Library, you need to preprocess the raw dataset, initialize some training and test datasets, initialize a model object you can use for testing, and then run the full suite of tests.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "### Prepocess the raw dataset\n", - "\n", - "Preprocessing performs a number of operations to get ready for the subsequent steps:\n", - "\n", - "- Preprocess the data: Splits the DataFrame (`df`) into multiple datasets (`train_df`, `validation_df`, and `test_df`) using `demo_dataset.preprocess` to simplify preprocessing.\n", - "- Separate features and targets: Drops the target column to create feature sets (`x_train`, `x_val`) and target sets (`y_train`, `y_val`).\n", - "- Initialize XGBoost classifier: Creates an `XGBClassifier` object with early stopping rounds set to 10.\n", - "- Set evaluation metrics: Specifies metrics for model evaluation as \"error,\" \"logloss,\" and \"auc.\"\n", - "- Fit the model: Trains the model on `x_train` and `y_train` using the validation set `(x_val, y_val)`. Verbose output is disabled.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "train_df, validation_df, test_df = customer_churn.preprocess(raw_df)\n", - "\n", - "x_train = train_df.drop(customer_churn.target_column, axis=1)\n", - "y_train = train_df[customer_churn.target_column]\n", - "x_val = validation_df.drop(customer_churn.target_column, axis=1)\n", - "y_val = validation_df[customer_churn.target_column]\n", - "\n", - "model = xgb.XGBClassifier(early_stopping_rounds=10)\n", - "model.set_params(\n", - " eval_metric=[\"error\", \"logloss\", \"auc\"],\n", - ")\n", - "model.fit(\n", - " x_train,\n", - " y_train,\n", - " eval_set=[(x_val, y_val)],\n", - " verbose=False,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "### Initialize the ValidMind datasets\n", - "\n", - "Before you can run tests, you must first initialize a ValidMind dataset object using the [`init_dataset`](https://docs.validmind.ai/validmind/validmind.html#init_dataset) function from the ValidMind (`vm`) module.\n", - "\n", - "This function takes a number of arguments:\n", - "\n", - "- `dataset` — the raw dataset that you want to provide as input to tests\n", - "- `input_id` - a unique identifier that allows tracking what inputs are used when running each individual test\n", - "- `target_column` — a required argument if tests require access to true values. This is the name of the target column in the dataset\n", - "- `class_labels` — an optional value to map predicted classes to class labels\n", - "\n", - "With all datasets ready, you can now initialize the raw, training and test datasets (`raw_df`, `train_df` and `test_df`) created earlier into their own dataset objects using [`vm.init_dataset()`](https://docs.validmind.ai/validmind/validmind.html#init_dataset):\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vm_raw_dataset = vm.init_dataset(\n", - " dataset=raw_df,\n", - " input_id=\"raw_dataset\",\n", - " target_column=customer_churn.target_column,\n", - " class_labels=customer_churn.class_labels,\n", - ")\n", - "\n", - "vm_train_ds = vm.init_dataset(\n", - " dataset=train_df,\n", - " input_id=\"train_dataset\",\n", - " target_column=customer_churn.target_column,\n", - ")\n", - "\n", - "vm_test_ds = vm.init_dataset(\n", - " dataset=test_df, input_id=\"test_dataset\", target_column=customer_churn.target_column\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "### Initialize a model object\n", - "\n", - "Additionally, you need to initialize a ValidMind model object (`vm_model`) that can be passed to other functions for analysis and tests on the data. You simply intialize this model object with [`vm.init_model()`](https://docs.validmind.ai/validmind/validmind.html#init_model):\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vm_model = vm.init_model(\n", - " model,\n", - " input_id=\"model\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "### Assign predictions to the datasets\n", - "\n", - "We can now use the assign_predictions() method from the Dataset object to link existing predictions to any model. If no prediction values are passed, the method will compute predictions automatically:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vm_train_ds.assign_predictions(\n", - " model=vm_model,\n", - ")\n", - "\n", - "vm_test_ds.assign_predictions(\n", - " model=vm_model,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "### Run the full suite of tests\n", - "\n", - "This is where it all comes together: you are now ready to run the documentation tests for the model as defined by the documentation template you looked at earlier.\n", - "\n", - "The [`vm.run_documentation_tests`](https://docs.validmind.ai/validmind/validmind.html#run_documentation_tests) function finds and runs every test specified in the template and then uploads all the documentation and test artifacts that get generated to the ValidMind Platform.\n", - "\n", - "The function requires information about the inputs to use on every test. These inputs can be passed as an `inputs` argument if we want to use the same inputs for all tests. It's also possible to pass a `config` argument that has information about the `params` and `inputs` that each test requires. The `config` parameter is a dictionary with the following structure:\n", - "\n", - "```python\n", - "config = {\n", - " \"\": {\n", - " \"params\": {\n", - " \"param1\": \"value1\",\n", - " \"param2\": \"value2\",\n", - " ...\n", - " },\n", - " \"inputs\": {\n", - " \"input1\": \"value1\",\n", - " \"input2\": \"value2\",\n", - " ...\n", - " }\n", - " },\n", - " ...\n", - "}\n", - "```\n", - "\n", - "Each `` above corresponds to the test driven block identifiers shown by `vm.preview_template()`. For this model, we will use the default parameters for all tests, but we'll need to specify the input configuration for each one. The method `get_demo_test_config()` below constructs the default input configuration for our demo.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from validmind.utils import preview_test_config\n", - "\n", - "test_config = customer_churn.get_demo_test_config()\n", - "preview_test_config(test_config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can pass the input configuration to `vm.run_documentation_tests()` and run the full suite of tests. The variable `full_suite` then holds the result of these tests.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "full_suite = vm.run_documentation_tests(config=test_config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "## Next steps\n", - "\n", - "You can look at the results of this test suite right in the notebook where you ran the code, as you would expect. But there is a better way — use the ValidMind Platform to work with your model documentation.\n", - "\n", - "\n", - "\n", - "### Work with your model documentation\n", - "\n", - "1. From the **Model Inventory** in the ValidMind Platform, go to the model you registered earlier. ([Need more help?](https://docs.validmind.ai/guide/model-inventory/working-with-model-inventory.html))\n", - "\n", - "2. Click and expand the **Model Development** section.\n", - "\n", - "What you see is the full draft of your model documentation in a more easily consumable version. From here, you can make qualitative edits to model documentation, view guidelines, collaborate with validators, and submit your model documentation for approval when it's ready. [Learn more ...](https://docs.validmind.ai/guide/model-documentation/working-with-model-documentation.html)\n", - "\n", - "\n", - "\n", - "### Discover more learning resources\n", - "\n", - "We offer many interactive notebooks to help you document models:\n", - "\n", - "- [Run tests & test suites](https://docs.validmind.ai/developer/model-testing/testing-overview.html)\n", - "- [Code samples](https://docs.validmind.ai/developer/samples-jupyter-notebooks.html)\n", - "\n", - "Or, visit our [documentation](https://docs.validmind.ai/) to learn more about ValidMind." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "## Upgrade ValidMind\n", - "\n", - "
After installing ValidMind, you’ll want to periodically make sure you are on the latest version to access any new features and other enhancements.
\n", - "\n", - "Retrieve the information for the currently installed version of ValidMind:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%pip show validmind" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If the version returned is lower than the version indicated in our [production open-source code](https://github.com/validmind/validmind-library/blob/prod/validmind/__version__.py), restart your notebook and run:\n", - "\n", - "```bash\n", - "%pip install --upgrade validmind\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You may need to restart your kernel after running the upgrade package for changes to be applied." - ] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "gpuClass": "standard", - "kernelspec": { - "display_name": "ValidMind Library", - "language": "python", - "name": "validmind" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/code_sharing/quickstart_model_documentation.ipynb b/notebooks/code_sharing/quickstart_model_documentation.ipynb index 5bca86bc7..09844928a 100644 --- a/notebooks/code_sharing/quickstart_model_documentation.ipynb +++ b/notebooks/code_sharing/quickstart_model_documentation.ipynb @@ -275,9 +275,24 @@ "id": "a2c5b527", "metadata": {}, "source": [ - "## Import sample dataset\n", + "## Documenting a model\n", "\n", - "Then, let's import the public [Bank Customer Churn Prediction](https://www.kaggle.com/datasets/shantanudhakadd/bank-customer-churn-prediction) dataset from Kaggle. \n", + "To document a model with the ValidMind Library, we'll:\n", + "\n", + "1. Import a sample dataset and preprocess it\n", + "2. Split the dataset into training and testing datasets and initialize them for use with ValidMind\n", + "3. Initialize a model object for use with testing\n", + "4. Run a full suite of tests as defined by our documentation template, which will send the results of those tests to the ValidMind Platform" + ] + }, + { + "cell_type": "markdown", + "id": "cfe30139", + "metadata": {}, + "source": [ + "### Import the sample dataset\n", + "\n", + "To start, let's import the public [Bank Customer Churn Prediction](https://www.kaggle.com/datasets/shantanudhakadd/bank-customer-churn-prediction) dataset from Kaggle. \n", "\n", "In our below example, note that: \n", "\n", @@ -306,7 +321,214 @@ "cell_type": "markdown", "id": "fdae3a7b", "metadata": {}, - "source": [] + "source": [ + "#### Preprocess the raw dataset\n", + "\n", + "Then, let's preprocess the imported dataset to get ready for the subsequent steps:\n", + "\n", + "- **Preprocess the data: using [`preprocess()`](https://docs.validmind.ai/validmind/validmind/datasets/classification/customer_churn.html#preprocess)** Splits the DataFrame (`df`) into multiple datasets (`train_df`, `validation_df`, and `test_df`) using `demo_dataset.preprocess` to simplify preprocessing.\n", + "- **Separate features and targets:** Drops the target column to create feature sets (`x_train`, `x_val`) and target sets (`y_train`, `y_val`).\n", + "- **Initialize XGBoost classifier:** Creates an `XGBClassifier` object with early stopping rounds set to 10.\n", + "- **Set evaluation metrics:** Specifies metrics for model evaluation as \"error,\" \"logloss,\" and \"auc.\"\n", + "- **Fit the model:** Trains the model on `x_train` and `y_train` using the validation set `(x_val, y_val)`. Verbose output is disabled.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2454559e", + "metadata": {}, + "outputs": [], + "source": [ + "train_df, validation_df, test_df = customer_churn.preprocess(raw_df)\n", + "\n", + "x_train = train_df.drop(customer_churn.target_column, axis=1)\n", + "y_train = train_df[customer_churn.target_column]\n", + "x_val = validation_df.drop(customer_churn.target_column, axis=1)\n", + "y_val = validation_df[customer_churn.target_column]\n", + "\n", + "model = xgb.XGBClassifier(early_stopping_rounds=10)\n", + "model.set_params(\n", + " eval_metric=[\"error\", \"logloss\", \"auc\"],\n", + ")\n", + "model.fit(\n", + " x_train,\n", + " y_train,\n", + " eval_set=[(x_val, y_val)],\n", + " verbose=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "5c9071d6", + "metadata": {}, + "source": [ + "### Initialize the ValidMind datasets\n", + "\n", + "Before you can run tests with your preprocessed datasets, you must first initialize a ValidMind `Dataset` object using the [`init_dataset`](https://docs.validmind.ai/validmind/validmind.html#init_dataset) function from the ValidMind (`vm`) module. **This step is always necessary every time you want to connect a dataset to documentation and produce test results through ValidMind,** but you only need to do it once per dataset.\n", + "\n", + "For this example, we'll pass in the following arguments:\n", + "\n", + "- **`dataset`** — The raw dataset that you want to provide as input to tests.\n", + "- **`input_id`** — A unique identifier that allows tracking what inputs are used when running each individual test.\n", + "- **`target_column`** — A required argument if tests require access to true values. This is the name of the target column in the dataset.\n", + "- **`class_labels`** — An optional value to map predicted classes to class labels." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "df034911", + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the raw dataset\n", + "vm_raw_dataset = vm.init_dataset(\n", + " dataset=raw_df,\n", + " input_id=\"raw_dataset\",\n", + " target_column=customer_churn.target_column,\n", + " class_labels=customer_churn.class_labels,\n", + ")\n", + "\n", + "# Initialize the training dataset\n", + "vm_train_ds = vm.init_dataset(\n", + " dataset=train_df,\n", + " input_id=\"train_dataset\",\n", + " target_column=customer_churn.target_column,\n", + ")\n", + "\n", + "# Initialize the testing dataset\n", + "vm_test_ds = vm.init_dataset(\n", + " dataset=test_df,\n", + " input_id=\"test_dataset\",\n", + " target_column=customer_churn.target_column\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "a14260db", + "metadata": {}, + "source": [ + "### Initialize a model object\n", + "\n", + "You'll also need to initialize a ValidMind model object (`vm_model`) that can be passed to other functions for analysis and tests on the data for our model.\n", + "\n", + "You simply initialize this model object with [`vm.init_model()`](https://docs.validmind.ai/validmind/validmind.html#init_model):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f6a7c68c", + "metadata": {}, + "outputs": [], + "source": [ + "vm_model = vm.init_model(\n", + " model,\n", + " input_id=\"model\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "42b74d2e", + "metadata": {}, + "source": [ + "#### Assign predictions\n", + "\n", + "Once the model has been registered you can assign model predictions to the training and testing datasets.\n", + "\n", + "- The [`assign_predictions()` method](https://docs.validmind.ai/validmind/validmind/vm_models.html#assign_predictions) from the `Dataset` object can link existing predictions to any number of models.\n", + "- This method links the model's class prediction values and probabilities to our `vm_train_ds` and `vm_test_ds` datasets.\n", + "\n", + "If no prediction values are passed, the method will compute predictions automatically:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "17c7411a", + "metadata": {}, + "outputs": [], + "source": [ + "vm_train_ds.assign_predictions(\n", + " model=vm_model,\n", + ")\n", + "\n", + "vm_test_ds.assign_predictions(\n", + " model=vm_model,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "443e914d", + "metadata": {}, + "source": [ + "### Run the full suite of tests\n", + "\n", + "This is where it all comes together — you are now ready to **run the documentation tests for the model as defined by the documentation template** you looked at earlier.\n", + "\n", + "The [`vm.run_documentation_tests`](https://docs.validmind.ai/validmind/validmind.html#run_documentation_tests) function finds and runs every test specified in the template and then uploads all the documentation and test artifacts that get generated to the ValidMind Platform:\n", + "\n", + "- The function requires information about the inputs to use on every test. These inputs can be passed as an `inputs` argument if we want to use the same inputs for all tests. \n", + "- It's also possible to pass a `config` argument that has information about the `params` and `inputs` that each test requires. The `config` parameter is a dictionary with the following structure:\n", + "\n", + " ```python\n", + " config = {\n", + " \"\": {\n", + " \"params\": {\n", + " \"param1\": \"value1\",\n", + " \"param2\": \"value2\",\n", + " ...\n", + " },\n", + " \"inputs\": {\n", + " \"input1\": \"value1\",\n", + " \"input2\": \"value2\",\n", + " ...\n", + " }\n", + " },\n", + " ...\n", + " }\n", + " ```\n", + "\n", + " Each `` above corresponds to the test driven block identifiers shown by `vm.preview_template()`. For this model, we will use the default parameters for all tests, but we'll need to specify the input configuration for each one. The method `get_demo_test_config()` below constructs the default input configuration for our demo." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "79e11135", + "metadata": {}, + "outputs": [], + "source": [ + "from validmind.utils import preview_test_config\n", + "\n", + "test_config = customer_churn.get_demo_test_config()\n", + "preview_test_config(test_config)" + ] + }, + { + "cell_type": "markdown", + "id": "70830a61", + "metadata": {}, + "source": [ + "Now we can pass the input configuration to `vm.run_documentation_tests()` and run the full suite of tests.\n", + "\n", + "The variable `full_suite` then holds the result of these tests:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8174830b", + "metadata": {}, + "outputs": [], + "source": [ + "full_suite = vm.run_documentation_tests(config=test_config)" + ] }, { "cell_type": "markdown", diff --git a/notebooks/tutorials/model_development/2-start_development_process.ipynb b/notebooks/tutorials/model_development/2-start_development_process.ipynb index 4711399be..6c7e55588 100644 --- a/notebooks/tutorials/model_development/2-start_development_process.ipynb +++ b/notebooks/tutorials/model_development/2-start_development_process.ipynb @@ -883,7 +883,7 @@ "\n", "### Assign predictions\n", "\n", - "Once the model has been registered you can assign model predictions to the training and test datasets.\n", + "Once the model has been registered you can assign model predictions to the training and testing datasets.\n", "\n", "- The [`assign_predictions()` method](https://docs.validmind.ai/validmind/validmind/vm_models.html#assign_predictions) from the `Dataset` object can link existing predictions to any number of models.\n", "- This method links the model's class prediction values and probabilities to our `vm_train_ds` and `vm_test_ds` datasets.\n", From e917246b6afb93f4d3ccfa957ed720fde103b417 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Tue, 13 May 2025 16:55:05 -0700 Subject: [PATCH 05/26] Edits --- .../quickstart_model_documentation.ipynb | 77 +++++++++++-------- notebooks/templates/next-steps.ipynb | 2 +- .../2-start_development_process.ipynb | 2 +- 3 files changed, 48 insertions(+), 33 deletions(-) diff --git a/notebooks/code_sharing/quickstart_model_documentation.ipynb b/notebooks/code_sharing/quickstart_model_documentation.ipynb index 09844928a..8880c2a62 100644 --- a/notebooks/code_sharing/quickstart_model_documentation.ipynb +++ b/notebooks/code_sharing/quickstart_model_documentation.ipynb @@ -7,7 +7,14 @@ "source": [ "# Quickstart for model documentation\n", "\n", - "Learn the basics of using ValidMind to document models as part of a model development workflow. Set up the ValidMind Library in your environment, and generate a draft of documentation using ValidMind tests for a binary classification model." + "Learn the basics of using ValidMind to document models as part of a model development workflow. Set up the ValidMind Library in your environment, and generate a draft of documentation using ValidMind tests for a binary classification model.\n", + "\n", + "To document a model with the ValidMind Library, we'll:\n", + "\n", + "1. Import a sample dataset and preprocess it\n", + "2. Split the dataset into training and testing datasets and initialize them for use with ValidMind\n", + "3. Initialize a model object for use with testing\n", + "4. Run a full suite of tests as defined by our documentation template, which will send the results of those tests to the ValidMind Platform" ] }, { @@ -272,27 +279,12 @@ }, { "cell_type": "markdown", - "id": "a2c5b527", - "metadata": {}, - "source": [ - "## Documenting a model\n", - "\n", - "To document a model with the ValidMind Library, we'll:\n", - "\n", - "1. Import a sample dataset and preprocess it\n", - "2. Split the dataset into training and testing datasets and initialize them for use with ValidMind\n", - "3. Initialize a model object for use with testing\n", - "4. Run a full suite of tests as defined by our documentation template, which will send the results of those tests to the ValidMind Platform" - ] - }, - { - "cell_type": "markdown", - "id": "cfe30139", + "id": "bd309b56", "metadata": {}, "source": [ - "### Import the sample dataset\n", + "## Import the sample dataset\n", "\n", - "To start, let's import the public [Bank Customer Churn Prediction](https://www.kaggle.com/datasets/shantanudhakadd/bank-customer-churn-prediction) dataset from Kaggle. \n", + "First, let's import the public [Bank Customer Churn Prediction](https://www.kaggle.com/datasets/shantanudhakadd/bank-customer-churn-prediction) dataset from Kaggle so that we have something to work with.\n", "\n", "In our below example, note that: \n", "\n", @@ -303,7 +295,7 @@ { "cell_type": "code", "execution_count": null, - "id": "5050b3a7", + "id": "c952eb18", "metadata": {}, "outputs": [], "source": [ @@ -319,24 +311,24 @@ }, { "cell_type": "markdown", - "id": "fdae3a7b", + "id": "7a87224a", "metadata": {}, "source": [ - "#### Preprocess the raw dataset\n", + "## Preprocess the raw dataset\n", "\n", - "Then, let's preprocess the imported dataset to get ready for the subsequent steps:\n", + "Next, let's preprocess the imported dataset to get ready for running tests with ValidMind:\n", "\n", "- **Preprocess the data: using [`preprocess()`](https://docs.validmind.ai/validmind/validmind/datasets/classification/customer_churn.html#preprocess)** Splits the DataFrame (`df`) into multiple datasets (`train_df`, `validation_df`, and `test_df`) using `demo_dataset.preprocess` to simplify preprocessing.\n", "- **Separate features and targets:** Drops the target column to create feature sets (`x_train`, `x_val`) and target sets (`y_train`, `y_val`).\n", "- **Initialize XGBoost classifier:** Creates an `XGBClassifier` object with early stopping rounds set to 10.\n", "- **Set evaluation metrics:** Specifies metrics for model evaluation as \"error,\" \"logloss,\" and \"auc.\"\n", - "- **Fit the model:** Trains the model on `x_train` and `y_train` using the validation set `(x_val, y_val)`. Verbose output is disabled.\n" + "- **Fit the model:** Trains the model on `x_train` and `y_train` using the validation set `(x_val, y_val)`. Verbose output is disabled." ] }, { "cell_type": "code", "execution_count": null, - "id": "2454559e", + "id": "c6027506", "metadata": {}, "outputs": [], "source": [ @@ -364,7 +356,7 @@ "id": "5c9071d6", "metadata": {}, "source": [ - "### Initialize the ValidMind datasets\n", + "## Initialize the ValidMind datasets\n", "\n", "Before you can run tests with your preprocessed datasets, you must first initialize a ValidMind `Dataset` object using the [`init_dataset`](https://docs.validmind.ai/validmind/validmind.html#init_dataset) function from the ValidMind (`vm`) module. **This step is always necessary every time you want to connect a dataset to documentation and produce test results through ValidMind,** but you only need to do it once per dataset.\n", "\n", @@ -411,7 +403,7 @@ "id": "a14260db", "metadata": {}, "source": [ - "### Initialize a model object\n", + "## Initialize a model object\n", "\n", "You'll also need to initialize a ValidMind model object (`vm_model`) that can be passed to other functions for analysis and tests on the data for our model.\n", "\n", @@ -436,7 +428,7 @@ "id": "42b74d2e", "metadata": {}, "source": [ - "#### Assign predictions\n", + "### Assign predictions\n", "\n", "Once the model has been registered you can assign model predictions to the training and testing datasets.\n", "\n", @@ -467,7 +459,7 @@ "id": "443e914d", "metadata": {}, "source": [ - "### Run the full suite of tests\n", + "## Run the full suite of tests\n", "\n", "This is where it all comes together — you are now ready to **run the documentation tests for the model as defined by the documentation template** you looked at earlier.\n", "\n", @@ -530,6 +522,24 @@ "full_suite = vm.run_documentation_tests(config=test_config)" ] }, + { + "cell_type": "markdown", + "id": "e5401c1f", + "metadata": {}, + "source": [ + "## In summary\n", + "\n", + "In this notebook, you learned how to:\n", + "\n", + "- [x] Register a model within the ValidMind Platform\n", + "- [x] Install and initialize the ValidMind Library\n", + "- [x] Preview the documentation template for your model\n", + "- [x] Import a sample dataset\n", + "- [x] Initialize ValidMind datasets and model objects\n", + "- [x] Assign model predictions to your ValidMind model objects\n", + "- [x] Run a full suite of documentation tests\n" + ] + }, { "cell_type": "markdown", "id": "next-steps-9782d94b-b8dd-4894-b7ca-c3d92ec548fd", @@ -537,7 +547,7 @@ "source": [ "## Next steps\n", "\n", - "You can look at the output produced by the ValidMind Library right in the notebook where you ran the code, as you would expect. But there is a better way: use the ValidMind Platform to work with your model documentation." + "You can look at the output produced by the ValidMind Library right in the notebook where you ran the code, as you would expect. But there is a better way — use the ValidMind Platform to work with your model documentation." ] }, { @@ -561,7 +571,12 @@ "source": [ "### Discover more learning resources\n", "\n", - "We offer many interactive notebooks to help you document models:\n", + "For a more in-depth introduction to using the ValidMind Library for development, check out our introductory development series and the accompanying interactive training:\n", + "\n", + "- **[ValidMind for model development](https://docs.validmind.ai/developer/validmind-library.html#for-model-development)**\n", + "- **[Developer Fundamentals](https://docs.validmind.ai/training/developer-fundamentals/developer-fundamentals-register.html)**\n", + "\n", + "We also offer many interactive notebooks to help you document models:\n", "\n", "- [Run tests & test suites](https://docs.validmind.ai/guide/testing-overview.html)\n", "- [Code samples](https://docs.validmind.ai/guide/samples-jupyter-notebooks.html)\n", diff --git a/notebooks/templates/next-steps.ipynb b/notebooks/templates/next-steps.ipynb index 8f0a42756..e87363fb6 100644 --- a/notebooks/templates/next-steps.ipynb +++ b/notebooks/templates/next-steps.ipynb @@ -7,7 +7,7 @@ "source": [ "## Next steps\n", "\n", - "You can look at the output produced by the ValidMind Library right in the notebook where you ran the code, as you would expect. But there is a better way: use the ValidMind Platform to work with your model documentation." + "You can look at the output produced by the ValidMind Library right in the notebook where you ran the code, as you would expect. But there is a better way — use the ValidMind Platform to work with your model documentation." ] }, { diff --git a/notebooks/tutorials/model_development/2-start_development_process.ipynb b/notebooks/tutorials/model_development/2-start_development_process.ipynb index 6c7e55588..5f6c39d65 100644 --- a/notebooks/tutorials/model_development/2-start_development_process.ipynb +++ b/notebooks/tutorials/model_development/2-start_development_process.ipynb @@ -955,7 +955,7 @@ "\n", "- [x] Import a sample dataset\n", "- [x] Identify which tests you might want to run with ValidMind\n", - "- [x] Initialize ValidMind datasets\n", + "- [x] Initialize ValidMind datasets and model objects\n", "- [x] Run individual tests\n", "- [x] Utilize the output from tests you've run\n", "- [x] Log test results from sets of or individual tests as evidence to the ValidMind Platform\n", From 98edc814a5ccf6e2c98cdcdbb949493ce55b71d9 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Tue, 13 May 2025 17:00:30 -0700 Subject: [PATCH 06/26] Moving notebook --- .../quickstart_model_documentation.ipynb | 12 ++++-------- 1 file changed, 4 insertions(+), 8 deletions(-) rename notebooks/{code_sharing => code_samples}/quickstart_model_documentation.ipynb (99%) diff --git a/notebooks/code_sharing/quickstart_model_documentation.ipynb b/notebooks/code_samples/quickstart_model_documentation.ipynb similarity index 99% rename from notebooks/code_sharing/quickstart_model_documentation.ipynb rename to notebooks/code_samples/quickstart_model_documentation.ipynb index 8880c2a62..cf3467117 100644 --- a/notebooks/code_sharing/quickstart_model_documentation.ipynb +++ b/notebooks/code_samples/quickstart_model_documentation.ipynb @@ -600,11 +600,7 @@ "cell_type": "code", "execution_count": null, "id": "upgrade-show-5f530fed-178d-4c73-adad-0bc191b0aee3", - "metadata": { - "vscode": { - "languageId": "plaintext" - } - }, + "metadata": {}, "outputs": [], "source": [ "%pip show validmind" @@ -633,13 +629,13 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "ValidMind Library", "language": "python", - "name": "python3" + "name": "validmind" }, "language_info": { "name": "python", - "version": "3.10" + "version": "3.10.13" } }, "nbformat": 4, From 252aa092f480d860d9e1aded505481ef3131b6d1 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Wed, 14 May 2025 08:18:08 -0700 Subject: [PATCH 07/26] Quickstart dir & link patch --- notebooks/README.md | 5 +- .../custom_tests/implement_custom_tests.ipynb | 2 +- ...quickstart_customer_churn_full_suite.ipynb | 533 +----------------- .../quickstart_model_documentation.ipynb | 178 ++++-- 4 files changed, 146 insertions(+), 572 deletions(-) rename notebooks/{code_samples => quickstart}/quickstart_model_documentation.ipynb (88%) diff --git a/notebooks/README.md b/notebooks/README.md index d4b8b9dbc..a641f3e27 100644 --- a/notebooks/README.md +++ b/notebooks/README.md @@ -4,9 +4,10 @@ Our [Jupyter Notebook](https://jupyter.org/) code samples are designed to showca Sample notebooks are organized into the following folders: -* `notebooks/code_samples` — Showcase end-to-end functionality for documenting models -* `notebooks/how_to` — Learn how to use specific ValidMind features, e.g. how to list all test suites +* `notebooks/quickstart` — Quick guides to get you started with ValidMind * `notebooks/tutorials` — Get step-by-step instructions and learn about ValidMind concepts in depth +* `notebooks/how_to` — Learn how to use specific ValidMind features, for example how to list all test suites +* `notebooks/code_samples` — Showcase end-to-end functionality for documenting or validating models * `notebooks/code_sharing` — Share your own notebooks or document code internally ## Getting started diff --git a/notebooks/code_samples/custom_tests/implement_custom_tests.ipynb b/notebooks/code_samples/custom_tests/implement_custom_tests.ipynb index 963179129..fdeb2d566 100644 --- a/notebooks/code_samples/custom_tests/implement_custom_tests.ipynb +++ b/notebooks/code_samples/custom_tests/implement_custom_tests.ipynb @@ -426,7 +426,7 @@ "source": [ "Just so we can run all of the tests in the template, let's initialize the train and raw dataset.\n", "\n", - "(see the `quickstart_customer_churn_full_suite.ipynb` notebook and the ValidMind docs for more information on what we are doing here)" + "(Refer to [**Quickstart for model documentation**](../../quickstart/quickstart_model_documentation.ipynb) and the ValidMind docs for more information on what we are doing here)" ] }, { diff --git a/notebooks/code_samples/quickstart_customer_churn_full_suite.ipynb b/notebooks/code_samples/quickstart_customer_churn_full_suite.ipynb index d7a9c6b87..a420570a3 100644 --- a/notebooks/code_samples/quickstart_customer_churn_full_suite.ipynb +++ b/notebooks/code_samples/quickstart_customer_churn_full_suite.ipynb @@ -4,538 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Quickstart for model documentation\n", + "# Quickstart for model documentation — DEPRECATED\n", "\n", - "Welcome! Let's get you started with the basic process of documenting models with ValidMind.\n", - "\n", - "You will learn how to initialize the ValidMind Library, load a sample dataset to train a simple classification model, and then run a ValidMind test suite to quickly generate documentation about the data and model.\n", - "\n", - "This notebook uses the [Bank Customer Churn Prediction](https://www.kaggle.com/code/kmalit/bank-customer-churn-prediction/data) sample dataset from Kaggle to train the classification model." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "## Contents \n", - "- [About ValidMind](#toc1_) \n", - " - [Before you begin](#toc1_1_) \n", - " - [New to ValidMind?](#toc1_2_) \n", - " - [Key concepts](#toc1_3_) \n", - "- [Install the ValidMind Library](#toc2_)\n", - "- [Initialize the client library](#toc3_) \n", - " - [Get your code snippet](#toc3_1_)\n", - "- [Initialize the Python environment](#toc4_) \n", - " - [Preview the documentation template](#toc4_1_) \n", - "- [Load the sample dataset](#toc5_) \n", - "- [Document the model](#toc6_) \n", - " - [Prepocess the raw dataset](#toc6_1_) \n", - " - [Initialize the ValidMind datasets](#toc6_2_) \n", - " - [Initialize a model object](#toc6_3_) \n", - " - [Assign predictions to the datasets](#toc6_4_) \n", - " - [Run the full suite of tests](#toc6_5_) \n", - "- [Next steps](#toc7_) \n", - " - [Work with your model documentation](#toc7_1_) \n", - " - [Discover more learning resources](#toc7_2_) \n", - "- [Upgrade ValidMind](#toc8_) \n", - "\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "## About ValidMind\n", - "\n", - "ValidMind is a suite of tools for managing model risk, including risk associated with AI and statistical models.\n", - "\n", - "You use the ValidMind Library to automate documentation and validation tests, and then use the ValidMind Platform to collaborate on model documentation. Together, these products simplify model risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and model validators.\n", - "\n", - "\n", - "\n", - "### Before you begin\n", - "\n", - "This notebook assumes you have basic familiarity with Python, including an understanding of how functions work. If you are new to Python, you can still run the notebook but we recommend further familiarizing yourself with the language. \n", - "\n", - "If you encounter errors due to missing modules in your Python environment, install the modules with `pip install`, and then re-run the notebook. For more help, refer to [Installing Python Modules](https://docs.python.org/3/installing/index.html).\n", - "\n", - "\n", - "\n", - "### New to ValidMind?\n", - "\n", - "If you haven't already seen our documentation on the [ValidMind Library](https://docs.validmind.ai/developer/validmind-library.html), we recommend you begin by exploring the available resources in this section. There, you can learn more about documenting models and running tests, as well as find code samples and our Python Library API reference.\n", - "\n", - "
For access to all features available in this notebook, create a free ValidMind account.\n", - "

\n", - "Signing up is FREE — Register with ValidMind
\n", - "\n", - "\n", - "\n", - "### Key concepts\n", - "\n", - "**Model documentation**: A structured and detailed record pertaining to a model, encompassing key components such as its underlying assumptions, methodologies, data sources, inputs, performance metrics, evaluations, limitations, and intended uses. It serves to ensure transparency, adherence to regulatory requirements, and a clear understanding of potential risks associated with the model’s application.\n", - "\n", - "**Documentation template**: Functions as a test suite and lays out the structure of model documentation, segmented into various sections and sub-sections. Documentation templates define the structure of your model documentation, specifying the tests that should be run, and how the results should be displayed.\n", - "\n", - "**Tests**: A function contained in the ValidMind Library, designed to run a specific quantitative test on the dataset or model. Tests are the building blocks of ValidMind, used to evaluate and document models and datasets, and can be run individually or as part of a suite defined by your model documentation template.\n", - "\n", - "**Custom tests**: Custom tests are functions that you define to evaluate your model or dataset. These functions can be registered via the ValidMind Library to be used with the ValidMind Platform.\n", - "\n", - "**Inputs**: Objects to be evaluated and documented in the ValidMind Library. They can be any of the following:\n", - "\n", - " - **model**: A single model that has been initialized in ValidMind with [`vm.init_model()`](https://docs.validmind.ai/validmind/validmind.html#init_model).\n", - " - **dataset**: Single dataset that has been initialized in ValidMind with [`vm.init_dataset()`](https://docs.validmind.ai/validmind/validmind.html#init_dataset).\n", - " - **models**: A list of ValidMind models - usually this is used when you want to compare multiple models in your custom test.\n", - " - **datasets**: A list of ValidMind datasets - usually this is used when you want to compare multiple datasets in your custom test. See this [example](https://docs.validmind.ai/notebooks/how_to/run_tests_that_require_multiple_datasets.html) for more information.\n", - "\n", - "**Parameters**: Additional arguments that can be passed when running a ValidMind test, used to pass additional information to a test, customize its behavior, or provide additional context.\n", - "\n", - "**Outputs**: Custom tests can return elements like tables or plots. Tables may be a list of dictionaries (each representing a row) or a pandas DataFrame. Plots may be matplotlib or plotly figures.\n", - "\n", - "**Test suites**: Collections of tests designed to run together to automate and generate model documentation end-to-end for specific use-cases.\n", - "\n", - "Example: the [`classifier_full_suite`](https://docs.validmind.ai/validmind/validmind/test_suites/classifier.html#ClassifierFullSuite) test suite runs tests from the [`tabular_dataset`](https://docs.validmind.ai/validmind/validmind/test_suites/tabular_datasets.html) and [`classifier`](https://docs.validmind.ai/validmind/validmind/test_suites/classifier.html) test suites to fully document the data and model sections for binary classification model use-cases.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "## Install the ValidMind Library\n", - "\n", - "To install the library:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%pip install -q validmind" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "## Initialize the ValidMind Library\n", - "\n", - "ValidMind generates a unique _code snippet_ for each registered model to connect with your developer environment. You initialize the ValidMind Library with this code snippet, which ensures that your documentation and tests are uploaded to the correct model when you run the notebook.\n", - "\n", - "\n", - "\n", - "### Get your code snippet\n", - "\n", - "1. In a browser, [log in to ValidMind](https://docs.validmind.ai/guide/configuration/log-in-to-validmind.html).\n", - "\n", - "2. In the left sidebar, navigate to **Model Inventory** and click **+ Register Model**.\n", - "\n", - "3. Enter the model details and click **Continue**. ([Need more help?](https://docs.validmind.ai/guide/model-inventory/register-models-in-inventory.html))\n", - "\n", - " For example, to register a model for use with this notebook, select:\n", - "\n", - " - Documentation template: `Binary classification`\n", - " - Use case: `Marketing/Sales - Attrition/Churn Management`\n", - "\n", - " You can fill in other options according to your preference.\n", - "\n", - "4. Go to **Getting Started** and click **Copy snippet to clipboard**.\n", - "\n", - "Next, [load your model identifier credentials from an `.env` file](https://docs.validmind.ai/developer/model-documentation/store-credentials-in-env-file.html) or replace the placeholder with your own code snippet:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Load your model identifier credentials from an `.env` file\n", - "\n", - "%load_ext dotenv\n", - "%dotenv .env\n", - "\n", - "# Or replace with your code snippet\n", - "\n", - "import validmind as vm\n", - "\n", - "vm.init(\n", - " # api_host=\"...\",\n", - " # api_key=\"...\",\n", - " # api_secret=\"...\",\n", - " # model=\"...\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "## Initialize the Python environment\n", - "\n", - "Next, let's import the necessary libraries and set up your Python environment for data analysis:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import xgboost as xgb\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "### Preview the documentation template\n", - "\n", - "A template predefines sections for your model documentation and provides a general outline to follow, making the documentation process much easier.\n", - "\n", - "You will upload documentation and test results into this template later on. For now, take a look at the structure that the template provides with the `vm.preview_template()` function from the ValidMind library and note the empty sections:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vm.preview_template()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "## Load the sample dataset\n", - "\n", - "The sample dataset used here is provided by the ValidMind library. To be able to use it, you need to import the dataset and load it into a pandas [DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html), a two-dimensional tabular data structure that makes use of rows and columns:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import the sample dataset from the library\n", - "\n", - "from validmind.datasets.classification import customer_churn\n", - "\n", - "print(\n", - " f\"Loaded demo dataset with: \\n\\n\\t• Target column: '{customer_churn.target_column}' \\n\\t• Class labels: {customer_churn.class_labels}\"\n", - ")\n", - "\n", - "raw_df = customer_churn.load_data()\n", - "raw_df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "## Document the model\n", - "\n", - "As part of documenting the model with the ValidMind Library, you need to preprocess the raw dataset, initialize some training and test datasets, initialize a model object you can use for testing, and then run the full suite of tests.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "### Prepocess the raw dataset\n", - "\n", - "Preprocessing performs a number of operations to get ready for the subsequent steps:\n", - "\n", - "- Preprocess the data: Splits the DataFrame (`df`) into multiple datasets (`train_df`, `validation_df`, and `test_df`) using `demo_dataset.preprocess` to simplify preprocessing.\n", - "- Separate features and targets: Drops the target column to create feature sets (`x_train`, `x_val`) and target sets (`y_train`, `y_val`).\n", - "- Initialize XGBoost classifier: Creates an `XGBClassifier` object with early stopping rounds set to 10.\n", - "- Set evaluation metrics: Specifies metrics for model evaluation as \"error,\" \"logloss,\" and \"auc.\"\n", - "- Fit the model: Trains the model on `x_train` and `y_train` using the validation set `(x_val, y_val)`. Verbose output is disabled.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "train_df, validation_df, test_df = customer_churn.preprocess(raw_df)\n", - "\n", - "x_train = train_df.drop(customer_churn.target_column, axis=1)\n", - "y_train = train_df[customer_churn.target_column]\n", - "x_val = validation_df.drop(customer_churn.target_column, axis=1)\n", - "y_val = validation_df[customer_churn.target_column]\n", - "\n", - "model = xgb.XGBClassifier(early_stopping_rounds=10)\n", - "model.set_params(\n", - " eval_metric=[\"error\", \"logloss\", \"auc\"],\n", - ")\n", - "model.fit(\n", - " x_train,\n", - " y_train,\n", - " eval_set=[(x_val, y_val)],\n", - " verbose=False,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "### Initialize the ValidMind datasets\n", - "\n", - "Before you can run tests, you must first initialize a ValidMind dataset object using the [`init_dataset`](https://docs.validmind.ai/validmind/validmind.html#init_dataset) function from the ValidMind (`vm`) module.\n", - "\n", - "This function takes a number of arguments:\n", - "\n", - "- `dataset` — the raw dataset that you want to provide as input to tests\n", - "- `input_id` - a unique identifier that allows tracking what inputs are used when running each individual test\n", - "- `target_column` — a required argument if tests require access to true values. This is the name of the target column in the dataset\n", - "- `class_labels` — an optional value to map predicted classes to class labels\n", - "\n", - "With all datasets ready, you can now initialize the raw, training and test datasets (`raw_df`, `train_df` and `test_df`) created earlier into their own dataset objects using [`vm.init_dataset()`](https://docs.validmind.ai/validmind/validmind.html#init_dataset):\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vm_raw_dataset = vm.init_dataset(\n", - " dataset=raw_df,\n", - " input_id=\"raw_dataset\",\n", - " target_column=customer_churn.target_column,\n", - " class_labels=customer_churn.class_labels,\n", - ")\n", - "\n", - "vm_train_ds = vm.init_dataset(\n", - " dataset=train_df,\n", - " input_id=\"train_dataset\",\n", - " target_column=customer_churn.target_column,\n", - ")\n", - "\n", - "vm_test_ds = vm.init_dataset(\n", - " dataset=test_df, input_id=\"test_dataset\", target_column=customer_churn.target_column\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "### Initialize a model object\n", - "\n", - "Additionally, you need to initialize a ValidMind model object (`vm_model`) that can be passed to other functions for analysis and tests on the data. You simply intialize this model object with [`vm.init_model()`](https://docs.validmind.ai/validmind/validmind.html#init_model):\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vm_model = vm.init_model(\n", - " model,\n", - " input_id=\"model\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "### Assign predictions to the datasets\n", - "\n", - "We can now use the assign_predictions() method from the Dataset object to link existing predictions to any model. If no prediction values are passed, the method will compute predictions automatically:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vm_train_ds.assign_predictions(\n", - " model=vm_model,\n", - ")\n", - "\n", - "vm_test_ds.assign_predictions(\n", - " model=vm_model,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "### Run the full suite of tests\n", - "\n", - "This is where it all comes together: you are now ready to run the documentation tests for the model as defined by the documentation template you looked at earlier.\n", - "\n", - "The [`vm.run_documentation_tests`](https://docs.validmind.ai/validmind/validmind.html#run_documentation_tests) function finds and runs every test specified in the template and then uploads all the documentation and test artifacts that get generated to the ValidMind Platform.\n", - "\n", - "The function requires information about the inputs to use on every test. These inputs can be passed as an `inputs` argument if we want to use the same inputs for all tests. It's also possible to pass a `config` argument that has information about the `params` and `inputs` that each test requires. The `config` parameter is a dictionary with the following structure:\n", - "\n", - "```python\n", - "config = {\n", - " \"\": {\n", - " \"params\": {\n", - " \"param1\": \"value1\",\n", - " \"param2\": \"value2\",\n", - " ...\n", - " },\n", - " \"inputs\": {\n", - " \"input1\": \"value1\",\n", - " \"input2\": \"value2\",\n", - " ...\n", - " }\n", - " },\n", - " ...\n", - "}\n", - "```\n", - "\n", - "Each `` above corresponds to the test driven block identifiers shown by `vm.preview_template()`. For this model, we will use the default parameters for all tests, but we'll need to specify the input configuration for each one. The method `get_demo_test_config()` below constructs the default input configuration for our demo.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from validmind.utils import preview_test_config\n", - "\n", - "test_config = customer_churn.get_demo_test_config()\n", - "preview_test_config(test_config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can pass the input configuration to `vm.run_documentation_tests()` and run the full suite of tests. The variable `full_suite` then holds the result of these tests.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "full_suite = vm.run_documentation_tests(config=test_config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "## Next steps\n", - "\n", - "You can look at the results of this test suite right in the notebook where you ran the code, as you would expect. But there is a better way — use the ValidMind Platform to work with your model documentation.\n", - "\n", - "\n", - "\n", - "### Work with your model documentation\n", - "\n", - "1. From the **Model Inventory** in the ValidMind Platform, go to the model you registered earlier. ([Need more help?](https://docs.validmind.ai/guide/model-inventory/working-with-model-inventory.html))\n", - "\n", - "2. Click and expand the **Model Development** section.\n", - "\n", - "What you see is the full draft of your model documentation in a more easily consumable version. From here, you can make qualitative edits to model documentation, view guidelines, collaborate with validators, and submit your model documentation for approval when it's ready. [Learn more ...](https://docs.validmind.ai/guide/model-documentation/working-with-model-documentation.html)\n", - "\n", - "\n", - "\n", - "### Discover more learning resources\n", - "\n", - "We offer many interactive notebooks to help you document models:\n", - "\n", - "- [Run tests & test suites](https://docs.validmind.ai/developer/model-testing/testing-overview.html)\n", - "- [Code samples](https://docs.validmind.ai/developer/samples-jupyter-notebooks.html)\n", - "\n", - "Or, visit our [documentation](https://docs.validmind.ai/) to learn more about ValidMind." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "## Upgrade ValidMind\n", - "\n", - "
After installing ValidMind, you’ll want to periodically make sure you are on the latest version to access any new features and other enhancements.
\n", - "\n", - "Retrieve the information for the currently installed version of ValidMind:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%pip show validmind" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If the version returned is lower than the version indicated in our [production open-source code](https://github.com/validmind/validmind-library/blob/prod/validmind/__version__.py), restart your notebook and run:\n", - "\n", - "```bash\n", - "%pip install --upgrade validmind\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You may need to restart your kernel after running the upgrade package for changes to be applied." + "This notebook is outdated — please refer to the new [**Quickstart for model documentation**](../quickstart/quickstart_model_documentation.ipynb) instead." ] } ], diff --git a/notebooks/code_samples/quickstart_model_documentation.ipynb b/notebooks/quickstart/quickstart_model_documentation.ipynb similarity index 88% rename from notebooks/code_samples/quickstart_model_documentation.ipynb rename to notebooks/quickstart/quickstart_model_documentation.ipynb index cf3467117..d261fa185 100644 --- a/notebooks/code_samples/quickstart_model_documentation.ipynb +++ b/notebooks/quickstart/quickstart_model_documentation.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "21a28d0f", + "id": "930f895c", "metadata": {}, "source": [ "# Quickstart for model documentation\n", @@ -19,9 +19,54 @@ }, { "cell_type": "markdown", - "id": "5928bca4", + "id": "f52d9a79", + "metadata": {}, + "source": [ + "::: {.content-hidden when-format=\"html\"}\n", + "## Contents \n", + "- [Introduction](#toc1_) \n", + "- [About ValidMind](#toc2_) \n", + " - [Before you begin](#toc2_1_) \n", + " - [New to ValidMind?](#toc2_2_) \n", + " - [Key concepts](#toc2_3_) \n", + "- [Setting up](#toc3_) \n", + " - [Install the ValidMind Library](#toc3_1_) \n", + " - [Initialize the ValidMind Library](#toc3_2_) \n", + " - [Get your code snippet](#toc3_2_1_) \n", + " - [Initialize the Python environment](#toc3_3_) \n", + "- [Getting to know ValidMind](#toc4_) \n", + " - [Preview the documentation template](#toc4_1_) \n", + " - [View model documentation in the ValidMind Platform](#toc4_2_) \n", + "- [Import the sample dataset](#toc5_) \n", + "- [Preprocess the raw dataset](#toc6_) \n", + "- [Initialize the ValidMind datasets](#toc7_) \n", + "- [Initialize a model object](#toc8_) \n", + " - [Assign predictions](#toc8_1_) \n", + "- [Run the full suite of tests](#toc9_) \n", + "- [In summary](#toc10_) \n", + "- [Next steps](#toc11_) \n", + " - [Work with your model documentation](#toc11_1_) \n", + " - [Discover more learning resources](#toc11_2_) \n", + "- [Upgrade ValidMind](#toc12_) \n", + "\n", + ":::\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "dc60685b", "metadata": {}, "source": [ + "\n", + "\n", "## Introduction\n", "\n", "Model development aims to produce a fit-for-purpose *champion model* by conducting thorough testing and analysis, supporting the capabilities of the model with evidence in the form of documentation and test results. Model documentation should be clear and comprehensive, ideally following a structure or template covering all aspects of compliance with model risk regulation.\n", @@ -34,9 +79,11 @@ }, { "cell_type": "markdown", - "id": "about-intro-00609311-15b1-4baa-a507-b0a9945928eb", + "id": "11bbf160", "metadata": {}, "source": [ + "\n", + "\n", "## About ValidMind\n", "\n", "ValidMind is a suite of tools for managing model risk, including risk associated with AI and statistical models. \n", @@ -46,9 +93,11 @@ }, { "cell_type": "markdown", - "id": "about-begin-08a29872-53e7-414f-ba12-f46adf588e7a", + "id": "589563b0", "metadata": {}, "source": [ + "\n", + "\n", "### Before you begin\n", "\n", "This notebook assumes you have basic familiarity with Python, including an understanding of how functions work. If you are new to Python, you can still run the notebook but we recommend further familiarizing yourself with the language. \n", @@ -58,9 +107,11 @@ }, { "cell_type": "markdown", - "id": "about-signup-a52b1b8f-723c-40f6-9e43-cee7bfda8ba6", + "id": "5610d41a", "metadata": {}, "source": [ + "\n", + "\n", "### New to ValidMind?\n", "\n", "If you haven't already seen our documentation on the [ValidMind Library](https://docs.validmind.ai/developer/validmind-library.html), we recommend you begin by exploring the available resources in this section. There, you can learn more about documenting models and running tests, as well as find code samples and our Python Library API reference.\n", @@ -72,9 +123,11 @@ }, { "cell_type": "markdown", - "id": "about-concepts-ab2cff73-e26c-44d6-82ee-cd32519cfe8b", + "id": "31611cc7", "metadata": {}, "source": [ + "\n", + "\n", "### Key concepts\n", "\n", "**Model documentation**: A structured and detailed record pertaining to a model, encompassing key components such as its underlying assumptions, methodologies, data sources, inputs, performance metrics, evaluations, limitations, and intended uses. It serves to ensure transparency, adherence to regulatory requirements, and a clear understanding of potential risks associated with the model’s application.\n", @@ -105,17 +158,20 @@ }, { "cell_type": "markdown", - "id": "63023166", + "id": "d636c0f8", "metadata": {}, "source": [ + "\n", + "\n", "## Setting up" ] }, { "cell_type": "markdown", - "id": "install-library-bc77eb94-9f35-4fb0-8a7c-3cae29acd64a", "metadata": {}, "source": [ + "\n", + "\n", "### Install the ValidMind Library\n", "\n", "
Recommended Python versions\n", @@ -128,7 +184,7 @@ { "cell_type": "code", "execution_count": null, - "id": "install-python-ebd70fee-b6be-462e-a43a-fbe14f18fb62", + "id": "137a184c", "metadata": {}, "outputs": [], "source": [ @@ -137,9 +193,11 @@ }, { "cell_type": "markdown", - "id": "install-initialize-3db79826-578b-48d3-8cca-3f3e69bd0ada", + "id": "fcba7f6a", "metadata": {}, "source": [ + "\n", + "\n", "### Initialize the ValidMind Library\n", "\n", "ValidMind generates a unique _code snippet_ for each registered model to connect with your developer environment. You initialize the ValidMind Library with this code snippet, which ensures that your documentation and tests are uploaded to the correct model when you run the notebook." @@ -147,9 +205,10 @@ }, { "cell_type": "markdown", - "id": "install-snippet-6eafc0c3-27cc-4e1e-8179-7dff1686d8d4", "metadata": {}, "source": [ + "\n", + "\n", "#### Get your code snippet\n", "\n", "1. In a browser, [log in to ValidMind](https://docs.validmind.ai/guide/configuration/log-in-to-validmind.html).\n", @@ -185,7 +244,7 @@ { "cell_type": "code", "execution_count": null, - "id": "install-init-a90debbe-251c-46cc-b9ea-5c8588467df6", + "id": "676525d3", "metadata": {}, "outputs": [], "source": [ @@ -208,9 +267,10 @@ }, { "cell_type": "markdown", - "id": "aa61686d", "metadata": {}, "source": [ + "\n", + "\n", "### Initialize the Python environment\n", "\n", "Next, let's import the necessary libraries and set up your Python environment for data analysis:\n", @@ -222,7 +282,7 @@ { "cell_type": "code", "execution_count": null, - "id": "881f26af", + "id": "16d6590c", "metadata": {}, "outputs": [], "source": [ @@ -233,17 +293,20 @@ }, { "cell_type": "markdown", - "id": "7fc8ea4c", + "id": "3b712d70", "metadata": {}, "source": [ + "\n", + "\n", "## Getting to know ValidMind" ] }, { "cell_type": "markdown", - "id": "af99b2cb", "metadata": {}, "source": [ + "\n", + "\n", "### Preview the documentation template\n", "\n", "Let's verify that you have connected the ValidMind Library to the ValidMind Platform and that the appropriate *template* is selected for your model. A template predefines sections for your model documentation and provides a general outline to follow, making the documentation process much easier.\n", @@ -254,7 +317,7 @@ { "cell_type": "code", "execution_count": null, - "id": "a14b9091", + "id": "ebe60f73", "metadata": {}, "outputs": [], "source": [ @@ -263,9 +326,11 @@ }, { "cell_type": "markdown", - "id": "d155b6a4", + "id": "19b4bc15", "metadata": {}, "source": [ + "\n", + "\n", "### View model documentation in the ValidMind Platform\n", "\n", "Next, let's head to the ValidMind Platform to see the template in action:\n", @@ -279,9 +344,10 @@ }, { "cell_type": "markdown", - "id": "bd309b56", "metadata": {}, "source": [ + "\n", + "\n", "## Import the sample dataset\n", "\n", "First, let's import the public [Bank Customer Churn Prediction](https://www.kaggle.com/datasets/shantanudhakadd/bank-customer-churn-prediction) dataset from Kaggle so that we have something to work with.\n", @@ -295,7 +361,7 @@ { "cell_type": "code", "execution_count": null, - "id": "c952eb18", + "id": "fe4b41d1", "metadata": {}, "outputs": [], "source": [ @@ -311,9 +377,10 @@ }, { "cell_type": "markdown", - "id": "7a87224a", "metadata": {}, "source": [ + "\n", + "\n", "## Preprocess the raw dataset\n", "\n", "Next, let's preprocess the imported dataset to get ready for running tests with ValidMind:\n", @@ -332,17 +399,39 @@ "metadata": {}, "outputs": [], "source": [ - "train_df, validation_df, test_df = customer_churn.preprocess(raw_df)\n", - "\n", + "# Split the datasets\n", + "train_df, validation_df, test_df = customer_churn.preprocess(raw_df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "14a41fec", + "metadata": {}, + "outputs": [], + "source": [ + "# Separate features and targets\n", "x_train = train_df.drop(customer_churn.target_column, axis=1)\n", "y_train = train_df[customer_churn.target_column]\n", "x_val = validation_df.drop(customer_churn.target_column, axis=1)\n", - "y_val = validation_df[customer_churn.target_column]\n", - "\n", + "y_val = validation_df[customer_churn.target_column]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d2d98065", + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize XGBoost classifier\n", "model = xgb.XGBClassifier(early_stopping_rounds=10)\n", + "\n", + "# Set evaluation metrics\n", "model.set_params(\n", " eval_metric=[\"error\", \"logloss\", \"auc\"],\n", ")\n", + "# Fit the model\n", "model.fit(\n", " x_train,\n", " y_train,\n", @@ -353,9 +442,10 @@ }, { "cell_type": "markdown", - "id": "5c9071d6", "metadata": {}, "source": [ + "\n", + "\n", "## Initialize the ValidMind datasets\n", "\n", "Before you can run tests with your preprocessed datasets, you must first initialize a ValidMind `Dataset` object using the [`init_dataset`](https://docs.validmind.ai/validmind/validmind.html#init_dataset) function from the ValidMind (`vm`) module. **This step is always necessary every time you want to connect a dataset to documentation and produce test results through ValidMind,** but you only need to do it once per dataset.\n", @@ -371,7 +461,7 @@ { "cell_type": "code", "execution_count": null, - "id": "df034911", + "id": "6044e6fd", "metadata": {}, "outputs": [], "source": [ @@ -400,9 +490,10 @@ }, { "cell_type": "markdown", - "id": "a14260db", "metadata": {}, "source": [ + "\n", + "\n", "## Initialize a model object\n", "\n", "You'll also need to initialize a ValidMind model object (`vm_model`) that can be passed to other functions for analysis and tests on the data for our model.\n", @@ -413,7 +504,7 @@ { "cell_type": "code", "execution_count": null, - "id": "f6a7c68c", + "id": "65cac73f", "metadata": {}, "outputs": [], "source": [ @@ -425,9 +516,10 @@ }, { "cell_type": "markdown", - "id": "42b74d2e", "metadata": {}, "source": [ + "\n", + "\n", "### Assign predictions\n", "\n", "Once the model has been registered you can assign model predictions to the training and testing datasets.\n", @@ -441,7 +533,7 @@ { "cell_type": "code", "execution_count": null, - "id": "17c7411a", + "id": "efccd449", "metadata": {}, "outputs": [], "source": [ @@ -456,9 +548,10 @@ }, { "cell_type": "markdown", - "id": "443e914d", "metadata": {}, "source": [ + "\n", + "\n", "## Run the full suite of tests\n", "\n", "This is where it all comes together — you are now ready to **run the documentation tests for the model as defined by the documentation template** you looked at earlier.\n", @@ -515,7 +608,7 @@ { "cell_type": "code", "execution_count": null, - "id": "8174830b", + "id": "4fa0d340", "metadata": {}, "outputs": [], "source": [ @@ -524,9 +617,11 @@ }, { "cell_type": "markdown", - "id": "e5401c1f", + "id": "52170211", "metadata": {}, "source": [ + "\n", + "\n", "## In summary\n", "\n", "In this notebook, you learned how to:\n", @@ -542,9 +637,11 @@ }, { "cell_type": "markdown", - "id": "next-steps-9782d94b-b8dd-4894-b7ca-c3d92ec548fd", + "id": "81c2798e", "metadata": {}, "source": [ + "\n", + "\n", "## Next steps\n", "\n", "You can look at the output produced by the ValidMind Library right in the notebook where you ran the code, as you would expect. But there is a better way — use the ValidMind Platform to work with your model documentation." @@ -552,9 +649,11 @@ }, { "cell_type": "markdown", - "id": "next-docs-bdd1e7e2-32f7-4a95-b39f-e8b4aa2e1214", + "id": "4336ff4b", "metadata": {}, "source": [ + "\n", + "\n", "### Work with your model documentation\n", "\n", "1. From the **Inventory** in the ValidMind Platform, go to the model you registered earlier. ([Need more help?](https://docs.validmind.ai/guide/model-inventory/working-with-model-inventory.html))\n", @@ -566,9 +665,11 @@ }, { "cell_type": "markdown", - "id": "next-resources-9214c11f-23b8-48d3-8c56-e96c675dea8c", + "id": "b5a53889", "metadata": {}, "source": [ + "\n", + "\n", "### Discover more learning resources\n", "\n", "For a more in-depth introduction to using the ValidMind Library for development, check out our introductory development series and the accompanying interactive training:\n", @@ -586,9 +687,10 @@ }, { "cell_type": "markdown", - "id": "upgrade-vm-5193df48-8e35-43df-9f30-76c1156707a2", "metadata": {}, "source": [ + "\n", + "\n", "## Upgrade ValidMind\n", "\n", "
After installing ValidMind, you’ll want to periodically make sure you are on the latest version to access any new features and other enhancements.
\n", From a9ae24c063b6f8dd6f0a0729e67aae37e1ffad92 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Wed, 14 May 2025 08:47:37 -0700 Subject: [PATCH 08/26] Editing --- .../quickstart_model_documentation.ipynb | 217 ++++++++++++------ 1 file changed, 153 insertions(+), 64 deletions(-) diff --git a/notebooks/quickstart/quickstart_model_documentation.ipynb b/notebooks/quickstart/quickstart_model_documentation.ipynb index d261fa185..d0acdd4ca 100644 --- a/notebooks/quickstart/quickstart_model_documentation.ipynb +++ b/notebooks/quickstart/quickstart_model_documentation.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "930f895c", + "id": "f2c17b2d", "metadata": {}, "source": [ "# Quickstart for model documentation\n", @@ -12,14 +12,14 @@ "To document a model with the ValidMind Library, we'll:\n", "\n", "1. Import a sample dataset and preprocess it\n", - "2. Split the dataset into training and testing datasets and initialize them for use with ValidMind\n", + "2. Split the datasets and initialize them for use with ValidMind\n", "3. Initialize a model object for use with testing\n", "4. Run a full suite of tests as defined by our documentation template, which will send the results of those tests to the ValidMind Platform" ] }, { "cell_type": "markdown", - "id": "f52d9a79", + "id": "05dce32a", "metadata": {}, "source": [ "::: {.content-hidden when-format=\"html\"}\n", @@ -38,16 +38,21 @@ " - [Preview the documentation template](#toc4_1_) \n", " - [View model documentation in the ValidMind Platform](#toc4_2_) \n", "- [Import the sample dataset](#toc5_) \n", - "- [Preprocess the raw dataset](#toc6_) \n", - "- [Initialize the ValidMind datasets](#toc7_) \n", - "- [Initialize a model object](#toc8_) \n", - " - [Assign predictions](#toc8_1_) \n", - "- [Run the full suite of tests](#toc9_) \n", - "- [In summary](#toc10_) \n", - "- [Next steps](#toc11_) \n", - " - [Work with your model documentation](#toc11_1_) \n", - " - [Discover more learning resources](#toc11_2_) \n", - "- [Upgrade ValidMind](#toc12_) \n", + "- [Preprocessing the raw dataset](#toc6_) \n", + " - [Split the dataset](#toc6_1_) \n", + " - [Separate features and targets](#toc6_2_) \n", + "- [Training an XGBoost classifier model](#toc7_) \n", + " - [Set evaluation metrics](#toc7_1_) \n", + " - [Fit the model](#toc7_2_) \n", + "- [Initialize the ValidMind datasets](#toc8_) \n", + "- [Initialize a model object](#toc9_) \n", + " - [Assign predictions](#toc9_1_) \n", + "- [Run the full suite of tests](#toc10_) \n", + "- [In summary](#toc11_) \n", + "- [Next steps](#toc12_) \n", + " - [Work with your model documentation](#toc12_1_) \n", + " - [Discover more learning resources](#toc12_2_) \n", + "- [Upgrade ValidMind](#toc13_) \n", "\n", ":::\n", "