From 77011d68762acecbba33216ca60a2104edf50caa Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Fri, 28 Mar 2025 09:30:47 -0700 Subject: [PATCH 01/71] Moved the introductory notebooks to their own PR --- .../111-import_champion_model.ipynb | 468 +++++++ .../112-develop_challenger_models.ipynb | 726 +++++++++++ .../113-perform_validation_tests.ipynb | 942 ++++++++++++++ .../114-finalize_testing_reporting.ipynb | 1081 +++++++++++++++++ .../model_validation/compliance-summary.png | Bin 0 -> 61626 bytes .../inserted-minimum-f1-scores.png | Bin 0 -> 110789 bytes .../link-validator-evidence.png | Bin 0 -> 398858 bytes .../selecting-minimum-f1-scores.png | Bin 0 -> 327835 bytes 8 files changed, 3217 insertions(+) create mode 100644 notebooks/tutorials/model_validation/111-import_champion_model.ipynb create mode 100644 notebooks/tutorials/model_validation/112-develop_challenger_models.ipynb create mode 100644 notebooks/tutorials/model_validation/113-perform_validation_tests.ipynb create mode 100644 notebooks/tutorials/model_validation/114-finalize_testing_reporting.ipynb create mode 100644 notebooks/tutorials/model_validation/compliance-summary.png create mode 100644 notebooks/tutorials/model_validation/inserted-minimum-f1-scores.png create mode 100644 notebooks/tutorials/model_validation/link-validator-evidence.png create mode 100644 notebooks/tutorials/model_validation/selecting-minimum-f1-scores.png diff --git a/notebooks/tutorials/model_validation/111-import_champion_model.ipynb b/notebooks/tutorials/model_validation/111-import_champion_model.ipynb new file mode 100644 index 000000000..730a25129 --- /dev/null +++ b/notebooks/tutorials/model_validation/111-import_champion_model.ipynb @@ -0,0 +1,468 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "248883b1", + "metadata": {}, + "source": [ + "# ValidMind for model validation — 111 Import the champion model\n", + "\n", + "Learn how to use ValidMind for your end-to-end model validation process based on common scenarios with our series of four introductory notebooks. In this first notebook, set up the ValidMind Library in preparation for validating a champion model.\n", + "\n", + "These notebooks use an application scorecard model as an example, but the same principles shown here apply to other model types." + ] + }, + { + "cell_type": "markdown", + "id": "e1657d54", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "Model validation aims to independently assess the compliance of *champion models* created by model developers with regulatory guidance by conducting thorough testing and analysis, potentially including the use of challenger models to benchmark performance. Assessments, presented in the form of a validation report, typically include *model findings* and recommendations to address those issues.\n", + "\n", + "An *application scorecard model* is a type of statistical model used in credit scoring to evaluate the creditworthiness of potential borrowers by generating a score based on various characteristics of an applicant such as credit history, income, employment status, and other relevant financial data.\n", + "\n", + " - This score assists lenders in making informed decisions about whether to approve or reject loan applications, as well as in determining the terms of the loan, including interest rates and credit limits.\n", + " - Effective validation of application scorecard models ensures that lenders can manage risk efficiently while maintaining a fast and transparent loan application process for applicants." + ] + }, + { + "cell_type": "markdown", + "id": "434bea70", + "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 comparison and other validation tests, and then use the ValidMind Platform to submit compliance assessments of champion models via comprehensive validation reports. Together, these products simplify model risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and model developers." + ] + }, + { + "cell_type": "markdown", + "id": "662d6027", + "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": "5a1a2fa8", + "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": "aa9b1d8e", + "metadata": {}, + "source": [ + "### Key concepts\n", + "\n", + "**Validation report**: A comprehensive and structured assessment of a model’s development and performance, focusing on verifying its integrity, appropriateness, and alignment with its intended use. It includes analyses of model assumptions, data quality, performance metrics, outcomes of testing procedures, and risk considerations. The validation report supports transparency, regulatory compliance, and informed decision-making by documenting the validator’s independent review and conclusions.\n", + "\n", + "**Validation report template**: Serves as a standardized framework for conducting and documenting model validation activities. It outlines the required sections, recommended analyses, and expected validation tests, ensuring consistency and completeness across validation reports. The template helps guide validators through a systematic review process while promoting comparability and traceability of validation outcomes.\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.\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." + ] + }, + { + "cell_type": "markdown", + "id": "057c1417", + "metadata": {}, + "source": [ + "## Setting up" + ] + }, + { + "cell_type": "markdown", + "id": "ef1f5ce4", + "metadata": {}, + "source": [ + "### Register a sample model\n", + "\n", + "In a usual model lifecycle, a champion model will have been independently registered in your model inventory and submitted to you for validation by your model development team as part of the effective challenge process. (**Learn more:** [Submit for approval](https://docs.validmind.ai/guide/model-documentation/submit-for-approval.html))\n", + "\n", + "For this series of notebooks, we'll have you register a dummy model in the ValidMind Platform inventory and assign yourself as the validator to familiarize you with the ValidMind interface and circumvent the need for an existing model:\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:\n", + "\n", + " - Documentation template: `Credit Risk Scorecard`\n", + " - Use case: `Credit Risk — CECL`\n", + "\n", + " You can fill in other options according to your preference." + ] + }, + { + "cell_type": "markdown", + "id": "9743927c", + "metadata": {}, + "source": [ + "#### Assign validator credentials\n", + "\n", + "In order to log tests as a validator instead of as a developer, on the model details page that appears after you've successfully registered your sample model:\n", + "\n", + "1. Remove yourself as a developer: \n", + "\n", + " - Click on the **DEVELOPERS** tile.\n", + " - Click the **x** next to your name to remove yourself from that model's role.\n", + " - Click **Save** to apply your changes to that role.\n", + "\n", + "2. Add yourself as a validator: \n", + "\n", + " - Click on the **VALIDATORS** tile.\n", + " - Select your name from the drop-down menu.\n", + " - Click **Save** to apply your changes to that role." + ] + }, + { + "cell_type": "markdown", + "id": "37a94e48", + "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": "cef99db1", + "metadata": {}, + "outputs": [], + "source": [ + "%pip install -q validmind" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize the ValidMind Library\n", + "\n", + "ValidMind generates a unique _code snippet_ for each registered model to connect with your validation environment. You initialize the ValidMind Library with this code snippet, which ensures that your test results are uploaded to the correct model when you run the notebook." + ] + }, + { + "cell_type": "markdown", + "id": "30803184", + "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 select the model you registered for this \"ValidMind for model validation\" series of notebooks.\n", + "\n", + "3. 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": "d5de450a", + "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": [ + "## Getting to know ValidMind" + ] + }, + { + "cell_type": "markdown", + "id": "d22ef356", + "metadata": {}, + "source": [ + "### Preview the validation report 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 model validation. A template predefines sections for your validation report and provides a general outline to follow, making the validation process much easier.\n", + "\n", + "You will attach evidence to this template in the form of risk assessment notes, findings, and test results 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:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38592086", + "metadata": {}, + "outputs": [], + "source": [ + "vm.preview_template()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### View validation report 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 \"ValidMind for model validation\" series of notebooks.\n", + "\n", + "3. Click on the **Validation Report** for your model and note:\n", + "\n", + " - [ ] The risk assessment compliance summary at the top of the report (screenshot below)\n", + " - [ ] How the structure of the validation report reflects the previewed template\n", + "\n", + " \"Screenshot\n", + "

" + ] + }, + { + "cell_type": "markdown", + "id": "943a45d6", + "metadata": {}, + "source": [ + "## Importing the champion model\n", + "\n", + "With the ValidMind Library set up and ready to go, let's go ahead and import the champion model submitted by the model development team in the format of a `.pkl` file: **[xgb_model_champion.pkl](xgb_model_champion.pkl)**\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3f9e197e", + "metadata": {}, + "outputs": [], + "source": [ + "import xgboost as xgb\n", + "\n", + "#Load the saved model\n", + "xgb_model = xgb.XGBClassifier()\n", + "xgb_model.load_model(\"xgb_model_champion.pkl\")\n", + "xgb_model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b6887d38", + "metadata": {}, + "outputs": [], + "source": [ + "# Ensure that we have to appropriate order in feature names from Champion model and dataset\n", + "cols_when_model_builds = xgb_model.get_booster().feature_names" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load the sample dataset\n", + "\n", + "Let's next import the public [Lending Club](https://www.kaggle.com/datasets/devanshi23/loan-data-2007-2014/data) dataset from Kaggle, which was used to develop the dummy champion model.\n", + "\n", + "- We'll use this dataset to review steps that should have been conducted during the initial development and documentation of the model to ensure that the model was built correctly.\n", + "- By independently performing steps such as preprocessing and feature engineering, we can confirm whether the model was built using appropriate and properly processed data.\n", + "\n", + "To be able to use the dataset, you'll 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:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "40e73b7c", + "metadata": {}, + "outputs": [], + "source": [ + "from validmind.datasets.credit_risk import lending_club\n", + "\n", + "df = lending_club.load_data(source=\"offline\")\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Preprocess the dataset\n", + "\n", + "We'll first quickly preprocess the dataset for data quality testing purposes using `lending_club.preprocess`. This function performs the following operations:\n", + "\n", + "- Filters the dataset to include only loans for debt consolidation or credit card purposes\n", + "- Removes loans classified under the riskier grades \"F\" and \"G\"\n", + "- Excludes uncommon home ownership types and standardizes employment length and loan terms into numerical formats\n", + "- Discards unnecessary fields and any entries with missing information to maintain a clean and robust dataset for modeling" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e436e862", + "metadata": {}, + "outputs": [], + "source": [ + "preprocess_df = lending_club.preprocess(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Apply feature engineering to the dataset\n", + "\n", + "Feature engineering improves the dataset's structure to better match what our model expects, and ensures that the model performs optimally by leveraging additional insights from raw data.\n", + "\n", + "We'll apply the following transformations using the `ending_club.feature_engineering()` function to optimize the dataset for predictive modeling in our application scorecard:\n", + "\n", + "- **WoE encoding**: Converts both numerical and categorical features into Weight of Evidence (WoE) values. WoE is a statistical measure used in scorecard modeling that quantifies the relationship between a predictor variable and the binary target variable. It calculates the ratio of the distribution of good outcomes to the distribution of bad outcomes for each category or bin of a feature. This transformation helps to ensure that the features are predictive and consistent in their contribution to the model.\n", + "- **Integration of WoE bins**: Ensures that the WoE transformed values are integrated throughout the dataset, replacing the original feature values while excluding the target variable from this transformation. This transformation is used to maintain a consistent scale and impact of each variable within the model, which helps make the predictions more stable and accurate." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9c6b641c", + "metadata": {}, + "outputs": [], + "source": [ + "fe_df = lending_club.feature_engineering(preprocess_df)\n", + "fe_df.head()" + ] + }, + { + "cell_type": "markdown", + "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": "10272aa9", + "metadata": {}, + "outputs": [], + "source": [ + "%pip show validmind" + ] + }, + { + "cell_type": "markdown", + "id": "upgrade-version-d64591ca-3073-4b3e-9586-d3577adda203", + "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": "6e12584a", + "metadata": {}, + "source": [ + "You may need to restart your kernel after running the upgrade package for changes to be applied." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## In summary\n", + "\n", + "In this first notebook, you learned how to:\n", + "\n", + "- [ ] Register a model within the ValidMind Platform and assign yourself as the validator\n", + "- [ ] Install and initialize the ValidMind Library\n", + "- [ ] Preview the validation report template for your model\n", + "- [ ] Locate the validation report for your model within the ValidMind Platform\n" + ] + }, + { + "cell_type": "markdown", + "id": "78f8161b", + "metadata": {}, + "source": [ + "## Next steps\n", + "\n", + "### Develop potential challenger models\n", + "\n", + "Now that the ValidMind Library is connected to your model in the ValidMind Library with the correct template applied, we can go ahead and start the model validation process: **[112 Develop potential challenger models](112-develop_challenger_models.ipynb)**" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ValidMind Library", + "language": "python", + "name": "validmind" + }, + "language_info": { + "name": "python", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/tutorials/model_validation/112-develop_challenger_models.ipynb b/notebooks/tutorials/model_validation/112-develop_challenger_models.ipynb new file mode 100644 index 000000000..68ac41403 --- /dev/null +++ b/notebooks/tutorials/model_validation/112-develop_challenger_models.ipynb @@ -0,0 +1,726 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ValidMind for model validation — 112 Develop potential challenger models\n", + "\n", + "Learn how to use ValidMind for your end-to-end model validation process with our series of four introductory notebooks. In this second notebook, develop potential challenger models and then pass your models and their predictions to ValidMind.\n", + "\n", + "A *challenger model* is an alternate model that attempt to outperform the champion model, ensuring that the best performing fit-for-purpose model is always considered for deployment. Challenger models also help avoid over-reliance on a single model, and allow testing of new features, algorithms, or data sources without disrupting the production lifecycle." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "\n", + "In order to continue on the model validation journey with notebook, you'll need to first have:\n", + "\n", + "- [ ] Registered a model within the ValidMind Platform and granted yourself access to the model as a validator\n", + "- [ ] Installed the ValidMind Library in your local environment, allowing you to access all its features\n", + "\n", + "
Need help with the above steps?\n", + "

\n", + "Refer to the first notebook in this series: 111 Import the champion model
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting up" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize the ValidMind Library\n", + "\n", + "First, let's connect up the ValidMind Library to our model we previously registered in the ValidMind Platform:\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 \"ValidMind for model validation\" series of notebooks.\n", + "\n", + "3. 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, + "metadata": {}, + "outputs": [], + "source": [ + "# Make sure the ValidMind Library is installed\n", + "\n", + "%pip install -q validmind\n", + "\n", + "# 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": [ + "### Import the champion model\n", + "\n", + "Next, we'll import the champion model submitted by the model development team as we used in the last notebook (**[xgb_model_champion.pkl](xgb_model_champion.pkl)**) and load in the sample [Lending Club](https://www.kaggle.com/datasets/devanshi23/loan-data-2007-2014/data) dataset used to develop the model, and independently preprocess and feature engineer the dataset to confirm that the model was built using appropriate and properly processed data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import xgboost as xgb\n", + "\n", + "#Load the saved model\n", + "xgb_model = xgb.XGBClassifier()\n", + "xgb_model.load_model(\"xgb_model_champion.pkl\")\n", + "xgb_model\n", + "\n", + "# Ensure that we have to appropriate order in feature names from Champion model and dataset\n", + "cols_when_model_builds = xgb_model.get_booster().feature_names" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import the Lending Club dataset from Kaggle\n", + "from validmind.datasets.credit_risk import lending_club\n", + "\n", + "df = lending_club.load_data(source=\"offline\")\n", + "df.head()\n", + "\n", + "# Preprocess the dataset for data quality testing purposes\n", + "preprocess_df = lending_club.preprocess(df)\n", + "\n", + "# Apply feature engineering to the dataset\n", + "fe_df = lending_club.feature_engineering(preprocess_df)\n", + "fe_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Split the feature engineered dataset\n", + "\n", + "With our dummy model imported and our independently preprocessed and feature engineered dataset ready to go, let's now **spilt our dataset into train and test** to start the validation testing process.\n", + "\n", + "Splitting our dataset into training and testing is essential for proper validation testing, as this helps assess how well the model generalizes to unseen data:\n", + "\n", + "- We begin by dividing our data, which is based on Weight of Evidence (WoE) features, into training and testing sets (`train_df`, `test_df`).\n", + "- With `lending_club.split`, we employ a simple random split, randomly allocating data points to each set to ensure a mix of examples in both." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data\n", + "train_df, test_df = lending_club.split(fe_df, test_size=0.2)\n", + "\n", + "x_train = train_df.drop(lending_club.target_column, axis=1)\n", + "y_train = train_df[lending_club.target_column]\n", + "\n", + "x_test = test_df.drop(lending_club.target_column, axis=1)\n", + "y_test = test_df[lending_club.target_column]\n", + "\n", + "# Now let's apply the order of features from the champion model construction\n", + "x_train = x_train[cols_when_model_builds]\n", + "x_test = x_test[cols_when_model_builds]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cols_use = ['annual_inc_woe',\n", + " 'verification_status_woe',\n", + " 'emp_length_woe',\n", + " 'installment_woe',\n", + " 'term_woe',\n", + " 'home_ownership_woe',\n", + " 'purpose_woe',\n", + " 'open_acc_woe',\n", + " 'total_acc_woe',\n", + " 'int_rate_woe',\n", + " 'sub_grade_woe',\n", + " 'grade_woe','loan_status']\n", + "\n", + "\n", + "train_df = train_df[cols_use]\n", + "test_df = test_df[cols_use]\n", + "test_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Investigate potential challenger models\n", + "\n", + "We're curious how alternate models compare to our champion model, so let's train two challenger models as basis for our testing.\n", + "\n", + "Our selected options below offer decreased complexity in terms of implementation — such as lessened manual preprocessing — which can reduce the amount of risk for implementation. However, model risk is not calculated in isolation from a single factor, but rather in consideration with trade-offs in predictive performance, ease of interpretability, and overall alignment with business objectives." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Random forest classification model\n", + "\n", + "A *random forest classification model* is an ensemble machine learning algorithm that uses multiple decision trees to classify data. In ensemble learning, multiple models are combined to improve prediction accuracy and robustness.\n", + "\n", + "Random forest classification models generally have higher accuracy because they capture complex, non-linear relationships, but as a result they lack transparency in their predictions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import the Random Forest Classification model\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "# Create the model instance with 50 decision trees\n", + "rf_model = RandomForestClassifier(\n", + " n_estimators=50,\n", + " random_state=42,\n", + ")\n", + "\n", + "# Train the model\n", + "rf_model.fit(x_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Logistic regression model\n", + "\n", + "A *logistic regression model* is a statistical machine learning algorithm that uses a linear equation (straight-line relationship between variables) and the logistic function (or sigmoid function, which maps any real-valued number to a range between `0` and `1`) to classify data. In statistical modeling, a single equation is used to estimate the probability of an outcome based on input features.\n", + "\n", + "Logistic regression models are simple and interpretable because they provide clear probability estimates and feature coefficients (numerical value that represents the influence of a particular input feature on the model's prediction), but they may struggle with capturing complex, non-linear relationships in the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import the Logistic Regression model\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "# Logistic Regression grid params\n", + "log_reg_params = {\n", + " \"penalty\": [\"l1\", \"l2\"],\n", + " \"C\": [0.001, 0.01, 0.1, 1, 10, 100, 1000],\n", + " \"solver\": [\"liblinear\"],\n", + "}\n", + "\n", + "# Grid search for Logistic Regression\n", + "from sklearn.model_selection import GridSearchCV\n", + "\n", + "grid_log_reg = GridSearchCV(LogisticRegression(), log_reg_params)\n", + "grid_log_reg.fit(x_train, y_train)\n", + "\n", + "# Logistic Regression best estimator\n", + "log_reg = grid_log_reg.best_estimator_\n", + "log_reg" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Extract predicted probabilities\n", + "\n", + "With our challenger models trained, let's extract the predicted probabilities from our three models:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Champion — Application scorecard model\n", + "train_xgb_prob = xgb_model.predict_proba(x_train)[:, 1]\n", + "test_xgb_prob = xgb_model.predict_proba(x_test)[:, 1]\n", + "\n", + "# Challenger — Random forest classification model\n", + "train_rf_prob = rf_model.predict_proba(x_train)[:, 1]\n", + "test_rf_prob = rf_model.predict_proba(x_test)[:, 1]\n", + "\n", + "# Challenger — Logistic regression model\n", + "train_log_prob = log_reg.predict_proba(x_train)[:, 1]\n", + "test_log_prob = log_reg.predict_proba(x_test)[:, 1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute binary predictions\n", + "\n", + "Next, we'll convert the probability predictions from our three models into a binary, based on a threshold of `0.3`:\n", + "\n", + "- If the probability is greater than `0.3`, the prediction becomes `1` (positive).\n", + "- Otherwise, it becomes `0` (negative)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cut_off_threshold = 0.3\n", + "\n", + "# Champion — Application scorecard model\n", + "train_xgb_binary_predictions = (train_xgb_prob > cut_off_threshold).astype(int)\n", + "test_xgb_binary_predictions = (test_xgb_prob > cut_off_threshold).astype(int)\n", + "\n", + "# Challenger — Random forest classification model\n", + "train_rf_binary_predictions = (train_rf_prob > cut_off_threshold).astype(int)\n", + "test_rf_binary_predictions = (test_rf_prob > cut_off_threshold).astype(int)\n", + "\n", + "# Challenger — Logistic regression model\n", + "train_log_binary_predictions = (train_log_prob > cut_off_threshold).astype(int)\n", + "test_log_binary_predictions = (test_log_prob > cut_off_threshold).astype(int)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initializing the ValidMind objects" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize the ValidMind datasets\n", + "\n", + "Before you can run tests, you'll need to connect your data with a ValidMind `Dataset` object. **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", + "Initialize a ValidMind dataset object using the [`init_dataset` function](https://docs.validmind.ai/validmind/validmind.html#init_dataset) from the ValidMind (`vm`) module. 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." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the raw dataset\n", + "vm_raw_dataset = vm.init_dataset(\n", + " dataset=df,\n", + " input_id=\"raw_dataset\",\n", + " target_column=lending_club.target_column,\n", + ")\n", + "\n", + "# Initialize the preprocessed dataset\n", + "vm_preprocess_dataset = vm.init_dataset(\n", + " dataset=preprocess_df,\n", + " input_id=\"preprocess_dataset\",\n", + " target_column=lending_club.target_column,\n", + ")\n", + "\n", + "# Initialize the feature engineered dataset\n", + "vm_fe_dataset = vm.init_dataset(\n", + " dataset=fe_df,\n", + " input_id=\"fe_dataset\",\n", + " target_column=lending_club.target_column,\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=lending_club.target_column,\n", + ")\n", + "\n", + "# Initialize the test dataset\n", + "vm_test_ds = vm.init_dataset(\n", + " dataset=test_df,\n", + " input_id=\"test_dataset\",\n", + " target_column=lending_club.target_column,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After initialization, you can pass the ValidMind `Dataset` objects `vm_raw_dataset`, `vm_preprocess_dataset`, `vm_fe_dataset`, `vm_train_ds`, and `vm_test_ds` into any ValidMind tests." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize the model objects\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 each of our three models.\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, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the champion application scorecard model\n", + "vm_xgb_model = vm.init_model(\n", + " xgb_model,\n", + " input_id=\"xgb_model_developer_champion\",\n", + ")\n", + "\n", + "# Initialize the challenger random forest classification model\n", + "vm_rf_model = vm.init_model(\n", + " rf_model,\n", + " input_id=\"rf_model\",\n", + ")\n", + "\n", + "# Initialize the challenger logistic regression model\n", + "vm_log_model = vm.init_model(\n", + " log_reg,\n", + " input_id=\"log_model\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Assign predictions\n", + "\n", + "With our models registered, we'll move on to assigning both the predictive probabilities coming directly from each model's predictions, and the binary prediction after applying the cutoff threshold described in the Compute binary predictions step above.\n", + "\n", + "- The [`assign_predictions()` method](https://docs.validmind.ai/validmind/validmind/vm_models.html#VMDataset.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." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Champion — Application scorecard model\n", + "vm_train_ds.assign_predictions(\n", + " model=vm_xgb_model,\n", + " prediction_values=train_xgb_binary_predictions,\n", + " prediction_probabilities=train_xgb_prob,\n", + ")\n", + "\n", + "vm_test_ds.assign_predictions(\n", + " model=vm_xgb_model,\n", + " prediction_values=test_xgb_binary_predictions,\n", + " prediction_probabilities=test_xgb_prob,\n", + ")\n", + "\n", + "# Challenger — Random forest classification model\n", + "vm_train_ds.assign_predictions(\n", + " model=vm_rf_model,\n", + " prediction_values=train_rf_binary_predictions,\n", + " prediction_probabilities=train_rf_prob,\n", + ")\n", + "\n", + "vm_test_ds.assign_predictions(\n", + " model=vm_rf_model,\n", + " prediction_values=test_rf_binary_predictions,\n", + " prediction_probabilities=test_rf_prob,\n", + ")\n", + "\n", + "\n", + "# Challenger — Logistic regression model\n", + "vm_train_ds.assign_predictions(\n", + " model=vm_log_model,\n", + " prediction_values=train_log_binary_predictions,\n", + " prediction_probabilities=train_log_prob,\n", + ")\n", + "\n", + "vm_test_ds.assign_predictions(\n", + " model=vm_log_model,\n", + " prediction_values=test_log_binary_predictions,\n", + " prediction_probabilities=test_log_prob,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute credit risk scores\n", + "\n", + "Finally, we'll translate model predictions into actionable scores using probability estimates generated by our trained model:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Compute the scores\n", + "train_xgb_scores = lending_club.compute_scores(train_xgb_prob)\n", + "test_xgb_scores = lending_club.compute_scores(test_xgb_prob)\n", + "train_rf_scores = lending_club.compute_scores(train_rf_prob)\n", + "test_rf_scores = lending_club.compute_scores(test_rf_prob)\n", + "train_log_scores = lending_club.compute_scores(train_log_prob)\n", + "test_log_scores = lending_club.compute_scores(test_log_prob)\n", + "\n", + "# Assign scores to the datasets\n", + "vm_train_ds.add_extra_column(\"xgb_scores\", train_xgb_scores)\n", + "vm_test_ds.add_extra_column(\"xgb_scores\", test_xgb_scores)\n", + "vm_train_ds.add_extra_column(\"rf_scores\", train_rf_scores)\n", + "vm_test_ds.add_extra_column(\"rf_scores\", test_rf_scores)\n", + "vm_train_ds.add_extra_column(\"log_scores\", train_log_scores)\n", + "vm_test_ds.add_extra_column(\"log_scores\", test_log_scores)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Enabling custom context for test descriptions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When you run ValidMind tests, test descriptions are automatically generated with LLM using the test results, the test name, and the static test definitions provided in the test’s docstring. While this metadata offers valuable high-level overviews of tests, insights produced by the LLM-based descriptions may not always align with your specific use cases or incorporate organizational policy requirements.\n", + "\n", + "Here, we'll include some initial custom use case context, improving the relevancy, insight, and format of the test descriptions returned.\n", + "\n", + "
Want to learn more about setting custom context for LLM-generated test descriptions?\n", + "

\n", + "Refer to our extended walkthrough notebook: Add context to LLM-generated test descriptions\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Review default LLM-generated descriptions\n", + "\n", + "By default, custom context for LLM-generated descriptions is disabled, meaning that the output will not include any additional context.\n", + "\n", + "Let's generate an initial test description for the `HighPearsonCorrelation` test with our `vm_test_ds` dataset for comparison with a later iteration to demonstrate. We'll go over running tests in more detail in a subsequent notebook:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vm.tests.run_test(\n", + " \"validmind.data_validation.HighPearsonCorrelation\",\n", + " inputs={\n", + " \"dataset\": vm_test_ds,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Enable use case context\n", + "\n", + "To enable custom use case context, set the `VALIDMIND_LLM_DESCRIPTIONS_CONTEXT_ENABLED` environment variable to `1`.\n", + "\n", + "This is a global setting that will affect all tests for your linked model:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.environ[\"VALIDMIND_LLM_DESCRIPTIONS_CONTEXT_ENABLED\"] = \"1\"" + ] + }, + { + "cell_type": "markdown", + "id": "0d1e90ba", + "metadata": {}, + "source": [ + "Enabling use case context allows you to pass in additional context to the LLM-generated text descriptions within `context`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "87f7fb2e", + "metadata": {}, + "outputs": [], + "source": [ + "context = \"\"\"\n", + "FORMAT FOR THE LLM DESCRIPTIONS: \n", + " **** is designed to .\n", + "\n", + " The test operates by \n", + "\n", + " The primary advantages of this test include \n", + "\n", + " Users should be aware that \n", + "\n", + " **Key Insights:**\n", + "\n", + " The test results reveal:\n", + "\n", + " - ****: \n", + " - ****: \n", + " ...\n", + "\n", + " Based on these results, \n", + "\n", + "ADDITIONAL INSTRUCTIONS:\n", + " Present insights in order from general to specific, with each insight as a single bullet point with bold title.\n", + " You are a model validator and the goal is to identify risk and/or suggest room for improvements or recommendations on what Model Developer should do in order to improve outcomes and reduce risk\n", + "\n", + " For each metric in the test results, include in the test overview:\n", + " - The metric's purpose and what it measures\n", + " - Its mathematical formula\n", + " - The range of possible values\n", + " - What constitutes good/bad performance\n", + " - How to interpret different values\n", + "\n", + " Each insight should progressively cover:\n", + " 1. Overall scope and distribution\n", + " 2. Complete breakdown of all elements with specific values\n", + " 3. Natural groupings and patterns\n", + " 4. Comparative analysis between datasets/categories\n", + " 5. Stability and variations\n", + " 6. Notable relationships or dependencies\n", + "\n", + " Remember:\n", + " - Keep all insights at the same level (no sub-bullets or nested structures)\n", + " - Make each insight complete and self-contained\n", + " - Include specific numerical values and ranges\n", + " - Cover all elements in the results comprehensively\n", + " - Maintain clear, concise language\n", + " - Use only \"- **Title**: Description\" format for insights\n", + " - Progress naturally from general to specific observations\n", + "\n", + "\"\"\".strip()\n", + "\n", + "os.environ[\"VALIDMIND_LLM_DESCRIPTIONS_CONTEXT\"] = context" + ] + }, + { + "cell_type": "markdown", + "id": "d6e38bd2", + "metadata": {}, + "source": [ + "With the use case context set, generate an updated test description for the `HighPearsonCorrelation` test for comparision with default output:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vm.tests.run_test(\n", + " \"validmind.data_validation.HighPearsonCorrelation\",\n", + " inputs={\n", + " \"dataset\": vm_test_ds,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## In summary\n", + "\n", + "In this second notebook, you learned how to:\n", + "\n", + "- [ ] Initialize ValidMind datasets\n", + "- [ ] Initialize ValidMind model objects\n", + "- [ ] Assign predictions and probabilities to your ValidMind datasets\n", + "- [ ] Enable custom context for test descriptions generated by ValidMind" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Next steps\n", + "\n", + "### Perform model validation tests\n", + "\n", + "Now that you're familiar with the basics of using the ValidMind Library, let's learn how to identify and run validation tests: **[113 Perform model validation tests](113-perform_validation_tests.ipynb)**" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ValidMind Library", + "language": "python", + "name": "validmind" + }, + "language_info": { + "name": "python", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/tutorials/model_validation/113-perform_validation_tests.ipynb b/notebooks/tutorials/model_validation/113-perform_validation_tests.ipynb new file mode 100644 index 000000000..eb96a5f74 --- /dev/null +++ b/notebooks/tutorials/model_validation/113-perform_validation_tests.ipynb @@ -0,0 +1,942 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ValidMind for model validation — 113 Perform model validation tests\n", + "\n", + "Learn how to use ValidMind for your end-to-end model validation process with our series of four introductory notebooks. In this third notebook, you'll identify relevant validation tests, run them, then add the log results to your validation report.\n", + "\n", + "You'll become familiar with the tests available in ValidMind, as well as how to run them. Running tests during model validation is crucial to the effective challenge process, as we want to independently evaluate the evidence and assessments provided by the model development team.\n", + "\n", + "While running our tests, we'll focus on:\n", + "\n", + "- Ensuring that data used for training and testing the model is of appropriate data quality\n", + "- Ensuring that the raw data has been preprocessed appropriately and that the resulting feature engineered dataset reflects this\n", + "- Comprehensive testing around model performance of both the champion ahd challenger models\n", + "\n", + "**For a full list of out-of-the-box tests,** refer to our [Test descriptions](https://docs.validmind.ai/developer/model-testing/test-descriptions.html) or try the interactive [Test sandbox](https://docs.validmind.ai/developer/model-testing/test-sandbox.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "\n", + "In order to log tests results or evidence to your validation report with this notebook, you'll need to first have:\n", + "\n", + "- [ ] Registered a model within the ValidMind Platform and granted yourself access to the model as a validator\n", + "- [ ] Installed the ValidMind Library in your local environment, allowing you to access all its features\n", + "- [ ] Learned how to import and initialize datasets for use with ValidMind\n", + "- [ ] Understood the basics of how to enable custom context for test descriptions generated by ValidMind\n", + "\n", + "
Need help with the above steps?\n", + "

\n", + "Refer to the first two notebooks in this series:\n", + "\n", + "
    \n", + "
  1. 111 Import the champion model
    2. \n", + "
  2. 112 Develop potential challenger models
    3. \n", + "
\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting up\n", + "\n", + "This section should be quite familiar to you — as we performed the same actions in the previous notebook, **[112 Develop potential challenger models](112-develop_challenger_models.ipynb)**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize the ValidMind Library\n", + "\n", + "As usual, let's first connect up the ValidMind Library to our model we previously registered in the ValidMind Platform:\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 \"ValidMind for model validation\" series of notebooks.\n", + "\n", + "3. 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, + "metadata": {}, + "outputs": [], + "source": [ + "# Make sure the ValidMind Library is installed\n", + "\n", + "%pip install -q validmind\n", + "\n", + "# 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": [ + "### Import the champion model\n", + "\n", + "Next, we'll import the champion model submitted by the model development team as we used in the last notebooks (**[xgb_model_champion.pkl](xgb_model_champion.pkl)**) and load in the sample [Lending Club](https://www.kaggle.com/datasets/devanshi23/loan-data-2007-2014/data) dataset used to develop the model, and independently preprocess and feature engineer the dataset to confirm that the model was built using appropriate and properly processed data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import xgboost as xgb\n", + "\n", + "#Load the saved model\n", + "xgb_model = xgb.XGBClassifier()\n", + "xgb_model.load_model(\"xgb_model_champion.pkl\")\n", + "xgb_model\n", + "\n", + "# Ensure that we have to appropriate order in feature names from Champion model and dataset\n", + "cols_when_model_builds = xgb_model.get_booster().feature_names" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import the Lending Club dataset from Kaggle\n", + "from validmind.datasets.credit_risk import lending_club\n", + "\n", + "df = lending_club.load_data(source=\"offline\")\n", + "df.head()\n", + "\n", + "# Preprocess the dataset for data quality testing purposes\n", + "preprocess_df = lending_club.preprocess(df)\n", + "\n", + "# Apply feature engineering to the dataset\n", + "fe_df = lending_club.feature_engineering(preprocess_df)\n", + "fe_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll also spilt our dataset into train and test to start the validation testing process:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data\n", + "train_df, test_df = lending_club.split(fe_df, test_size=0.2)\n", + "\n", + "x_train = train_df.drop(lending_club.target_column, axis=1)\n", + "y_train = train_df[lending_club.target_column]\n", + "\n", + "x_test = test_df.drop(lending_club.target_column, axis=1)\n", + "y_test = test_df[lending_club.target_column]\n", + "\n", + "# Now let's apply the order of features from the champion model construction\n", + "x_train = x_train[cols_when_model_builds]\n", + "x_test = x_test[cols_when_model_builds]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cols_use = ['annual_inc_woe',\n", + " 'verification_status_woe',\n", + " 'emp_length_woe',\n", + " 'installment_woe',\n", + " 'term_woe',\n", + " 'home_ownership_woe',\n", + " 'purpose_woe',\n", + " 'open_acc_woe',\n", + " 'total_acc_woe',\n", + " 'int_rate_woe',\n", + " 'sub_grade_woe',\n", + " 'grade_woe','loan_status']\n", + "\n", + "\n", + "train_df = train_df[cols_use]\n", + "test_df = test_df[cols_use]\n", + "test_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train challenger models\n", + "\n", + "Just like in the last notebook, we'll train two challenger models as the basis for our testing:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import the Random Forest Classification model\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "# Create the model instance with 50 decision trees\n", + "rf_model = RandomForestClassifier(\n", + " n_estimators=50,\n", + " random_state=42,\n", + ")\n", + "\n", + "# Train the model\n", + "rf_model.fit(x_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import the Logistic Regression model\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "# Logistic Regression grid params\n", + "log_reg_params = {\n", + " \"penalty\": [\"l1\", \"l2\"],\n", + " \"C\": [0.001, 0.01, 0.1, 1, 10, 100, 1000],\n", + " \"solver\": [\"liblinear\"],\n", + "}\n", + "\n", + "# Grid search for Logistic Regression\n", + "from sklearn.model_selection import GridSearchCV\n", + "\n", + "grid_log_reg = GridSearchCV(LogisticRegression(), log_reg_params)\n", + "grid_log_reg.fit(x_train, y_train)\n", + "\n", + "# Logistic Regression best estimator\n", + "log_reg = grid_log_reg.best_estimator_\n", + "log_reg" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Extract predicted probabilities\n", + "\n", + "With our challenger models trained, let's extract the predicted probabilities from our three models and convert the probability predictions into a binary:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Champion — Application scorecard model\n", + "train_xgb_prob = xgb_model.predict_proba(x_train)[:, 1]\n", + "test_xgb_prob = xgb_model.predict_proba(x_test)[:, 1]\n", + "\n", + "# Challenger — Random forest classification model\n", + "train_rf_prob = rf_model.predict_proba(x_train)[:, 1]\n", + "test_rf_prob = rf_model.predict_proba(x_test)[:, 1]\n", + "\n", + "# Challenger — Logistic regression model\n", + "train_log_prob = log_reg.predict_proba(x_train)[:, 1]\n", + "test_log_prob = log_reg.predict_proba(x_test)[:, 1]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# If probability > 0.3 = 1 (positive)\n", + "cut_off_threshold = 0.3\n", + "\n", + "# Champion — Application scorecard model\n", + "train_xgb_binary_predictions = (train_xgb_prob > cut_off_threshold).astype(int)\n", + "test_xgb_binary_predictions = (test_xgb_prob > cut_off_threshold).astype(int)\n", + "\n", + "# Challenger — Random forest classification model\n", + "train_rf_binary_predictions = (train_rf_prob > cut_off_threshold).astype(int)\n", + "test_rf_binary_predictions = (test_rf_prob > cut_off_threshold).astype(int)\n", + "\n", + "# Challenger — Logistic regression model\n", + "train_log_binary_predictions = (train_log_prob > cut_off_threshold).astype(int)\n", + "test_log_binary_predictions = (test_log_prob > cut_off_threshold).astype(int)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize the ValidMind objects\n", + "\n", + "Let's initialize the ValidMind `Dataset` and `Model` objects in preparation for assigning model predictions to each dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the raw dataset\n", + "vm_raw_dataset = vm.init_dataset(\n", + " dataset=df,\n", + " input_id=\"raw_dataset\",\n", + " target_column=lending_club.target_column,\n", + ")\n", + "\n", + "# Initialize the preprocessed dataset\n", + "vm_preprocess_dataset = vm.init_dataset(\n", + " dataset=preprocess_df,\n", + " input_id=\"preprocess_dataset\",\n", + " target_column=lending_club.target_column,\n", + ")\n", + "\n", + "# Initialize the feature engineered dataset\n", + "vm_fe_dataset = vm.init_dataset(\n", + " dataset=fe_df,\n", + " input_id=\"fe_dataset\",\n", + " target_column=lending_club.target_column,\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=lending_club.target_column,\n", + ")\n", + "\n", + "# Initialize the test dataset\n", + "vm_test_ds = vm.init_dataset(\n", + " dataset=test_df,\n", + " input_id=\"test_dataset\",\n", + " target_column=lending_club.target_column,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the champion application scorecard model\n", + "vm_xgb_model = vm.init_model(\n", + " xgb_model,\n", + " input_id=\"xgb_model_developer_champion\",\n", + ")\n", + "\n", + "# Initialize the challenger random forest classification model\n", + "vm_rf_model = vm.init_model(\n", + " rf_model,\n", + " input_id=\"rf_model\",\n", + ")\n", + "\n", + "# Initialize the challenger logistic regression model\n", + "vm_log_model = vm.init_model(\n", + " log_reg,\n", + " input_id=\"log_model\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Assign predictions\n", + "\n", + "With our models registered, we'll move on to assigning both the predictive probabilities coming directly from each model's predictions, and the binary prediction after applying the cutoff threshold:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Champion — Application scorecard model\n", + "vm_train_ds.assign_predictions(\n", + " model=vm_xgb_model,\n", + " prediction_values=train_xgb_binary_predictions,\n", + " prediction_probabilities=train_xgb_prob,\n", + ")\n", + "\n", + "vm_test_ds.assign_predictions(\n", + " model=vm_xgb_model,\n", + " prediction_values=test_xgb_binary_predictions,\n", + " prediction_probabilities=test_xgb_prob,\n", + ")\n", + "\n", + "# Challenger — Random forest classification model\n", + "vm_train_ds.assign_predictions(\n", + " model=vm_rf_model,\n", + " prediction_values=train_rf_binary_predictions,\n", + " prediction_probabilities=train_rf_prob,\n", + ")\n", + "\n", + "vm_test_ds.assign_predictions(\n", + " model=vm_rf_model,\n", + " prediction_values=test_rf_binary_predictions,\n", + " prediction_probabilities=test_rf_prob,\n", + ")\n", + "\n", + "# Challenger — Logistic regression model\n", + "vm_train_ds.assign_predictions(\n", + " model=vm_log_model,\n", + " prediction_values=train_log_binary_predictions,\n", + " prediction_probabilities=train_log_prob,\n", + ")\n", + "\n", + "vm_test_ds.assign_predictions(\n", + " model=vm_log_model,\n", + " prediction_values=test_log_binary_predictions,\n", + " prediction_probabilities=test_log_prob,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we'll translate model predictions into actionable scores using probability estimates generated by our trained model:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Compute the scores\n", + "train_xgb_scores = lending_club.compute_scores(train_xgb_prob)\n", + "test_xgb_scores = lending_club.compute_scores(test_xgb_prob)\n", + "train_rf_scores = lending_club.compute_scores(train_rf_prob)\n", + "test_rf_scores = lending_club.compute_scores(test_rf_prob)\n", + "train_log_scores = lending_club.compute_scores(train_log_prob)\n", + "test_log_scores = lending_club.compute_scores(test_log_prob)\n", + "\n", + "# Assign scores to the datasets\n", + "vm_train_ds.add_extra_column(\"xgb_scores\", train_xgb_scores)\n", + "vm_test_ds.add_extra_column(\"xgb_scores\", test_xgb_scores)\n", + "vm_train_ds.add_extra_column(\"rf_scores\", train_rf_scores)\n", + "vm_test_ds.add_extra_column(\"rf_scores\", test_rf_scores)\n", + "vm_train_ds.add_extra_column(\"log_scores\", train_log_scores)\n", + "vm_test_ds.add_extra_column(\"log_scores\", test_log_scores)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Enable use case context\n", + "\n", + "Here, we'll include some initial custom use case context, improving the relevancy, insight, and format of the test descriptions returned in preparation for running our tests:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.environ[\"VALIDMIND_LLM_DESCRIPTIONS_CONTEXT_ENABLED\"] = \"1\"\n", + "\n", + "context = \"\"\"\n", + "FORMAT FOR THE LLM DESCRIPTIONS: \n", + " **** is designed to .\n", + "\n", + " The test operates by \n", + "\n", + " The primary advantages of this test include \n", + "\n", + " Users should be aware that \n", + "\n", + " **Key Insights:**\n", + "\n", + " The test results reveal:\n", + "\n", + " - ****: \n", + " - ****: \n", + " ...\n", + "\n", + " Based on these results, \n", + "\n", + "ADDITIONAL INSTRUCTIONS:\n", + " Present insights in order from general to specific, with each insight as a single bullet point with bold title.\n", + " You are a model validator and the goal is to identify risk and/or suggest room for improvements or recommendations on what Model Developer should do in order to improve outcomes and reduce risk\n", + "\n", + " For each metric in the test results, include in the test overview:\n", + " - The metric's purpose and what it measures\n", + " - Its mathematical formula\n", + " - The range of possible values\n", + " - What constitutes good/bad performance\n", + " - How to interpret different values\n", + "\n", + " Each insight should progressively cover:\n", + " 1. Overall scope and distribution\n", + " 2. Complete breakdown of all elements with specific values\n", + " 3. Natural groupings and patterns\n", + " 4. Comparative analysis between datasets/categories\n", + " 5. Stability and variations\n", + " 6. Notable relationships or dependencies\n", + "\n", + " Remember:\n", + " - Keep all insights at the same level (no sub-bullets or nested structures)\n", + " - Make each insight complete and self-contained\n", + " - Include specific numerical values and ranges\n", + " - Cover all elements in the results comprehensively\n", + " - Maintain clear, concise language\n", + " - Use only \"- **Title**: Description\" format for insights\n", + " - Progress naturally from general to specific observations\n", + "\n", + "\"\"\".strip()\n", + "\n", + "os.environ[\"VALIDMIND_LLM_DESCRIPTIONS_CONTEXT\"] = context" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run data quality tests\n", + "\n", + "With everything ready to go, let's explore some of ValidMind's available tests. Using ValidMind’s repository of tests streamlines your validation testing, and helps you ensure that your models are being validated appropriately." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Identify qualitative tests\n", + "\n", + "Let's first explore the list of all available tests in the ValidMind Library with [the `vm.tests.list_tests()` function](https://docs.validmind.ai/validmind/validmind/tests.html#list_tests) — we'll then narrow down the tests we want to run from this list." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vm.tests.list_tests()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Next, we'll use the [`vm.tests.list_tasks_and_tags()` function](https://docs.validmind.ai/validmind/validmind/tests.html#list_tasks_and_tags) to list which `tags` are associated with each `task` type:\n", + "\n", + "- **`tasks`** represent the kind of modeling task associated with a test. Here we'll focus on `classification` tasks.\n", + "- **`tags`** are free-form descriptions providing more details about the test, for example, what category the test falls into. Here we'll focus on the `data_quality` tag." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vm.tests.list_tasks_and_tags()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we'll call [`vm.tests.list_tests()`](https://docs.validmind.ai/validmind/validmind/tests.html#list_tests) to list all the data quality tests for classification:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vm.tests.list_tests(\n", + " tags=[\"data_quality\"], task=\"classification\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
Want to learn more about navigating ValidMind tests?\n", + "

\n", + "Refer to our notebook outlining the utilities available for viewing and understanding available ValidMind tests: Explore tests
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Log data quality tests\n", + "\n", + "We want to run all the tests that are returned, so we'll store the identified tests in `dq` in preparation for batch running these tests and logging their results to the ValidMind Platform:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dq = vm.tests.list_tests(tags=[\"data_quality\"], task=\"classification\",pretty=False)\n", + "dq" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "With our data quality tests identified, let's run our first batch of tests using our previously initialized preprocessed dataset (`vm_preprocess_dataset`) and log their results.\n", + "\n", + "- You run validation tests by calling [the `run_test` function](https://docs.validmind.ai/validmind/validmind/tests.html#run_test) provided by the `validmind.tests` module.\n", + "- Every test result returned by the `run_test()` function has a [`.log()` method](https://docs.validmind.ai/validmind/validmind/vm_models.html#TestResult.log) that can be used to send the test results to the ValidMind Platform:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for test in dq:\n", + " vm.tests.run_test(\n", + " test,\n", + " inputs={\n", + " \"dataset\": vm_preprocess_dataset\n", + " }\n", + " ).log()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
Note the output returned indicating that a test-driven block doesn't currently exist in your model's documentation for some test IDs. \n", + "

\n", + "That's expected, as when we run validations tests the results logged need to be manually added to your report as part of your compliance assessment process within the ValidMind Platform.
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run comparison tests\n", + "\n", + "Next, let's reuse the tests in `dq` to perform comparison tests between the raw (`vm_raw_dataset`) and preprocessed (`vm_preprocess_dataset`) dataset, again logging the results to the ValidMind Platform:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for test in dq:\n", + " vm.tests.run_test(\n", + " test,\n", + " input_grid={\n", + " \"dataset\": [vm_raw_dataset,vm_preprocess_dataset]\n", + " }\n", + " ).log()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run performance tests\n", + "\n", + "We'll also run some performance tests, beginning with independent testing of our champion application scorecard model, then moving on to our potential challenger models.\n", + "\n", + "Use `vm.tests.list_tests()` to this time identify all the model performance tests for classification:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "vm.tests.list_tests(tags=[\"model_performance\"], task=\"classification\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll isolate the specific tests we want to run in `mpt`:\n", + "\n", + "- [`ClassifierPerformance`](https://docs.validmind.ai/tests/model_validation/sklearn/ClassifierPerformance.html)\n", + "- [`ConfusionMatrix`](https://docs.validmind.ai/tests/model_validation/sklearn/ConfusionMatrix.html)\n", + "- [`MinimumAccuracy`](https://docs.validmind.ai/tests/model_validation/sklearn/MinimumAccuracy.html)\n", + "- [`MinimumF1Score`](https://docs.validmind.ai/tests/model_validation/sklearn/MinimumF1Score.html)\n", + "- [`ROCCurve`](https://docs.validmind.ai/tests/model_validation/sklearn/ROCCurve.html)\n", + "\n", + "When running individual tests, you can use a custom `result_id` to tag the individual result with a unique identifier by appending this `result_id` to the `test_id` with a `:` separator. We'll append an identifier for our champion model here:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mpt = [\n", + " \"validmind.model_validation.sklearn.ClassifierPerformance:xgboost_champion\",\n", + " \"validmind.model_validation.sklearn.ConfusionMatrix:xgboost_champion\",\n", + " \"validmind.model_validation.sklearn.MinimumAccuracy:xgboost_champion\",\n", + " \"validmind.model_validation.sklearn.MinimumF1Score:xgboost_champion\",\n", + " \"validmind.model_validation.sklearn.ROCCurve:xgboost_champion\"\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluate performance of the champion model\n", + "\n", + "Now, let's run and log our batch of model performance tests using our testing dataset (`vm_test_ds`) for our champion model:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for test in mpt:\n", + " vm.tests.run_test(\n", + " test,\n", + " inputs={\n", + " \"dataset\": vm_test_ds, \"model\" : vm_xgb_model, \n", + " },\n", + " ).log()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluate performance of challenger models\n", + "\n", + "We've now conducted similar tests as the model development team for our champion model, with the aim of verifying their test results.\n", + "\n", + "Next, let's see how our challenger models compare. We'll use the same batch of tests here as we did in `mpt`, but append a different `result_id` to indicate that these results should be associated with our challenger models:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mpt_chall = [\n", + " \"validmind.model_validation.sklearn.ClassifierPerformance:xgboost_champion_vs_challengers\",\n", + " \"validmind.model_validation.sklearn.ConfusionMatrix:xgboost_champion_vs_challengers\",\n", + " \"validmind.model_validation.sklearn.MinimumAccuracy:xgboost_champion_vs_challengers\",\n", + " \"validmind.model_validation.sklearn.MinimumF1Score:xgboost_champion_vs_challengers\",\n", + " \"validmind.model_validation.sklearn.ROCCurve:xgboost_champion_vs_challengers\"\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll also adjust the use case context to focus on this comparison for tests going forward:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.environ[\"VALIDMIND_LLM_DESCRIPTIONS_CONTEXT_ENABLED\"] = \"1\"\n", + "\n", + "context = \"\"\"\n", + "FORMAT FOR THE LLM DESCRIPTIONS: \n", + " **** is designed to .\n", + "\n", + " The test operates by \n", + "\n", + " The primary advantages of this test include \n", + "\n", + " Users should be aware that \n", + "\n", + " **Key Insights:**\n", + "\n", + " The test results reveal:\n", + "\n", + " - ****: \n", + " - ****: \n", + " ...\n", + "\n", + " Based on these results, \n", + "\n", + "ADDITIONAL INSTRUCTIONS:\n", + "\n", + " The champion model as the basis for comparison is called \"xgb_model_developer_champion\" and emphasis should be on the following:\n", + " - The metrics for the champion model compared against the challenger models\n", + " - Which model potentially outperforms the champion model based on the metrics, this should be highlighted and emphasized\n", + "\n", + "\n", + " For each metric in the test results, include in the test overview:\n", + " - The metric's purpose and what it measures\n", + " - Its mathematical formula\n", + " - The range of possible values\n", + " - What constitutes good/bad performance\n", + " - How to interpret different values\n", + "\n", + " Each insight should progressively cover:\n", + " 1. Overall scope and distribution\n", + " 2. Complete breakdown of all elements with specific values\n", + " 3. Natural groupings and patterns\n", + " 4. Comparative analysis between datasets/categories\n", + " 5. Stability and variations\n", + " 6. Notable relationships or dependencies\n", + "\n", + " Remember:\n", + " - Champion model (xgb_model_developer_champion) is the selection and challenger models are used to challenge the selection\n", + " - Keep all insights at the same level (no sub-bullets or nested structures)\n", + " - Make each insight complete and self-contained\n", + " - Include specific numerical values and ranges\n", + " - Cover all elements in the results comprehensively\n", + " - Maintain clear, concise language\n", + " - Use only \"- **Title**: Description\" format for insights\n", + " - Progress naturally from general to specific observations\n", + "\n", + "\"\"\".strip()\n", + "\n", + "os.environ[\"VALIDMIND_LLM_DESCRIPTIONS_CONTEXT\"] = context" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll run each test once for each model with the same `vm_test_ds` dataset to compare them:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for test in mpt_chall:\n", + " vm.tests.run_test(\n", + " test,\n", + " input_grid={\n", + " \"dataset\": [vm_test_ds], \"model\" : [vm_xgb_model,vm_log_model,vm_rf_model],\n", + " }\n", + " ).log()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
Based on the performance metrics, we can conclude that the random forest classification model is not a viable candidate for our use case and can be disregarded in our tests going forward.\n", + "

\n", + "In the final notebook, we'll dive a bit deeper into some tests comparing our champion application scorecard model and our remaining challenger logistic regression model, including tests that will allow us to customize parameters and thresholds for performance standards.
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## In summary\n", + "\n", + "In this third notebook, you learned how to:\n", + "\n", + "- [ ] Identify and run different types of validation tests\n", + "- [ ] Log test results as evidence to the ValidMind Platform\n", + "- [ ] Use tests from ValidMind to evaluate the potential of challenger models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Next steps\n", + "\n", + "### Finalize testing and reporting\n", + "\n", + "Now that you're familiar with the basics of using the ValidMind Library to run and log validation tests, let's learn how to implement some custom tests and wrap up our validation: **[104 Finalize testing and reporting](114-finalize_testing_reporting.ipynb)**" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ValidMind Library", + "language": "python", + "name": "validmind" + }, + "language_info": { + "name": "python", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/tutorials/model_validation/114-finalize_testing_reporting.ipynb b/notebooks/tutorials/model_validation/114-finalize_testing_reporting.ipynb new file mode 100644 index 000000000..5446026dc --- /dev/null +++ b/notebooks/tutorials/model_validation/114-finalize_testing_reporting.ipynb @@ -0,0 +1,1081 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ValidMind for model validation — 114 Finalize testing and reporting\n", + "\n", + "Learn how to use ValidMind for your end-to-end model validation process with our series of four introductory notebooks. In this last notebook, you'll configure and run some custom tests, then add logged test results to your validation report.\n", + "\n", + "As we concluded in [113 Perform validation tests](113-perform_validation_tests.ipynb), our challenger random forest classification model was not a viable candidate for our use case and was eliminated as a contender. We'll finish up by comparing our champion application scorecard model against our remaining challenger logistic regression model, then use the ValidMind Platform to draft our validation report supplemented by our logged test results as evidence." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "\n", + "In order to finalize the validation testing and reporting for your sample model, you'll need to first have:\n", + "\n", + "- [ ] Registered a model within the ValidMind Platform and granted yourself access to the model as a validator\n", + "- [ ] Installed the ValidMind Library in your local environment, allowing you to access all its features\n", + "- [ ] Learned how to import and initialize datasets for use with ValidMind\n", + "- [ ] Learned how to enable custom context for test descriptions generated by ValidMind\n", + "- [ ] Understood the basics of how to identify and run validation tests\n", + "- [ ] Run data quality and model performance tests for your champion and challenger models, and logged the results of those tests to the ValidMind Platform\n", + "\n", + "
Need help with the above steps?\n", + "

\n", + "Refer to the first three notebooks in this series:\n", + "\n", + "
    \n", + "
  1. 111 Import the champion model
    2. \n", + "
  2. 112 Develop potential challenger models
    3. \n", + "
  3. 113 Perform validation tests
    4. \n", + "
\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting up\n", + "\n", + "This section should be very familiar to you now — as we performed the same actions in the previous two notebooks in this series." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize the ValidMind Library\n", + "\n", + "As usual, let's first connect up the ValidMind Library to our model we previously registered in the ValidMind Platform:\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 \"ValidMind for model validation\" series of notebooks.\n", + "\n", + "3. 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, + "metadata": {}, + "outputs": [], + "source": [ + "# Make sure the ValidMind Library is installed\n", + "\n", + "%pip install -q validmind\n", + "\n", + "# 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": [ + "### Import the champion model\n", + "\n", + "Next, we'll import the champion model submitted by the model development team as we used in the last notebooks (**[xgb_model_champion.pkl](xgb_model_champion.pkl)**) and load in the sample [Lending Club](https://www.kaggle.com/datasets/devanshi23/loan-data-2007-2014/data) dataset used to develop the model, and independently preprocess and feature engineer the dataset to confirm that the model was built using appropriate and properly processed data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import xgboost as xgb\n", + "\n", + "#Load the saved model\n", + "xgb_model = xgb.XGBClassifier()\n", + "xgb_model.load_model(\"xgb_model_champion.pkl\")\n", + "xgb_model\n", + "\n", + "# Ensure that we have to appropriate order in feature names from Champion model and dataset\n", + "cols_when_model_builds = xgb_model.get_booster().feature_names" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import the Lending Club dataset from Kaggle\n", + "from validmind.datasets.credit_risk import lending_club\n", + "\n", + "df = lending_club.load_data(source=\"offline\")\n", + "df.head()\n", + "\n", + "# Preprocess the dataset for data quality testing purposes\n", + "preprocess_df = lending_club.preprocess(df)\n", + "\n", + "# Apply feature engineering to the dataset\n", + "fe_df = lending_club.feature_engineering(preprocess_df)\n", + "fe_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Split our dataset into train and test to start the validation testing process\n", + "train_df, test_df = lending_club.split(fe_df, test_size=0.2)\n", + "\n", + "x_train = train_df.drop(lending_club.target_column, axis=1)\n", + "y_train = train_df[lending_club.target_column]\n", + "\n", + "x_test = test_df.drop(lending_club.target_column, axis=1)\n", + "y_test = test_df[lending_club.target_column]\n", + "\n", + "# Now let's apply the order of features from the champion model construction\n", + "x_train = x_train[cols_when_model_builds]\n", + "x_test = x_test[cols_when_model_builds]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cols_use = ['annual_inc_woe',\n", + " 'verification_status_woe',\n", + " 'emp_length_woe',\n", + " 'installment_woe',\n", + " 'term_woe',\n", + " 'home_ownership_woe',\n", + " 'purpose_woe',\n", + " 'open_acc_woe',\n", + " 'total_acc_woe',\n", + " 'int_rate_woe',\n", + " 'sub_grade_woe',\n", + " 'grade_woe','loan_status']\n", + "\n", + "\n", + "train_df = train_df[cols_use]\n", + "test_df = test_df[cols_use]\n", + "test_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train the challenger model\n", + "\n", + "As we eliminated the random forest classification model as a challenger, we'll only train our logistic regression model here:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import the Logistic Regression model\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "# Logistic Regression grid params\n", + "log_reg_params = {\n", + " \"penalty\": [\"l1\", \"l2\"],\n", + " \"C\": [0.001, 0.01, 0.1, 1, 10, 100, 1000],\n", + " \"solver\": [\"liblinear\"],\n", + "}\n", + "\n", + "# Grid search for Logistic Regression\n", + "from sklearn.model_selection import GridSearchCV\n", + "\n", + "grid_log_reg = GridSearchCV(LogisticRegression(), log_reg_params)\n", + "grid_log_reg.fit(x_train, y_train)\n", + "\n", + "# Logistic Regression best estimator\n", + "log_reg = grid_log_reg.best_estimator_\n", + "log_reg" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Extract predicted probabilities\n", + "\n", + "With our challenger model trained, let's extract the predicted probabilities from our two models and convert the probability predictions into a binary:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Champion — Application scorecard model\n", + "train_xgb_prob = xgb_model.predict_proba(x_train)[:, 1]\n", + "test_xgb_prob = xgb_model.predict_proba(x_test)[:, 1]\n", + "\n", + "# Challenger — Logistic regression model\n", + "train_log_prob = log_reg.predict_proba(x_train)[:, 1]\n", + "test_log_prob = log_reg.predict_proba(x_test)[:, 1]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# If probability > 0.3 = 1 (positive)\n", + "cut_off_threshold = 0.3\n", + "\n", + "# Champion — Application scorecard model\n", + "train_xgb_binary_predictions = (train_xgb_prob > cut_off_threshold).astype(int)\n", + "test_xgb_binary_predictions = (test_xgb_prob > cut_off_threshold).astype(int)\n", + "\n", + "# Challenger — Logistic regression model\n", + "train_log_binary_predictions = (train_log_prob > cut_off_threshold).astype(int)\n", + "test_log_binary_predictions = (test_log_prob > cut_off_threshold).astype(int)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize the ValidMind objects\n", + "\n", + "Let's initialize the ValidMind `Dataset` and `Model` objects in preparation for assigning model predictions to each dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the raw dataset\n", + "vm_raw_dataset = vm.init_dataset(\n", + " dataset=df,\n", + " input_id=\"raw_dataset\",\n", + " target_column=lending_club.target_column,\n", + ")\n", + "\n", + "# Initialize the preprocessed dataset\n", + "vm_preprocess_dataset = vm.init_dataset(\n", + " dataset=preprocess_df,\n", + " input_id=\"preprocess_dataset\",\n", + " target_column=lending_club.target_column,\n", + ")\n", + "\n", + "# Initialize the feature engineered dataset\n", + "vm_fe_dataset = vm.init_dataset(\n", + " dataset=fe_df,\n", + " input_id=\"fe_dataset\",\n", + " target_column=lending_club.target_column,\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=lending_club.target_column,\n", + ")\n", + "\n", + "# Initialize the test dataset\n", + "vm_test_ds = vm.init_dataset(\n", + " dataset=test_df,\n", + " input_id=\"test_dataset\",\n", + " target_column=lending_club.target_column,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the champion application scorecard model\n", + "vm_xgb_model = vm.init_model(\n", + " xgb_model,\n", + " input_id=\"xgb_model_developer_champion\",\n", + ")\n", + "\n", + "# Initialize the challenger logistic regression model\n", + "vm_log_model = vm.init_model(\n", + " log_reg,\n", + " input_id=\"log_model\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Assign predictions\n", + "\n", + "With our models registered, we'll move on to assigning both the predictive probabilities coming directly from each model's predictions, and the binary prediction after applying the cutoff threshold:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Champion — Application scorecard model\n", + "vm_train_ds.assign_predictions(\n", + " model=vm_xgb_model,\n", + " prediction_values=train_xgb_binary_predictions,\n", + " prediction_probabilities=train_xgb_prob,\n", + ")\n", + "\n", + "vm_test_ds.assign_predictions(\n", + " model=vm_xgb_model,\n", + " prediction_values=test_xgb_binary_predictions,\n", + " prediction_probabilities=test_xgb_prob,\n", + ")\n", + "\n", + "# Challenger — Logistic regression model\n", + "vm_train_ds.assign_predictions(\n", + " model=vm_log_model,\n", + " prediction_values=train_log_binary_predictions,\n", + " prediction_probabilities=train_log_prob,\n", + ")\n", + "\n", + "vm_test_ds.assign_predictions(\n", + " model=vm_log_model,\n", + " prediction_values=test_log_binary_predictions,\n", + " prediction_probabilities=test_log_prob,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Compute the scores\n", + "train_xgb_scores = lending_club.compute_scores(train_xgb_prob)\n", + "test_xgb_scores = lending_club.compute_scores(test_xgb_prob)\n", + "train_log_scores = lending_club.compute_scores(train_log_prob)\n", + "test_log_scores = lending_club.compute_scores(test_log_prob)\n", + "\n", + "# Assign scores to the datasets\n", + "vm_train_ds.add_extra_column(\"xgb_scores\", train_xgb_scores)\n", + "vm_test_ds.add_extra_column(\"xgb_scores\", test_xgb_scores)\n", + "vm_train_ds.add_extra_column(\"log_scores\", train_log_scores)\n", + "vm_test_ds.add_extra_column(\"log_scores\", test_log_scores)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Enable use case context\n", + "\n", + "We'll also adjust the use case context to focus on comparison between our models for tests going forward:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.environ[\"VALIDMIND_LLM_DESCRIPTIONS_CONTEXT_ENABLED\"] = \"1\"\n", + "\n", + "context = \"\"\"\n", + "FORMAT FOR THE LLM DESCRIPTIONS: \n", + " **** is designed to .\n", + "\n", + " The test operates by \n", + "\n", + " The primary advantages of this test include \n", + "\n", + " Users should be aware that \n", + "\n", + " **Key Insights:**\n", + "\n", + " The test results reveal:\n", + "\n", + " - ****: \n", + " - ****: \n", + " ...\n", + "\n", + " Based on these results, \n", + "\n", + "ADDITIONAL INSTRUCTIONS:\n", + "\n", + " The champion model as the basis for comparison is called \"xgb_model_developer_champion\" and emphasis should be on the following:\n", + " - The metrics for the champion model compared against the challenger models\n", + " - Which model potentially outperforms the champion model based on the metrics, this should be highlighted and emphasized\n", + "\n", + "\n", + " For each metric in the test results, include in the test overview:\n", + " - The metric's purpose and what it measures\n", + " - Its mathematical formula\n", + " - The range of possible values\n", + " - What constitutes good/bad performance\n", + " - How to interpret different values\n", + "\n", + " Each insight should progressively cover:\n", + " 1. Overall scope and distribution\n", + " 2. Complete breakdown of all elements with specific values\n", + " 3. Natural groupings and patterns\n", + " 4. Comparative analysis between datasets/categories\n", + " 5. Stability and variations\n", + " 6. Notable relationships or dependencies\n", + "\n", + " Remember:\n", + " - Champion model (xgb_model_developer_champion) is the selection and challenger models are used to challenge the selection\n", + " - Keep all insights at the same level (no sub-bullets or nested structures)\n", + " - Make each insight complete and self-contained\n", + " - Include specific numerical values and ranges\n", + " - Cover all elements in the results comprehensively\n", + " - Maintain clear, concise language\n", + " - Use only \"- **Title**: Description\" format for insights\n", + " - Progress naturally from general to specific observations\n", + "\n", + "\"\"\".strip()\n", + "\n", + "os.environ[\"VALIDMIND_LLM_DESCRIPTIONS_CONTEXT\"] = context" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adjust a ValidMind test\n", + "\n", + "Let's dig deeper into the [`MinimumF1Score` test](https://docs.validmind.ai/tests/model_validation/sklearn/MinimumF1Score.html) we ran previously in [113 Perform validation tests](113-perform_validation_tests.ipynb) to ensure that the models maintain a minimum acceptable balance between *precision* and *recall*. Precision refers to how many out of the positive predictions made by the model were actually correct, and recall refers to how many out of the actual positive cases did the model correctly identify.\n", + "\n", + "Use [`run_test()`](https://docs.validmind.ai/validmind/validmind/tests.html#run_test) with our testing dataset (`vm_test_ds`) to run the test in isolation again for our two remaining models without logging the result to have the output to compare with a subsequent iteration:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vm.tests.run_test(\n", + " \"validmind.model_validation.sklearn.MinimumF1Score:xgboost_champion_vs_challengers\",\n", + " input_grid={\n", + " \"dataset\": [vm_test_ds],\n", + " \"model\": [vm_xgb_model, vm_log_model]\n", + " },\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As `MinimumF1Score` allows us to customize parameters and thresholds for performance standards, let's adjust the threshold to see if it improves metrics:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "result = vm.tests.run_test(\n", + " \"validmind.model_validation.sklearn.MinimumF1Score:AdjThreshold\",\n", + " input_grid={\n", + " \"dataset\": [vm_test_ds],\n", + " \"model\": [vm_xgb_model, vm_log_model],\n", + " \"params\": {\"min_threshold\": 0.35}\n", + " },\n", + ").log()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
Note the output returned indicating that a test-driven block doesn't currently exist in your model's documentation for some test IDs. \n", + "

\n", + "That's expected, as when we run validations tests the results logged need to be manually added to your report as part of your compliance assessment process within the ValidMind Platform.
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run diagnostic tests\n", + "\n", + "Next we want to inspect the robustness and stability testing comparison between our champion and challenger model.\n", + "\n", + "Use [`list_tests()`](https://docs.validmind.ai/validmind/validmind/tests.html#list_tests) to identify all the model diagnosis tests for classification:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vm.tests.list_tests(tags=[\"model_diagnosis\"], task=\"classification\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
Want to learn more about navigating ValidMind tests?\n", + "

\n", + "Refer to our notebook outlining the utilities available for viewing and understanding available ValidMind tests: Explore tests
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's see if models suffer from any *overfit* potentials and also where there are potential sub-segments of issues. Overfitting occurs when a model learns the training data too well, capturing not only the true pattern but noise and random fluctuations resulting in excellent performance on the training dataset but poor generalization to new, unseen data.\n", + "\n", + "We'll select the following two tests:\n", + "\n", + "- **[`TrainingTestDegradation`](https://docs.validmind.ai/tests/model_validation/sklearn/TrainingTestDegradation.html):** Evaulates how much the models' performance degrade when moving from the training to the test datasets.\n", + "- **[`OverfitDiagnosis`](https://docs.validmind.ai/tests/model_validation/sklearn/OverfitDiagnosis.html):** Dives into detecting overfitting beyond performance degradation by examining learning curves, model complexity v.s. generalization, and cross-validation results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Store our desired 2 tests\n", + "overfit_testing = [\n", + " \"validmind.model_validation.sklearn.TrainingTestDegradation:Champion_vs_LogRegression\",\n", + " \"validmind.model_validation.sklearn.OverfitDiagnosis:Champion_vs_LogRegression\"\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run and log our 2 tests for both models for the training and testing datasets\n", + "for test in overfit_testing:\n", + " vm.tests.run_test(\n", + " test,\n", + " input_grid={\n", + " \"datasets\": [[vm_train_ds,vm_test_ds]],\n", + " \"model\" : [vm_xgb_model,vm_log_model]\n", + " }\n", + " ).log()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's also conduct *robustness* and *stability* testing of the two models. Robustness refers to a model's ability to maintain consistent performance, and stability refers to a model's ability to produce consistent outputs over time across different data subsets.\n", + "\n", + "We'll select the following two tests:\n", + "\n", + "- **[`RobustnessDiagnosis`](https://docs.validmind.ai/tests/model_validation/sklearn/RobustnessDiagnosis.html):** Evaluates whether the models are resilient to small *perturbations* (small, controlled modifications) or *variations* (broader subgroup-level or distributional changes) in input data.\n", + "- **[`WeakspotsDiagnosis`](https://docs.validmind.ai/tests/model_validation/sklearn/WeakspotsDiagnosis.html):** Identifies specific conditions where the models consistently underperform." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Store our desired 2 tests\n", + "stab_robust = [\n", + " \"validmind.model_validation.sklearn.RobustnessDiagnosis:Champion_vs_LogRegression\",\n", + " \"validmind.model_validation.sklearn.WeakspotsDiagnosis:Champion_vs_LogRegression\"\n", + " ]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run and log our 2 tests for both models for the training and testing datasets\n", + "for test in stab_robust:\n", + " vm.tests.run_test(\n", + " test,\n", + " input_grid={\n", + " \"datasets\": [[vm_train_ds,vm_test_ds]],\n", + " \"model\" : [vm_xgb_model,vm_log_model]\n", + " },\n", + " ).log()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run feature importance tests\n", + "\n", + "We want to verify the relative influence of different input features on our models' predictions, as well as inspect the differences between our champion and challenger model to see if a certain model offers more understandable or logical importance scores for features.\n", + "\n", + "Use `list_tests()` to identify all the feature importance tests for classification:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Store the feature importance tests\n", + "FI = vm.tests.list_tests(tags=[\"feature_importance\"], task=\"classification\",pretty=False)\n", + "FI" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run and log our feature importance tests for both models for the testing dataset\n", + "for test in FI:\n", + " vm.tests.run_test(\n", + " \"\".join((test,':Champion_vs_LogisticRegression')),\n", + " input_grid={\n", + " \"dataset\": [vm_test_ds], \"model\" : [vm_xgb_model,vm_log_model]\n", + " },\n", + " ).log()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Implement a custom test\n", + "\n", + "Let's finish up testing by implementing a custom *inline test* that outputs a FICO score-type score. An inline test refers to a test written and executed within the same environment as the code being tested — in this case, right in this Jupyter Notebook — without requiring a separate test file or framework.\n", + "\n", + "The [`@vm.test` wrapper](https://docs.validmind.ai/validmind/validmind.html#test) allows you to create a reusable test:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import plotly.graph_objects as go\n", + "\n", + "@vm.test(\"my_custom_tests.ScoreToOdds\")\n", + "def score_to_odds_analysis(dataset, score_column='score', score_bands=[410, 440, 470]):\n", + " \"\"\"\n", + " Analyzes the relationship between score bands and odds (good:bad ratio).\n", + " Good odds = (1 - default_rate) / default_rate\n", + " \n", + " Higher scores should correspond to higher odds of being good.\n", + "\n", + " If there are multiple scores provided through score_column, this means that there are two different models and the scores reflect each model\n", + "\n", + " If there are more scores provided in the score_column then focus the assessment on the differences between the two scores and indicate through evidence which one is preferred.\n", + " \"\"\"\n", + " df = dataset.df\n", + " \n", + " # Create score bands\n", + " df['score_band'] = pd.cut(\n", + " df[score_column],\n", + " bins=[-np.inf] + score_bands + [np.inf],\n", + " labels=[f'<{score_bands[0]}'] + \n", + " [f'{score_bands[i]}-{score_bands[i+1]}' for i in range(len(score_bands)-1)] +\n", + " [f'>{score_bands[-1]}']\n", + " )\n", + " \n", + " # Calculate metrics per band\n", + " results = df.groupby('score_band').agg({\n", + " dataset.target_column: ['mean', 'count']\n", + " })\n", + " \n", + " results.columns = ['Default Rate', 'Total']\n", + " results['Good Count'] = results['Total'] - (results['Default Rate'] * results['Total'])\n", + " results['Bad Count'] = results['Default Rate'] * results['Total']\n", + " results['Odds'] = results['Good Count'] / results['Bad Count']\n", + " \n", + " # Create visualization\n", + " fig = go.Figure()\n", + " \n", + " # Add odds bars\n", + " fig.add_trace(go.Bar(\n", + " name='Odds (Good:Bad)',\n", + " x=results.index,\n", + " y=results['Odds'],\n", + " marker_color='blue'\n", + " ))\n", + " \n", + " fig.update_layout(\n", + " title='Score-to-Odds Analysis',\n", + " yaxis=dict(title='Odds Ratio (Good:Bad)'),\n", + " showlegend=False\n", + " )\n", + " \n", + " return fig" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the custom test available, run and log the test for our champion and challenger models with our testing dataset (`vm_test_ds`):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "result = vm.tests.run_test(\n", + " \"my_custom_tests.ScoreToOdds:Champion_vs_Challenger\",\n", + " inputs={\n", + " \"dataset\": vm_test_ds,\n", + " },\n", + " param_grid={\n", + " \"score_column\": [\"xgb_scores\",\"log_scores\"],\n", + " \"score_bands\": [[500, 540, 570]],\n", + " },\n", + ").log()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
Want to learn more about custom tests?\n", + "

\n", + "Refer to our in-depth introduction to custom tests: Implement custom tests
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Verify test runs\n", + "\n", + "Our final task is to verify that all the tests provided by the model development team were run and reported accurately. Note the appended `result_ids` to delineate which dataset we ran the test with for the relevant tests.\n", + "\n", + "Here, we'll specify all the tests we'd like to independently rerun in a dictionary called `test_config`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "test_config = {\n", + " # Run with the raw dataset\n", + " 'validmind.data_validation.DatasetDescription:raw_data': {\n", + " 'inputs': {'dataset': 'raw_dataset'}\n", + " },\n", + " 'validmind.data_validation.DescriptiveStatistics:raw_data': {\n", + " 'inputs': {'dataset': 'raw_dataset'}\n", + " },\n", + " 'validmind.data_validation.MissingValues:raw_data': {\n", + " 'inputs': {'dataset': 'raw_dataset'},\n", + " 'params': {'min_threshold': 1}\n", + " },\n", + " 'validmind.data_validation.ClassImbalance:raw_data': {\n", + " 'inputs': {'dataset': 'raw_dataset'},\n", + " 'params': {'min_percent_threshold': 10}\n", + " },\n", + " 'validmind.data_validation.Duplicates:raw_data': {\n", + " 'inputs': {'dataset': 'raw_dataset'},\n", + " 'params': {'min_threshold': 1}\n", + " },\n", + " 'validmind.data_validation.HighCardinality:raw_data': {\n", + " 'inputs': {'dataset': 'raw_dataset'},\n", + " 'params': {\n", + " 'num_threshold': 100,\n", + " 'percent_threshold': 0.1,\n", + " 'threshold_type': 'percent'\n", + " }\n", + " },\n", + " 'validmind.data_validation.Skewness:raw_data': {\n", + " 'inputs': {'dataset': 'raw_dataset'},\n", + " 'params': {'max_threshold': 1}\n", + " },\n", + " 'validmind.data_validation.UniqueRows:raw_data': {\n", + " 'inputs': {'dataset': 'raw_dataset'},\n", + " 'params': {'min_percent_threshold': 1}\n", + " },\n", + " 'validmind.data_validation.TooManyZeroValues:raw_data': {\n", + " 'inputs': {'dataset': 'raw_dataset'},\n", + " 'params': {'max_percent_threshold': 0.03}\n", + " },\n", + " 'validmind.data_validation.IQROutliersTable:raw_data': {\n", + " 'inputs': {'dataset': 'raw_dataset'},\n", + " 'params': {'threshold': 5}\n", + " },\n", + " # Run with the preprocessed dataset\n", + " 'validmind.data_validation.DescriptiveStatistics:preprocessed_data': {\n", + " 'inputs': {'dataset': 'preprocess_dataset'}\n", + " },\n", + " 'validmind.data_validation.TabularDescriptionTables:preprocessed_data': {\n", + " 'inputs': {'dataset': 'preprocess_dataset'}\n", + " },\n", + " 'validmind.data_validation.MissingValues:preprocessed_data': {\n", + " 'inputs': {'dataset': 'preprocess_dataset'},\n", + " 'params': {'min_threshold': 1}\n", + " },\n", + " 'validmind.data_validation.TabularNumericalHistograms:preprocessed_data': {\n", + " 'inputs': {'dataset': 'preprocess_dataset'}\n", + " },\n", + " 'validmind.data_validation.TabularCategoricalBarPlots:preprocessed_data': {\n", + " 'inputs': {'dataset': 'preprocess_dataset'}\n", + " },\n", + " 'validmind.data_validation.TargetRateBarPlots:preprocessed_data': {\n", + " 'inputs': {'dataset': 'preprocess_dataset'},\n", + " 'params': {'default_column': 'loan_status'}\n", + " },\n", + " # Run with the training and test datasets\n", + " 'validmind.data_validation.DescriptiveStatistics:development_data': {\n", + " 'input_grid': {'dataset': ['train_dataset', 'test_dataset']}\n", + " },\n", + " 'validmind.data_validation.TabularDescriptionTables:development_data': {\n", + " 'input_grid': {'dataset': ['train_dataset', 'test_dataset']}\n", + " },\n", + " 'validmind.data_validation.ClassImbalance:development_data': {\n", + " 'input_grid': {'dataset': ['train_dataset', 'test_dataset']},\n", + " 'params': {'min_percent_threshold': 10}\n", + " },\n", + " 'validmind.data_validation.UniqueRows:development_data': {\n", + " 'input_grid': {'dataset': ['train_dataset', 'test_dataset']},\n", + " 'params': {'min_percent_threshold': 1}\n", + " },\n", + " 'validmind.data_validation.TabularNumericalHistograms:development_data': {\n", + " 'input_grid': {'dataset': ['train_dataset', 'test_dataset']}\n", + " },\n", + " 'validmind.data_validation.MutualInformation:development_data': {\n", + " 'input_grid': {'dataset': ['train_dataset', 'test_dataset']},\n", + " 'params': {'min_threshold': 0.01}\n", + " },\n", + " 'validmind.data_validation.PearsonCorrelationMatrix:development_data': {\n", + " 'input_grid': {'dataset': ['train_dataset', 'test_dataset']}\n", + " },\n", + " 'validmind.data_validation.HighPearsonCorrelation:development_data': {\n", + " 'input_grid': {'dataset': ['train_dataset', 'test_dataset']},\n", + " 'params': {'max_threshold': 0.3, 'top_n_correlations': 10}\n", + " },\n", + " 'validmind.model_validation.ModelMetadata': {\n", + " 'input_grid': {'model': ['xgb_model', 'rf_model']}\n", + " },\n", + " 'validmind.model_validation.sklearn.ModelParameters': {\n", + " 'input_grid': {'model': ['xgb_model', 'rf_model']}\n", + " },\n", + " 'validmind.model_validation.sklearn.ROCCurve': {\n", + " 'input_grid': {'dataset': ['train_dataset', 'test_dataset'], 'model': ['xgb_model']}\n", + " },\n", + " 'validmind.model_validation.sklearn.MinimumROCAUCScore': {\n", + " 'input_grid': {'dataset': ['train_dataset', 'test_dataset'], 'model': ['xgb_model']},\n", + " 'params': {'min_threshold': 0.5}\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then batch run and log our tests in `test_config`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for t in test_config:\n", + " print(t)\n", + " try:\n", + " # Check if test has input_grid\n", + " if 'input_grid' in test_config[t]:\n", + " # For tests with input_grid, pass the input_grid configuration\n", + " if 'params' in test_config[t]:\n", + " vm.tests.run_test(t, input_grid=test_config[t]['input_grid'], params=test_config[t]['params']).log()\n", + " else:\n", + " vm.tests.run_test(t, input_grid=test_config[t]['input_grid']).log()\n", + " else:\n", + " # Original logic for regular inputs\n", + " if 'params' in test_config[t]:\n", + " vm.tests.run_test(t, inputs=test_config[t]['inputs'], params=test_config[t]['params']).log()\n", + " else:\n", + " vm.tests.run_test(t, inputs=test_config[t]['inputs']).log()\n", + " except Exception as e:\n", + " print(f\"Error running test {t}: {str(e)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add test results to reporting\n", + "\n", + "With all the test results logged, let's head to the model we connected to at the beginning of this notebook and insert some test results into our validation report ([Need more help?](https://docs.validmind.ai/guide/model-validation/assess-compliance.html#link-validator-evidence)):\n", + "\n", + "1. From the **Inventory** in the ValidMind Platform, go to the model you connected to earlier.\n", + "\n", + "2. In the left sidebar that appears for your model, click **Validation Report**.\n", + "\n", + "3. Locate the Data Preparation section and click on **2.2.2. Model Performance** to expand that section.\n", + "\n", + "4. Under the Model Performance Metrics section, locate Validator Evidence then click **Link Evidence to Report**:\n", + "\n", + " \"Screenshot\n", + "

\n", + "\n", + "5. Select the Minimum F1 Score test results we logged:\n", + "\n", + " - **ValidMind Model Validation Sklearn Minimum F 1 Score Adj Threshold**\n", + " - **ValidMind Model Validation Sklearn Minimum F 1 Score Xgboost Champion**\n", + " - **ValidMind Model Validation Sklearn Minimum F 1 Score Xgboost Champion Vs Challengers**\n", + "\n", + " \"Screenshot\n", + "

\n", + "\n", + "6. Finally, click **Update Linked Evidence** to add the test results to the validation report.\n", + "\n", + " Confirm that the results for the minimum F1 score tests have been correctly inserted into section **2.2.2. Model Performance** of the report:\n", + "\n", + " \"Screenshot\n", + "

\n", + "\n", + "7. Continue to work on your validation report by:\n", + "\n", + " - **Adding risk assessment notes:** Click under **Risk Assessment Notes** in any validation report section to access the text editor and content editing toolbar, including an option to generate a draft with AI. (Learn more: [Work with content blocks](https://docs.validmind.ai/guide/model-documentation/work-with-content-blocks.html#content-editing-toolbar))\n", + " - **Adding findings:** Click on **Link Finding to Report** in any validation report section, then click **+ Create New Finding**. (Learn more: [Add and manage model findings](https://docs.validmind.ai/guide/model-validation/add-manage-model-findings.html))\n", + " - **Assessing compliance:** Under the Guideline for any validation report section, click **ASSESSMENT** and select the compliance status from the drop-down menu. (Learn more: [Assess compliance](https://docs.validmind.ai/guide/model-validation/assess-compliance.html#provide-compliance-assessments))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## In summary\n", + "\n", + "In this final notebook, you learned how to:\n", + "\n", + "- [ ] Adjust an out-of-the box ValidMind test\n", + "- [ ] Implement a custom inline test\n", + "- [ ] Re-run tests provided by your model development team to verify that they were run and reported accurately\n", + "- [ ] Link your logged test results as evidence on your validation report\n", + "\n", + "With our ValidMind for model validation series of notebooks, you learned how to validate a model end-to-end with the ValidMind Library by running through some common scenarios in a typical model validation setting:\n", + "\n", + "- Verifying the data quality steps performed by the model development team\n", + "- Independently replicating the champion model's results and conducting additional tests to assess performance, stability, and robustness\n", + "- Setting up test inputs and challenger models for comparative analysis\n", + "- Running validation tests, analyzing results, and logging findings to ValidMind" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Next steps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Work with your validation report\n", + "\n", + "Now that you've logged all your test results and verified the work done by the model development team, head to the ValidMind Platform to wrap up your validation report. Include risk assessment notes, add findings, and assess compliance, then submit your report for review when it's ready. **Learn more:** [Preparing validation reports](https://docs.validmind.ai/guide/model-validation/preparing-validation-reports.html)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Learn more\n", + "\n", + "Now that you're familiar with the basics, you can explore the following notebooks to get a deeper understanding on how the ValidMind Library assists you in streamlining model validation:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### More how-to guides and code samples\n", + "\n", + "- [Explore available tests in detail](../../how_to/explore_tests.ipynb)\n", + "- [In-depth guide on running dataset based tests](../../how_to/run_tests/1_run_dataset_based_tests.ipynb)\n", + "- [In-depth guide for running comparison tests](../../how_to/run_tests/2_run_comparison_tests.ipynb)\n", + "- [In-depth guide for implementing custom tests](../../code_samples/custom_tests/implement_custom_tests.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Discover more learning resources\n", + "\n", + "All notebook samples can be found in the following directories of the ValidMind Library GitHub repository:\n", + "\n", + "- [Code samples](https://github.com/validmind/validmind-library/tree/main/notebooks/code_samples)\n", + "- [How-to guides](https://github.com/validmind/validmind-library/tree/main/notebooks/how_to)\n", + "\n", + "Or, visit our [documentation](https://docs.validmind.ai/) to learn more about ValidMind." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ValidMind Library", + "language": "python", + "name": "validmind" + }, + "language_info": { + "name": "python", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/tutorials/model_validation/compliance-summary.png 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zL-N~C(DUv#&8YBeOt9R#oEpb3Wa;&uis<}N(9uQuWB|CViujLEMPz_@22=w*CCF3A=d z)+aTqN*z#XFxq{~PWYxb_&4lRBPuB|FEqc!L*>gY{;^%5LJ%rt=y}L~V3h4qAAKf@ z+9f7jyFbfr=i}Yu>x;dgNyA!3J^A(O@X;dfB>~08XCYx@t1!#42)FbI8En&1u7)1O zx-{`Q9X39OZtE3JTPPJkK@bap4+{KcHIE>IJ+BSzSS&7^A1iU<;)%liT6NS|t1Gp7YXP(Da0T_|@H9QAom}Uu z4=A2ga>Z@eYhn8*PF7ib1(}!FBD$3jdZH!_s$}vh0f>MMAPF4XwC|n?N419I+ zaP!%SwekbS!OKI?>Cp0P#AB`?fpi&7gp(>S+Uv18;I&;XzaTgo;0f#Tro_om{zJ?*{$irZ4I`Y&o=dd=4P zkGXzD5NIZ_s%6XH3Y1zd;Syj<@d2v7MpKCs1# literal 0 HcmV?d00001 From a267d30a62966d21d6ea44c373d4fca582e225b4 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Fri, 28 Mar 2025 11:10:16 -0700 Subject: [PATCH 02/71] Renamed the old model development series --- ...up_validmind.ipynb => 1-set_up_validmind.ipynb} | 4 ++-- ...ess.ipynb => 2-start_development_process.ipynb} | 6 +++--- ..._tests.ipynb => 3-integrate_custom_tests.ipynb} | 14 ++++++-------- ...pynb => 4-finalize_testing_documentation.ipynb} | 14 ++++++-------- 4 files changed, 17 insertions(+), 21 deletions(-) rename notebooks/tutorials/model_development/{101-set_up_validmind.ipynb => 1-set_up_validmind.ipynb} (98%) rename notebooks/tutorials/model_development/{102-start_development_process.ipynb => 2-start_development_process.ipynb} (99%) rename notebooks/tutorials/model_development/{103-integrate_custom_tests.ipynb => 3-integrate_custom_tests.ipynb} (98%) rename notebooks/tutorials/model_development/{104-finalize_testing_documentation.ipynb => 4-finalize_testing_documentation.ipynb} (97%) diff --git a/notebooks/tutorials/model_development/101-set_up_validmind.ipynb b/notebooks/tutorials/model_development/1-set_up_validmind.ipynb similarity index 98% rename from notebooks/tutorials/model_development/101-set_up_validmind.ipynb rename to notebooks/tutorials/model_development/1-set_up_validmind.ipynb index 9a5936350..63881b517 100644 --- a/notebooks/tutorials/model_development/101-set_up_validmind.ipynb +++ b/notebooks/tutorials/model_development/1-set_up_validmind.ipynb @@ -5,7 +5,7 @@ "id": "97710f2a", "metadata": {}, "source": [ - "# ValidMind for model development — 101 Set up the ValidMind Library\n", + "# ValidMind for model development 1 — Set up the ValidMind Library\n", "\n", "Learn how to use ValidMind for your end-to-end model documentation process based on common model development scenarios with our series of four introductory notebooks. This first notebook walks you through the initial setup of the ValidMind Library.\n", "\n", @@ -388,7 +388,7 @@ "\n", "### Start the model development process\n", "\n", - "Now that the ValidMind Library is connected to your model in the ValidMind Library with the correct template applied, we can go ahead and start the model development process: **[102 Start the model development process](102-start_development_process.ipynb)**" + "Now that the ValidMind Library is connected to your model in the ValidMind Library with the correct template applied, we can go ahead and start the model development process: **[2 — Start the model development process](2-start_development_process.ipynb)**" ] } ], diff --git a/notebooks/tutorials/model_development/102-start_development_process.ipynb b/notebooks/tutorials/model_development/2-start_development_process.ipynb similarity index 99% rename from notebooks/tutorials/model_development/102-start_development_process.ipynb rename to notebooks/tutorials/model_development/2-start_development_process.ipynb index 4cdfa0247..8ee80f611 100644 --- a/notebooks/tutorials/model_development/102-start_development_process.ipynb +++ b/notebooks/tutorials/model_development/2-start_development_process.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# ValidMind for model development — 102 Start the model development process\n", + "# ValidMind for model development 2 — Start the model development process\n", "\n", "Learn how to use ValidMind for your end-to-end model documentation process with our series of four introductory notebooks. In this second notebook, you'll run tests and investigate results, then add the results or evidence to your documentation.\n", "\n", @@ -67,7 +67,7 @@ "\n", "

Need help with the above steps?\n", "

\n", - "Refer to the first notebook in this series: 101 Set up ValidMind
\n" + "Refer to the first notebook in this series: 1 — Set up ValidMind\n" ] }, { @@ -930,7 +930,7 @@ "\n", "### Integrate custom tests\n", "\n", - "Now that you're familiar with the basics of using the ValidMind Library to run and log tests to provide evidence for your model documentation, let's learn how to incorporate your own custom tests into ValidMind: **[103 Integrate custom tests](103-integrate_custom_tests.ipynb)**" + "Now that you're familiar with the basics of using the ValidMind Library to run and log tests to provide evidence for your model documentation, let's learn how to incorporate your own custom tests into ValidMind: **[3 — Integrate custom tests](3-integrate_custom_tests.ipynb)**" ] } ], diff --git a/notebooks/tutorials/model_development/103-integrate_custom_tests.ipynb b/notebooks/tutorials/model_development/3-integrate_custom_tests.ipynb similarity index 98% rename from notebooks/tutorials/model_development/103-integrate_custom_tests.ipynb rename to notebooks/tutorials/model_development/3-integrate_custom_tests.ipynb index bcde58c62..751220c99 100644 --- a/notebooks/tutorials/model_development/103-integrate_custom_tests.ipynb +++ b/notebooks/tutorials/model_development/3-integrate_custom_tests.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# ValidMind for model development — 103 Integrate custom tests\n", + "# ValidMind for model development 3 — Integrate custom tests\n", "\n", "Learn how to use ValidMind for your end-to-end model documentation process with our series of four introductory notebooks. In this third notebook, supplement ValidMind tests with your own and include them as additional evidence in your documentation.\n", "\n", @@ -77,10 +77,8 @@ "

\n", "Refer to the first two notebooks in this series:\n", "\n", - "
    \n", - "
  1. 101 Set up ValidMind
    2. \n", - "
  2. 102 Start the model development process
    3. \n", - "
\n", + "- 1 — Set up ValidMind\n", + "- 2 — Start the model development process\n", "\n", "\n" ] @@ -93,7 +91,7 @@ "\n", "## Setting up\n", "\n", - "This section should be quite familiar to you — as we performed the same actions in the previous notebook, **[102 Start the model development process](102-start_development_process.ipynb)**." + "This section should be quite familiar to you — as we performed the same actions in the previous notebook, **[2 — Start the model development process](2-start_development_process.ipynb)**." ] }, { @@ -652,7 +650,7 @@ "\n", "### Log the confusion matrix results\n", "\n", - "As we learned in **[102 Start the model development process](102-start_development_process.ipynb)** under **Documenting results** > **Run and log an individual tests**, you can log any result to the ValidMind Platform with the [`.log()` method](https://docs.validmind.ai/validmind/validmind/vm_models.html#TestResult.log) of the result object, allowing you to then add the result to the documentation.\n", + "As we learned in **[2 — Start the model development process](2-start_development_process.ipynb)** under **Documenting results** > **Run and log an individual tests**, you can log any result to the ValidMind Platform with the [`.log()` method](https://docs.validmind.ai/validmind/validmind/vm_models.html#TestResult.log) of the result object, allowing you to then add the result to the documentation.\n", "\n", "You can now do the same for the confusion matrix results:\n" ] @@ -958,7 +956,7 @@ "\n", "### Finalize testing and documentation\n", "\n", - "Now that you're proficient at using the ValidMind Library to run and log tests, let's put the last pieces in place to prepare our fully documented sample model for review: **[104 Finalize testing and documentation](104-finalize_testing_documentation.ipynb)**" + "Now that you're proficient at using the ValidMind Library to run and log tests, let's put the last pieces in place to prepare our fully documented sample model for review: **[4 — Finalize testing and documentation](4-finalize_testing_documentation.ipynb)**" ] } ], diff --git a/notebooks/tutorials/model_development/104-finalize_testing_documentation.ipynb b/notebooks/tutorials/model_development/4-finalize_testing_documentation.ipynb similarity index 97% rename from notebooks/tutorials/model_development/104-finalize_testing_documentation.ipynb rename to notebooks/tutorials/model_development/4-finalize_testing_documentation.ipynb index fd17733fc..cb7d1f4e7 100644 --- a/notebooks/tutorials/model_development/104-finalize_testing_documentation.ipynb +++ b/notebooks/tutorials/model_development/4-finalize_testing_documentation.ipynb @@ -4,11 +4,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# ValidMind for model development — 104 Finalize testing and documentation\n", + "# ValidMind for model development 4 — Finalize testing and documentation\n", "\n", "Learn how to use ValidMind for your end-to-end model documentation process with our introductory notebook series. In this last notebook, finalize the testing and documentation of your model and have a fully documented sample model ready for review.\n", "\n", - "We'll first use [`run_documentation_tests()`](https://docs.validmind.ai/validmind/validmind.html#run_documentation_tests) previously covered in **[102 Start the model development process](102-start_development_process.ipynb)** to ensure that your custom test results generated in **[103 Integrate custom tests](103-integrate_custom_tests.ipynb)** are included in your documentation. Then, we'll view and update the configuration for the entire model documentation template to suit your needs.\n" + "We'll first use [`run_documentation_tests()`](https://docs.validmind.ai/validmind/validmind.html#run_documentation_tests) previously covered in **[2 — Start the model development process](2-start_development_process.ipynb)** to ensure that your custom test results generated in **[3 — Integrate custom tests](3-integrate_custom_tests.ipynb)** are included in your documentation. Then, we'll view and update the configuration for the entire model documentation template to suit your needs.\n" ] }, { @@ -74,11 +74,9 @@ "

\n", "Refer to the first three notebooks in this series:\n", "\n", - "
    \n", - "
  1. 101 Set up ValidMind
    2. \n", - "
  2. 102 Start the model development process
    3. \n", - "
  3. 103 Integrate custom tests
    4. \n", - "
\n", + "- 1 — Set up ValidMind\n", + "- 2 — Start the model development process\n", + "- 3 — Integrate custom tests\n", "\n", "" ] @@ -638,7 +636,7 @@ "\n", "Let's run all tests in the Model Evaluation section of the documentation. Note that we have been running the sample custom confusion matrix with `normalize=True` to demonstrate the ability to provide custom parameters.\n", "\n", - "In the **Run the model evaluation tests** section of **[102 Start the model development process](102-start_development_process.ipynb)**, you learned how to assign inputs to individual tests with [`run_documentation_tests()`](https://docs.validmind.ai/validmind/validmind.html#run_documentation_tests). Assigning parameters is similar, you only need to provide assign a `params` dictionary to a given test ID, `my_test_provider.ConfusionMatrix` in this case.\n" + "In the **Run the model evaluation tests** section of **[2 — Start the model development process](2-start_development_process.ipynb)**, you learned how to assign inputs to individual tests with [`run_documentation_tests()`](https://docs.validmind.ai/validmind/validmind.html#run_documentation_tests). Assigning parameters is similar, you only need to provide assign a `params` dictionary to a given test ID, `my_test_provider.ConfusionMatrix` in this case.\n" ] }, { From 575711fa9ee54d54b6317cee883f98cddf0680a6 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Fri, 28 Mar 2025 11:19:24 -0700 Subject: [PATCH 03/71] Renamed the new model validation series --- ...n_model.ipynb => 1-import_champion_model.ipynb} | 4 ++-- ...dels.ipynb => 2-develop_challenger_model.ipynb} | 6 +++--- ...ests.ipynb => 3-perform_validation_tests.ipynb} | 13 ++++++------- ...ng.ipynb => 4-finalize_testing_reporting.ipynb} | 14 ++++++-------- 4 files changed, 17 insertions(+), 20 deletions(-) rename notebooks/tutorials/model_validation/{111-import_champion_model.ipynb => 1-import_champion_model.ipynb} (99%) rename notebooks/tutorials/model_validation/{112-develop_challenger_models.ipynb => 2-develop_challenger_model.ipynb} (99%) rename notebooks/tutorials/model_validation/{113-perform_validation_tests.ipynb => 3-perform_validation_tests.ipynb} (98%) rename notebooks/tutorials/model_validation/{114-finalize_testing_reporting.ipynb => 4-finalize_testing_reporting.ipynb} (98%) diff --git a/notebooks/tutorials/model_validation/111-import_champion_model.ipynb b/notebooks/tutorials/model_validation/1-import_champion_model.ipynb similarity index 99% rename from notebooks/tutorials/model_validation/111-import_champion_model.ipynb rename to notebooks/tutorials/model_validation/1-import_champion_model.ipynb index 730a25129..b7fc79e05 100644 --- a/notebooks/tutorials/model_validation/111-import_champion_model.ipynb +++ b/notebooks/tutorials/model_validation/1-import_champion_model.ipynb @@ -5,7 +5,7 @@ "id": "248883b1", "metadata": {}, "source": [ - "# ValidMind for model validation — 111 Import the champion model\n", + "# ValidMind for model validation 1 — Import the champion model\n", "\n", "Learn how to use ValidMind for your end-to-end model validation process based on common scenarios with our series of four introductory notebooks. In this first notebook, set up the ValidMind Library in preparation for validating a champion model.\n", "\n", @@ -448,7 +448,7 @@ "\n", "### Develop potential challenger models\n", "\n", - "Now that the ValidMind Library is connected to your model in the ValidMind Library with the correct template applied, we can go ahead and start the model validation process: **[112 Develop potential challenger models](112-develop_challenger_models.ipynb)**" + "Now that the ValidMind Library is connected to your model in the ValidMind Library with the correct template applied, we can go ahead and start the model validation process: **[2 — Develop potential challenger model](2-develop_challenger_model.ipynb)**" ] } ], diff --git a/notebooks/tutorials/model_validation/112-develop_challenger_models.ipynb b/notebooks/tutorials/model_validation/2-develop_challenger_model.ipynb similarity index 99% rename from notebooks/tutorials/model_validation/112-develop_challenger_models.ipynb rename to notebooks/tutorials/model_validation/2-develop_challenger_model.ipynb index 68ac41403..d4a2529d3 100644 --- a/notebooks/tutorials/model_validation/112-develop_challenger_models.ipynb +++ b/notebooks/tutorials/model_validation/2-develop_challenger_model.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# ValidMind for model validation — 112 Develop potential challenger models\n", + "# ValidMind for model validation 2 — Develop potential challenger model\n", "\n", "Learn how to use ValidMind for your end-to-end model validation process with our series of four introductory notebooks. In this second notebook, develop potential challenger models and then pass your models and their predictions to ValidMind.\n", "\n", @@ -24,7 +24,7 @@ "\n", "
Need help with the above steps?\n", "

\n", - "Refer to the first notebook in this series: 111 Import the champion model
\n" + "Refer to the first notebook in this series: 1 — Import the champion model\n" ] }, { @@ -706,7 +706,7 @@ "\n", "### Perform model validation tests\n", "\n", - "Now that you're familiar with the basics of using the ValidMind Library, let's learn how to identify and run validation tests: **[113 Perform model validation tests](113-perform_validation_tests.ipynb)**" + "Now that you're familiar with the basics of using the ValidMind Library, let's learn how to identify and run validation tests: **[3 — Perform model validation tests](3-perform_validation_tests.ipynb)**" ] } ], diff --git a/notebooks/tutorials/model_validation/113-perform_validation_tests.ipynb b/notebooks/tutorials/model_validation/3-perform_validation_tests.ipynb similarity index 98% rename from notebooks/tutorials/model_validation/113-perform_validation_tests.ipynb rename to notebooks/tutorials/model_validation/3-perform_validation_tests.ipynb index eb96a5f74..9721c14fe 100644 --- a/notebooks/tutorials/model_validation/113-perform_validation_tests.ipynb +++ b/notebooks/tutorials/model_validation/3-perform_validation_tests.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# ValidMind for model validation — 113 Perform model validation tests\n", + "# ValidMind for model validation 3 — Perform model validation tests\n", "\n", "Learn how to use ValidMind for your end-to-end model validation process with our series of four introductory notebooks. In this third notebook, you'll identify relevant validation tests, run them, then add the log results to your validation report.\n", "\n", @@ -36,10 +36,9 @@ "

\n", "Refer to the first two notebooks in this series:\n", "\n", - "
    \n", - "
  1. 111 Import the champion model
    2. \n", - "
  2. 112 Develop potential challenger models
    3. \n", - "
\n", + "- 1 — Import the champion model\n", + "- 2 — Develop potential challenger model\n", + "\n", "\n", "" ] @@ -50,7 +49,7 @@ "source": [ "## Setting up\n", "\n", - "This section should be quite familiar to you — as we performed the same actions in the previous notebook, **[112 Develop potential challenger models](112-develop_challenger_models.ipynb)**." + "This section should be quite familiar to you — as we performed the same actions in the previous notebook, **[2 — Develop potential challenger model](2-develop_challenger_model.ipynb)**." ] }, { @@ -922,7 +921,7 @@ "\n", "### Finalize testing and reporting\n", "\n", - "Now that you're familiar with the basics of using the ValidMind Library to run and log validation tests, let's learn how to implement some custom tests and wrap up our validation: **[104 Finalize testing and reporting](114-finalize_testing_reporting.ipynb)**" + "Now that you're familiar with the basics of using the ValidMind Library to run and log validation tests, let's learn how to implement some custom tests and wrap up our validation: **[4 — Finalize testing and reporting](4-finalize_testing_reporting.ipynb)**" ] } ], diff --git a/notebooks/tutorials/model_validation/114-finalize_testing_reporting.ipynb b/notebooks/tutorials/model_validation/4-finalize_testing_reporting.ipynb similarity index 98% rename from notebooks/tutorials/model_validation/114-finalize_testing_reporting.ipynb rename to notebooks/tutorials/model_validation/4-finalize_testing_reporting.ipynb index 5446026dc..0b312a071 100644 --- a/notebooks/tutorials/model_validation/114-finalize_testing_reporting.ipynb +++ b/notebooks/tutorials/model_validation/4-finalize_testing_reporting.ipynb @@ -4,11 +4,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# ValidMind for model validation — 114 Finalize testing and reporting\n", + "# ValidMind for model validation 4 — Finalize testing and reporting\n", "\n", "Learn how to use ValidMind for your end-to-end model validation process with our series of four introductory notebooks. In this last notebook, you'll configure and run some custom tests, then add logged test results to your validation report.\n", "\n", - "As we concluded in [113 Perform validation tests](113-perform_validation_tests.ipynb), our challenger random forest classification model was not a viable candidate for our use case and was eliminated as a contender. We'll finish up by comparing our champion application scorecard model against our remaining challenger logistic regression model, then use the ValidMind Platform to draft our validation report supplemented by our logged test results as evidence." + "As we concluded in [3 — Perform validation tests](3-perform_validation_tests.ipynb), our challenger random forest classification model was not a viable candidate for our use case and was eliminated as a contender. We'll finish up by comparing our champion application scorecard model against our remaining challenger logistic regression model, then use the ValidMind Platform to draft our validation report supplemented by our logged test results as evidence." ] }, { @@ -30,11 +30,9 @@ "

\n", "Refer to the first three notebooks in this series:\n", "\n", - "
    \n", - "
  1. 111 Import the champion model
    2. \n", - "
  2. 112 Develop potential challenger models
    3. \n", - "
  3. 113 Perform validation tests
    4. \n", - "
\n", + "- 1 — Import the champion model\n", + "- 2 — Develop potential challenger model\n", + "- 3 — Perform validation tests\n", "\n", "" ] @@ -479,7 +477,7 @@ "source": [ "## Adjust a ValidMind test\n", "\n", - "Let's dig deeper into the [`MinimumF1Score` test](https://docs.validmind.ai/tests/model_validation/sklearn/MinimumF1Score.html) we ran previously in [113 Perform validation tests](113-perform_validation_tests.ipynb) to ensure that the models maintain a minimum acceptable balance between *precision* and *recall*. Precision refers to how many out of the positive predictions made by the model were actually correct, and recall refers to how many out of the actual positive cases did the model correctly identify.\n", + "Let's dig deeper into the [`MinimumF1Score` test](https://docs.validmind.ai/tests/model_validation/sklearn/MinimumF1Score.html) we ran previously in [3 — Perform validation tests](3-perform_validation_tests.ipynb) to ensure that the models maintain a minimum acceptable balance between *precision* and *recall*. Precision refers to how many out of the positive predictions made by the model were actually correct, and recall refers to how many out of the actual positive cases did the model correctly identify.\n", "\n", "Use [`run_test()`](https://docs.validmind.ai/validmind/validmind/tests.html#run_test) with our testing dataset (`vm_test_ds`) to run the test in isolation again for our two remaining models without logging the result to have the output to compare with a subsequent iteration:" ] From 5f30adb02167968c7dc2f60d3552452b6cbd93f2 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Fri, 28 Mar 2025 11:41:05 -0700 Subject: [PATCH 04/71] Exporting the champion model --- .gitignore | 2 ++ .../model_validation/lr_model_champion.pkl | Bin 0 -> 1020 bytes 2 files changed, 2 insertions(+) create mode 100644 notebooks/tutorials/model_validation/lr_model_champion.pkl diff --git a/.gitignore b/.gitignore index 7511a18ea..0155e5934 100644 --- a/.gitignore +++ b/.gitignore @@ -193,6 +193,8 @@ lending_club_loan_data_*.csv *.pkl # Sample application scorecard model for validation notebook — do not remove! !notebooks/code_samples/model_validation/xgb_model_champion.pkl +# Sample logistic regression model for validation series — do not remove! +!notebooks/tutorials/model_validation/lr_model_champion.pkl notebooks/llm/datasets/*.jsonl diff --git a/notebooks/tutorials/model_validation/lr_model_champion.pkl b/notebooks/tutorials/model_validation/lr_model_champion.pkl new file mode 100644 index 0000000000000000000000000000000000000000..9b81662b244ac048a42bed6e47b281a0e1908fb2 GIT binary patch literal 1020 zcmZuw%}*0S6mJWqwUm#Vcn}pYYVcqa{E8;V23As}P!PD1ak@KgS9fQd*;%j&8aN;{ znH$f?)($qoi9d3lPZJw z1aoqZ%ODd_u^B3SKp~NA3n$l{oQu#=tCt8WO$Xydo>A!uMmV*Zi@XV_ecZE4<)rQi zmh@{-At-8(6Tx(cyHa?%L~~TwKZr%SQ9rVbWfwpT=3o)Vp(hi}#1^1lZ+!ttkh2}%5{v|vYtJf z9}|v&WL&gqkh+@Cs4zgaUv~R+X-=~dX6I@ib`0Pp+i0vYf%yWpB+|?&2wEzZNty|p za@_0K-kT^i!iOqa^!xhay~nGYDL*B`{pQ_OPQ$3v i+ugCdnCAUxkB`<#8gH6l&-?FHg?p2yk|)8-C4U2-zM=O3 literal 0 HcmV?d00001 From c8df5e795b4870b93be49e083e876c7dbe83e33e Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Fri, 28 Mar 2025 11:53:37 -0700 Subject: [PATCH 05/71] Editing validation 1 --- .../1-import_champion_model.ipynb | 110 ++++++++++-------- 1 file changed, 61 insertions(+), 49 deletions(-) diff --git a/notebooks/tutorials/model_validation/1-import_champion_model.ipynb b/notebooks/tutorials/model_validation/1-import_champion_model.ipynb index b7fc79e05..4bd1047a4 100644 --- a/notebooks/tutorials/model_validation/1-import_champion_model.ipynb +++ b/notebooks/tutorials/model_validation/1-import_champion_model.ipynb @@ -9,7 +9,7 @@ "\n", "Learn how to use ValidMind for your end-to-end model validation process based on common scenarios with our series of four introductory notebooks. In this first notebook, set up the ValidMind Library in preparation for validating a champion model.\n", "\n", - "These notebooks use an application scorecard model as an example, but the same principles shown here apply to other model types." + "These notebooks use a binary classification model as an example, but the same principles shown here apply to other model types." ] }, { @@ -21,10 +21,10 @@ "\n", "Model validation aims to independently assess the compliance of *champion models* created by model developers with regulatory guidance by conducting thorough testing and analysis, potentially including the use of challenger models to benchmark performance. Assessments, presented in the form of a validation report, typically include *model findings* and recommendations to address those issues.\n", "\n", - "An *application scorecard model* is a type of statistical model used in credit scoring to evaluate the creditworthiness of potential borrowers by generating a score based on various characteristics of an applicant such as credit history, income, employment status, and other relevant financial data.\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 score assists lenders in making informed decisions about whether to approve or reject loan applications, as well as in determining the terms of the loan, including interest rates and credit limits.\n", - " - Effective validation of application scorecard models ensures that lenders can manage risk efficiently while maintaining a fast and transparent loan application process for applicants." + "- 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." ] }, { @@ -121,8 +121,8 @@ "\n", " For example, to register a model for use with this notebook, select:\n", "\n", - " - Documentation template: `Credit Risk Scorecard`\n", - " - Use case: `Credit Risk — CECL`\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." ] @@ -279,7 +279,7 @@ "source": [ "## Importing the champion model\n", "\n", - "With the ValidMind Library set up and ready to go, let's go ahead and import the champion model submitted by the model development team in the format of a `.pkl` file: **[xgb_model_champion.pkl](xgb_model_champion.pkl)**\n" + "With the ValidMind Library set up and ready to go, let's go ahead and import the champion model submitted by the model development team in the format of a `.pkl` file: **[lr_model_champion.pkl](lr_model_champion.pkl)**\n" ] }, { @@ -289,23 +289,10 @@ "metadata": {}, "outputs": [], "source": [ - "import xgboost as xgb\n", + "import pickle\n", "\n", - "#Load the saved model\n", - "xgb_model = xgb.XGBClassifier()\n", - "xgb_model.load_model(\"xgb_model_champion.pkl\")\n", - "xgb_model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b6887d38", - "metadata": {}, - "outputs": [], - "source": [ - "# Ensure that we have to appropriate order in feature names from Champion model and dataset\n", - "cols_when_model_builds = xgb_model.get_booster().feature_names" + "with open(\"lr_model_champion.pkl\", \"rb\") as f:\n", + " model = pickle.load(f)" ] }, { @@ -314,12 +301,14 @@ "source": [ "### Load the sample dataset\n", "\n", - "Let's next import the public [Lending Club](https://www.kaggle.com/datasets/devanshi23/loan-data-2007-2014/data) dataset from Kaggle, which was used to develop the dummy champion model.\n", + "Then, let's import the public [Bank Customer Churn Prediction](https://www.kaggle.com/datasets/shantanudhakadd/bank-customer-churn-prediction) dataset from Kaggle, which was used to develop the dummy champion model.\n", + "\n", + "We'll use this dataset to review steps that should have been conducted during the initial development and documentation of the model to ensure that the model was built correctly. By independently performing steps such as rebalancing and removing highly correlated features, we can confirm whether the model was built using appropriate and properly processed data.\n", "\n", - "- We'll use this dataset to review steps that should have been conducted during the initial development and documentation of the model to ensure that the model was built correctly.\n", - "- By independently performing steps such as preprocessing and feature engineering, we can confirm whether the model was built using appropriate and properly processed data.\n", + "In our below example, note that:\n", "\n", - "To be able to use the dataset, you'll 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:" + "- 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." ] }, { @@ -329,24 +318,21 @@ "metadata": {}, "outputs": [], "source": [ - "from validmind.datasets.credit_risk import lending_club\n", + "from validmind.datasets.classification import customer_churn as demo_dataset\n", "\n", - "df = lending_club.load_data(source=\"offline\")\n", - "df.head()" + "print(\n", + " f\"Loaded demo dataset with: \\n\\n\\t• Target column: '{demo_dataset.target_column}' \\n\\t• Class labels: {demo_dataset.class_labels}\"\n", + ")\n", + "\n", + "raw_df = demo_dataset.load_data()\n", + "raw_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### Preprocess the dataset\n", - "\n", - "We'll first quickly preprocess the dataset for data quality testing purposes using `lending_club.preprocess`. This function performs the following operations:\n", - "\n", - "- Filters the dataset to include only loans for debt consolidation or credit card purposes\n", - "- Removes loans classified under the riskier grades \"F\" and \"G\"\n", - "- Excludes uncommon home ownership types and standardizes employment length and loan terms into numerical formats\n", - "- Discards unnecessary fields and any entries with missing information to maintain a clean and robust dataset for modeling" + "#### Rebalance the dataset" ] }, { @@ -356,21 +342,23 @@ "metadata": {}, "outputs": [], "source": [ - "preprocess_df = lending_club.preprocess(df)" + "import pandas as pd\n", + "\n", + "raw_copy_df = raw_df.sample(frac=1) # Create a copy of the raw dataset\n", + "\n", + "# Create a balanced dataset with the same number of exited and not exited customers\n", + "exited_df = raw_copy_df.loc[raw_copy_df[\"Exited\"] == 1]\n", + "not_exited_df = raw_copy_df.loc[raw_copy_df[\"Exited\"] == 0].sample(n=exited_df.shape[0])\n", + "\n", + "balanced_raw_df = pd.concat([exited_df, not_exited_df])\n", + "balanced_raw_df = balanced_raw_df.sample(frac=1, random_state=42)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Apply feature engineering to the dataset\n", - "\n", - "Feature engineering improves the dataset's structure to better match what our model expects, and ensures that the model performs optimally by leveraging additional insights from raw data.\n", - "\n", - "We'll apply the following transformations using the `ending_club.feature_engineering()` function to optimize the dataset for predictive modeling in our application scorecard:\n", - "\n", - "- **WoE encoding**: Converts both numerical and categorical features into Weight of Evidence (WoE) values. WoE is a statistical measure used in scorecard modeling that quantifies the relationship between a predictor variable and the binary target variable. It calculates the ratio of the distribution of good outcomes to the distribution of bad outcomes for each category or bin of a feature. This transformation helps to ensure that the features are predictive and consistent in their contribution to the model.\n", - "- **Integration of WoE bins**: Ensures that the WoE transformed values are integrated throughout the dataset, replacing the original feature values while excluding the target variable from this transformation. This transformation is used to maintain a consistent scale and impact of each variable within the model, which helps make the predictions more stable and accurate." + "#### Remove highly correlated features" ] }, { @@ -380,8 +368,32 @@ "metadata": {}, "outputs": [], "source": [ - "fe_df = lending_club.feature_engineering(preprocess_df)\n", - "fe_df.head()" + "# Extract table from `corr_result.tables`\n", + "features_df = corr_result.tables[0].data\n", + "features_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "87d9ea2d", + "metadata": {}, + "outputs": [], + "source": [ + "# Extract list of features that failed the test\n", + "high_correlation_features = features_df[features_df[\"Pass/Fail\"] == \"Fail\"][\"Columns\"].tolist()\n", + "high_correlation_features" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb192ae5", + "metadata": {}, + "outputs": [], + "source": [ + "high_correlation_features = [feature.split(\",\")[0].strip(\"()\") for feature in high_correlation_features]\n", + "high_correlation_features" ] }, { From 4cc6d63db04ae233e09b6f7a23473201f78367c3 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Fri, 28 Mar 2025 12:05:51 -0700 Subject: [PATCH 06/71] Adjusting --- .../model_validation/1-import_champion_model.ipynb | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/notebooks/tutorials/model_validation/1-import_champion_model.ipynb b/notebooks/tutorials/model_validation/1-import_champion_model.ipynb index 4bd1047a4..9d904d476 100644 --- a/notebooks/tutorials/model_validation/1-import_champion_model.ipynb +++ b/notebooks/tutorials/model_validation/1-import_champion_model.ipynb @@ -396,6 +396,17 @@ "high_correlation_features" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "abacc888", + "metadata": {}, + "outputs": [], + "source": [ + "# Remove the highly correlated features from the dataset\n", + "balanced_raw_no_age_df = balanced_raw_df.drop(columns=high_correlation_features)" + ] + }, { "cell_type": "markdown", "metadata": {}, From a1b9ad960c0c76884a7920be3b835ff34d1e1960 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Fri, 28 Mar 2025 15:10:33 -0700 Subject: [PATCH 07/71] Test --- .../validate_application_scorecard.ipynb | 2 +- .../1-import_champion_model.ipynb | 79 ------------------- 2 files changed, 1 insertion(+), 80 deletions(-) diff --git a/notebooks/code_samples/model_validation/validate_application_scorecard.ipynb b/notebooks/code_samples/model_validation/validate_application_scorecard.ipynb index a2a6d900e..709109d70 100644 --- a/notebooks/code_samples/model_validation/validate_application_scorecard.ipynb +++ b/notebooks/code_samples/model_validation/validate_application_scorecard.ipynb @@ -388,7 +388,7 @@ "\n", "Feature engineering improves the dataset's structure to better match what our model expects, and ensures that the model performs optimally by leveraging additional insights from raw data.\n", "\n", - "We'll apply the following transformations using the `ending_club.feature_engineering()` function to optimize the dataset for predictive modeling in our application scorecard:\n", + "We'll apply the following transformations using the `lending_club.feature_engineering()` function to optimize the dataset for predictive modeling in our application scorecard:\n", "\n", "- **WoE encoding**: Converts both numerical and categorical features into Weight of Evidence (WoE) values. WoE is a statistical measure used in scorecard modeling that quantifies the relationship between a predictor variable and the binary target variable. It calculates the ratio of the distribution of good outcomes to the distribution of bad outcomes for each category or bin of a feature. This transformation helps to ensure that the features are predictive and consistent in their contribution to the model.\n", "- **Integration of WoE bins**: Ensures that the WoE transformed values are integrated throughout the dataset, replacing the original feature values while excluding the target variable from this transformation. This transformation is used to maintain a consistent scale and impact of each variable within the model, which helps make the predictions more stable and accurate." diff --git a/notebooks/tutorials/model_validation/1-import_champion_model.ipynb b/notebooks/tutorials/model_validation/1-import_champion_model.ipynb index 9d904d476..11392040a 100644 --- a/notebooks/tutorials/model_validation/1-import_champion_model.ipynb +++ b/notebooks/tutorials/model_validation/1-import_champion_model.ipynb @@ -328,85 +328,6 @@ "raw_df.head()" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Rebalance the dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e436e862", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "raw_copy_df = raw_df.sample(frac=1) # Create a copy of the raw dataset\n", - "\n", - "# Create a balanced dataset with the same number of exited and not exited customers\n", - "exited_df = raw_copy_df.loc[raw_copy_df[\"Exited\"] == 1]\n", - "not_exited_df = raw_copy_df.loc[raw_copy_df[\"Exited\"] == 0].sample(n=exited_df.shape[0])\n", - "\n", - "balanced_raw_df = pd.concat([exited_df, not_exited_df])\n", - "balanced_raw_df = balanced_raw_df.sample(frac=1, random_state=42)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Remove highly correlated features" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9c6b641c", - "metadata": {}, - "outputs": [], - "source": [ - "# Extract table from `corr_result.tables`\n", - "features_df = corr_result.tables[0].data\n", - "features_df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "87d9ea2d", - "metadata": {}, - "outputs": [], - "source": [ - "# Extract list of features that failed the test\n", - "high_correlation_features = features_df[features_df[\"Pass/Fail\"] == \"Fail\"][\"Columns\"].tolist()\n", - "high_correlation_features" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cb192ae5", - "metadata": {}, - "outputs": [], - "source": [ - "high_correlation_features = [feature.split(\",\")[0].strip(\"()\") for feature in high_correlation_features]\n", - "high_correlation_features" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "abacc888", - "metadata": {}, - "outputs": [], - "source": [ - "# Remove the highly correlated features from the dataset\n", - "balanced_raw_no_age_df = balanced_raw_df.drop(columns=high_correlation_features)" - ] - }, { "cell_type": "markdown", "metadata": {}, From 7faf0bb48168b3213a0780eab5ca98b24353458e Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Fri, 28 Mar 2025 15:48:22 -0700 Subject: [PATCH 08/71] WIP new validator 2 --- .../1-set_up_validmind.ipynb | 57 +++-- ...> 1-set_up_validmind_for_validation.ipynb} | 56 +---- .../2-develop_challenger_model.ipynb | 235 +++++------------- .../2-start_validation_process.ipynb | 18 ++ 4 files changed, 135 insertions(+), 231 deletions(-) rename notebooks/tutorials/model_validation/{1-import_champion_model.ipynb => 1-set_up_validmind_for_validation.ipynb} (87%) create mode 100644 notebooks/tutorials/model_validation/2-start_validation_process.ipynb diff --git a/notebooks/tutorials/model_development/1-set_up_validmind.ipynb b/notebooks/tutorials/model_development/1-set_up_validmind.ipynb index 5e248328c..d5ffef871 100644 --- a/notebooks/tutorials/model_development/1-set_up_validmind.ipynb +++ b/notebooks/tutorials/model_development/1-set_up_validmind.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "97710f2a", + "id": "b6fa2ac0", "metadata": {}, "source": [ "# ValidMind for model development 1 — Set up the ValidMind Library\n", @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "d3bb0ff8", + "id": "fe2e0eca", "metadata": {}, "source": [ "::: {.content-hidden when-format=\"html\"}\n", @@ -30,6 +30,7 @@ " - [Get your code snippet](#toc3_2_1_) \n", "- [Getting to know ValidMind](#toc4_) \n", " - [Preview the documentation template](#toc4_1_) \n", + " - [View model documentation in the ValidMind Platform](#toc4_1_1_) \n", " - [Explore available tests](#toc4_2_) \n", "- [Upgrade ValidMind](#toc5_) \n", "- [In summary](#toc6_) \n", @@ -49,7 +50,7 @@ }, { "cell_type": "markdown", - "id": "d78e3887", + "id": "814da22c", "metadata": {}, "source": [ "\n", @@ -66,7 +67,7 @@ }, { "cell_type": "markdown", - "id": "f40a5e0a", + "id": "4b966a95", "metadata": {}, "source": [ "\n", @@ -80,7 +81,7 @@ }, { "cell_type": "markdown", - "id": "12af6ba2", + "id": "87936431", "metadata": {}, "source": [ "\n", @@ -94,7 +95,7 @@ }, { "cell_type": "markdown", - "id": "5f9cc87c", + "id": "cb9f8dc1", "metadata": {}, "source": [ "\n", @@ -110,7 +111,7 @@ }, { "cell_type": "markdown", - "id": "31c5cde0", + "id": "a0d16aca", "metadata": {}, "source": [ "\n", @@ -145,7 +146,7 @@ }, { "cell_type": "markdown", - "id": "1c06378f", + "id": "215d62a7", "metadata": {}, "source": [ "\n", @@ -173,7 +174,7 @@ { "cell_type": "code", "execution_count": null, - "id": "8883bbc3", + "id": "827eb6bd", "metadata": {}, "outputs": [], "source": [ @@ -182,7 +183,7 @@ }, { "cell_type": "markdown", - "id": "780b6b39", + "id": "5e37f9fe", "metadata": {}, "source": [ "\n", @@ -211,7 +212,7 @@ }, { "cell_type": "markdown", - "id": "d00f6f07", + "id": "48eb92b3", "metadata": {}, "source": [ " - Documentation template: `Binary classification`\n", @@ -233,7 +234,7 @@ { "cell_type": "code", "execution_count": null, - "id": "5f22e91d", + "id": "a58d951f", "metadata": {}, "outputs": [], "source": [ @@ -256,7 +257,7 @@ }, { "cell_type": "markdown", - "id": "c3186121", + "id": "99cf2df8", "metadata": {}, "source": [ "\n", @@ -280,13 +281,31 @@ { "cell_type": "code", "execution_count": null, - "id": "32ab4cac", + "id": "819a40bc", "metadata": {}, "outputs": [], "source": [ "vm.preview_template()" ] }, + { + "cell_type": "markdown", + "id": "cf63d701", + "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", + "\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 \"ValidMind for model development\" series of notebooks.\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", "metadata": {}, @@ -303,7 +322,7 @@ { "cell_type": "code", "execution_count": null, - "id": "acf76128", + "id": "7ccc7776", "metadata": {}, "outputs": [], "source": [ @@ -326,7 +345,7 @@ { "cell_type": "code", "execution_count": null, - "id": "95bede03", + "id": "f5d3216d", "metadata": {}, "outputs": [], "source": [ @@ -347,7 +366,7 @@ }, { "cell_type": "markdown", - "id": "6a7bf101", + "id": "9b8aa1cc", "metadata": {}, "source": [ "You may need to restart your kernel after running the upgrade package for changes to be applied." @@ -355,7 +374,7 @@ }, { "cell_type": "markdown", - "id": "207875f2", + "id": "65ece5fb", "metadata": {}, "source": [ "\n", @@ -372,7 +391,7 @@ }, { "cell_type": "markdown", - "id": "29781eb4", + "id": "a262f940", "metadata": {}, "source": [ "\n", diff --git a/notebooks/tutorials/model_validation/1-import_champion_model.ipynb b/notebooks/tutorials/model_validation/1-set_up_validmind_for_validation.ipynb similarity index 87% rename from notebooks/tutorials/model_validation/1-import_champion_model.ipynb rename to notebooks/tutorials/model_validation/1-set_up_validmind_for_validation.ipynb index 11392040a..4d56ab195 100644 --- a/notebooks/tutorials/model_validation/1-import_champion_model.ipynb +++ b/notebooks/tutorials/model_validation/1-set_up_validmind_for_validation.ipynb @@ -5,7 +5,7 @@ "id": "248883b1", "metadata": {}, "source": [ - "# ValidMind for model validation 1 — Import the champion model\n", + "# ValidMind for model validation 1 — Set up the ValidMind Library for validation\n", "\n", "Learn how to use ValidMind for your end-to-end model validation process based on common scenarios with our series of four introductory notebooks. In this first notebook, set up the ValidMind Library in preparation for validating a champion model.\n", "\n", @@ -255,7 +255,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### View validation report in the ValidMind Platform\n", + "#### View validation report in the ValidMind Platform\n", "\n", "Next, let's head to the ValidMind Platform to see the template in action:\n", "\n", @@ -274,58 +274,22 @@ }, { "cell_type": "markdown", - "id": "943a45d6", + "id": "3fa2883d", "metadata": {}, "source": [ - "## Importing the champion model\n", + "### Explore available tests\n", "\n", - "With the ValidMind Library set up and ready to go, let's go ahead and import the champion model submitted by the model development team in the format of a `.pkl` file: **[lr_model_champion.pkl](lr_model_champion.pkl)**\n" + "Next, let's explore the list of all available tests in the ValidMind Library with [the `vm.tests.list_tests()` function](https://docs.validmind.ai/validmind/validmind/tests.html#list_tests) — we'll later narrow down the tests we want to run from this list when we learn to run tests." ] }, { "cell_type": "code", "execution_count": null, - "id": "3f9e197e", + "id": "c1107ca4", "metadata": {}, "outputs": [], "source": [ - "import pickle\n", - "\n", - "with open(\"lr_model_champion.pkl\", \"rb\") as f:\n", - " model = pickle.load(f)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load the 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, which was used to develop the dummy champion model.\n", - "\n", - "We'll use this dataset to review steps that should have been conducted during the initial development and documentation of the model to ensure that the model was built correctly. By independently performing steps such as rebalancing and removing highly correlated features, we can confirm whether the model was built using appropriate and properly processed data.\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." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "40e73b7c", - "metadata": {}, - "outputs": [], - "source": [ - "from validmind.datasets.classification import customer_churn as demo_dataset\n", - "\n", - "print(\n", - " f\"Loaded demo dataset with: \\n\\n\\t• Target column: '{demo_dataset.target_column}' \\n\\t• Class labels: {demo_dataset.class_labels}\"\n", - ")\n", - "\n", - "raw_df = demo_dataset.load_data()\n", - "raw_df.head()" + "vm.tests.list_tests()" ] }, { @@ -380,7 +344,7 @@ "- [ ] Register a model within the ValidMind Platform and assign yourself as the validator\n", "- [ ] Install and initialize the ValidMind Library\n", "- [ ] Preview the validation report template for your model\n", - "- [ ] Locate the validation report for your model within the ValidMind Platform\n" + "- [ ] Explore the available tests offered by the ValidMind Library\n" ] }, { @@ -390,9 +354,9 @@ "source": [ "## Next steps\n", "\n", - "### Develop potential challenger models\n", + "### Start the model validation process\n", "\n", - "Now that the ValidMind Library is connected to your model in the ValidMind Library with the correct template applied, we can go ahead and start the model validation process: **[2 — Develop potential challenger model](2-develop_challenger_model.ipynb)**" + "Now that the ValidMind Library is connected to your model in the ValidMind Library with the correct template applied, we can go ahead and start the model validation process: **[2 — Start the model validation process](2-start_validation_process.ipynb)**" ] } ], diff --git a/notebooks/tutorials/model_validation/2-develop_challenger_model.ipynb b/notebooks/tutorials/model_validation/2-develop_challenger_model.ipynb index d4a2529d3..ca0dfd287 100644 --- a/notebooks/tutorials/model_validation/2-develop_challenger_model.ipynb +++ b/notebooks/tutorials/model_validation/2-develop_challenger_model.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# ValidMind for model validation 2 — Develop potential challenger model\n", + "# ValidMind for model validation 2 — Start the model validation process\n", "\n", "Learn how to use ValidMind for your end-to-end model validation process with our series of four introductory notebooks. In this second notebook, develop potential challenger models and then pass your models and their predictions to ValidMind.\n", "\n", @@ -78,6 +78,73 @@ ")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# WIP" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing the champion model\n", + "\n", + "With the ValidMind Library set up and ready to go, let's go ahead and import the champion model submitted by the model development team in the format of a `.pkl` file: **[lr_model_champion.pkl](lr_model_champion.pkl)**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "\n", + "with open(\"lr_model_champion.pkl\", \"rb\") as f:\n", + " model = pickle.load(f)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load the 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, which was used to develop the dummy champion model.\n", + "\n", + "We'll use this dataset to review steps that should have been conducted during the initial development and documentation of the model to ensure that the model was built correctly. By independently performing steps such as rebalancing and removing highly correlated features, we can confirm whether the model was built using appropriate and properly processed data.\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." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from validmind.datasets.classification import customer_churn as demo_dataset\n", + "\n", + "print(\n", + " f\"Loaded demo dataset with: \\n\\n\\t• Target column: '{demo_dataset.target_column}' \\n\\t• Class labels: {demo_dataset.class_labels}\"\n", + ")\n", + "\n", + "raw_df = demo_dataset.load_data()\n", + "raw_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# WIP2" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -521,169 +588,6 @@ "vm_test_ds.add_extra_column(\"log_scores\", test_log_scores)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Enabling custom context for test descriptions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When you run ValidMind tests, test descriptions are automatically generated with LLM using the test results, the test name, and the static test definitions provided in the test’s docstring. While this metadata offers valuable high-level overviews of tests, insights produced by the LLM-based descriptions may not always align with your specific use cases or incorporate organizational policy requirements.\n", - "\n", - "Here, we'll include some initial custom use case context, improving the relevancy, insight, and format of the test descriptions returned.\n", - "\n", - "
Want to learn more about setting custom context for LLM-generated test descriptions?\n", - "

\n", - "Refer to our extended walkthrough notebook: Add context to LLM-generated test descriptions\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Review default LLM-generated descriptions\n", - "\n", - "By default, custom context for LLM-generated descriptions is disabled, meaning that the output will not include any additional context.\n", - "\n", - "Let's generate an initial test description for the `HighPearsonCorrelation` test with our `vm_test_ds` dataset for comparison with a later iteration to demonstrate. We'll go over running tests in more detail in a subsequent notebook:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vm.tests.run_test(\n", - " \"validmind.data_validation.HighPearsonCorrelation\",\n", - " inputs={\n", - " \"dataset\": vm_test_ds,\n", - " }\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Enable use case context\n", - "\n", - "To enable custom use case context, set the `VALIDMIND_LLM_DESCRIPTIONS_CONTEXT_ENABLED` environment variable to `1`.\n", - "\n", - "This is a global setting that will affect all tests for your linked model:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "os.environ[\"VALIDMIND_LLM_DESCRIPTIONS_CONTEXT_ENABLED\"] = \"1\"" - ] - }, - { - "cell_type": "markdown", - "id": "0d1e90ba", - "metadata": {}, - "source": [ - "Enabling use case context allows you to pass in additional context to the LLM-generated text descriptions within `context`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "87f7fb2e", - "metadata": {}, - "outputs": [], - "source": [ - "context = \"\"\"\n", - "FORMAT FOR THE LLM DESCRIPTIONS: \n", - " **** is designed to .\n", - "\n", - " The test operates by \n", - "\n", - " The primary advantages of this test include \n", - "\n", - " Users should be aware that \n", - "\n", - " **Key Insights:**\n", - "\n", - " The test results reveal:\n", - "\n", - " - ****: \n", - " - ****: \n", - " ...\n", - "\n", - " Based on these results, \n", - "\n", - "ADDITIONAL INSTRUCTIONS:\n", - " Present insights in order from general to specific, with each insight as a single bullet point with bold title.\n", - " You are a model validator and the goal is to identify risk and/or suggest room for improvements or recommendations on what Model Developer should do in order to improve outcomes and reduce risk\n", - "\n", - " For each metric in the test results, include in the test overview:\n", - " - The metric's purpose and what it measures\n", - " - Its mathematical formula\n", - " - The range of possible values\n", - " - What constitutes good/bad performance\n", - " - How to interpret different values\n", - "\n", - " Each insight should progressively cover:\n", - " 1. Overall scope and distribution\n", - " 2. Complete breakdown of all elements with specific values\n", - " 3. Natural groupings and patterns\n", - " 4. Comparative analysis between datasets/categories\n", - " 5. Stability and variations\n", - " 6. Notable relationships or dependencies\n", - "\n", - " Remember:\n", - " - Keep all insights at the same level (no sub-bullets or nested structures)\n", - " - Make each insight complete and self-contained\n", - " - Include specific numerical values and ranges\n", - " - Cover all elements in the results comprehensively\n", - " - Maintain clear, concise language\n", - " - Use only \"- **Title**: Description\" format for insights\n", - " - Progress naturally from general to specific observations\n", - "\n", - "\"\"\".strip()\n", - "\n", - "os.environ[\"VALIDMIND_LLM_DESCRIPTIONS_CONTEXT\"] = context" - ] - }, - { - "cell_type": "markdown", - "id": "d6e38bd2", - "metadata": {}, - "source": [ - "With the use case context set, generate an updated test description for the `HighPearsonCorrelation` test for comparision with default output:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vm.tests.run_test(\n", - " \"validmind.data_validation.HighPearsonCorrelation\",\n", - " inputs={\n", - " \"dataset\": vm_test_ds,\n", - " }\n", - ")" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -694,8 +598,7 @@ "\n", "- [ ] Initialize ValidMind datasets\n", "- [ ] Initialize ValidMind model objects\n", - "- [ ] Assign predictions and probabilities to your ValidMind datasets\n", - "- [ ] Enable custom context for test descriptions generated by ValidMind" + "- [ ] Assign predictions and probabilities to your ValidMind datasets" ] }, { diff --git a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb new file mode 100644 index 000000000..2422504e6 --- /dev/null +++ b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb @@ -0,0 +1,18 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ValidMind for model validation 2 — Start the model validation process\n" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From ad9f0e753548c792fb0f18c1b3eaeb498751e7ce Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Fri, 28 Mar 2025 15:57:52 -0700 Subject: [PATCH 09/71] copying some code into validator 2 --- .../2-start_validation_process.ipynb | 341 +++++++++++++++++- 1 file changed, 340 insertions(+), 1 deletion(-) diff --git a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb index 2422504e6..86961ad33 100644 --- a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb +++ b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb @@ -4,7 +4,346 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# ValidMind for model validation 2 — Start the model validation process\n" + "# ValidMind for model validation 2 — Start the model validation process\n", + "\n", + "Learn how to use ValidMind for your end-to-end model validation process with our series of four introductory notebooks. In this second notebook, \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "\n", + "In order to continue on the model validation journey with notebook, you'll need to first have:\n", + "\n", + "- [ ] Registered a model within the ValidMind Platform and granted yourself access to the model as a validator\n", + "- [ ] Installed the ValidMind Library in your local environment, allowing you to access all its features\n", + "\n", + "
Need help with the above steps?\n", + "

\n", + "Refer to the first notebook in this series: 1 — Set up the ValidMind Library for validation
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting up" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize the ValidMind Library\n", + "\n", + "First, let's connect up the ValidMind Library to our model we previously registered in the ValidMind Platform:\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 \"ValidMind for model validation\" series of notebooks.\n", + "\n", + "3. 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, + "metadata": {}, + "outputs": [], + "source": [ + "# Make sure the ValidMind Library is installed\n", + "\n", + "%pip install -q validmind\n", + "\n", + "# 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": [ + "## Import the champion model\n", + "\n", + "With the ValidMind Library set up and ready to go, let's go ahead and import the champion model submitted by the model development team in the format of a `.pkl` file: **[lr_model_champion.pkl](lr_model_champion.pkl)**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "\n", + "with open(\"lr_model_champion.pkl\", \"rb\") as f:\n", + " model = pickle.load(f)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load the 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, which was used to develop the dummy champion model.\n", + "\n", + "We'll use this dataset to review steps that should have been conducted during the initial development and documentation of the model to ensure that the model was built correctly. By independently performing steps such as rebalancing and removing highly correlated features, we can confirm whether the model was built using appropriate and properly processed data.\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." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from validmind.datasets.classification import customer_churn as demo_dataset\n", + "\n", + "print(\n", + " f\"Loaded demo dataset with: \\n\\n\\t• Target column: '{demo_dataset.target_column}' \\n\\t• Class labels: {demo_dataset.class_labels}\"\n", + ")\n", + "\n", + "raw_df = demo_dataset.load_data()\n", + "raw_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Identify qualitative tests\n", + "\n", + "Next, let's say we want to do some data quality assessments by running a few individual tests.\n", + "\n", + "Use the [`vm.tests.list_tests()` function](https://docs.validmind.ai/validmind/validmind/tests.html#list_tests) introduced by the first notebook in this series in combination with [`vm.tests.list_tags()`](https://docs.validmind.ai/validmind/validmind/tests.html#list_tags) and [`vm.tests.list_tasks()`](https://docs.validmind.ai/validmind/validmind/tests.html#list_tasks) to find which prebuilt tests are relevant for data quality assessment:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Get the list of available tags\n", + "sorted(vm.tests.list_tags())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Get the list of available task types\n", + "sorted(vm.tests.list_tasks())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can pass `tags` and `tasks` as parameters to the `vm.tests.list_tests()` function to filter the tests based on the tags and task types.\n", + "\n", + "For example, to find tests related to tabular data quality for classification models, you can call `list_tests()` like this:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vm.tests.list_tests(task=\"classification\", tags=[\"tabular_data\", \"data_quality\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
Want to learn more about navigating ValidMind tests?\n", + "

\n", + "Refer to our notebook outlining the utilities available for viewing and understanding available ValidMind tests: Explore tests
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialize the ValidMind datasets\n", + "\n", + "With the individual tests we want to run identified, the next step is to connect your data with a ValidMind `Dataset` object. **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", + "Initialize a ValidMind dataset object using the [`init_dataset` function](https://docs.validmind.ai/validmind/validmind.html#init_dataset) from the ValidMind (`vm`) module. 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." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# vm_raw_dataset is now a VMDataset object that you can pass to any ValidMind test\n", + "vm_raw_dataset = vm.init_dataset(\n", + " dataset=raw_df,\n", + " input_id=\"raw_dataset\",\n", + " target_column=\"Exited\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running tests\n", + "\n", + "Now that we know how to initialize a ValidMind `dataset` object, we're ready to run some tests!\n", + "\n", + "You run individual tests by calling [the `run_test` function](https://docs.validmind.ai/validmind/validmind/tests.html#run_test) provided by the `validmind.tests` module. For the examples below, we'll pass in the following arguments:\n", + "\n", + "- **`test_id`** — The ID of the test to run, as seen in the `ID` column when you run `list_tests`. \n", + "- **`params`** — A dictionary of parameters for the test. These will override any `default_params` set in the test definition. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run tabular data tests\n", + "\n", + "The inputs expected by a test can also be found in the test definition — let's take [`validmind.data_validation.DescriptiveStatistics`](https://docs.validmind.ai/tests/data_validation/DescriptiveStatistics.html) as an example.\n", + "\n", + "Note that the output of the [`describe_test()` function](https://docs.validmind.ai/validmind/validmind/tests.html#describe_test) below shows that this test expects a `dataset` as input:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vm.tests.describe_test(\"validmind.data_validation.DescriptiveStatistics\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's run a few tests to assess the quality of the dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "result = vm.tests.run_test(\n", + " test_id=\"validmind.data_validation.DescriptiveStatistics\",\n", + " inputs={\"dataset\": vm_raw_dataset},\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "result2 = vm.tests.run_test(\n", + " test_id=\"validmind.data_validation.ClassImbalance\",\n", + " inputs={\"dataset\": vm_raw_dataset},\n", + " params={\"min_percent_threshold\": 30},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The output above shows that [the class imbalance test](https://docs.validmind.ai/tests/data_validation/ClassImbalance.html) did not pass according to the value we set for `min_percent_threshold`.\n", + "\n", + "To address this issue, we'll re-run the test on some processed data. In this case let's apply a very simple rebalancing technique to the dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "raw_copy_df = raw_df.sample(frac=1) # Create a copy of the raw dataset\n", + "\n", + "# Create a balanced dataset with the same number of exited and not exited customers\n", + "exited_df = raw_copy_df.loc[raw_copy_df[\"Exited\"] == 1]\n", + "not_exited_df = raw_copy_df.loc[raw_copy_df[\"Exited\"] == 0].sample(n=exited_df.shape[0])\n", + "\n", + "balanced_raw_df = pd.concat([exited_df, not_exited_df])\n", + "balanced_raw_df = balanced_raw_df.sample(frac=1, random_state=42)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With this new balanced dataset, you can re-run the individual test to see if it now passes the class imbalance test requirement.\n", + "\n", + "As this is technically a different dataset, **remember to first initialize a new ValidMind `Dataset` object** to pass in as input as required by `run_test()`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Register new data and now 'balanced_raw_dataset' is the new dataset object of interest\n", + "vm_balanced_raw_dataset = vm.init_dataset(\n", + " dataset=balanced_raw_df,\n", + " input_id=\"balanced_raw_dataset\",\n", + " target_column=\"Exited\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Pass the initialized `balanced_raw_dataset` as input into the test run\n", + "result = vm.tests.run_test(\n", + " test_id=\"validmind.data_validation.ClassImbalance\",\n", + " inputs={\"dataset\": vm_balanced_raw_dataset},\n", + " params={\"min_percent_threshold\": 30},\n", + ")" ] } ], From 8cec0c1b3ce0e0a624d757642b5129559b2c5aa1 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Fri, 28 Mar 2025 16:06:28 -0700 Subject: [PATCH 10/71] Ugh --- .../2-start_development_process.ipynb | 2 +- .../2-start_validation_process.ipynb | 217 ------------------ 2 files changed, 1 insertion(+), 218 deletions(-) diff --git a/notebooks/tutorials/model_development/2-start_development_process.ipynb b/notebooks/tutorials/model_development/2-start_development_process.ipynb index 90e11e615..5564200aa 100644 --- a/notebooks/tutorials/model_development/2-start_development_process.ipynb +++ b/notebooks/tutorials/model_development/2-start_development_process.ipynb @@ -517,7 +517,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Re-running the test with the reduced feature set should pass the test:\n" + "Re-running the test with the reduced feature set should pass the test:" ] }, { diff --git a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb index 86961ad33..bcd2fe3e2 100644 --- a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb +++ b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb @@ -128,223 +128,6 @@ "raw_df = demo_dataset.load_data()\n", "raw_df.head()" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Identify qualitative tests\n", - "\n", - "Next, let's say we want to do some data quality assessments by running a few individual tests.\n", - "\n", - "Use the [`vm.tests.list_tests()` function](https://docs.validmind.ai/validmind/validmind/tests.html#list_tests) introduced by the first notebook in this series in combination with [`vm.tests.list_tags()`](https://docs.validmind.ai/validmind/validmind/tests.html#list_tags) and [`vm.tests.list_tasks()`](https://docs.validmind.ai/validmind/validmind/tests.html#list_tasks) to find which prebuilt tests are relevant for data quality assessment:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Get the list of available tags\n", - "sorted(vm.tests.list_tags())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Get the list of available task types\n", - "sorted(vm.tests.list_tasks())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can pass `tags` and `tasks` as parameters to the `vm.tests.list_tests()` function to filter the tests based on the tags and task types.\n", - "\n", - "For example, to find tests related to tabular data quality for classification models, you can call `list_tests()` like this:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vm.tests.list_tests(task=\"classification\", tags=[\"tabular_data\", \"data_quality\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
Want to learn more about navigating ValidMind tests?\n", - "

\n", - "Refer to our notebook outlining the utilities available for viewing and understanding available ValidMind tests: Explore tests
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize the ValidMind datasets\n", - "\n", - "With the individual tests we want to run identified, the next step is to connect your data with a ValidMind `Dataset` object. **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", - "Initialize a ValidMind dataset object using the [`init_dataset` function](https://docs.validmind.ai/validmind/validmind.html#init_dataset) from the ValidMind (`vm`) module. 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." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# vm_raw_dataset is now a VMDataset object that you can pass to any ValidMind test\n", - "vm_raw_dataset = vm.init_dataset(\n", - " dataset=raw_df,\n", - " input_id=\"raw_dataset\",\n", - " target_column=\"Exited\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Running tests\n", - "\n", - "Now that we know how to initialize a ValidMind `dataset` object, we're ready to run some tests!\n", - "\n", - "You run individual tests by calling [the `run_test` function](https://docs.validmind.ai/validmind/validmind/tests.html#run_test) provided by the `validmind.tests` module. For the examples below, we'll pass in the following arguments:\n", - "\n", - "- **`test_id`** — The ID of the test to run, as seen in the `ID` column when you run `list_tests`. \n", - "- **`params`** — A dictionary of parameters for the test. These will override any `default_params` set in the test definition. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run tabular data tests\n", - "\n", - "The inputs expected by a test can also be found in the test definition — let's take [`validmind.data_validation.DescriptiveStatistics`](https://docs.validmind.ai/tests/data_validation/DescriptiveStatistics.html) as an example.\n", - "\n", - "Note that the output of the [`describe_test()` function](https://docs.validmind.ai/validmind/validmind/tests.html#describe_test) below shows that this test expects a `dataset` as input:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vm.tests.describe_test(\"validmind.data_validation.DescriptiveStatistics\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let's run a few tests to assess the quality of the dataset:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "result = vm.tests.run_test(\n", - " test_id=\"validmind.data_validation.DescriptiveStatistics\",\n", - " inputs={\"dataset\": vm_raw_dataset},\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "result2 = vm.tests.run_test(\n", - " test_id=\"validmind.data_validation.ClassImbalance\",\n", - " inputs={\"dataset\": vm_raw_dataset},\n", - " params={\"min_percent_threshold\": 30},\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The output above shows that [the class imbalance test](https://docs.validmind.ai/tests/data_validation/ClassImbalance.html) did not pass according to the value we set for `min_percent_threshold`.\n", - "\n", - "To address this issue, we'll re-run the test on some processed data. In this case let's apply a very simple rebalancing technique to the dataset:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "raw_copy_df = raw_df.sample(frac=1) # Create a copy of the raw dataset\n", - "\n", - "# Create a balanced dataset with the same number of exited and not exited customers\n", - "exited_df = raw_copy_df.loc[raw_copy_df[\"Exited\"] == 1]\n", - "not_exited_df = raw_copy_df.loc[raw_copy_df[\"Exited\"] == 0].sample(n=exited_df.shape[0])\n", - "\n", - "balanced_raw_df = pd.concat([exited_df, not_exited_df])\n", - "balanced_raw_df = balanced_raw_df.sample(frac=1, random_state=42)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With this new balanced dataset, you can re-run the individual test to see if it now passes the class imbalance test requirement.\n", - "\n", - "As this is technically a different dataset, **remember to first initialize a new ValidMind `Dataset` object** to pass in as input as required by `run_test()`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Register new data and now 'balanced_raw_dataset' is the new dataset object of interest\n", - "vm_balanced_raw_dataset = vm.init_dataset(\n", - " dataset=balanced_raw_df,\n", - " input_id=\"balanced_raw_dataset\",\n", - " target_column=\"Exited\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Pass the initialized `balanced_raw_dataset` as input into the test run\n", - "result = vm.tests.run_test(\n", - " test_id=\"validmind.data_validation.ClassImbalance\",\n", - " inputs={\"dataset\": vm_balanced_raw_dataset},\n", - " params={\"min_percent_threshold\": 30},\n", - ")" - ] } ], "metadata": { From d8607d84436624a6e11fc51d93bce8bb3bb1387b Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Fri, 28 Mar 2025 16:55:02 -0700 Subject: [PATCH 11/71] I give up for this week --- .../2-start_validation_process.ipynb | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb index bcd2fe3e2..38f30b0cb 100644 --- a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb +++ b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb @@ -105,7 +105,7 @@ "\n", "Then, let's import the public [Bank Customer Churn Prediction](https://www.kaggle.com/datasets/shantanudhakadd/bank-customer-churn-prediction) dataset from Kaggle, which was used to develop the dummy champion model.\n", "\n", - "We'll use this dataset to review steps that should have been conducted during the initial development and documentation of the model to ensure that the model was built correctly. By independently performing steps such as rebalancing and removing highly correlated features, we can confirm whether the model was built using appropriate and properly processed data.\n", + "We'll use this dataset to review steps that should have been conducted during the initial development and documentation of the model to ensure that the model was built correctly. By independently performing steps taken by the model development team, we can confirm whether the model was built using appropriate and properly processed data.\n", "\n", "In our below example, note that:\n", "\n", @@ -128,6 +128,16 @@ "raw_df = demo_dataset.load_data()\n", "raw_df.head()" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Assess the quality of the dataset\n", + "\n", + "Let's say we know that the model development team rebalanced and removed highly correlated features from the raw dataset, and this modified dataset was used to train the champion model. We'll use the same data processing logic and training procedure to confirm that the model's results can be reproduced independently.\n", + "\n" + ] } ], "metadata": { From 1c7b819899023af0c1dd9dfca21368bedad0da95 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Wed, 2 Apr 2025 12:48:52 -0700 Subject: [PATCH 12/71] Validation 2 - Verify data quality assessments setup --- .../2-start_development_process.ipynb | 4 +- .../2-start_validation_process.ipynb | 369 +++++++++++++++++- 2 files changed, 369 insertions(+), 4 deletions(-) diff --git a/notebooks/tutorials/model_development/2-start_development_process.ipynb b/notebooks/tutorials/model_development/2-start_development_process.ipynb index 5564200aa..d20adbd8d 100644 --- a/notebooks/tutorials/model_development/2-start_development_process.ipynb +++ b/notebooks/tutorials/model_development/2-start_development_process.ipynb @@ -391,7 +391,9 @@ "\n", "### Utilize test output\n", "\n", - "You can utilize the output from a ValidMind test for further use, for example, if you want to remove highly correlated features. Below we demonstrate how to retrieve the list of features with the highest correlation coefficients and use them to reduce the final list of features for modeling.\n", + "You can utilize the output from a ValidMind test for further use, for example, if you want to remove highly correlated features. Removing highly correlated features helps make the model simpler, more stable, and easier to understand.\n", + "\n", + "Below we demonstrate how to retrieve the list of features with the highest correlation coefficients and use them to reduce the final list of features for modeling.\n", "\n", "First, we'll run [`validmind.data_validation.HighPearsonCorrelation`](https://docs.validmind.ai/tests/data_validation/HighPearsonCorrelation.html) with the `balanced_raw_dataset` we initialized previously as input as is for comparison with later runs:" ] diff --git a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb index 38f30b0cb..778f1941a 100644 --- a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb +++ b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb @@ -133,10 +133,373 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Assess the quality of the dataset\n", + "## Verify data quality adjustments\n", "\n", - "Let's say we know that the model development team rebalanced and removed highly correlated features from the raw dataset, and this modified dataset was used to train the champion model. We'll use the same data processing logic and training procedure to confirm that the model's results can be reproduced independently.\n", - "\n" + "Let's say that thanks to the documentation submitted by the model development team ([Learn more ...](https://docs.validmind.ai/developer/validmind-library.html#for-model-development)), we know that the sample dataset was first modified before being used to train the champion model. After performing some data quality assessments on the raw dataset, it was determined that the dataset required rebalancing, and highly correlated features were also removed." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Identify qualitative tests\n", + "\n", + "During model validation, we use the same data processing logic and training procedure to confirm that the model's results can be reproduced independently, so let's start by doing some data quality assessments by running a few individual tests just like the development team did.\n", + "\n", + "Use the [`vm.tests.list_tests()` function](https://docs.validmind.ai/validmind/validmind/tests.html#list_tests) introduced by the first notebook in this series in combination with [`vm.tests.list_tags()`](https://docs.validmind.ai/validmind/validmind/tests.html#list_tags) and [`vm.tests.list_tasks()`](https://docs.validmind.ai/validmind/validmind/tests.html#list_tasks) to find which prebuilt tests are relevant for data quality assessment:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Get the list of available tags\n", + "sorted(vm.tests.list_tags())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Get the list of available task types\n", + "sorted(vm.tests.list_tasks())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can pass `tags` and `tasks` as parameters to the `vm.tests.list_tests()` function to filter the tests based on the tags and task types.\n", + "\n", + "For example, to find tests related to tabular data quality for classification models, you can call `list_tests()` like this:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vm.tests.list_tests(task=\"classification\", tags=[\"tabular_data\", \"data_quality\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
Want to learn more about navigating ValidMind tests?\n", + "

\n", + "Refer to our notebook outlining the utilities available for viewing and understanding available ValidMind tests: Explore tests
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize the ValidMind datasets\n", + "\n", + "With the individual tests we want to run identified, the next step is to connect your data with a ValidMind `Dataset` object. **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", + "Initialize a ValidMind dataset object using the [`init_dataset` function](https://docs.validmind.ai/validmind/validmind.html#init_dataset) from the ValidMind (`vm`) module. 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." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# vm_raw_dataset is now a VMDataset object that you can pass to any ValidMind test\n", + "vm_raw_dataset = vm.init_dataset(\n", + " dataset=raw_df,\n", + " input_id=\"raw_dataset\",\n", + " target_column=\"Exited\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run data quality tests\n", + "\n", + "Now that we know how to initialize a ValidMind `dataset` object, we're ready to run some tests!\n", + "\n", + "You run individual tests by calling [the `run_test` function](https://docs.validmind.ai/validmind/validmind/tests.html#run_test) provided by the `validmind.tests` module. For the examples below, we'll pass in the following arguments:\n", + "\n", + "- **`test_id`** — The ID of the test to run, as seen in the `ID` column when you run `list_tests`. \n", + "- **`params`** — A dictionary of parameters for the test. These will override any `default_params` set in the test definition. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Run tabular data tests\n", + "\n", + "The inputs expected by a test can also be found in the test definition — let's take [`validmind.data_validation.DescriptiveStatistics`](https://docs.validmind.ai/tests/data_validation/DescriptiveStatistics.html) as an example.\n", + "\n", + "Note that the output of the [`describe_test()` function](https://docs.validmind.ai/validmind/validmind/tests.html#describe_test) below shows that this test expects a `dataset` as input:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vm.tests.describe_test(\"validmind.data_validation.DescriptiveStatistics\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's run a few tests to assess the quality of the dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "result2 = vm.tests.run_test(\n", + " test_id=\"validmind.data_validation.ClassImbalance\",\n", + " inputs={\"dataset\": vm_raw_dataset},\n", + " params={\"min_percent_threshold\": 30},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The output above shows that [the class imbalance test](https://docs.validmind.ai/tests/data_validation/ClassImbalance.html) did not pass according to the value we set for `min_percent_threshold` — great, this matches what was reported by the model development team.\n", + "\n", + "To address this issue, we'll re-run the test on some processed data. In this case let's apply a very simple rebalancing technique to the dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "raw_copy_df = raw_df.sample(frac=1) # Create a copy of the raw dataset\n", + "\n", + "# Create a balanced dataset with the same number of exited and not exited customers\n", + "exited_df = raw_copy_df.loc[raw_copy_df[\"Exited\"] == 1]\n", + "not_exited_df = raw_copy_df.loc[raw_copy_df[\"Exited\"] == 0].sample(n=exited_df.shape[0])\n", + "\n", + "balanced_raw_df = pd.concat([exited_df, not_exited_df])\n", + "balanced_raw_df = balanced_raw_df.sample(frac=1, random_state=42)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With this new balanced dataset, you can re-run the individual test to see if it now passes the class imbalance test requirement.\n", + "\n", + "As this is technically a different dataset, **remember to first initialize a new ValidMind `Dataset` object** to pass in as input as required by `run_test()`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Register new data and now 'balanced_raw_dataset' is the new dataset object of interest\n", + "vm_balanced_raw_dataset = vm.init_dataset(\n", + " dataset=balanced_raw_df,\n", + " input_id=\"balanced_raw_dataset\",\n", + " target_column=\"Exited\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Pass the initialized `balanced_raw_dataset` as input into the test run\n", + "result = vm.tests.run_test(\n", + " test_id=\"validmind.data_validation.ClassImbalance\",\n", + " inputs={\"dataset\": vm_balanced_raw_dataset},\n", + " params={\"min_percent_threshold\": 30},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Remove highly correlated features\n", + "\n", + "Next, let's also remove highly correlated features from our dataset as outlined by the development team. Removing highly correlated features helps make the model simpler, more stable, and easier to understand.\n", + "\n", + "You can utilize the output from a ValidMind test for further use — in this below example, to retrieve the list of features with the highest correlation coefficients and use them to reduce the final list of features for modeling.\n", + "\n", + "First, we'll run [`validmind.data_validation.HighPearsonCorrelation`](https://docs.validmind.ai/tests/data_validation/HighPearsonCorrelation.html) with the `balanced_raw_dataset` we initialized previously as input as is for comparison with later runs:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "corr_result = vm.tests.run_test(\n", + " test_id=\"validmind.data_validation.HighPearsonCorrelation\",\n", + " params={\"max_threshold\": 0.3},\n", + " inputs={\"dataset\": vm_balanced_raw_dataset},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`corr_result` is an object of type `TestResult`. We can inspect the result object to see what the test has produced:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(type(corr_result))\n", + "print(\"Result ID: \", corr_result.result_id)\n", + "print(\"Params: \", corr_result.params)\n", + "print(\"Passed: \", corr_result.passed)\n", + "print(\"Tables: \", corr_result.tables)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's remove the highly correlated features and create a new VM `dataset` object.\n", + "\n", + "We'll begin by checking out the table in the result and extracting a list of features that failed the test:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Extract table from `corr_result.tables`\n", + "features_df = corr_result.tables[0].data\n", + "features_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Extract list of features that failed the test\n", + "high_correlation_features = features_df[features_df[\"Pass/Fail\"] == \"Fail\"][\"Columns\"].tolist()\n", + "high_correlation_features" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, extract the feature names from the list of strings (example: `(Age, Exited)` > `Age`):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "high_correlation_features = [feature.split(\",\")[0].strip(\"()\") for feature in high_correlation_features]\n", + "high_correlation_features" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, it's time to re-initialize the dataset with the highly correlated features removed.\n", + "\n", + "**Note the use of a different `input_id`.** This allows tracking the inputs used when running each individual test." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Remove the highly correlated features from the dataset\n", + "balanced_raw_no_age_df = balanced_raw_df.drop(columns=high_correlation_features)\n", + "\n", + "# Re-initialize the dataset object\n", + "vm_raw_dataset_preprocessed = vm.init_dataset(\n", + " dataset=balanced_raw_no_age_df,\n", + " input_id=\"raw_dataset_preprocessed\",\n", + " target_column=\"Exited\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Re-running the test with the reduced feature set should pass the test:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "corr_result = vm.tests.run_test(\n", + " test_id=\"validmind.data_validation.HighPearsonCorrelation\",\n", + " params={\"max_threshold\": 0.3},\n", + " inputs={\"dataset\": vm_raw_dataset_preprocessed},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also plot the correlation matrix to visualize the new correlation between features:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "corr_result = vm.tests.run_test(\n", + " test_id=\"validmind.data_validation.PearsonCorrelationMatrix\",\n", + " inputs={\"dataset\": vm_raw_dataset_preprocessed},\n", + ")" ] } ], From a5820036059b6d27e6fc536172f5b4c70fabf285 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Wed, 2 Apr 2025 12:54:37 -0700 Subject: [PATCH 13/71] Validation 2 - Verify data quality assessments editing --- .../model_development/2-start_development_process.ipynb | 2 ++ .../tutorials/model_validation/2-start_validation_process.ipynb | 2 ++ 2 files changed, 4 insertions(+) diff --git a/notebooks/tutorials/model_development/2-start_development_process.ipynb b/notebooks/tutorials/model_development/2-start_development_process.ipynb index d20adbd8d..05ab48135 100644 --- a/notebooks/tutorials/model_development/2-start_development_process.ipynb +++ b/notebooks/tutorials/model_development/2-start_development_process.ipynb @@ -417,6 +417,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "The output above shows that the test did not pass according to the value we set for `max_threshold`.\n", + "\n", "`corr_result` is an object of type `TestResult`. We can inspect the result object to see what the test has produced:" ] }, diff --git a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb index 778f1941a..0d5f7e226 100644 --- a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb +++ b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb @@ -373,6 +373,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "The output above shows that the test did not pass according to the value we set for `max_threshold` — as reported and expected.\n", + "\n", "`corr_result` is an object of type `TestResult`. We can inspect the result object to see what the test has produced:" ] }, From 5ca1a38051f64d720f3ef7dbdb14b70f715ce9ff Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Wed, 2 Apr 2025 13:31:01 -0700 Subject: [PATCH 14/71] Validation 2 - Document test results WIP --- .../2-start_development_process.ipynb | 6 +- .../2-start_validation_process.ipynb | 139 +++++++++++++++++- 2 files changed, 141 insertions(+), 4 deletions(-) diff --git a/notebooks/tutorials/model_development/2-start_development_process.ipynb b/notebooks/tutorials/model_development/2-start_development_process.ipynb index 05ab48135..e050a9979 100644 --- a/notebooks/tutorials/model_development/2-start_development_process.ipynb +++ b/notebooks/tutorials/model_development/2-start_development_process.ipynb @@ -28,7 +28,7 @@ "- [Running tests](#toc3_) \n", " - [Run tabular data tests](#toc3_1_) \n", " - [Utilize test output](#toc3_2_) \n", - "- [Documenting results](#toc4_) \n", + "- [Documenting test results](#toc4_) \n", " - [Run and log multiple tests](#toc4_1_) \n", " - [Run and log an individual test](#toc4_2_) \n", " - [Add individual test results to model documentation](#toc4_2_1_) \n", @@ -564,9 +564,9 @@ "source": [ "\n", "\n", - "## Documenting results\n", + "## Documenting test results\n", "\n", - "We've now done some analysis on two different datasets, and we should be able to document why certain things were done to the raw data with testing to support it.\n", + "Now that we've done some analysis on two different datasets, we can use ValidMind to easily document why certain things were done to our raw data with testing to support it.\n", "\n", "Every test result returned by the `run_test()` function has a [`.log()` method](https://docs.validmind.ai/validmind/validmind/vm_models.html#TestResult.log) that can be used to send the test results to the ValidMind Platform:\n", "\n", diff --git a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb index 0d5f7e226..38cd560d3 100644 --- a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb +++ b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb @@ -133,7 +133,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Verify data quality adjustments\n", + "## Verifying data quality adjustments\n", "\n", "Let's say that thanks to the documentation submitted by the model development team ([Learn more ...](https://docs.validmind.ai/developer/validmind-library.html#for-model-development)), we know that the sample dataset was first modified before being used to train the champion model. After performing some data quality assessments on the raw dataset, it was determined that the dataset required rebalancing, and highly correlated features were also removed." ] @@ -503,6 +503,143 @@ " inputs={\"dataset\": vm_raw_dataset_preprocessed},\n", ")" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Document test results\n", + "\n", + "Now that we've done some analysis on two different datasets, we can use ValidMind to easily document why certain things were done to our raw data with testing to support it:\n", + "\n", + "- Every test result returned by the `run_test()` function has a [`.log()` method](https://docs.validmind.ai/validmind/validmind/vm_models.html#TestResult.log) that can be used to send the test results to the ValidMind Platform.\n", + "- When logging validation test results to the platform, you'll need to manually add those results to the desired section of the validation report.\n", + "\n", + "Below, we'll run the tests using the previously initialized `vm_raw_dataset_preprocessed` as input, and we'll pass in the following arguments:\n", + "\n", + "- **`inputs`:** Any inputs to be passed to the tests.\n", + "- **`config`:** A dictionary `:` that allows configuring each test individually. Each test config requires the following:\n", + " - **`params`:** Individual test parameters.\n", + " - **`inputs`:** Individual test inputs. This overrides any inputs passed from the `run_documentation_tests()` function.\n", + "\n", + "When including explicit configuration for individual tests, you'll need to specify the `inputs` even if they mirror what is included in your global configuration." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Individual test config with inputs specified\n", + "test_config = {\n", + " \"validmind.data_validation.ClassImbalance\": {\n", + " \"params\": {\"min_percent_threshold\": 30},\n", + " \"inputs\": {\"dataset\": vm_raw_dataset_preprocessed},\n", + " },\n", + " \"validmind.data_validation.HighPearsonCorrelation\": {\n", + " \"params\": {\"max_threshold\": 0.3},\n", + " \"inputs\": {\"dataset\": vm_raw_dataset_preprocessed},\n", + " },\n", + "}\n", + "\n", + "# Then batch run and log our tests in test_config\n", + "for t in test_config:\n", + " print(t)\n", + " try:\n", + " # Check if test has input_grid\n", + " if 'input_grid' in test_config[t]:\n", + " # For tests with input_grid, pass the input_grid configuration\n", + " if 'params' in test_config[t]:\n", + " vm.tests.run_test(t, input_grid=test_config[t]['input_grid'], params=test_config[t]['params']).log()\n", + " else:\n", + " vm.tests.run_test(t, input_grid=test_config[t]['input_grid']).log()\n", + " else:\n", + " # Original logic for regular inputs\n", + " if 'params' in test_config[t]:\n", + " vm.tests.run_test(t, inputs=test_config[t]['inputs'], params=test_config[t]['params']).log()\n", + " else:\n", + " vm.tests.run_test(t, inputs=test_config[t]['inputs']).log()\n", + " except Exception as e:\n", + " print(f\"Error running test {t}: {str(e)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
Note the output returned indicating that a test-driven block doesn't currently exist in your model's documentation for this particular test ID. \n", + "

\n", + "That's expected, as when we run individual tests the results logged need to be manually added to your documentation within the ValidMind Platform. You'll continue to see this message throughout this notebook as we run and log more tests.
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we'll use the previously initialized `vm_balanced_raw_dataset` (that had a highly correlated `Age` column) as input to run an individual test, then log the result to the ValidMind Platform.\n", + "\n", + "When running individual tests, **you can use a custom `result_id` to tag the individual result with a unique identifier:**\n", + "\n", + "- This `result_id` can be appended to `test_id` with a `:` separator.\n", + "- The `balanced_raw_dataset` result identifier will correspond to the `balanced_raw_dataset` input, the dataset that still has the `Age` column." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "result = vm.tests.run_test(\n", + " test_id=\"validmind.data_validation.HighPearsonCorrelation:balanced_raw_dataset\",\n", + " params={\"max_threshold\": 0.3},\n", + " inputs={\"dataset\": vm_balanced_raw_dataset},\n", + ")\n", + "result.log()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Add test results to reporting*\n", + "\n", + "With our test results logged, let's head to the model we connected to at the beginning of this notebook and insert the test results into our validation report ([Need more help?](https://docs.validmind.ai/guide/model-validation/assess-compliance.html#link-validator-evidence)):\n", + "\n", + "1. From the **Inventory** in the ValidMind Platform, go to the model you connected to earlier.\n", + "\n", + "2. In the left sidebar that appears for your model, click **Validation Report**.\n", + "\n", + "3. Locate the Data Preparation section and click on **2.2.2. Model Performance** to expand that section.\n", + "\n", + "4. Under the Model Performance Metrics section, locate Validator Evidence then click **Link Evidence to Report**:\n", + "\n", + " \"Screenshot\n", + "

\n", + "\n", + "5. Select the Minimum F1 Score test results we logged:\n", + "\n", + " - **ValidMind Model Validation Sklearn Minimum F 1 Score Adj Threshold**\n", + " - **ValidMind Model Validation Sklearn Minimum F 1 Score Xgboost Champion**\n", + " - **ValidMind Model Validation Sklearn Minimum F 1 Score Xgboost Champion Vs Challengers**\n", + "\n", + " \"Screenshot\n", + "

\n", + "\n", + "6. Finally, click **Update Linked Evidence** to add the test results to the validation report.\n", + "\n", + " Confirm that the results for the minimum F1 score tests have been correctly inserted into section **2.2.2. Model Performance** of the report:\n", + "\n", + " \"Screenshot\n", + "

\n", + "\n", + "7. Continue to work on your validation report by:\n", + "\n", + " - **Adding risk assessment notes:** Click under **Risk Assessment Notes** in any validation report section to access the text editor and content editing toolbar, including an option to generate a draft with AI. (Learn more: [Work with content blocks](https://docs.validmind.ai/guide/model-documentation/work-with-content-blocks.html#content-editing-toolbar))\n", + " - **Adding findings:** Click on **Link Finding to Report** in any validation report section, then click **+ Create New Finding**. (Learn more: [Add and manage model findings](https://docs.validmind.ai/guide/model-validation/add-manage-model-findings.html))\n", + " - **Assessing compliance:** Under the Guideline for any validation report section, click **ASSESSMENT** and select the compliance status from the drop-down menu. (Learn more: [Assess compliance](https://docs.validmind.ai/guide/model-validation/assess-compliance.html#provide-compliance-assessments))\n" + ] } ], "metadata": { From 2411ae514956013afd2f3c00a73c8778c29c7eca Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Wed, 2 Apr 2025 13:56:41 -0700 Subject: [PATCH 15/71] Validation 2 - Document test results - add --- .../2-start_validation_process.ipynb | 78 +++++++++++------- .../inserted-class-imbalance-results.png | Bin 0 -> 230624 bytes .../link-validator-evidence.png | Bin 398858 -> 364135 bytes .../link-validator-evidence_OLD.png | Bin 0 -> 398858 bytes .../selecting-class-imbalance-results.png | Bin 0 -> 105815 bytes 5 files changed, 47 insertions(+), 31 deletions(-) create mode 100644 notebooks/tutorials/model_validation/inserted-class-imbalance-results.png create mode 100644 notebooks/tutorials/model_validation/link-validator-evidence_OLD.png create mode 100644 notebooks/tutorials/model_validation/selecting-class-imbalance-results.png diff --git a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb index 38cd560d3..ab50369f0 100644 --- a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb +++ b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb @@ -508,21 +508,26 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Document test results\n", + "## Documenting test results\n", "\n", "Now that we've done some analysis on two different datasets, we can use ValidMind to easily document why certain things were done to our raw data with testing to support it:\n", "\n", "- Every test result returned by the `run_test()` function has a [`.log()` method](https://docs.validmind.ai/validmind/validmind/vm_models.html#TestResult.log) that can be used to send the test results to the ValidMind Platform.\n", - "- When logging validation test results to the platform, you'll need to manually add those results to the desired section of the validation report.\n", + "- When logging validation test results to the platform, you'll need to manually add those results to the desired section of the validation report." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Configure and run multiple tests\n", "\n", - "Below, we'll run the tests using the previously initialized `vm_raw_dataset_preprocessed` as input, and we'll pass in the following arguments:\n", + "Below, we'll run the tests using the previously initialized `vm_raw_dataset_preprocessed` as input.\n", "\n", - "- **`inputs`:** Any inputs to be passed to the tests.\n", - "- **`config`:** A dictionary `:` that allows configuring each test individually. Each test config requires the following:\n", - " - **`params`:** Individual test parameters.\n", - " - **`inputs`:** Individual test inputs. This overrides any inputs passed from the `run_documentation_tests()` function.\n", + "We can specify all the tests we'd ike to run in a dictionary called `test_config`, and we'll pass in the following arguments for each test:\n", "\n", - "When including explicit configuration for individual tests, you'll need to specify the `inputs` even if they mirror what is included in your global configuration." + " - **`params`:** Individual test parameters.\n", + " - **`inputs`:** Individual test inputs." ] }, { @@ -541,9 +546,22 @@ " \"params\": {\"max_threshold\": 0.3},\n", " \"inputs\": {\"dataset\": vm_raw_dataset_preprocessed},\n", " },\n", - "}\n", - "\n", - "# Then batch run and log our tests in test_config\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then batch run and log our tests in `test_config`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "for t in test_config:\n", " print(t)\n", " try:\n", @@ -577,6 +595,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "### Log tests with a unique identifiers\n", + "\n", "Next, we'll use the previously initialized `vm_balanced_raw_dataset` (that had a highly correlated `Age` column) as input to run an individual test, then log the result to the ValidMind Platform.\n", "\n", "When running individual tests, **you can use a custom `result_id` to tag the individual result with a unique identifier:**\n", @@ -603,48 +623,44 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Add test results to reporting*\n", + "### Add test results to reporting\n", "\n", - "With our test results logged, let's head to the model we connected to at the beginning of this notebook and insert the test results into our validation report ([Need more help?](https://docs.validmind.ai/guide/model-validation/assess-compliance.html#link-validator-evidence)):\n", + "With some test results logged, let's head to the model we connected to at the beginning of this notebook and learn how to insert a test result into our validation report ([Need more help?](https://docs.validmind.ai/guide/model-validation/assess-compliance.html#link-validator-evidence)):\n", "\n", "1. From the **Inventory** in the ValidMind Platform, go to the model you connected to earlier.\n", "\n", "2. In the left sidebar that appears for your model, click **Validation Report**.\n", "\n", - "3. Locate the Data Preparation section and click on **2.2.2. Model Performance** to expand that section.\n", + "3. Locate the Data Preparation section and click on **2.2.1. Data Quality** to expand that section.\n", "\n", - "4. Under the Model Performance Metrics section, locate Validator Evidence then click **Link Evidence to Report**:\n", + "4. Under the Class Imbalance Assessment section, locate Validator Evidence then click **Link Evidence to Report**:\n", "\n", " \"Screenshot\n", "

\n", "\n", - "5. Select the Minimum F1 Score test results we logged:\n", + "5. Select the Class Imbalance test results we logged: **ValidMind Data Validation Class Imbalance**\n", "\n", - " - **ValidMind Model Validation Sklearn Minimum F 1 Score Adj Threshold**\n", - " - **ValidMind Model Validation Sklearn Minimum F 1 Score Xgboost Champion**\n", - " - **ValidMind Model Validation Sklearn Minimum F 1 Score Xgboost Champion Vs Challengers**\n", - "\n", - " \"Screenshot\n", + " \"Screenshot\n", "

\n", "\n", "6. Finally, click **Update Linked Evidence** to add the test results to the validation report.\n", "\n", - " Confirm that the results for the minimum F1 score tests have been correctly inserted into section **2.2.2. Model Performance** of the report:\n", + " Confirm that the results for the Class Imbalance score tests have been correctly inserted into section **2.2.1. Data Quality** of the report:\n", "\n", - " \"Screenshot\n", - "

\n", - "\n", - "7. Continue to work on your validation report by:\n", - "\n", - " - **Adding risk assessment notes:** Click under **Risk Assessment Notes** in any validation report section to access the text editor and content editing toolbar, including an option to generate a draft with AI. (Learn more: [Work with content blocks](https://docs.validmind.ai/guide/model-documentation/work-with-content-blocks.html#content-editing-toolbar))\n", - " - **Adding findings:** Click on **Link Finding to Report** in any validation report section, then click **+ Create New Finding**. (Learn more: [Add and manage model findings](https://docs.validmind.ai/guide/model-validation/add-manage-model-findings.html))\n", - " - **Assessing compliance:** Under the Guideline for any validation report section, click **ASSESSMENT** and select the compliance status from the drop-down menu. (Learn more: [Assess compliance](https://docs.validmind.ai/guide/model-validation/assess-compliance.html#provide-compliance-assessments))\n" + " \"Screenshot\n", + "

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QPvEC_!RWW5^Y*v@3yI)o*8l(j literal 0 HcmV?d00001 From ffb1486fbd93ff77162c38235003ce4c5d47d49d Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Wed, 2 Apr 2025 14:41:21 -0700 Subject: [PATCH 16/71] Validation 3 - setup --- .../validate_application_scorecard.ipynb | 8 +- .../1-set_up_validmind.ipynb | 8 +- .../2-start_development_process.ipynb | 20 ++-- .../3-integrate_custom_tests.ipynb | 24 ++-- .../4-finalize_testing_documentation.ipynb | 22 ++-- .../1-set_up_validmind_for_validation.ipynb | 12 +- ...b => 2-develop_challenger_model_OLD.ipynb} | 0 .../2-start_validation_process.ipynb | 36 +++++- .../3-developing_challenger_models.ipynb | 110 ++++++++++++++++++ 9 files changed, 193 insertions(+), 47 deletions(-) rename notebooks/tutorials/model_validation/{2-develop_challenger_model.ipynb => 2-develop_challenger_model_OLD.ipynb} (100%) create mode 100644 notebooks/tutorials/model_validation/3-developing_challenger_models.ipynb diff --git a/notebooks/code_samples/model_validation/validate_application_scorecard.ipynb b/notebooks/code_samples/model_validation/validate_application_scorecard.ipynb index 709109d70..99dda7693 100644 --- a/notebooks/code_samples/model_validation/validate_application_scorecard.ipynb +++ b/notebooks/code_samples/model_validation/validate_application_scorecard.ipynb @@ -1816,8 +1816,14 @@ } ], "metadata": { + "kernelspec": { + "display_name": "ValidMind Library", + "language": "python", + "name": "validmind" + }, "language_info": { - "name": "python" + "name": "python", + "version": "3.10.13" } }, "nbformat": 4, diff --git a/notebooks/tutorials/model_development/1-set_up_validmind.ipynb b/notebooks/tutorials/model_development/1-set_up_validmind.ipynb index d5ffef871..46a002a83 100644 --- a/notebooks/tutorials/model_development/1-set_up_validmind.ipynb +++ b/notebooks/tutorials/model_development/1-set_up_validmind.ipynb @@ -383,10 +383,10 @@ "\n", "In this first notebook, you learned how to:\n", "\n", - "- [ ] Register a model within the ValidMind Platform\n", - "- [ ] Install and initialize the ValidMind Library\n", - "- [ ] Preview the documentation template for your model\n", - "- [ ] Explore the available tests offered by the ValidMind Library" + "- [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] Explore the available tests offered by the ValidMind Library" ] }, { diff --git a/notebooks/tutorials/model_development/2-start_development_process.ipynb b/notebooks/tutorials/model_development/2-start_development_process.ipynb index e050a9979..31a5c5bd9 100644 --- a/notebooks/tutorials/model_development/2-start_development_process.ipynb +++ b/notebooks/tutorials/model_development/2-start_development_process.ipynb @@ -62,8 +62,8 @@ "\n", "In order to log test results or evidence to your model documentation with this notebook, you'll need to first have:\n", "\n", - "- [ ] Registered a model within the ValidMind Platform with a predefined documentation template\n", - "- [ ] Installed the ValidMind Library in your local environment, allowing you to access all its features\n", + "- [x] Registered a model within the ValidMind Platform with a predefined documentation template\n", + "- [x] Installed the ValidMind Library in your local environment, allowing you to access all its features\n", "\n", "

    Need help with the above steps?\n", "

    \n", @@ -921,14 +921,14 @@ "\n", "In this second notebook, you learned how to:\n", "\n", - "- [ ] Import a sample dataset\n", - "- [ ] Identify which tests you might want to run with ValidMind\n", - "- [ ] Initialize ValidMind datasets\n", - "- [ ] Run individual tests\n", - "- [ ] Utilize the output from tests you've run\n", - "- [ ] Log test results from sets of or individual tests as evidence to the ValidMind Platform\n", - "- [ ] Add supplementary individual test results to your documentation\n", - "- [ ] Assign model predictions to your ValidMind datasets\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] 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", + "- [x] Add supplementary individual test results to your documentation\n", + "- [x] Assign model predictions to your ValidMind datasets\n" ] }, { diff --git a/notebooks/tutorials/model_development/3-integrate_custom_tests.ipynb b/notebooks/tutorials/model_development/3-integrate_custom_tests.ipynb index 4b5a366e0..54bbaa823 100644 --- a/notebooks/tutorials/model_development/3-integrate_custom_tests.ipynb +++ b/notebooks/tutorials/model_development/3-integrate_custom_tests.ipynb @@ -67,11 +67,11 @@ "\n", "In order to integrate custom tests with your model documentation with this notebook, you'll need to first have:\n", "\n", - "- [ ] Registered a model within the ValidMind Platform with a predefined documentation template\n", - "- [ ] Installed the ValidMind Library in your local environment, allowing you to access all its features\n", - "- [ ] Learned how to import and initialize datasets for use with ValidMind\n", - "- [ ] Understood the basics of how to run and log tests with ValidMind\n", - "- [ ] Inserted a test-driven block for the results of your `HighPearsonCorrelation:balanced_raw_dataset` test into your model's documentation\n", + "- [x] Registered a model within the ValidMind Platform with a predefined documentation template\n", + "- [x] Installed the ValidMind Library in your local environment, allowing you to access all its features\n", + "- [x] Learned how to import and initialize datasets for use with ValidMind\n", + "- [x] Understood the basics of how to run and log tests with ValidMind\n", + "- [x] Inserted a test-driven block for the results of your `HighPearsonCorrelation:balanced_raw_dataset` test into your model's documentation\n", "\n", "
    Need help with the above steps?\n", "

    \n", @@ -779,8 +779,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "- [ ] Confirm that the `save()` method saved the `confusion_matrix` function to a file named `ConfusionMatrix.py` in the `my_tests` folder.\n", - "- [ ] Note that the new file provides some context on the origin of the test, which is useful for traceability:\n", + "- [x] Confirm that the `save()` method saved the `confusion_matrix` function to a file named `ConfusionMatrix.py` in the `my_tests` folder.\n", + "- [x] Note that the new file provides some context on the origin of the test, which is useful for traceability:\n", "\n", " ```\n", " # Saved from __main__.confusion_matrix\n", @@ -788,7 +788,7 @@ " # New Test ID: .ConfusionMatrix\n", " ```\n", "\n", - "- [ ] Additionally, the new test function has been stripped off its decorator, as it now resides in a file that will be loaded by the test provider:\n", + "- [x] Additionally, the new test function has been stripped off its decorator, as it now resides in a file that will be loaded by the test provider:\n", "\n", " ```python\n", " def ConfusionMatrix(dataset, model, normalize=False):\n", @@ -933,10 +933,10 @@ "\n", "In this third notebook, you learned how to:\n", "\n", - "- [ ] Implement a custom inline test\n", - "- [ ] Run and log your custom inline tests\n", - "- [ ] Use external custom test providers\n", - "- [ ] Run and log tests from your custom test providers" + "- [x] Implement a custom inline test\n", + "- [x] Run and log your custom inline tests\n", + "- [x] Use external custom test providers\n", + "- [x] Run and log tests from your custom test providers" ] }, { diff --git a/notebooks/tutorials/model_development/4-finalize_testing_documentation.ipynb b/notebooks/tutorials/model_development/4-finalize_testing_documentation.ipynb index a18f5bf15..dfbd01089 100644 --- a/notebooks/tutorials/model_development/4-finalize_testing_documentation.ipynb +++ b/notebooks/tutorials/model_development/4-finalize_testing_documentation.ipynb @@ -61,14 +61,14 @@ "\n", "In order to finalize the testing and documentation for your sample model, you'll need to first have:\n", "\n", - "- [ ] Registered a model within the ValidMind Platform with a predefined documentation template\n", - "- [ ] Installed the ValidMind Library in your local environment, allowing you to access all its features\n", - "- [ ] Learned how to import and initialize datasets for use with ValidMind\n", - "- [ ] Learned how to run and log default and custom tests with ValidMind, including from external test providers\n", - "- [ ] Inserted test-driven blocks for the results of the following tests into your model's documentation:\n", - " - [ ] `HighPearsonCorrelation:balanced_raw_dataset`\n", - " - [ ] `my_test_provider.ConfusionMatrix`\n", - " - [ ] `my_custom_tests.ConfusionMatrix:test_dataset_normalized`\n", + "- [x] Registered a model within the ValidMind Platform with a predefined documentation template\n", + "- [x] Installed the ValidMind Library in your local environment, allowing you to access all its features\n", + "- [x] Learned how to import and initialize datasets for use with ValidMind\n", + "- [x] Learned how to run and log default and custom tests with ValidMind, including from external test providers\n", + "- [x] Inserted test-driven blocks for the results of the following tests into your model's documentation:\n", + " - [x] `HighPearsonCorrelation:balanced_raw_dataset`\n", + " - [x] `my_test_provider.ConfusionMatrix`\n", + " - [x] `my_custom_tests.ConfusionMatrix:test_dataset_normalized`\n", "\n", "
    Need help with the above steps?\n", "

    \n", @@ -862,9 +862,9 @@ "\n", "In this final notebook, you learned how to:\n", "\n", - "- [ ] Refresh the connection from the ValidMind Library to the ValidMind Platform after you've inserted test-driven blocks to your documentation\n", - "- [ ] Include custom test results in your model documentation\n", - "- [ ] View and configure the configuration for your model documentation template\n", + "- [x] Refresh the connection from the ValidMind Library to the ValidMind Platform after you've inserted test-driven blocks to your documentation\n", + "- [x] Include custom test results in your model documentation\n", + "- [x] View and configure the configuration for your model documentation template\n", "\n", "With our ValidMind for model development series of notebooks, you learned how to document a model end-to-end with the ValidMind Library by running through some common scenarios in a typical model development setting:\n", "\n", diff --git a/notebooks/tutorials/model_validation/1-set_up_validmind_for_validation.ipynb b/notebooks/tutorials/model_validation/1-set_up_validmind_for_validation.ipynb index 4d56ab195..769149cfa 100644 --- a/notebooks/tutorials/model_validation/1-set_up_validmind_for_validation.ipynb +++ b/notebooks/tutorials/model_validation/1-set_up_validmind_for_validation.ipynb @@ -265,8 +265,8 @@ "\n", "3. Click on the **Validation Report** for your model and note:\n", "\n", - " - [ ] The risk assessment compliance summary at the top of the report (screenshot below)\n", - " - [ ] How the structure of the validation report reflects the previewed template\n", + " - [x] The risk assessment compliance summary at the top of the report (screenshot below)\n", + " - [x] How the structure of the validation report reflects the previewed template\n", "\n", " \"Screenshot\n", "

    " @@ -341,10 +341,10 @@ "\n", "In this first notebook, you learned how to:\n", "\n", - "- [ ] Register a model within the ValidMind Platform and assign yourself as the validator\n", - "- [ ] Install and initialize the ValidMind Library\n", - "- [ ] Preview the validation report template for your model\n", - "- [ ] Explore the available tests offered by the ValidMind Library\n" + "- [x] Register a model within the ValidMind Platform and assign yourself as the validator\n", + "- [x] Install and initialize the ValidMind Library\n", + "- [x] Preview the validation report template for your model\n", + "- [x] Explore the available tests offered by the ValidMind Library\n" ] }, { diff --git a/notebooks/tutorials/model_validation/2-develop_challenger_model.ipynb b/notebooks/tutorials/model_validation/2-develop_challenger_model_OLD.ipynb similarity index 100% rename from notebooks/tutorials/model_validation/2-develop_challenger_model.ipynb rename to notebooks/tutorials/model_validation/2-develop_challenger_model_OLD.ipynb diff --git a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb index ab50369f0..6ba40a525 100644 --- a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb +++ b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb @@ -6,7 +6,9 @@ "source": [ "# ValidMind for model validation 2 — Start the model validation process\n", "\n", - "Learn how to use ValidMind for your end-to-end model validation process with our series of four introductory notebooks. In this second notebook, \n" + "Learn how to use ValidMind for your end-to-end model validation process with our series of four introductory notebooks. In this second notebook, independently verify the data quality tests performed on the dataset used to train the champion model.\n", + "\n", + "You'll learn how to run relevant validation tests with ValidMind, log the results of those tests to the ValidMind Platform, and insert your logged test results as evidence into your validation report." ] }, { @@ -17,8 +19,8 @@ "\n", "In order to continue on the model validation journey with notebook, you'll need to first have:\n", "\n", - "- [ ] Registered a model within the ValidMind Platform and granted yourself access to the model as a validator\n", - "- [ ] Installed the ValidMind Library in your local environment, allowing you to access all its features\n", + "- [x] Registered a model within the ValidMind Platform and granted yourself access to the model as a validator\n", + "- [x] Installed the ValidMind Library in your local environment, allowing you to access all its features\n", "\n", "
    Need help with the above steps?\n", "

    \n", @@ -650,6 +652,34 @@ " \"Screenshot\n", "

    " ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## In summary\n", + "\n", + "In this second notebook, you learned how to:\n", + "\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] Run individual tests\n", + "- [x] Utilize the output from tests you’ve run\n", + "- [x] Log test results as evidence to the ValidMind Platform\n", + "- [x] Insert test results into your validation report" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Next steps\n", + "\n", + "### Develop potential challenger models\n", + "\n", + "Now that you're familiar with the basics of using the ValidMind Library, let's use it to develop some challenger models: **[3 — Developing potential challenger models](3-developing_challenger_models.ipynb)**" + ] } ], "metadata": { diff --git a/notebooks/tutorials/model_validation/3-developing_challenger_models.ipynb b/notebooks/tutorials/model_validation/3-developing_challenger_models.ipynb new file mode 100644 index 000000000..95f4e6be3 --- /dev/null +++ b/notebooks/tutorials/model_validation/3-developing_challenger_models.ipynb @@ -0,0 +1,110 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ValidMind for model validation 3 — Developing potential challenger models\n", + "\n", + "Learn how to use ValidMind for your end-to-end model validation process with our series of four introductory notebooks. In this third notebook, develop potential challenger models and then pass your models and their predictions to ValidMind.\n", + "\n", + "A *challenger model* is an alternate model that attempt to outperform the champion model, ensuring that the best performing fit-for-purpose model is always considered for deployment. Challenger models also help avoid over-reliance on a single model, and allow testing of new features, algorithms, or data sources without disrupting the production lifecycle." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting up" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Split the preprocessed dataset\n", + "\n", + "With our dummy model imported, raw dataset rebalanced with highly correlated features removed, let's now **spilt our dataset into train and test** in preparation for training potential challenger models.\n", + "\n", + "To start, let's grab the first few rows from the `balanced_raw_no_age_df` dataset we initialized earlier:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "balanced_raw_no_age_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before training the models, we need to encode the categorical features in the dataset:\n", + "\n", + "- Use the `OneHotEncoder` class from the `sklearn.preprocessing` module to encode the categorical features.\n", + "- The categorical features in the dataset are `Geography` and `Gender`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "balanced_raw_no_age_df = pd.get_dummies(\n", + " balanced_raw_no_age_df, columns=[\"Geography\", \"Gender\"], drop_first=True\n", + ")\n", + "balanced_raw_no_age_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Splitting our dataset into training and testing is essential for proper validation testing, as this helps assess how well the model generalizes to unseen data:\n", + "\n", + "- We begin by dividing our preprocessed dataset into feature variables (`X`) and the target variable (`y`), which indicates whether a customer exited.\n", + "- Using `train_test_split`, we randomly allocate 80% of the data to the training set (`X_train`, `y_train`) and 20% to the test set (`X_test`, `y_test`), ensuring both sets are representative of the full distribution." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "# Split the input and target variables\n", + "X = balanced_raw_no_age_df.drop(\"Exited\", axis=1)\n", + "y = balanced_raw_no_age_df[\"Exited\"]\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " X,\n", + " y,\n", + " test_size=0.2,\n", + " random_state=42,\n", + ")" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From e63b94ae0bcecd4f78370c9546f24181a847eeba Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Wed, 2 Apr 2025 14:59:03 -0700 Subject: [PATCH 17/71] Validation 3 - Setting up WIP --- .../2-start_development_process.ipynb | 2 +- .../3-integrate_custom_tests.ipynb | 2 +- .../4-finalize_testing_documentation.ipynb | 2 +- .../3-developing_challenger_models.ipynb | 267 +++++++++++++++++- 4 files changed, 269 insertions(+), 4 deletions(-) diff --git a/notebooks/tutorials/model_development/2-start_development_process.ipynb b/notebooks/tutorials/model_development/2-start_development_process.ipynb index 31a5c5bd9..627cdc458 100644 --- a/notebooks/tutorials/model_development/2-start_development_process.ipynb +++ b/notebooks/tutorials/model_development/2-start_development_process.ipynb @@ -67,7 +67,7 @@ "\n", "
    Need help with the above steps?\n", "

    \n", - "Refer to the first notebook in this series:     1 — Set up ValidMind
    \n" + "Refer to the first notebook in this series: 1 — Set up the ValidMind Library
    " ] }, { diff --git a/notebooks/tutorials/model_development/3-integrate_custom_tests.ipynb b/notebooks/tutorials/model_development/3-integrate_custom_tests.ipynb index 54bbaa823..bfaaa8597 100644 --- a/notebooks/tutorials/model_development/3-integrate_custom_tests.ipynb +++ b/notebooks/tutorials/model_development/3-integrate_custom_tests.ipynb @@ -77,7 +77,7 @@ "

    \n", "Refer to the first two notebooks in this series:\n", "\n", - "- 1 — Set up ValidMind\n", + "- 1 — Set up the ValidMind Library\n", "- 2 — Start the model development process\n", "\n", "
    \n" diff --git a/notebooks/tutorials/model_development/4-finalize_testing_documentation.ipynb b/notebooks/tutorials/model_development/4-finalize_testing_documentation.ipynb index dfbd01089..efa7ed671 100644 --- a/notebooks/tutorials/model_development/4-finalize_testing_documentation.ipynb +++ b/notebooks/tutorials/model_development/4-finalize_testing_documentation.ipynb @@ -74,7 +74,7 @@ "

    \n", "Refer to the first three notebooks in this series:\n", "\n", - "- 1 — Set up ValidMind\n", + "- 1 — Set up the ValidMind Library\n", "- 2 — Start the model development process\n", "- 3 — Integrate custom tests\n", "\n", diff --git a/notebooks/tutorials/model_validation/3-developing_challenger_models.ipynb b/notebooks/tutorials/model_validation/3-developing_challenger_models.ipynb index 95f4e6be3..9f3b595f7 100644 --- a/notebooks/tutorials/model_validation/3-developing_challenger_models.ipynb +++ b/notebooks/tutorials/model_validation/3-developing_challenger_models.ipynb @@ -15,7 +15,272 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Setting up" + "## Prerequisites\n", + "\n", + "In order to develop potential challenger models with this notebook, you'll need to first have:\n", + "\n", + "- [x] Registered a model within the ValidMind Platform and granted yourself access to the model as a validator\n", + "- [x] Installed the ValidMind Library in your local environment, allowing you to access all its features\n", + "- [x] Learned how to import and initialize datasets for use with ValidMind\n", + "- [x] Understood the basics of how to run and log tests with ValidMind\n", + "\n", + "
    Need help with the above steps?\n", + "

    \n", + "Refer to the first two notebooks in this series:\n", + "\n", + "- 1 — Set up the ValidMind Library for validation\n", + "- 2 — Start the model validation process\n", + "\n", + "
    \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting up\n", + "\n", + "This section should be quite familiar to you — as we performed the same actions in the previous notebook, **[2 — Start the model validation process](2-start_validation_process.ipynb)**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize the ValidMind Library\n", + "\n", + "As usual, let's first connect up the ValidMind Library to our model we previously registered in the ValidMind Platform:\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 \"ValidMind for model validation\" series of notebooks.\n", + "\n", + "3. 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, + "metadata": {}, + "outputs": [], + "source": [ + "# Make sure the ValidMind Library is installed\n", + "\n", + "%pip install -q validmind\n", + "\n", + "# 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": [ + "### Import the champion model\n", + "\n", + "Next, we'll import the champion model submitted by the model development team as we used in the last notebooks (**[lr_model_champion.pkl](lr_model_champion.pkl)**) and load in the sample [Bank Customer Churn Prediction](https://www.kaggle.com/datasets/shantanudhakadd/bank-customer-churn-prediction) dataset used to develop the model that we will independently preprocess:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import the champion model\n", + "import pickle\n", + "\n", + "with open(\"lr_model_champion.pkl\", \"rb\") as f:\n", + " model = pickle.load(f)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the sample dataset\n", + "from validmind.datasets.classification import customer_churn as demo_dataset\n", + "\n", + "print(\n", + " f\"Loaded demo dataset with: \\n\\n\\t• Target column: '{demo_dataset.target_column}' \\n\\t• Class labels: {demo_dataset.class_labels}\"\n", + ")\n", + "\n", + "raw_df = demo_dataset.load_data()\n", + "raw_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the ValidMind dataset object\n", + "vm_raw_dataset = vm.init_dataset(\n", + " dataset=raw_df,\n", + " input_id=\"raw_dataset\",\n", + " target_column=\"Exited\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Preprocess the dataset\n", + "\n", + "We’ll apply a simple rebalancing technique to the dataset before continuing:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "raw_copy_df = raw_df.sample(frac=1) # Create a copy of the raw dataset\n", + "\n", + "# Create a balanced dataset with the same number of exited and not exited customers\n", + "exited_df = raw_copy_df.loc[raw_copy_df[\"Exited\"] == 1]\n", + "not_exited_df = raw_copy_df.loc[raw_copy_df[\"Exited\"] == 0].sample(n=exited_df.shape[0])\n", + "\n", + "balanced_raw_df = pd.concat([exited_df, not_exited_df])\n", + "balanced_raw_df = balanced_raw_df.sample(frac=1, random_state=42)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let’s also quickly remove highly correlated features from the dataset using the output from a ValidMind test.\n", + "\n", + "As you know, before we can run tests you’ll need to initialize a ValidMind dataset object:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Register new data and now 'balanced_raw_dataset' is the new dataset object of interest\n", + "vm_balanced_raw_dataset = vm.init_dataset(\n", + " dataset=balanced_raw_df,\n", + " input_id=\"balanced_raw_dataset\",\n", + " target_column=\"Exited\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With our balanced dataset initialized, we can then run our test and utilize the output to help us identify the features we want to remove:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run HighPearsonCorrelation test with our balanced dataset as input and return a result object\n", + "corr_result = vm.tests.run_test(\n", + " test_id=\"validmind.data_validation.HighPearsonCorrelation\",\n", + " params={\"max_threshold\": 0.3},\n", + " inputs={\"dataset\": vm_balanced_raw_dataset},\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# From result object, extract table from `corr_result.tables`\n", + "features_df = corr_result.tables[0].data\n", + "features_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Extract list of features that failed the test\n", + "high_correlation_features = features_df[features_df[\"Pass/Fail\"] == \"Fail\"][\"Columns\"].tolist()\n", + "high_correlation_features" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Extract feature names from the list of strings\n", + "high_correlation_features = [feature.split(\",\")[0].strip(\"()\") for feature in high_correlation_features]\n", + "high_correlation_features" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then re-initialize the dataset with a different input_id and the highly correlated features removed and re-run the test for confirmation:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Remove the highly correlated features from the dataset\n", + "balanced_raw_no_age_df = balanced_raw_df.drop(columns=high_correlation_features)\n", + "\n", + "# Re-initialize the dataset object\n", + "vm_raw_dataset_preprocessed = vm.init_dataset(\n", + " dataset=balanced_raw_no_age_df,\n", + " input_id=\"raw_dataset_preprocessed\",\n", + " target_column=\"Exited\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Re-run the test with the reduced feature set\n", + "corr_result = vm.tests.run_test(\n", + " test_id=\"validmind.data_validation.HighPearsonCorrelation\",\n", + " params={\"max_threshold\": 0.3},\n", + " inputs={\"dataset\": vm_raw_dataset_preprocessed},\n", + ")" ] }, { From 04e5196fca725f0e915aac0c7db80c249e3b843d Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Wed, 2 Apr 2025 15:01:52 -0700 Subject: [PATCH 18/71] Validation 3 - Testing... --- .../3-developing_challenger_models.ipynb | 20 +++++-------------- 1 file changed, 5 insertions(+), 15 deletions(-) diff --git a/notebooks/tutorials/model_validation/3-developing_challenger_models.ipynb b/notebooks/tutorials/model_validation/3-developing_challenger_models.ipynb index 9f3b595f7..6f056db6d 100644 --- a/notebooks/tutorials/model_validation/3-developing_challenger_models.ipynb +++ b/notebooks/tutorials/model_validation/3-developing_challenger_models.ipynb @@ -297,11 +297,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "vscode": { - "languageId": "plaintext" - } - }, + "metadata": {}, "outputs": [], "source": [ "balanced_raw_no_age_df.head()" @@ -320,11 +316,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "vscode": { - "languageId": "plaintext" - } - }, + "metadata": {}, "outputs": [], "source": [ "balanced_raw_no_age_df = pd.get_dummies(\n", @@ -346,13 +338,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "vscode": { - "languageId": "plaintext" - } - }, + "metadata": {}, "outputs": [], "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", "# Split the input and target variables\n", "X = balanced_raw_no_age_df.drop(\"Exited\", axis=1)\n", "y = balanced_raw_no_age_df[\"Exited\"]\n", From 913e4d1e2698ad3cc61110eb08b81db4c46d8333 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Wed, 2 Apr 2025 16:27:28 -0700 Subject: [PATCH 19/71] Save point --- .../2-start_validation_process.ipynb | 8 +- ...nb => 3-developing_challenger_model.ipynb} | 226 +++++++++++++++++- 2 files changed, 222 insertions(+), 12 deletions(-) rename notebooks/tutorials/model_validation/{3-developing_challenger_models.ipynb => 3-developing_challenger_model.ipynb} (59%) diff --git a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb index 6ba40a525..376c487be 100644 --- a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb +++ b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb @@ -93,10 +93,10 @@ "metadata": {}, "outputs": [], "source": [ - "import pickle\n", + "import pickle as pkl\n", "\n", - "with open(\"lr_model_champion.pkl\", \"rb\") as f:\n", - " model = pickle.load(f)" + "with open(\"lr_model_champion.pkl\", \"rb\") as log_reg:\n", + " model = pkl.load(log_reg)" ] }, { @@ -678,7 +678,7 @@ "\n", "### Develop potential challenger models\n", "\n", - "Now that you're familiar with the basics of using the ValidMind Library, let's use it to develop some challenger models: **[3 — Developing potential challenger models](3-developing_challenger_models.ipynb)**" + "Now that you're familiar with the basics of using the ValidMind Library, let's use it to develop a challenger model: **[3 — Developing a potential challenger model](3-developing_challenger_model.ipynb)**" ] } ], diff --git a/notebooks/tutorials/model_validation/3-developing_challenger_models.ipynb b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb similarity index 59% rename from notebooks/tutorials/model_validation/3-developing_challenger_models.ipynb rename to notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb index 6f056db6d..ecc251022 100644 --- a/notebooks/tutorials/model_validation/3-developing_challenger_models.ipynb +++ b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb @@ -4,9 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# ValidMind for model validation 3 — Developing potential challenger models\n", + "# ValidMind for model validation 3 — Developing a potential challenger model\n", "\n", - "Learn how to use ValidMind for your end-to-end model validation process with our series of four introductory notebooks. In this third notebook, develop potential challenger models and then pass your models and their predictions to ValidMind.\n", + "Learn how to use ValidMind for your end-to-end model validation process with our series of four introductory notebooks. In this third notebook, develop a potential challenger model and then pass your model and its predictions to ValidMind.\n", "\n", "A *challenger model* is an alternate model that attempt to outperform the champion model, ensuring that the best performing fit-for-purpose model is always considered for deployment. Challenger models also help avoid over-reliance on a single model, and allow testing of new features, algorithms, or data sources without disrupting the production lifecycle." ] @@ -104,10 +104,10 @@ "outputs": [], "source": [ "# Import the champion model\n", - "import pickle\n", + "import pickle as pkl\n", "\n", - "with open(\"lr_model_champion.pkl\", \"rb\") as f:\n", - " model = pickle.load(f)" + "with open(\"lr_model_champion.pkl\", \"rb\") as log_reg:\n", + " model = pkl.load(log_reg)" ] }, { @@ -249,7 +249,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can then re-initialize the dataset with a different input_id and the highly correlated features removed and re-run the test for confirmation:" + "We can then re-initialize the dataset with a different `input_id` and the highly correlated features removed and re-run the test for confirmation:" ] }, { @@ -289,7 +289,7 @@ "source": [ "## Split the preprocessed dataset\n", "\n", - "With our dummy model imported, raw dataset rebalanced with highly correlated features removed, let's now **spilt our dataset into train and test** in preparation for training potential challenger models.\n", + "With our dummy model imported, raw dataset rebalanced with highly correlated features removed, let's now **spilt our dataset into train and test** in preparation for training our potential challenger model.\n", "\n", "To start, let's grab the first few rows from the `balanced_raw_no_age_df` dataset we initialized earlier:" ] @@ -307,7 +307,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Before training the models, we need to encode the categorical features in the dataset:\n", + "Before training the model, we need to encode the categorical features in the dataset:\n", "\n", "- Use the `OneHotEncoder` class from the `sklearn.preprocessing` module to encode the categorical features.\n", "- The categorical features in the dataset are `Geography` and `Gender`." @@ -353,6 +353,216 @@ " random_state=42,\n", ")" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize the split datasets\n", + "\n", + "Next, let's initialize the training and testing datasets so they are available for use:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train_df = X_train\n", + "train_df[\"Exited\"] = y_train\n", + "test_df = X_test\n", + "test_df[\"Exited\"] = y_test\n", + "\n", + "vm_train_ds = vm.init_dataset(\n", + " input_id=\"train_dataset_final\",\n", + " dataset=train_df,\n", + " target_column=\"Exited\",\n", + ")\n", + "\n", + "vm_test_ds = vm.init_dataset(\n", + " input_id=\"test_dataset_final\",\n", + " dataset=test_df,\n", + " target_column=\"Exited\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training a potential challenger model\n", + "\n", + "We're curious how an alternate model compare to our champion model, so let's train a challenger random forest classification model as a basis for our testing.\n", + "\n", + "Our selected option below offers decreased complexity in terms of implementation — such as lessened manual preprocessing — which can reduce the amount of risk for implementation. However, model risk is not calculated in isolation from a single factor, but rather in consideration with trade-offs in predictive performance, ease of interpretability, and overall alignment with business objectives." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Random forest classification model\n", + "\n", + "A *random forest classification model* is an ensemble machine learning algorithm that uses multiple decision trees to classify data. In ensemble learning, multiple models are combined to improve prediction accuracy and robustness.\n", + "\n", + "Random forest classification models generally have higher accuracy because they capture complex, non-linear relationships, but as a result they lack transparency in their predictions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import the Random Forest Classification model\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "# Create the model instance with 50 decision trees\n", + "rf_model = RandomForestClassifier(\n", + " n_estimators=50,\n", + " random_state=42,\n", + ")\n", + "\n", + "# Train the model\n", + "rf_model.fit(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Extract predicted probabilities\n", + "\n", + "With our challenger model trained, let's extract the predicted probabilities from our two models:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Champion — Logistic regression model\n", + "train_log_prob = log_reg.predict_proba(X_train)[:, 1]\n", + "test_log_prob = log_reg.predict_proba(X_test)[:, 1]\n", + "\n", + "# Challenger — Random forest classification model\n", + "train_rf_prob = rf_model.predict_proba(X_train)[:, 1]\n", + "test_rf_prob = rf_model.predict_proba(X_test)[:, 1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Compute binary predictions\n", + "\n", + "Next, we'll convert the probability predictions from our three models into a binary, based on a threshold of `0.3`:\n", + "\n", + "If the probability is greater than `0.3`, the prediction becomes `1` (positive).\n", + "Otherwise, it becomes `0` (negative)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "cut_off_threshold = 0.3\n", + "\n", + "# Champion — Logistic regression model\n", + "train_log_binary_predictions = (train_log_prob > cut_off_threshold).astype(int)\n", + "test_log_binary_predictions = (test_log_prob > cut_off_threshold).astype(int)\n", + "\n", + "# Challenger — Random forest classification model\n", + "train_rf_binary_predictions = (train_rf_prob > cut_off_threshold).astype(int)\n", + "test_rf_binary_predictions = (test_rf_prob > cut_off_threshold).astype(int)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initializing the model objects" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize the model objects\n", + "\n", + "In addition to the initialized datasets, 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 each of our two models.\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, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the champion logistic regression model\n", + "vm_log_model = vm.init_model(\n", + " log_reg,\n", + " input_id=\"log_model_champion\",\n", + ")\n", + "\n", + "# Initialize the challenger random forest classification model\n", + "vm_rf_model = vm.init_model(\n", + " rf_model,\n", + " input_id=\"rf_model\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Assign predictions*\n", + "\n", + "With our models registered, we'll move on to assigning both the predictive probabilities coming directly from each model's predictions, and the binary prediction after applying the cutoff threshold described in the Compute binary predictions step above.\n", + "\n", + "- The [`assign_predictions()` method](https://docs.validmind.ai/validmind/validmind/vm_models.html#VMDataset.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." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Champion — Logistic regression model\n", + "vm_train_ds.assign_predictions(\n", + " model=vm_log_model,\n", + " prediction_values=train_log_binary_predictions,\n", + " prediction_probabilities=train_log_prob,\n", + ")\n", + "\n", + "vm_test_ds.assign_predictions(\n", + " model=vm_log_model,\n", + " prediction_values=test_log_binary_predictions,\n", + " prediction_probabilities=test_log_prob,\n", + ")\n", + "\n", + "# Challenger — Random forest classification model\n", + "vm_train_ds.assign_predictions(\n", + " model=vm_rf_model,\n", + " prediction_values=train_rf_binary_predictions,\n", + " prediction_probabilities=train_rf_prob,\n", + ")\n", + "\n", + "vm_test_ds.assign_predictions(\n", + " model=vm_rf_model,\n", + " prediction_values=test_rf_binary_predictions,\n", + " prediction_probabilities=test_rf_prob,\n", + ")" + ] } ], "metadata": { From 87f4e09f23ac4ac717bf9f55256c61fdcbfd1fc8 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Wed, 2 Apr 2025 16:29:39 -0700 Subject: [PATCH 20/71] Save point --- .../2-start_validation_process.ipynb | 25 ++----------- .../3-developing_challenger_model.ipynb | 35 ++++++++++++------- 2 files changed, 24 insertions(+), 36 deletions(-) diff --git a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb index 376c487be..a7a799bc5 100644 --- a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb +++ b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb @@ -82,30 +82,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Import the champion model\n", + "## Load the sample dataset\n", "\n", - "With the ValidMind Library set up and ready to go, let's go ahead and import the champion model submitted by the model development team in the format of a `.pkl` file: **[lr_model_champion.pkl](lr_model_champion.pkl)**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pickle as pkl\n", - "\n", - "with open(\"lr_model_champion.pkl\", \"rb\") as log_reg:\n", - " model = pkl.load(log_reg)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load the 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, which was used to develop the dummy champion model.\n", + "Let's first import the public [Bank Customer Churn Prediction](https://www.kaggle.com/datasets/shantanudhakadd/bank-customer-churn-prediction) dataset from Kaggle, which was used to develop the dummy champion model.\n", "\n", "We'll use this dataset to review steps that should have been conducted during the initial development and documentation of the model to ensure that the model was built correctly. By independently performing steps taken by the model development team, we can confirm whether the model was built using appropriate and properly processed data.\n", "\n", diff --git a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb index ecc251022..007ffc8c0 100644 --- a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb +++ b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb @@ -97,19 +97,6 @@ "Next, we'll import the champion model submitted by the model development team as we used in the last notebooks (**[lr_model_champion.pkl](lr_model_champion.pkl)**) and load in the sample [Bank Customer Churn Prediction](https://www.kaggle.com/datasets/shantanudhakadd/bank-customer-churn-prediction) dataset used to develop the model that we will independently preprocess:" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import the champion model\n", - "import pickle as pkl\n", - "\n", - "with open(\"lr_model_champion.pkl\", \"rb\") as log_reg:\n", - " model = pkl.load(log_reg)" - ] - }, { "cell_type": "code", "execution_count": null, @@ -283,6 +270,28 @@ ")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import the champion model\n", + "\n", + "With our raw dataset assessed and preprocessed, let's go ahead and import the champion model submitted by the model development team in the format of a `.pkl` file: **[lr_model_champion.pkl](lr_model_champion.pkl)**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import the champion model\n", + "import pickle as pkl\n", + "\n", + "with open(\"lr_model_champion.pkl\", \"rb\") as log_reg:\n", + " model = pkl.load(log_reg)" + ] + }, { "cell_type": "markdown", "metadata": {}, From 750e09c7cc9dff6743378a389f8da4becdf4aed2 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Wed, 2 Apr 2025 16:57:07 -0700 Subject: [PATCH 21/71] Save point --- .../3-developing_challenger_model.ipynb | 25 ++++--------------- 1 file changed, 5 insertions(+), 20 deletions(-) diff --git a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb index 007ffc8c0..0b809d637 100644 --- a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb +++ b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb @@ -92,9 +92,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Import the champion model\n", + "### Import the sample dataset\n", "\n", - "Next, we'll import the champion model submitted by the model development team as we used in the last notebooks (**[lr_model_champion.pkl](lr_model_champion.pkl)**) and load in the sample [Bank Customer Churn Prediction](https://www.kaggle.com/datasets/shantanudhakadd/bank-customer-churn-prediction) dataset used to develop the model that we will independently preprocess:" + "Next, we'll load in the sample [Bank Customer Churn Prediction](https://www.kaggle.com/datasets/shantanudhakadd/bank-customer-churn-prediction) dataset used to develop the champion model that we will independently preprocess:" ] }, { @@ -110,29 +110,14 @@ " f\"Loaded demo dataset with: \\n\\n\\t• Target column: '{demo_dataset.target_column}' \\n\\t• Class labels: {demo_dataset.class_labels}\"\n", ")\n", "\n", - "raw_df = demo_dataset.load_data()\n", - "raw_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize the ValidMind dataset object\n", - "vm_raw_dataset = vm.init_dataset(\n", - " dataset=raw_df,\n", - " input_id=\"raw_dataset\",\n", - " target_column=\"Exited\",\n", - ")" + "raw_df = demo_dataset.load_data()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Preprocess the dataset\n", + "#### Preprocess the dataset\n", "\n", "We’ll apply a simple rebalancing technique to the dataset before continuing:" ] @@ -161,7 +146,7 @@ "source": [ "Let’s also quickly remove highly correlated features from the dataset using the output from a ValidMind test.\n", "\n", - "As you know, before we can run tests you’ll need to initialize a ValidMind dataset object:" + "As you know, before we can run tests you’ll need to initialize a ValidMind dataset object with the [`init_dataset` function](https://docs.validmind.ai/validmind/validmind.html#init_dataset):" ] }, { From 8070d6d0aa62f2c4840fa335bb792d02bc16f626 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Wed, 2 Apr 2025 17:03:03 -0700 Subject: [PATCH 22/71] Testing something --- .../3-developing_challenger_model.ipynb | 74 +++++++++++++------ 1 file changed, 53 insertions(+), 21 deletions(-) diff --git a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb index 0b809d637..38491cce5 100644 --- a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb +++ b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb @@ -438,12 +438,12 @@ "outputs": [], "source": [ "# Champion — Logistic regression model\n", - "train_log_prob = log_reg.predict_proba(X_train)[:, 1]\n", - "test_log_prob = log_reg.predict_proba(X_test)[:, 1]\n", + "# train_log_prob = log_reg.predict_proba(X_train)[:, 1]\n", + "# test_log_prob = log_reg.predict_proba(X_test)[:, 1]\n", "\n", "# Challenger — Random forest classification model\n", - "train_rf_prob = rf_model.predict_proba(X_train)[:, 1]\n", - "test_rf_prob = rf_model.predict_proba(X_test)[:, 1]" + "# train_rf_prob = rf_model.predict_proba(X_train)[:, 1]\n", + "# test_rf_prob = rf_model.predict_proba(X_test)[:, 1]" ] }, { @@ -465,15 +465,15 @@ "outputs": [], "source": [ "\n", - "cut_off_threshold = 0.3\n", + "# cut_off_threshold = 0.3\n", "\n", "# Champion — Logistic regression model\n", - "train_log_binary_predictions = (train_log_prob > cut_off_threshold).astype(int)\n", - "test_log_binary_predictions = (test_log_prob > cut_off_threshold).astype(int)\n", + "# train_log_binary_predictions = (train_log_prob > cut_off_threshold).astype(int)\n", + "# test_log_binary_predictions = (test_log_prob > cut_off_threshold).astype(int)\n", "\n", "# Challenger — Random forest classification model\n", - "train_rf_binary_predictions = (train_rf_prob > cut_off_threshold).astype(int)\n", - "test_rf_binary_predictions = (test_rf_prob > cut_off_threshold).astype(int)" + "# train_rf_binary_predictions = (train_rf_prob > cut_off_threshold).astype(int)\n", + "# test_rf_binary_predictions = (test_rf_prob > cut_off_threshold).astype(int)" ] }, { @@ -525,6 +525,46 @@ "- This method links the model's class prediction values and probabilities to our `vm_train_ds` and `vm_test_ds` datasets." ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Champion — Logistic regression model\n", + "# vm_train_ds.assign_predictions(\n", + "# model=vm_log_model,\n", + "# prediction_values=train_log_binary_predictions,\n", + "# prediction_probabilities=train_log_prob,\n", + "# )\n", + "\n", + "# vm_test_ds.assign_predictions(\n", + "# model=vm_log_model,\n", + "# prediction_values=test_log_binary_predictions,\n", + "# prediction_probabilities=test_log_prob,\n", + "# )\n", + "\n", + "# Challenger — Random forest classification model\n", + "# vm_train_ds.assign_predictions(\n", + "# model=vm_rf_model,\n", + "# prediction_values=train_rf_binary_predictions,\n", + "# prediction_probabilities=train_rf_prob,\n", + "# )\n", + "\n", + "# vm_test_ds.assign_predictions(\n", + "# model=vm_rf_model,\n", + "# prediction_values=test_rf_binary_predictions,\n", + "# prediction_probabilities=test_rf_prob,\n", + "# )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If no prediction values are passed, the method will compute predictions automatically:" + ] + }, { "cell_type": "code", "execution_count": null, @@ -533,28 +573,20 @@ "source": [ "# Champion — Logistic regression model\n", "vm_train_ds.assign_predictions(\n", - " model=vm_log_model,\n", - " prediction_values=train_log_binary_predictions,\n", - " prediction_probabilities=train_log_prob,\n", + " model=vm_log_model\n", ")\n", "\n", "vm_test_ds.assign_predictions(\n", - " model=vm_log_model,\n", - " prediction_values=test_log_binary_predictions,\n", - " prediction_probabilities=test_log_prob,\n", + " model=vm_log_model\n", ")\n", "\n", "# Challenger — Random forest classification model\n", "vm_train_ds.assign_predictions(\n", - " model=vm_rf_model,\n", - " prediction_values=train_rf_binary_predictions,\n", - " prediction_probabilities=train_rf_prob,\n", + " model=vm_rf_model\n", ")\n", "\n", "vm_test_ds.assign_predictions(\n", - " model=vm_rf_model,\n", - " prediction_values=test_rf_binary_predictions,\n", - " prediction_probabilities=test_rf_prob,\n", + " model=vm_rf_model\n", ")" ] } From 1264ed6b06777ede493573552ca980f9ab0f521e Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Wed, 2 Apr 2025 17:08:53 -0700 Subject: [PATCH 23/71] Testing more --- .../model_validation/3-developing_challenger_model.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb index 38491cce5..c22f3c317 100644 --- a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb +++ b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb @@ -273,8 +273,8 @@ "# Import the champion model\n", "import pickle as pkl\n", "\n", - "with open(\"lr_model_champion.pkl\", \"rb\") as log_reg:\n", - " model = pkl.load(log_reg)" + "with open(\"lr_model_champion.pkl\", \"rb\") as f:\n", + " log_reg = pkl.load(f)" ] }, { From 8d73b9665d9dab6568c50971c17d8b00b216918c Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Thu, 3 Apr 2025 16:46:30 -0700 Subject: [PATCH 24/71] Applying Andres' fixes --- .../2-start_development_process.ipynb | 37 +++++++++++++------ .../3-developing_challenger_model.ipynb | 24 ++++-------- 2 files changed, 34 insertions(+), 27 deletions(-) diff --git a/notebooks/tutorials/model_development/2-start_development_process.ipynb b/notebooks/tutorials/model_development/2-start_development_process.ipynb index 627cdc458..2e3c8963a 100644 --- a/notebooks/tutorials/model_development/2-start_development_process.ipynb +++ b/notebooks/tutorials/model_development/2-start_development_process.ipynb @@ -767,7 +767,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Using `GridSearchCV`, we'll find the best-performing hyperparameters or settings and save them:" + "We'll split our preprocessed dataset into training and testing, to help assess how well the model generalizes to unseen data:\n", + "\n", + "- We start by dividing our preprocessed DataFrame (`balanced_raw_no_age_df`) into training and test subsets using `train_test_split`, with 80% of the data allocated to training (`train_df`) and 20% to testing (`test_df`).\n", + "- From each subset, we separate the features (all columns except \"Exited\") into `X_train` and `X_test`, and the target column (\"Exited\") into `y_train` and `y_test`." ] }, { @@ -776,18 +779,30 @@ "metadata": {}, "outputs": [], "source": [ - "from sklearn.linear_model import LogisticRegression\n", "from sklearn.model_selection import train_test_split\n", "\n", - "# Split the input and target variables\n", - "X = balanced_raw_no_age_df.drop(\"Exited\", axis=1)\n", - "y = balanced_raw_no_age_df[\"Exited\"]\n", - "X_train, X_test, y_train, y_test = train_test_split(\n", - " X,\n", - " y,\n", - " test_size=0.2,\n", - " random_state=42,\n", - ")\n", + "train_df, test_df = train_test_split(balanced_raw_no_age_df, test_size=0.20)\n", + "\n", + "X_train = train_df.drop(\"Exited\", axis=1)\n", + "y_train = train_df[\"Exited\"]\n", + "X_test = test_df.drop(\"Exited\", axis=1)\n", + "y_test = test_df[\"Exited\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then using `GridSearchCV`, we'll find the best-performing hyperparameters or settings and save them:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import LogisticRegression\n", "\n", "# Logistic Regression grid params\n", "log_reg_params = {\n", diff --git a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb index c22f3c317..9745afb7c 100644 --- a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb +++ b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb @@ -325,8 +325,8 @@ "source": [ "Splitting our dataset into training and testing is essential for proper validation testing, as this helps assess how well the model generalizes to unseen data:\n", "\n", - "- We begin by dividing our preprocessed dataset into feature variables (`X`) and the target variable (`y`), which indicates whether a customer exited.\n", - "- Using `train_test_split`, we randomly allocate 80% of the data to the training set (`X_train`, `y_train`) and 20% to the test set (`X_test`, `y_test`), ensuring both sets are representative of the full distribution." + "- We start by dividing our preprocessed DataFrame (`balanced_raw_no_age_df`) into training and test subsets using `train_test_split`, with 80% of the data allocated to training (`train_df`) and 20% to testing (`test_df`).\n", + "- From each subset, we separate the features (all columns except \"Exited\") into `X_train` and `X_test`, and the target column (\"Exited\") into `y_train` and `y_test`." ] }, { @@ -337,15 +337,12 @@ "source": [ "from sklearn.model_selection import train_test_split\n", "\n", - "# Split the input and target variables\n", - "X = balanced_raw_no_age_df.drop(\"Exited\", axis=1)\n", - "y = balanced_raw_no_age_df[\"Exited\"]\n", - "X_train, X_test, y_train, y_test = train_test_split(\n", - " X,\n", - " y,\n", - " test_size=0.2,\n", - " random_state=42,\n", - ")" + "train_df, test_df = train_test_split(balanced_raw_no_age_df, test_size=0.20)\n", + "\n", + "X_train = train_df.drop(\"Exited\", axis=1)\n", + "y_train = train_df[\"Exited\"]\n", + "X_test = test_df.drop(\"Exited\", axis=1)\n", + "y_test = test_df[\"Exited\"]" ] }, { @@ -363,11 +360,6 @@ "metadata": {}, "outputs": [], "source": [ - "train_df = X_train\n", - "train_df[\"Exited\"] = y_train\n", - "test_df = X_test\n", - "test_df[\"Exited\"] = y_test\n", - "\n", "vm_train_ds = vm.init_dataset(\n", " input_id=\"train_dataset_final\",\n", " dataset=train_df,\n", From 3efcc337301de049c348b53eaad6062ce4d8cf34 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Thu, 3 Apr 2025 17:02:44 -0700 Subject: [PATCH 25/71] Validation 4 setup --- .../2-start_development_process.ipynb | 5 +- .../3-developing_challenger_model.ipynb | 135 +++++------------- ...b => 3-perform_validation_tests_OLD.ipynb} | 0 ...=> 4-finalize_testing_reporting_OLD.ipynb} | 0 .../4-finalize_validation_reporting.ipynb | 88 ++++++++++++ 5 files changed, 128 insertions(+), 100 deletions(-) rename notebooks/tutorials/model_validation/{3-perform_validation_tests.ipynb => 3-perform_validation_tests_OLD.ipynb} (100%) rename notebooks/tutorials/model_validation/{4-finalize_testing_reporting.ipynb => 4-finalize_testing_reporting_OLD.ipynb} (100%) create mode 100644 notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb diff --git a/notebooks/tutorials/model_development/2-start_development_process.ipynb b/notebooks/tutorials/model_development/2-start_development_process.ipynb index 2e3c8963a..a2ed2cd69 100644 --- a/notebooks/tutorials/model_development/2-start_development_process.ipynb +++ b/notebooks/tutorials/model_development/2-start_development_process.ipynb @@ -869,7 +869,10 @@ "\n", "### Assign predictions\n", "\n", - "Once the model has been registered you can assign model predictions to the training and test datasets. The [`assign_predictions()` method](https://docs.validmind.ai/validmind/validmind/vm_models.html#VMDataset.assign_predictions) from the `Dataset` object can link existing predictions to any number of models.\n", + "Once the model has been registered you can assign model predictions to the training and test datasets.\n", + "\n", + "- The [`assign_predictions()` method](https://docs.validmind.ai/validmind/validmind/vm_models.html#VMDataset.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:\n" ] diff --git a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb index 9745afb7c..793c04c1b 100644 --- a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb +++ b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb @@ -379,9 +379,9 @@ "source": [ "## Training a potential challenger model\n", "\n", - "We're curious how an alternate model compare to our champion model, so let's train a challenger random forest classification model as a basis for our testing.\n", + "We're curious how an alternate model compares to our champion model, so let's train a challenger model as a basis for our testing.\n", "\n", - "Our selected option below offers decreased complexity in terms of implementation — such as lessened manual preprocessing — which can reduce the amount of risk for implementation. However, model risk is not calculated in isolation from a single factor, but rather in consideration with trade-offs in predictive performance, ease of interpretability, and overall alignment with business objectives." + "Our champion *logistic regression model* is a simpler, parametric model that assumes a linear relationship between the independent variables and the log-odds of the outcome. While logistic regression may not capture complex patterns as effectively, it offers a high degree of interpretability and is easier to explain to stakeholders. However, model risk is not calculated in isolation from a single factor, but rather in consideration with trade-offs in predictive performance, ease of interpretability, and overall alignment with business objectives." ] }, { @@ -414,60 +414,6 @@ "rf_model.fit(X_train, y_train)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Extract predicted probabilities\n", - "\n", - "With our challenger model trained, let's extract the predicted probabilities from our two models:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Champion — Logistic regression model\n", - "# train_log_prob = log_reg.predict_proba(X_train)[:, 1]\n", - "# test_log_prob = log_reg.predict_proba(X_test)[:, 1]\n", - "\n", - "# Challenger — Random forest classification model\n", - "# train_rf_prob = rf_model.predict_proba(X_train)[:, 1]\n", - "# test_rf_prob = rf_model.predict_proba(X_test)[:, 1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Compute binary predictions\n", - "\n", - "Next, we'll convert the probability predictions from our three models into a binary, based on a threshold of `0.3`:\n", - "\n", - "If the probability is greater than `0.3`, the prediction becomes `1` (positive).\n", - "Otherwise, it becomes `0` (negative)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "# cut_off_threshold = 0.3\n", - "\n", - "# Champion — Logistic regression model\n", - "# train_log_binary_predictions = (train_log_prob > cut_off_threshold).astype(int)\n", - "# test_log_binary_predictions = (test_log_prob > cut_off_threshold).astype(int)\n", - "\n", - "# Challenger — Random forest classification model\n", - "# train_rf_binary_predictions = (train_rf_prob > cut_off_threshold).astype(int)\n", - "# test_rf_binary_predictions = (test_rf_prob > cut_off_threshold).astype(int)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -509,12 +455,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Assign predictions*\n", + "### Assign predictions\n", "\n", "With our models registered, we'll move on to assigning both the predictive probabilities coming directly from each model's predictions, and the binary prediction after applying the cutoff threshold described in the Compute binary predictions step above.\n", "\n", "- The [`assign_predictions()` method](https://docs.validmind.ai/validmind/validmind/vm_models.html#VMDataset.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." + "- 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:" ] }, { @@ -524,62 +472,51 @@ "outputs": [], "source": [ "# Champion — Logistic regression model\n", - "# vm_train_ds.assign_predictions(\n", - "# model=vm_log_model,\n", - "# prediction_values=train_log_binary_predictions,\n", - "# prediction_probabilities=train_log_prob,\n", - "# )\n", - "\n", - "# vm_test_ds.assign_predictions(\n", - "# model=vm_log_model,\n", - "# prediction_values=test_log_binary_predictions,\n", - "# prediction_probabilities=test_log_prob,\n", - "# )\n", + "vm_train_ds.assign_predictions(model=vm_log_model)\n", + "\n", + "vm_test_ds.assign_predictions(model=vm_log_model)\n", "\n", "# Challenger — Random forest classification model\n", - "# vm_train_ds.assign_predictions(\n", - "# model=vm_rf_model,\n", - "# prediction_values=train_rf_binary_predictions,\n", - "# prediction_probabilities=train_rf_prob,\n", - "# )\n", + "vm_train_ds.assign_predictions(model=vm_rf_model)\n", "\n", - "# vm_test_ds.assign_predictions(\n", - "# model=vm_rf_model,\n", - "# prediction_values=test_rf_binary_predictions,\n", - "# prediction_probabilities=test_rf_prob,\n", - "# )" + "vm_test_ds.assign_predictions(model=vm_rf_model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "If no prediction values are passed, the method will compute predictions automatically:" + "## Running validation tests" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# Champion — Logistic regression model\n", - "vm_train_ds.assign_predictions(\n", - " model=vm_log_model\n", - ")\n", - "\n", - "vm_test_ds.assign_predictions(\n", - " model=vm_log_model\n", - ")\n", - "\n", - "# Challenger — Random forest classification model\n", - "vm_train_ds.assign_predictions(\n", - " model=vm_rf_model\n", - ")\n", + "### Run data quality tests" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run performance tests" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## In summary" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Next steps\n", "\n", - "vm_test_ds.assign_predictions(\n", - " model=vm_rf_model\n", - ")" + "### Finalize validation and reporting" ] } ], diff --git a/notebooks/tutorials/model_validation/3-perform_validation_tests.ipynb b/notebooks/tutorials/model_validation/3-perform_validation_tests_OLD.ipynb similarity index 100% rename from notebooks/tutorials/model_validation/3-perform_validation_tests.ipynb rename to notebooks/tutorials/model_validation/3-perform_validation_tests_OLD.ipynb diff --git a/notebooks/tutorials/model_validation/4-finalize_testing_reporting.ipynb b/notebooks/tutorials/model_validation/4-finalize_testing_reporting_OLD.ipynb similarity index 100% rename from notebooks/tutorials/model_validation/4-finalize_testing_reporting.ipynb rename to notebooks/tutorials/model_validation/4-finalize_testing_reporting_OLD.ipynb diff --git a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb new file mode 100644 index 000000000..150421c1c --- /dev/null +++ b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb @@ -0,0 +1,88 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ValidMind for model validation 4 — Finalize testing and reporting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting up" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adjust a ValidMind test" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run diagnostic tests" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run feature importance tests" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Implement a custom test" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Verify test runs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## In summary" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Next steps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Work with your validation report" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 0a425e535fd541076f8318414cf6cf546f5b4d94 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Thu, 3 Apr 2025 17:03:19 -0700 Subject: [PATCH 26/71] Testing --- .../model_development/2-start_development_process.ipynb | 5 ----- 1 file changed, 5 deletions(-) diff --git a/notebooks/tutorials/model_development/2-start_development_process.ipynb b/notebooks/tutorials/model_development/2-start_development_process.ipynb index a2ed2cd69..2a5c154ae 100644 --- a/notebooks/tutorials/model_development/2-start_development_process.ipynb +++ b/notebooks/tutorials/model_development/2-start_development_process.ipynb @@ -840,11 +840,6 @@ "metadata": {}, "outputs": [], "source": [ - "train_df = X_train\n", - "train_df[\"Exited\"] = y_train\n", - "test_df = X_test\n", - "test_df[\"Exited\"] = y_test\n", - "\n", "vm_train_ds = vm.init_dataset(\n", " input_id=\"train_dataset_final\",\n", " dataset=train_df,\n", From 7bfabdbd7612852ff450db32bcd0e6e4b2a19c75 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Fri, 4 Apr 2025 09:49:54 -0700 Subject: [PATCH 27/71] Applying changes to subsequent notebooks --- .../3-integrate_custom_tests.ipynb | 31 ++++++++++--------- .../4-finalize_testing_documentation.ipynb | 31 ++++++++++--------- 2 files changed, 32 insertions(+), 30 deletions(-) diff --git a/notebooks/tutorials/model_development/3-integrate_custom_tests.ipynb b/notebooks/tutorials/model_development/3-integrate_custom_tests.ipynb index bfaaa8597..0ade31439 100644 --- a/notebooks/tutorials/model_development/3-integrate_custom_tests.ipynb +++ b/notebooks/tutorials/model_development/3-integrate_custom_tests.ipynb @@ -340,18 +340,24 @@ "metadata": {}, "outputs": [], "source": [ - "from sklearn.linear_model import LogisticRegression\n", + "# Split the processed dataset into train and test\n", "from sklearn.model_selection import train_test_split\n", "\n", - "# Split the input and target variables\n", - "X = balanced_raw_no_age_df.drop(\"Exited\", axis=1)\n", - "y = balanced_raw_no_age_df[\"Exited\"]\n", - "X_train, X_test, y_train, y_test = train_test_split(\n", - " X,\n", - " y,\n", - " test_size=0.2,\n", - " random_state=42,\n", - ")\n", + "train_df, test_df = train_test_split(balanced_raw_no_age_df, test_size=0.20)\n", + "\n", + "X_train = train_df.drop(\"Exited\", axis=1)\n", + "y_train = train_df[\"Exited\"]\n", + "X_test = test_df.drop(\"Exited\", axis=1)\n", + "y_test = test_df[\"Exited\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import LogisticRegression\n", "\n", "# Logistic Regression grid params\n", "log_reg_params = {\n", @@ -387,11 +393,6 @@ "metadata": {}, "outputs": [], "source": [ - "train_df = X_train\n", - "train_df[\"Exited\"] = y_train\n", - "test_df = X_test\n", - "test_df[\"Exited\"] = y_test\n", - "\n", "# Initialize the datasets into their own dataset objects\n", "vm_train_ds = vm.init_dataset(\n", " input_id=\"train_dataset_final\",\n", diff --git a/notebooks/tutorials/model_development/4-finalize_testing_documentation.ipynb b/notebooks/tutorials/model_development/4-finalize_testing_documentation.ipynb index efa7ed671..13a4f1e14 100644 --- a/notebooks/tutorials/model_development/4-finalize_testing_documentation.ipynb +++ b/notebooks/tutorials/model_development/4-finalize_testing_documentation.ipynb @@ -338,18 +338,24 @@ "metadata": {}, "outputs": [], "source": [ - "from sklearn.linear_model import LogisticRegression\n", + "# Split the processed dataset into train and test\n", "from sklearn.model_selection import train_test_split\n", "\n", - "# Split the input and target variables\n", - "X = balanced_raw_no_age_df.drop(\"Exited\", axis=1)\n", - "y = balanced_raw_no_age_df[\"Exited\"]\n", - "X_train, X_test, y_train, y_test = train_test_split(\n", - " X,\n", - " y,\n", - " test_size=0.2,\n", - " random_state=42,\n", - ")\n", + "train_df, test_df = train_test_split(balanced_raw_no_age_df, test_size=0.20)\n", + "\n", + "X_train = train_df.drop(\"Exited\", axis=1)\n", + "y_train = train_df[\"Exited\"]\n", + "X_test = test_df.drop(\"Exited\", axis=1)\n", + "y_test = test_df[\"Exited\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import LogisticRegression\n", "\n", "# Logistic Regression grid params\n", "log_reg_params = {\n", @@ -385,11 +391,6 @@ "metadata": {}, "outputs": [], "source": [ - "train_df = X_train\n", - "train_df[\"Exited\"] = y_train\n", - "test_df = X_test\n", - "test_df[\"Exited\"] = y_test\n", - "\n", "# Initialize the datasets into their own dataset objects\n", "vm_train_ds = vm.init_dataset(\n", " input_id=\"train_dataset_final\",\n", From fe91a7037647b8bf6ee06408a9d20f58113ea59c Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Fri, 4 Apr 2025 09:56:51 -0700 Subject: [PATCH 28/71] Deleting old draft for validation 2 --- .../2-develop_challenger_model_OLD.ipynb | 629 ------------------ 1 file changed, 629 deletions(-) delete mode 100644 notebooks/tutorials/model_validation/2-develop_challenger_model_OLD.ipynb diff --git a/notebooks/tutorials/model_validation/2-develop_challenger_model_OLD.ipynb b/notebooks/tutorials/model_validation/2-develop_challenger_model_OLD.ipynb deleted file mode 100644 index ca0dfd287..000000000 --- a/notebooks/tutorials/model_validation/2-develop_challenger_model_OLD.ipynb +++ /dev/null @@ -1,629 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# ValidMind for model validation 2 — Start the model validation process\n", - "\n", - "Learn how to use ValidMind for your end-to-end model validation process with our series of four introductory notebooks. In this second notebook, develop potential challenger models and then pass your models and their predictions to ValidMind.\n", - "\n", - "A *challenger model* is an alternate model that attempt to outperform the champion model, ensuring that the best performing fit-for-purpose model is always considered for deployment. Challenger models also help avoid over-reliance on a single model, and allow testing of new features, algorithms, or data sources without disrupting the production lifecycle." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "In order to continue on the model validation journey with notebook, you'll need to first have:\n", - "\n", - "- [ ] Registered a model within the ValidMind Platform and granted yourself access to the model as a validator\n", - "- [ ] Installed the ValidMind Library in your local environment, allowing you to access all its features\n", - "\n", - "
    Need help with the above steps?\n", - "

    \n", - "Refer to the first notebook in this series: 1 — Import the champion model
    \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setting up" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initialize the ValidMind Library\n", - "\n", - "First, let's connect up the ValidMind Library to our model we previously registered in the ValidMind Platform:\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 \"ValidMind for model validation\" series of notebooks.\n", - "\n", - "3. 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, - "metadata": {}, - "outputs": [], - "source": [ - "# Make sure the ValidMind Library is installed\n", - "\n", - "%pip install -q validmind\n", - "\n", - "# 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": [ - "# WIP" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing the champion model\n", - "\n", - "With the ValidMind Library set up and ready to go, let's go ahead and import the champion model submitted by the model development team in the format of a `.pkl` file: **[lr_model_champion.pkl](lr_model_champion.pkl)**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pickle\n", - "\n", - "with open(\"lr_model_champion.pkl\", \"rb\") as f:\n", - " model = pickle.load(f)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load the 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, which was used to develop the dummy champion model.\n", - "\n", - "We'll use this dataset to review steps that should have been conducted during the initial development and documentation of the model to ensure that the model was built correctly. By independently performing steps such as rebalancing and removing highly correlated features, we can confirm whether the model was built using appropriate and properly processed data.\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." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from validmind.datasets.classification import customer_churn as demo_dataset\n", - "\n", - "print(\n", - " f\"Loaded demo dataset with: \\n\\n\\t• Target column: '{demo_dataset.target_column}' \\n\\t• Class labels: {demo_dataset.class_labels}\"\n", - ")\n", - "\n", - "raw_df = demo_dataset.load_data()\n", - "raw_df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# WIP2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Import the champion model\n", - "\n", - "Next, we'll import the champion model submitted by the model development team as we used in the last notebook (**[xgb_model_champion.pkl](xgb_model_champion.pkl)**) and load in the sample [Lending Club](https://www.kaggle.com/datasets/devanshi23/loan-data-2007-2014/data) dataset used to develop the model, and independently preprocess and feature engineer the dataset to confirm that the model was built using appropriate and properly processed data:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import xgboost as xgb\n", - "\n", - "#Load the saved model\n", - "xgb_model = xgb.XGBClassifier()\n", - "xgb_model.load_model(\"xgb_model_champion.pkl\")\n", - "xgb_model\n", - "\n", - "# Ensure that we have to appropriate order in feature names from Champion model and dataset\n", - "cols_when_model_builds = xgb_model.get_booster().feature_names" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import the Lending Club dataset from Kaggle\n", - "from validmind.datasets.credit_risk import lending_club\n", - "\n", - "df = lending_club.load_data(source=\"offline\")\n", - "df.head()\n", - "\n", - "# Preprocess the dataset for data quality testing purposes\n", - "preprocess_df = lending_club.preprocess(df)\n", - "\n", - "# Apply feature engineering to the dataset\n", - "fe_df = lending_club.feature_engineering(preprocess_df)\n", - "fe_df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Split the feature engineered dataset\n", - "\n", - "With our dummy model imported and our independently preprocessed and feature engineered dataset ready to go, let's now **spilt our dataset into train and test** to start the validation testing process.\n", - "\n", - "Splitting our dataset into training and testing is essential for proper validation testing, as this helps assess how well the model generalizes to unseen data:\n", - "\n", - "- We begin by dividing our data, which is based on Weight of Evidence (WoE) features, into training and testing sets (`train_df`, `test_df`).\n", - "- With `lending_club.split`, we employ a simple random split, randomly allocating data points to each set to ensure a mix of examples in both." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Split the data\n", - "train_df, test_df = lending_club.split(fe_df, test_size=0.2)\n", - "\n", - "x_train = train_df.drop(lending_club.target_column, axis=1)\n", - "y_train = train_df[lending_club.target_column]\n", - "\n", - "x_test = test_df.drop(lending_club.target_column, axis=1)\n", - "y_test = test_df[lending_club.target_column]\n", - "\n", - "# Now let's apply the order of features from the champion model construction\n", - "x_train = x_train[cols_when_model_builds]\n", - "x_test = x_test[cols_when_model_builds]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cols_use = ['annual_inc_woe',\n", - " 'verification_status_woe',\n", - " 'emp_length_woe',\n", - " 'installment_woe',\n", - " 'term_woe',\n", - " 'home_ownership_woe',\n", - " 'purpose_woe',\n", - " 'open_acc_woe',\n", - " 'total_acc_woe',\n", - " 'int_rate_woe',\n", - " 'sub_grade_woe',\n", - " 'grade_woe','loan_status']\n", - "\n", - "\n", - "train_df = train_df[cols_use]\n", - "test_df = test_df[cols_use]\n", - "test_df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Investigate potential challenger models\n", - "\n", - "We're curious how alternate models compare to our champion model, so let's train two challenger models as basis for our testing.\n", - "\n", - "Our selected options below offer decreased complexity in terms of implementation — such as lessened manual preprocessing — which can reduce the amount of risk for implementation. However, model risk is not calculated in isolation from a single factor, but rather in consideration with trade-offs in predictive performance, ease of interpretability, and overall alignment with business objectives." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Random forest classification model\n", - "\n", - "A *random forest classification model* is an ensemble machine learning algorithm that uses multiple decision trees to classify data. In ensemble learning, multiple models are combined to improve prediction accuracy and robustness.\n", - "\n", - "Random forest classification models generally have higher accuracy because they capture complex, non-linear relationships, but as a result they lack transparency in their predictions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import the Random Forest Classification model\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "\n", - "# Create the model instance with 50 decision trees\n", - "rf_model = RandomForestClassifier(\n", - " n_estimators=50,\n", - " random_state=42,\n", - ")\n", - "\n", - "# Train the model\n", - "rf_model.fit(x_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Logistic regression model\n", - "\n", - "A *logistic regression model* is a statistical machine learning algorithm that uses a linear equation (straight-line relationship between variables) and the logistic function (or sigmoid function, which maps any real-valued number to a range between `0` and `1`) to classify data. In statistical modeling, a single equation is used to estimate the probability of an outcome based on input features.\n", - "\n", - "Logistic regression models are simple and interpretable because they provide clear probability estimates and feature coefficients (numerical value that represents the influence of a particular input feature on the model's prediction), but they may struggle with capturing complex, non-linear relationships in the data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import the Logistic Regression model\n", - "from sklearn.linear_model import LogisticRegression\n", - "\n", - "# Logistic Regression grid params\n", - "log_reg_params = {\n", - " \"penalty\": [\"l1\", \"l2\"],\n", - " \"C\": [0.001, 0.01, 0.1, 1, 10, 100, 1000],\n", - " \"solver\": [\"liblinear\"],\n", - "}\n", - "\n", - "# Grid search for Logistic Regression\n", - "from sklearn.model_selection import GridSearchCV\n", - "\n", - "grid_log_reg = GridSearchCV(LogisticRegression(), log_reg_params)\n", - "grid_log_reg.fit(x_train, y_train)\n", - "\n", - "# Logistic Regression best estimator\n", - "log_reg = grid_log_reg.best_estimator_\n", - "log_reg" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Extract predicted probabilities\n", - "\n", - "With our challenger models trained, let's extract the predicted probabilities from our three models:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Champion — Application scorecard model\n", - "train_xgb_prob = xgb_model.predict_proba(x_train)[:, 1]\n", - "test_xgb_prob = xgb_model.predict_proba(x_test)[:, 1]\n", - "\n", - "# Challenger — Random forest classification model\n", - "train_rf_prob = rf_model.predict_proba(x_train)[:, 1]\n", - "test_rf_prob = rf_model.predict_proba(x_test)[:, 1]\n", - "\n", - "# Challenger — Logistic regression model\n", - "train_log_prob = log_reg.predict_proba(x_train)[:, 1]\n", - "test_log_prob = log_reg.predict_proba(x_test)[:, 1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute binary predictions\n", - "\n", - "Next, we'll convert the probability predictions from our three models into a binary, based on a threshold of `0.3`:\n", - "\n", - "- If the probability is greater than `0.3`, the prediction becomes `1` (positive).\n", - "- Otherwise, it becomes `0` (negative)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cut_off_threshold = 0.3\n", - "\n", - "# Champion — Application scorecard model\n", - "train_xgb_binary_predictions = (train_xgb_prob > cut_off_threshold).astype(int)\n", - "test_xgb_binary_predictions = (test_xgb_prob > cut_off_threshold).astype(int)\n", - "\n", - "# Challenger — Random forest classification model\n", - "train_rf_binary_predictions = (train_rf_prob > cut_off_threshold).astype(int)\n", - "test_rf_binary_predictions = (test_rf_prob > cut_off_threshold).astype(int)\n", - "\n", - "# Challenger — Logistic regression model\n", - "train_log_binary_predictions = (train_log_prob > cut_off_threshold).astype(int)\n", - "test_log_binary_predictions = (test_log_prob > cut_off_threshold).astype(int)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initializing the ValidMind objects" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initialize the ValidMind datasets\n", - "\n", - "Before you can run tests, you'll need to connect your data with a ValidMind `Dataset` object. **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", - "Initialize a ValidMind dataset object using the [`init_dataset` function](https://docs.validmind.ai/validmind/validmind.html#init_dataset) from the ValidMind (`vm`) module. 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." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize the raw dataset\n", - "vm_raw_dataset = vm.init_dataset(\n", - " dataset=df,\n", - " input_id=\"raw_dataset\",\n", - " target_column=lending_club.target_column,\n", - ")\n", - "\n", - "# Initialize the preprocessed dataset\n", - "vm_preprocess_dataset = vm.init_dataset(\n", - " dataset=preprocess_df,\n", - " input_id=\"preprocess_dataset\",\n", - " target_column=lending_club.target_column,\n", - ")\n", - "\n", - "# Initialize the feature engineered dataset\n", - "vm_fe_dataset = vm.init_dataset(\n", - " dataset=fe_df,\n", - " input_id=\"fe_dataset\",\n", - " target_column=lending_club.target_column,\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=lending_club.target_column,\n", - ")\n", - "\n", - "# Initialize the test dataset\n", - "vm_test_ds = vm.init_dataset(\n", - " dataset=test_df,\n", - " input_id=\"test_dataset\",\n", - " target_column=lending_club.target_column,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After initialization, you can pass the ValidMind `Dataset` objects `vm_raw_dataset`, `vm_preprocess_dataset`, `vm_fe_dataset`, `vm_train_ds`, and `vm_test_ds` into any ValidMind tests." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initialize the model objects\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 each of our three models.\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, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize the champion application scorecard model\n", - "vm_xgb_model = vm.init_model(\n", - " xgb_model,\n", - " input_id=\"xgb_model_developer_champion\",\n", - ")\n", - "\n", - "# Initialize the challenger random forest classification model\n", - "vm_rf_model = vm.init_model(\n", - " rf_model,\n", - " input_id=\"rf_model\",\n", - ")\n", - "\n", - "# Initialize the challenger logistic regression model\n", - "vm_log_model = vm.init_model(\n", - " log_reg,\n", - " input_id=\"log_model\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Assign predictions\n", - "\n", - "With our models registered, we'll move on to assigning both the predictive probabilities coming directly from each model's predictions, and the binary prediction after applying the cutoff threshold described in the Compute binary predictions step above.\n", - "\n", - "- The [`assign_predictions()` method](https://docs.validmind.ai/validmind/validmind/vm_models.html#VMDataset.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." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Champion — Application scorecard model\n", - "vm_train_ds.assign_predictions(\n", - " model=vm_xgb_model,\n", - " prediction_values=train_xgb_binary_predictions,\n", - " prediction_probabilities=train_xgb_prob,\n", - ")\n", - "\n", - "vm_test_ds.assign_predictions(\n", - " model=vm_xgb_model,\n", - " prediction_values=test_xgb_binary_predictions,\n", - " prediction_probabilities=test_xgb_prob,\n", - ")\n", - "\n", - "# Challenger — Random forest classification model\n", - "vm_train_ds.assign_predictions(\n", - " model=vm_rf_model,\n", - " prediction_values=train_rf_binary_predictions,\n", - " prediction_probabilities=train_rf_prob,\n", - ")\n", - "\n", - "vm_test_ds.assign_predictions(\n", - " model=vm_rf_model,\n", - " prediction_values=test_rf_binary_predictions,\n", - " prediction_probabilities=test_rf_prob,\n", - ")\n", - "\n", - "\n", - "# Challenger — Logistic regression model\n", - "vm_train_ds.assign_predictions(\n", - " model=vm_log_model,\n", - " prediction_values=train_log_binary_predictions,\n", - " prediction_probabilities=train_log_prob,\n", - ")\n", - "\n", - "vm_test_ds.assign_predictions(\n", - " model=vm_log_model,\n", - " prediction_values=test_log_binary_predictions,\n", - " prediction_probabilities=test_log_prob,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute credit risk scores\n", - "\n", - "Finally, we'll translate model predictions into actionable scores using probability estimates generated by our trained model:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Compute the scores\n", - "train_xgb_scores = lending_club.compute_scores(train_xgb_prob)\n", - "test_xgb_scores = lending_club.compute_scores(test_xgb_prob)\n", - "train_rf_scores = lending_club.compute_scores(train_rf_prob)\n", - "test_rf_scores = lending_club.compute_scores(test_rf_prob)\n", - "train_log_scores = lending_club.compute_scores(train_log_prob)\n", - "test_log_scores = lending_club.compute_scores(test_log_prob)\n", - "\n", - "# Assign scores to the datasets\n", - "vm_train_ds.add_extra_column(\"xgb_scores\", train_xgb_scores)\n", - "vm_test_ds.add_extra_column(\"xgb_scores\", test_xgb_scores)\n", - "vm_train_ds.add_extra_column(\"rf_scores\", train_rf_scores)\n", - "vm_test_ds.add_extra_column(\"rf_scores\", test_rf_scores)\n", - "vm_train_ds.add_extra_column(\"log_scores\", train_log_scores)\n", - "vm_test_ds.add_extra_column(\"log_scores\", test_log_scores)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## In summary\n", - "\n", - "In this second notebook, you learned how to:\n", - "\n", - "- [ ] Initialize ValidMind datasets\n", - "- [ ] Initialize ValidMind model objects\n", - "- [ ] Assign predictions and probabilities to your ValidMind datasets" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Next steps\n", - "\n", - "### Perform model validation tests\n", - "\n", - "Now that you're familiar with the basics of using the ValidMind Library, let's learn how to identify and run validation tests: **[3 — Perform model validation tests](3-perform_validation_tests.ipynb)**" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "ValidMind Library", - "language": "python", - "name": "validmind" - }, - "language_info": { - "name": "python", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 4504233114f1f036a06a0046679d597076801f3a Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Fri, 4 Apr 2025 10:19:38 -0700 Subject: [PATCH 29/71] Save point --- .../2-start_development_process.ipynb | 4 +- .../2-start_validation_process.ipynb | 11 +- .../3-developing_challenger_model.ipynb | 133 +++++++++++++++++- 3 files changed, 138 insertions(+), 10 deletions(-) diff --git a/notebooks/tutorials/model_development/2-start_development_process.ipynb b/notebooks/tutorials/model_development/2-start_development_process.ipynb index 2a5c154ae..54a522122 100644 --- a/notebooks/tutorials/model_development/2-start_development_process.ipynb +++ b/notebooks/tutorials/model_development/2-start_development_process.ipynb @@ -769,7 +769,7 @@ "source": [ "We'll split our preprocessed dataset into training and testing, to help assess how well the model generalizes to unseen data:\n", "\n", - "- We start by dividing our preprocessed DataFrame (`balanced_raw_no_age_df`) into training and test subsets using `train_test_split`, with 80% of the data allocated to training (`train_df`) and 20% to testing (`test_df`).\n", + "- We start by dividing our `balanced_raw_no_age_df` dataset into training and test subsets using `train_test_split`, with 80% of the data allocated to training (`train_df`) and 20% to testing (`test_df`).\n", "- From each subset, we separate the features (all columns except \"Exited\") into `X_train` and `X_test`, and the target column (\"Exited\") into `y_train` and `y_test`." ] }, @@ -941,7 +941,7 @@ "- [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", "- [x] Add supplementary individual test results to your documentation\n", - "- [x] Assign model predictions to your ValidMind datasets\n" + "- [x] Assign model predictions to your ValidMind model objects\n" ] }, { diff --git a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb index a7a799bc5..af16e1453 100644 --- a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb +++ b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb @@ -8,7 +8,14 @@ "\n", "Learn how to use ValidMind for your end-to-end model validation process with our series of four introductory notebooks. In this second notebook, independently verify the data quality tests performed on the dataset used to train the champion model.\n", "\n", - "You'll learn how to run relevant validation tests with ValidMind, log the results of those tests to the ValidMind Platform, and insert your logged test results as evidence into your validation report." + "You'll learn how to run relevant validation tests with ValidMind, log the results of those tests to the ValidMind Platform, and insert your logged test results as evidence into your validation report. You'll become familiar with the tests available in ValidMind, as well as how to run them. Running tests during model validation is crucial to the effective challenge process, as we want to independently evaluate the evidence and assessments provided by the model development team.\n", + "\n", + "While running our tests in this notebook, we'll focus on:\n", + "\n", + "- Ensuring that data used for training and testing the model is of appropriate data quality\n", + "- Ensuring that the raw data has been preprocessed appropriately and that the resulting final datasets reflects this\n", + "\n", + "**For a full list of out-of-the-box tests,** refer to our [Test descriptions](https://docs.validmind.ai/developer/model-testing/test-descriptions.html) or try the interactive [Test sandbox](https://docs.validmind.ai/developer/model-testing/test-sandbox.html)." ] }, { @@ -17,7 +24,7 @@ "source": [ "## Prerequisites\n", "\n", - "In order to continue on the model validation journey with notebook, you'll need to first have:\n", + "In order to independently assess the quality of your datasets with notebook, you'll need to first have:\n", "\n", "- [x] Registered a model within the ValidMind Platform and granted yourself access to the model as a validator\n", "- [x] Installed the ValidMind Library in your local environment, allowing you to access all its features\n", diff --git a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb index 793c04c1b..f7de82ed5 100644 --- a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb +++ b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb @@ -325,7 +325,7 @@ "source": [ "Splitting our dataset into training and testing is essential for proper validation testing, as this helps assess how well the model generalizes to unseen data:\n", "\n", - "- We start by dividing our preprocessed DataFrame (`balanced_raw_no_age_df`) into training and test subsets using `train_test_split`, with 80% of the data allocated to training (`train_df`) and 20% to testing (`test_df`).\n", + "- We start by dividing our `balanced_raw_no_age_df` dataset into training and test subsets using `train_test_split`, with 80% of the data allocated to training (`train_df`) and 20% to testing (`test_df`).\n", "- From each subset, we separate the features (all columns except \"Exited\") into `X_train` and `X_test`, and the target column (\"Exited\") into `y_train` and `y_test`." ] }, @@ -473,12 +473,10 @@ "source": [ "# Champion — Logistic regression model\n", "vm_train_ds.assign_predictions(model=vm_log_model)\n", - "\n", "vm_test_ds.assign_predictions(model=vm_log_model)\n", "\n", "# Challenger — Random forest classification model\n", "vm_train_ds.assign_predictions(model=vm_rf_model)\n", - "\n", "vm_test_ds.assign_predictions(model=vm_rf_model)" ] }, @@ -486,14 +484,131 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Running validation tests" + "## Running validation tests\n", + "\n", + "With everything ready for us, let's run the rest of our validation tests. We'll focus on comprehensive testing around model performance of both the champion and challenger models going forward as we've already verified the data quality of the datasets used to train the champion model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run data quality tests\n", + "\n", + "This time, we'll use the [`vm.tests.list_tasks_and_tags()` function](https://docs.validmind.ai/validmind/validmind/tests.html#list_tasks_and_tags) to list which `tags` are associated with each `task` type:\n", + "\n", + "- **`tasks`** represent the kind of modeling task associated with a test. Here we'll focus on `classification` tasks.\n", + "- **`tags`** are free-form descriptions providing more details about the test, for example, what category the test falls into. Here we'll focus on the `data_quality` tag." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vm.tests.list_tasks_and_tags()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we'll call the familiar [`vm.tests.list_tests()`](https://docs.validmind.ai/validmind/validmind/tests.html#list_tests) to list all the data quality tests for classification:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vm.tests.list_tests(\n", + " tags=[\"data_quality\"], task=\"classification\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
    Want to learn more about navigating ValidMind tests?\n", + "

    \n", + "Refer to our notebook outlining the utilities available for viewing and understanding available ValidMind tests: Explore tests
    " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We want to run all the tests that are returned, so we'll store the identified tests in `dq` in preparation for batch running these tests and logging their results to the ValidMind Platform:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dq = vm.tests.list_tests(tags=[\"data_quality\"], task=\"classification\",pretty=False)\n", + "dq" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "With our data quality tests identified, let's run our first batch of tests using our previously initialized preprocessed dataset (`balanced_raw_no_age_df`) and log their results.\n", + "\n", + "- You run validation tests by calling [the `run_test` function](https://docs.validmind.ai/validmind/validmind/tests.html#run_test) provided by the `validmind.tests` module.\n", + "- Every test result returned by the `run_test()` function has a [`.log()` method](https://docs.validmind.ai/validmind/validmind/vm_models.html#TestResult.log) that can be used to send the test results to the ValidMind Platform:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for test in dq:\n", + " vm.tests.run_test(\n", + " test,\n", + " inputs={\n", + " \"dataset\": balanced_raw_no_age_df\n", + " }\n", + " ).log()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
    Note the output returned indicating that a test-driven block doesn't currently exist in your model's documentation for this particular test ID. \n", + "

    \n", + "That's expected, as when we run individual tests the results logged need to be manually added to your documentation within the ValidMind Platform. You'll continue to see this message throughout this notebook as we run and log more tests.
    " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Run data quality tests" + "Next, let's reuse the tests in `dq` to perform comparison tests between the raw balanced (`vm_balanced_raw_dataset`) and preprocessed (`balanced_raw_no_age_df`) dataset, again logging the results to the ValidMind Platform:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for test in dq:\n", + " vm.tests.run_test(\n", + " test,\n", + " input_grid={\n", + " \"dataset\": [vm_balanced_raw_dataset,balanced_raw_no_age_df]\n", + " }\n", + " ).log()" ] }, { @@ -507,7 +622,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## In summary" + "## In summary\n", + "\n", + "In this third notebook, you learned how to:\n", + "\n", + "- [x] Initialize ValidMind model objects\n", + "- [x] Assign predictions and probabilities to your ValidMind model objects\n", + "- [x] Use tests from ValidMind to evaluate the potential of challenger models" ] }, { From bf5fed37e2fbbb46d4ee857562469ec360c60cd7 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Fri, 4 Apr 2025 11:16:51 -0700 Subject: [PATCH 30/71] Save point --- .../2-start_development_process.ipynb | 13 +- .../2-start_validation_process.ipynb | 119 ++++++++++++++++-- .../3-developing_challenger_model.ipynb | 2 +- 3 files changed, 120 insertions(+), 14 deletions(-) diff --git a/notebooks/tutorials/model_development/2-start_development_process.ipynb b/notebooks/tutorials/model_development/2-start_development_process.ipynb index 54a522122..d2c0756fb 100644 --- a/notebooks/tutorials/model_development/2-start_development_process.ipynb +++ b/notebooks/tutorials/model_development/2-start_development_process.ipynb @@ -167,7 +167,10 @@ "\n", "Next, let's say we want to do some data quality assessments by running a few individual tests.\n", "\n", - "Use the [`vm.tests.list_tests()` function](https://docs.validmind.ai/validmind/validmind/tests.html#list_tests) introduced by the first notebook in this series in combination with [`vm.tests.list_tags()`](https://docs.validmind.ai/validmind/validmind/tests.html#list_tags) and [`vm.tests.list_tasks()`](https://docs.validmind.ai/validmind/validmind/tests.html#list_tasks) to find which prebuilt tests are relevant for data quality assessment:\n" + "Use the [`vm.tests.list_tests()` function](https://docs.validmind.ai/validmind/validmind/tests.html#list_tests) introduced by the first notebook in this series in combination with [`vm.tests.list_tags()`](https://docs.validmind.ai/validmind/validmind/tests.html#list_tags) and [`vm.tests.list_tasks()`](https://docs.validmind.ai/validmind/validmind/tests.html#list_tasks) to find which prebuilt tests are relevant for data quality assessment:\n", + "\n", + "- **`tasks`** represent the kind of modeling task associated with a test. Here we'll focus on `classification` tasks.\n", + "- **`tags`** are free-form descriptions providing more details about the test, for example, what category the test falls into. Here we'll focus on the `data_quality` tag.\n" ] }, { @@ -176,8 +179,8 @@ "metadata": {}, "outputs": [], "source": [ - "# Get the list of available tags\n", - "sorted(vm.tests.list_tags())" + "# Get the list of available task types\n", + "sorted(vm.tests.list_tasks())" ] }, { @@ -186,8 +189,8 @@ "metadata": {}, "outputs": [], "source": [ - "# Get the list of available task types\n", - "sorted(vm.tests.list_tasks())" + "# Get the list of available tags\n", + "sorted(vm.tests.list_tags())" ] }, { diff --git a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb index af16e1453..3807b8789 100644 --- a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb +++ b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb @@ -134,7 +134,10 @@ "\n", "During model validation, we use the same data processing logic and training procedure to confirm that the model's results can be reproduced independently, so let's start by doing some data quality assessments by running a few individual tests just like the development team did.\n", "\n", - "Use the [`vm.tests.list_tests()` function](https://docs.validmind.ai/validmind/validmind/tests.html#list_tests) introduced by the first notebook in this series in combination with [`vm.tests.list_tags()`](https://docs.validmind.ai/validmind/validmind/tests.html#list_tags) and [`vm.tests.list_tasks()`](https://docs.validmind.ai/validmind/validmind/tests.html#list_tasks) to find which prebuilt tests are relevant for data quality assessment:" + "Use the [`vm.tests.list_tests()` function](https://docs.validmind.ai/validmind/validmind/tests.html#list_tests) introduced by the first notebook in this series in combination with [`vm.tests.list_tags()`](https://docs.validmind.ai/validmind/validmind/tests.html#list_tags) and [`vm.tests.list_tasks()`](https://docs.validmind.ai/validmind/validmind/tests.html#list_tasks) to find which prebuilt tests are relevant for data quality assessment:\n", + "\n", + "- **`tasks`** represent the kind of modeling task associated with a test. Here we'll focus on `classification` tasks.\n", + "- **`tags`** are free-form descriptions providing more details about the test, for example, what category the test falls into. Here we'll focus on the `data_quality` tag." ] }, { @@ -143,8 +146,8 @@ "metadata": {}, "outputs": [], "source": [ - "# Get the list of available tags\n", - "sorted(vm.tests.list_tags())" + "# Get the list of available task types\n", + "sorted(vm.tests.list_tasks())" ] }, { @@ -153,8 +156,8 @@ "metadata": {}, "outputs": [], "source": [ - "# Get the list of available task types\n", - "sorted(vm.tests.list_tasks())" + "# Get the list of available tags\n", + "sorted(vm.tests.list_tags())" ] }, { @@ -492,16 +495,116 @@ ")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Verify quality of adjusted datasets\n", + "\n", + "We were able to independently reproduce the results reported by the model development that led to the dataset being adjusted, so next let's evaluate the quality of the balanced dataset with the highly correlated features removed (`balanced_raw_no_age_df`).\n", + "\n", + "This time, we'll use the [`vm.tests.list_tasks_and_tags()` function](https://docs.validmind.ai/validmind/validmind/tests.html#list_tasks_and_tags) to list which `tags` are associated with each `task` type:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vm.tests.list_tasks_and_tags()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we'll call the familiar `list_tests()` to list only the data quality tests for classification:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vm.tests.list_tests(\n", + " tags=[\"data_quality\"], task=\"classification\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We want to run all the tests that are returned, so we'll store the identified tests in `dq` in preparation for batch running these tests and logging their results to the ValidMind Platform:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dq = vm.tests.list_tests(tags=[\"data_quality\"], task=\"classification\",pretty=False)\n", + "dq" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With our data quality tests identified, let's run our first batch of tests using our previously initialized preprocessed dataset (`vm_raw_dataset_preprocessed`) and log their results.\n", + "\n", + "Every test result returned by the `run_test()` function has a [`.log()` method](https://docs.validmind.ai/validmind/validmind/vm_models.html#TestResult.log) that can be used to send the test results to the ValidMind Platform:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for test in dq:\n", + " vm.tests.run_test(\n", + " test,\n", + " inputs={\n", + " \"dataset\": vm_raw_dataset_preprocessed\n", + " }\n", + " ).log()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, let's reuse the tests in `dq` to perform comparison tests between the original raw dataset (`vm_raw_dataset`) and the final preprocessed (`vm_raw_dataset_preprocessed`) dataset, again logging the results to the ValidMind Platform:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for test in dq:\n", + " vm.tests.run_test(\n", + " test,\n", + " input_grid={\n", + " \"dataset\": [vm_raw_dataset,vm_raw_dataset_preprocessed]\n", + " }\n", + " ).log()" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Documenting test results\n", "\n", - "Now that we've done some analysis on two different datasets, we can use ValidMind to easily document why certain things were done to our raw data with testing to support it:\n", + "Now that we've done some analysis on two different datasets, we can use ValidMind to easily document why certain things were done to our raw data with testing to support it. As we learned above, every test result returned by the `run_test()` function has a `.log()` method that can be used to send the test results to the ValidMind Platform.\n", "\n", - "- Every test result returned by the `run_test()` function has a [`.log()` method](https://docs.validmind.ai/validmind/validmind/vm_models.html#TestResult.log) that can be used to send the test results to the ValidMind Platform.\n", - "- When logging validation test results to the platform, you'll need to manually add those results to the desired section of the validation report." + "When logging validation test results to the platform, you'll need to manually add those results to the desired section of the validation report. To demonstrate how to add test results to your validation report, we'll log some specific tests and insert the results via the ValidMind Platform." ] }, { diff --git a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb index f7de82ed5..31f4289ef 100644 --- a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb +++ b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb @@ -615,7 +615,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Run performance tests" + "### Run model performance tests" ] }, { From d865c83711177d546efa60ab36b3e30d04d6da14 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Fri, 4 Apr 2025 11:19:09 -0700 Subject: [PATCH 31/71] Save point --- .../tutorials/model_validation/2-start_validation_process.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb index 3807b8789..1cbfaa17e 100644 --- a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb +++ b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb @@ -501,7 +501,7 @@ "source": [ "### Verify quality of adjusted datasets\n", "\n", - "We were able to independently reproduce the results reported by the model development that led to the dataset being adjusted, so next let's evaluate the quality of the balanced dataset with the highly correlated features removed (`balanced_raw_no_age_df`).\n", + "We were able to independently reproduce the results reported by the model development that led to the dataset being adjusted, so next let's evaluate the quality of the balanced dataset with the highly correlated features removed (`vm_raw_dataset_preprocessed`).\n", "\n", "This time, we'll use the [`vm.tests.list_tasks_and_tags()` function](https://docs.validmind.ai/validmind/validmind/tests.html#list_tasks_and_tags) to list which `tags` are associated with each `task` type:" ] From bf6375a593e09e8159a89618ab0796d58560d6f4 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Fri, 4 Apr 2025 11:29:07 -0700 Subject: [PATCH 32/71] Save point --- .../tutorials/model_validation/2-start_validation_process.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb index 1cbfaa17e..c382a14d3 100644 --- a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb +++ b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb @@ -501,7 +501,7 @@ "source": [ "### Verify quality of adjusted datasets\n", "\n", - "We were able to independently reproduce the results reported by the model development that led to the dataset being adjusted, so next let's evaluate the quality of the balanced dataset with the highly correlated features removed (`vm_raw_dataset_preprocessed`).\n", + "We were able to independently reproduce the results reported by the model development team that led to the dataset being adjusted, so next let's evaluate the quality of the balanced dataset with the highly correlated features removed (`vm_raw_dataset_preprocessed`).\n", "\n", "This time, we'll use the [`vm.tests.list_tasks_and_tags()` function](https://docs.validmind.ai/validmind/validmind/tests.html#list_tasks_and_tags) to list which `tags` are associated with each `task` type:" ] From e2a18a99e0b4ee10bb6ca210c7ac69122663e26a Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Fri, 4 Apr 2025 11:36:48 -0700 Subject: [PATCH 33/71] Testing? --- .../2-start_validation_process.ipynb | 52 +++++++++++++++++++ 1 file changed, 52 insertions(+) diff --git a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb index c382a14d3..4556fd67a 100644 --- a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb +++ b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb @@ -673,6 +673,58 @@ " print(f\"Error running test {t}: {str(e)}\")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, let's reuse the same tests to perform comparison tests between the original raw dataset (`vm_raw_dataset`) and the final preprocessed (`vm_raw_dataset_preprocessed`) dataset, again logging the results to the ValidMind Platform:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Individual test config with inputs specified\n", + "comp_test = {\n", + " \"validmind.data_validation.ClassImbalance\": {\n", + " 'input_grid': {'dataset': ['vm_raw_dataset', 'vm_raw_dataset_preprocessed']},\n", + " \"params\": {\"min_percent_threshold\": 30}\n", + " },\n", + " \"validmind.data_validation.HighPearsonCorrelation\": {\n", + " 'input_grid': {'dataset': ['vm_raw_dataset', 'vm_raw_dataset_preprocessed']},\n", + " \"params\": {\"max_threshold\": 0.3}\n", + " },\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for t in comp_test:\n", + " print(t)\n", + " try:\n", + " # Check if test has input_grid\n", + " if 'input_grid' in test_config[t]:\n", + " # For tests with input_grid, pass the input_grid configuration\n", + " if 'params' in test_config[t]:\n", + " vm.tests.run_test(t, input_grid=test_config[t]['input_grid'], params=test_config[t]['params']).log()\n", + " else:\n", + " vm.tests.run_test(t, input_grid=test_config[t]['input_grid']).log()\n", + " else:\n", + " # Original logic for regular inputs\n", + " if 'params' in test_config[t]:\n", + " vm.tests.run_test(t, inputs=test_config[t]['inputs'], params=test_config[t]['params']).log()\n", + " else:\n", + " vm.tests.run_test(t, inputs=test_config[t]['inputs']).log()\n", + " except Exception as e:\n", + " print(f\"Error running test {t}: {str(e)}\")" + ] + }, { "cell_type": "markdown", "metadata": {}, From d970d9b6a3d2662f5be315a31093f6818d7712cd Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Fri, 4 Apr 2025 11:42:02 -0700 Subject: [PATCH 34/71] Testing... --- .../2-start_validation_process.ipynb | 105 +----------------- 1 file changed, 2 insertions(+), 103 deletions(-) diff --git a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb index 4556fd67a..0f73cfd88 100644 --- a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb +++ b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb @@ -495,107 +495,6 @@ ")" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Verify quality of adjusted datasets\n", - "\n", - "We were able to independently reproduce the results reported by the model development team that led to the dataset being adjusted, so next let's evaluate the quality of the balanced dataset with the highly correlated features removed (`vm_raw_dataset_preprocessed`).\n", - "\n", - "This time, we'll use the [`vm.tests.list_tasks_and_tags()` function](https://docs.validmind.ai/validmind/validmind/tests.html#list_tasks_and_tags) to list which `tags` are associated with each `task` type:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vm.tests.list_tasks_and_tags()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then we'll call the familiar `list_tests()` to list only the data quality tests for classification:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vm.tests.list_tests(\n", - " tags=[\"data_quality\"], task=\"classification\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We want to run all the tests that are returned, so we'll store the identified tests in `dq` in preparation for batch running these tests and logging their results to the ValidMind Platform:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dq = vm.tests.list_tests(tags=[\"data_quality\"], task=\"classification\",pretty=False)\n", - "dq" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With our data quality tests identified, let's run our first batch of tests using our previously initialized preprocessed dataset (`vm_raw_dataset_preprocessed`) and log their results.\n", - "\n", - "Every test result returned by the `run_test()` function has a [`.log()` method](https://docs.validmind.ai/validmind/validmind/vm_models.html#TestResult.log) that can be used to send the test results to the ValidMind Platform:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for test in dq:\n", - " vm.tests.run_test(\n", - " test,\n", - " inputs={\n", - " \"dataset\": vm_raw_dataset_preprocessed\n", - " }\n", - " ).log()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, let's reuse the tests in `dq` to perform comparison tests between the original raw dataset (`vm_raw_dataset`) and the final preprocessed (`vm_raw_dataset_preprocessed`) dataset, again logging the results to the ValidMind Platform:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for test in dq:\n", - " vm.tests.run_test(\n", - " test,\n", - " input_grid={\n", - " \"dataset\": [vm_raw_dataset,vm_raw_dataset_preprocessed]\n", - " }\n", - " ).log()" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -689,11 +588,11 @@ "# Individual test config with inputs specified\n", "comp_test = {\n", " \"validmind.data_validation.ClassImbalance\": {\n", - " 'input_grid': {'dataset': ['vm_raw_dataset', 'vm_raw_dataset_preprocessed']},\n", + " \"input_grid\": {\"dataset\": [\"vm_raw_dataset\", \"vm_raw_dataset_preprocessed\"]},\n", " \"params\": {\"min_percent_threshold\": 30}\n", " },\n", " \"validmind.data_validation.HighPearsonCorrelation\": {\n", - " 'input_grid': {'dataset': ['vm_raw_dataset', 'vm_raw_dataset_preprocessed']},\n", + " \"input_grid\": {\"dataset\": [\"vm_raw_dataset\", \"vm_raw_dataset_preprocessed\"]},\n", " \"params\": {\"max_threshold\": 0.3}\n", " },\n", "}" From 8a5df0c1ffa96fd8631feae05d0f7a76e5b283bc Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Fri, 4 Apr 2025 11:57:17 -0700 Subject: [PATCH 35/71] Editing... --- .../2-start_development_process.ipynb | 2 +- .../2-start_validation_process.ipynb | 78 ++++--------------- 2 files changed, 15 insertions(+), 65 deletions(-) diff --git a/notebooks/tutorials/model_development/2-start_development_process.ipynb b/notebooks/tutorials/model_development/2-start_development_process.ipynb index d2c0756fb..5afc8e75e 100644 --- a/notebooks/tutorials/model_development/2-start_development_process.ipynb +++ b/notebooks/tutorials/model_development/2-start_development_process.ipynb @@ -636,7 +636,7 @@ "\n", "### Run and log an individual test\n", "\n", - "Next, we'll use the previously initialized `vm_balanced_raw_dataset` (that had a highly correlated `Age` column) as input to run an individual test, then log the result to the ValidMind Platform.\n", + "Next, we'll use the previously initialized `vm_balanced_raw_dataset` (that still has a highly correlated `Age` column) as input to run an individual test, then log the result to the ValidMind Platform.\n", "\n", "When running individual tests, **you can use a custom `result_id` to tag the individual result with a unique identifier:** \n", "\n", diff --git a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb index 0f73cfd88..aa9d3c24b 100644 --- a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb +++ b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb @@ -503,21 +503,21 @@ "\n", "Now that we've done some analysis on two different datasets, we can use ValidMind to easily document why certain things were done to our raw data with testing to support it. As we learned above, every test result returned by the `run_test()` function has a `.log()` method that can be used to send the test results to the ValidMind Platform.\n", "\n", - "When logging validation test results to the platform, you'll need to manually add those results to the desired section of the validation report. To demonstrate how to add test results to your validation report, we'll log some specific tests and insert the results via the ValidMind Platform." + "When logging validation test results to the platform, you'll need to manually add those results to the desired section of the validation report. To demonstrate how to add test results to your validation report, we'll log our data quality tests and insert the results via the ValidMind Platform." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Configure and run multiple tests\n", + "### Configure and run comparison tests\n", "\n", - "Below, we'll run the tests using the previously initialized `vm_raw_dataset_preprocessed` as input.\n", + "Below, we'll perform comparison tests between the original raw dataset (`vm_raw_dataset`) and the final preprocessed (`vm_raw_dataset_preprocessed`) dataset, again logging the results to the ValidMind Platform.\n", "\n", "We can specify all the tests we'd ike to run in a dictionary called `test_config`, and we'll pass in the following arguments for each test:\n", "\n", " - **`params`:** Individual test parameters.\n", - " - **`inputs`:** Individual test inputs." + " - **`input_grid`:** Individual test inputs to compare. In this case, we'll input our two datasets for comparison." ] }, { @@ -529,12 +529,12 @@ "# Individual test config with inputs specified\n", "test_config = {\n", " \"validmind.data_validation.ClassImbalance\": {\n", - " \"params\": {\"min_percent_threshold\": 30},\n", - " \"inputs\": {\"dataset\": vm_raw_dataset_preprocessed},\n", + " \"input_grid\": {\"dataset\": [\"vm_raw_dataset\", \"vm_raw_dataset_preprocessed\"]},\n", + " \"params\": {\"min_percent_threshold\": 30}\n", " },\n", " \"validmind.data_validation.HighPearsonCorrelation\": {\n", - " \"params\": {\"max_threshold\": 0.3},\n", - " \"inputs\": {\"dataset\": vm_raw_dataset_preprocessed},\n", + " \"input_grid\": {\"dataset\": [\"vm_raw_dataset\", \"vm_raw_dataset_preprocessed\"]},\n", + " \"params\": {\"max_threshold\": 0.3}\n", " },\n", "}" ] @@ -572,58 +572,6 @@ " print(f\"Error running test {t}: {str(e)}\")" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, let's reuse the same tests to perform comparison tests between the original raw dataset (`vm_raw_dataset`) and the final preprocessed (`vm_raw_dataset_preprocessed`) dataset, again logging the results to the ValidMind Platform:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Individual test config with inputs specified\n", - "comp_test = {\n", - " \"validmind.data_validation.ClassImbalance\": {\n", - " \"input_grid\": {\"dataset\": [\"vm_raw_dataset\", \"vm_raw_dataset_preprocessed\"]},\n", - " \"params\": {\"min_percent_threshold\": 30}\n", - " },\n", - " \"validmind.data_validation.HighPearsonCorrelation\": {\n", - " \"input_grid\": {\"dataset\": [\"vm_raw_dataset\", \"vm_raw_dataset_preprocessed\"]},\n", - " \"params\": {\"max_threshold\": 0.3}\n", - " },\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for t in comp_test:\n", - " print(t)\n", - " try:\n", - " # Check if test has input_grid\n", - " if 'input_grid' in test_config[t]:\n", - " # For tests with input_grid, pass the input_grid configuration\n", - " if 'params' in test_config[t]:\n", - " vm.tests.run_test(t, input_grid=test_config[t]['input_grid'], params=test_config[t]['params']).log()\n", - " else:\n", - " vm.tests.run_test(t, input_grid=test_config[t]['input_grid']).log()\n", - " else:\n", - " # Original logic for regular inputs\n", - " if 'params' in test_config[t]:\n", - " vm.tests.run_test(t, inputs=test_config[t]['inputs'], params=test_config[t]['params']).log()\n", - " else:\n", - " vm.tests.run_test(t, inputs=test_config[t]['inputs']).log()\n", - " except Exception as e:\n", - " print(f\"Error running test {t}: {str(e)}\")" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -639,7 +587,7 @@ "source": [ "### Log tests with a unique identifiers\n", "\n", - "Next, we'll use the previously initialized `vm_balanced_raw_dataset` (that had a highly correlated `Age` column) as input to run an individual test, then log the result to the ValidMind Platform.\n", + "Next, we'll use the previously initialized `vm_balanced_raw_dataset` (that still has a highly correlated `Age` column) as input to run an individual test, then log the result to the ValidMind Platform.\n", "\n", "When running individual tests, **you can use a custom `result_id` to tag the individual result with a unique identifier:**\n", "\n", @@ -667,7 +615,9 @@ "source": [ "### Add test results to reporting\n", "\n", - "With some test results logged, let's head to the model we connected to at the beginning of this notebook and learn how to insert a test result into our validation report ([Need more help?](https://docs.validmind.ai/guide/model-validation/assess-compliance.html#link-validator-evidence)):\n", + "With some test results logged, let's head to the model we connected to at the beginning of this notebook and learn how to insert a test result into our validation report ([Need more help?](https://docs.validmind.ai/guide/model-validation/assess-compliance.html#link-validator-evidence)).\n", + "\n", + "While the example below focuses on a specific test result, you can follow the same general procedure for your other results:\n", "\n", "1. From the **Inventory** in the ValidMind Platform, go to the model you connected to earlier.\n", "\n", @@ -680,14 +630,14 @@ " \"Screenshot\n", "

    \n", "\n", - "5. Select the Class Imbalance test results we logged: **ValidMind Data Validation Class Imbalance**\n", + "5. Select the Class Imbalance test results we logged: **ValidMind Data Validation Class Imbalance** \n", "\n", " \"Screenshot\n", "

    \n", "\n", "6. Finally, click **Update Linked Evidence** to add the test results to the validation report.\n", "\n", - " Confirm that the results for the Class Imbalance score tests have been correctly inserted into section **2.2.1. Data Quality** of the report:\n", + " Confirm that the results for the Class Imbalance test you inserted has been correctly inserted into section **2.2.1. Data Quality** of the report:\n", "\n", " \"Screenshot\n", "

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QPvEC_!RWW5^Y*v@3yI)o*8l(j From db8abc421624fd50327a6ec4e7f3911692b01abb Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Fri, 4 Apr 2025 13:43:05 -0700 Subject: [PATCH 37/71] Save point --- .../validate_application_scorecard.ipynb | 10 ++-------- .../model_validation/2-start_validation_process.ipynb | 6 +++--- 2 files changed, 5 insertions(+), 11 deletions(-) diff --git a/notebooks/code_samples/model_validation/validate_application_scorecard.ipynb b/notebooks/code_samples/model_validation/validate_application_scorecard.ipynb index 99dda7693..a2a6d900e 100644 --- a/notebooks/code_samples/model_validation/validate_application_scorecard.ipynb +++ b/notebooks/code_samples/model_validation/validate_application_scorecard.ipynb @@ -388,7 +388,7 @@ "\n", "Feature engineering improves the dataset's structure to better match what our model expects, and ensures that the model performs optimally by leveraging additional insights from raw data.\n", "\n", - "We'll apply the following transformations using the `lending_club.feature_engineering()` function to optimize the dataset for predictive modeling in our application scorecard:\n", + "We'll apply the following transformations using the `ending_club.feature_engineering()` function to optimize the dataset for predictive modeling in our application scorecard:\n", "\n", "- **WoE encoding**: Converts both numerical and categorical features into Weight of Evidence (WoE) values. WoE is a statistical measure used in scorecard modeling that quantifies the relationship between a predictor variable and the binary target variable. It calculates the ratio of the distribution of good outcomes to the distribution of bad outcomes for each category or bin of a feature. This transformation helps to ensure that the features are predictive and consistent in their contribution to the model.\n", "- **Integration of WoE bins**: Ensures that the WoE transformed values are integrated throughout the dataset, replacing the original feature values while excluding the target variable from this transformation. This transformation is used to maintain a consistent scale and impact of each variable within the model, which helps make the predictions more stable and accurate." @@ -1816,14 +1816,8 @@ } ], "metadata": { - "kernelspec": { - "display_name": "ValidMind Library", - "language": "python", - "name": "validmind" - }, "language_info": { - "name": "python", - "version": "3.10.13" + "name": "python" } }, "nbformat": 4, diff --git a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb index aa9d3c24b..117da59fd 100644 --- a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb +++ b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb @@ -512,7 +512,7 @@ "source": [ "### Configure and run comparison tests\n", "\n", - "Below, we'll perform comparison tests between the original raw dataset (`vm_raw_dataset`) and the final preprocessed (`vm_raw_dataset_preprocessed`) dataset, again logging the results to the ValidMind Platform.\n", + "Below, we'll perform comparison tests between the original raw dataset (`raw_dataset`) and the final preprocessed (`raw_dataset_preprocessed`) dataset, again logging the results to the ValidMind Platform. **Note here that the `input_grid` expects the `input_id` of the dataset as the value rather than the variable name we specified.**\n", "\n", "We can specify all the tests we'd ike to run in a dictionary called `test_config`, and we'll pass in the following arguments for each test:\n", "\n", @@ -529,11 +529,11 @@ "# Individual test config with inputs specified\n", "test_config = {\n", " \"validmind.data_validation.ClassImbalance\": {\n", - " \"input_grid\": {\"dataset\": [\"vm_raw_dataset\", \"vm_raw_dataset_preprocessed\"]},\n", + " \"input_grid\": {\"dataset\": [\"raw_dataset\", \"raw_dataset_preprocessed\"]},\n", " \"params\": {\"min_percent_threshold\": 30}\n", " },\n", " \"validmind.data_validation.HighPearsonCorrelation\": {\n", - " \"input_grid\": {\"dataset\": [\"vm_raw_dataset\", \"vm_raw_dataset_preprocessed\"]},\n", + " \"input_grid\": {\"dataset\": [\"raw_dataset\", \"raw_dataset_preprocessed\"]},\n", " \"params\": {\"max_threshold\": 0.3}\n", " },\n", "}" From 23cf3fc62fd91eff26346db8995bf884a97c3634 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Fri, 4 Apr 2025 13:49:01 -0700 Subject: [PATCH 38/71] Editing...... --- .../validate_application_scorecard.ipynb | 2 +- .../2-start_development_process.ipynb | 23 +++++++++++++++---- .../2-start_validation_process.ipynb | 6 +++-- 3 files changed, 24 insertions(+), 7 deletions(-) diff --git a/notebooks/code_samples/model_validation/validate_application_scorecard.ipynb b/notebooks/code_samples/model_validation/validate_application_scorecard.ipynb index a2a6d900e..e6e7bd406 100644 --- a/notebooks/code_samples/model_validation/validate_application_scorecard.ipynb +++ b/notebooks/code_samples/model_validation/validate_application_scorecard.ipynb @@ -1580,7 +1580,7 @@ "\n", "Our final task is to verify that all the tests provided by the model development team were run and reported accurately. Note the appended `result_ids` to delineate which dataset we ran the test with for the relevant tests.\n", "\n", - "Here, we'll specify all the tests we'd like to independently rerun in a dictionary called `test_config`:" + "Here, we'll specify all the tests we'd like to independently rerun in a dictionary called `test_config`. **Note here that `inputs` and `input_grid` expect the `input_id` of the dataset or model as the value rather than the variable name we specified**:" ] }, { diff --git a/notebooks/tutorials/model_development/2-start_development_process.ipynb b/notebooks/tutorials/model_development/2-start_development_process.ipynb index 5afc8e75e..28ce3ea1e 100644 --- a/notebooks/tutorials/model_development/2-start_development_process.ipynb +++ b/notebooks/tutorials/model_development/2-start_development_process.ipynb @@ -832,9 +832,7 @@ "\n", "### Initialize model evaluation objects\n", "\n", - "The last step for evaluating the model's performance is to initialize the ValidMind `Dataset` and `Model` objects in preparation for assigning model predictions to each dataset.\n", - "\n", - "Use the `init_dataset` and [`init_model`](https://docs.validmind.ai/validmind/validmind.html#init_model) functions to initialize these objects:\n" + "The last step for evaluating the model's performance is to initialize the ValidMind `Dataset` and `Model` objects in preparation for assigning model predictions to each dataset." ] }, { @@ -843,6 +841,7 @@ "metadata": {}, "outputs": [], "source": [ + "# Initialize the datasets into their own dataset objects\n", "vm_train_ds = vm.init_dataset(\n", " input_id=\"train_dataset_final\",\n", " dataset=train_df,\n", @@ -853,8 +852,24 @@ " input_id=\"test_dataset_final\",\n", " dataset=test_df,\n", " target_column=\"Exited\",\n", - ")\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "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 each of our three models.\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, + "metadata": {}, + "outputs": [], + "source": [ "# Register the model\n", "vm_model = vm.init_model(log_reg, input_id=\"log_reg_model_v1\")" ] diff --git a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb index 117da59fd..c40714df9 100644 --- a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb +++ b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb @@ -512,12 +512,14 @@ "source": [ "### Configure and run comparison tests\n", "\n", - "Below, we'll perform comparison tests between the original raw dataset (`raw_dataset`) and the final preprocessed (`raw_dataset_preprocessed`) dataset, again logging the results to the ValidMind Platform. **Note here that the `input_grid` expects the `input_id` of the dataset as the value rather than the variable name we specified.**\n", + "Below, we'll perform comparison tests between the original raw dataset (`raw_dataset`) and the final preprocessed (`raw_dataset_preprocessed`) dataset, again logging the results to the ValidMind Platform. \n", "\n", "We can specify all the tests we'd ike to run in a dictionary called `test_config`, and we'll pass in the following arguments for each test:\n", "\n", " - **`params`:** Individual test parameters.\n", - " - **`input_grid`:** Individual test inputs to compare. In this case, we'll input our two datasets for comparison." + " - **`input_grid`:** Individual test inputs to compare. In this case, we'll input our two datasets for comparison.\n", + "\n", + "**Note here that the `input_grid` expects the `input_id` of the dataset as the value rather than the variable name we specified:**" ] }, { From 59e9e3519efcc662145ddf77732625b8e272e04f Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Fri, 4 Apr 2025 13:59:08 -0700 Subject: [PATCH 39/71] Validation 2 cleanup --- .../2-start_validation_process.ipynb | 20 ++++++++++++++---- .../class-imbalance-results-detail.png | Bin 0 -> 340258 bytes 2 files changed, 16 insertions(+), 4 deletions(-) create mode 100644 notebooks/tutorials/model_validation/class-imbalance-results-detail.png diff --git a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb index c40714df9..86a73df2a 100644 --- a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb +++ b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb @@ -634,15 +634,27 @@ "\n", "5. Select the Class Imbalance test results we logged: **ValidMind Data Validation Class Imbalance** \n", "\n", - " \"Screenshot\n", + " \"Screenshot\n", "

    \n", "\n", - "6. Finally, click **Update Linked Evidence** to add the test results to the validation report.\n", + "6. Click **Update Linked Evidence** to add the test results to the validation report.\n", "\n", " Confirm that the results for the Class Imbalance test you inserted has been correctly inserted into section **2.2.1. Data Quality** of the report:\n", "\n", - " \"Screenshot\n", - "

    " + " \"Screenshot\n", + "

    \n", + "\n", + "7. Note that these test results are flagged as **Requires Attention** — as they include comparative results from our initial raw dataset.\n", + "\n", + " Click **See evidence details** to review the LLM-generated description that summarizes the test results, that confirm that our final preprocessed dataset actually passes our test:\n", + "\n", + " \"Screenshot\n", + "

    \n", + "\n", + "\n", + "

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z;_~EP_`pRTfnJw~eDK!czp-NyAs2I5;BoSPusdX!W^ov|ByOajdS5=Zv#;mO@7gHR zlmz%1zumLh*zZUE&Go-~5VZy_!O8#aHV`sUtzS7s1nLwDOoos?9{NL`s=g&HB(?@ z*!Rca@fU+7?Eh`l{$KA#VnS`Tc4BktShas?oPR}w+1*_z-vO2UzdTL;N!_`A{rsCDhVodL2m(h*+$Kd_2ORcZ=&Ve k4PN!6>#%C(T;|g|_iOMam7X)(J Date: Fri, 4 Apr 2025 14:13:22 -0700 Subject: [PATCH 40/71] Validataion 3 editing --- .../2-start_validation_process.ipynb | 13 +- .../3-developing_challenger_model.ipynb | 112 +++++++++++------- .../3-perform_validation_tests_OLD.ipynb | 4 +- .../4-finalize_validation_reporting.ipynb | 21 ---- 4 files changed, 75 insertions(+), 75 deletions(-) diff --git a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb index 86a73df2a..50b31f6f5 100644 --- a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb +++ b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb @@ -274,6 +274,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "
    Note the output returned indicating that a test-driven block doesn't currently exist in your model's documentation for this particular test ID. \n", + "

    \n", + "That's expected, as when we run individual tests the results logged need to be manually added to your documentation within the ValidMind Platform. You'll continue to see this message throughout this notebook as we run and log more tests.
    \n", + "\n", "The output above shows that [the class imbalance test](https://docs.validmind.ai/tests/data_validation/ClassImbalance.html) did not pass according to the value we set for `min_percent_threshold` — great, this matches what was reported by the model development team.\n", "\n", "To address this issue, we'll re-run the test on some processed data. In this case let's apply a very simple rebalancing technique to the dataset:" @@ -574,15 +578,6 @@ " print(f\"Error running test {t}: {str(e)}\")" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
    Note the output returned indicating that a test-driven block doesn't currently exist in your model's documentation for this particular test ID. \n", - "

    \n", - "That's expected, as when we run individual tests the results logged need to be manually added to your documentation within the ValidMind Platform. You'll continue to see this message throughout this notebook as we run and log more tests.
    " - ] - }, { "cell_type": "markdown", "metadata": {}, diff --git a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb index 31f4289ef..ffbc1e7e4 100644 --- a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb +++ b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb @@ -184,6 +184,15 @@ ")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
    Note the output returned indicating that a test-driven block doesn't currently exist in your model's documentation for this particular test ID. \n", + "

    \n", + "That's expected, as when we run individual tests the results logged need to be manually added to your documentation within the ValidMind Platform. You'll continue to see this message throughout this notebook as we run and log more tests.
    " + ] + }, { "cell_type": "code", "execution_count": null, @@ -484,7 +493,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Running validation tests\n", + "## Running model validation tests\n", "\n", "With everything ready for us, let's run the rest of our validation tests. We'll focus on comprehensive testing around model performance of both the champion and challenger models going forward as we've already verified the data quality of the datasets used to train the champion model." ] @@ -493,12 +502,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Run data quality tests\n", + "### Run model performance tests\n", "\n", - "This time, we'll use the [`vm.tests.list_tasks_and_tags()` function](https://docs.validmind.ai/validmind/validmind/tests.html#list_tasks_and_tags) to list which `tags` are associated with each `task` type:\n", + "Let's run some performance tests, beginning with independent testing of our champion logistic regression model, then moving on to our potential challenger model.\n", "\n", - "- **`tasks`** represent the kind of modeling task associated with a test. Here we'll focus on `classification` tasks.\n", - "- **`tags`** are free-form descriptions providing more details about the test, for example, what category the test falls into. Here we'll focus on the `data_quality` tag." + "Use [`vm.tests.list_tests()`](https://docs.validmind.ai/validmind/validmind/tests.html#list_tests) to identify all the model performance tests for classification:" ] }, { @@ -507,14 +515,23 @@ "metadata": {}, "outputs": [], "source": [ - "vm.tests.list_tasks_and_tags()" + "\n", + "vm.tests.list_tests(tags=[\"model_performance\"], task=\"classification\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Then we'll call the familiar [`vm.tests.list_tests()`](https://docs.validmind.ai/validmind/validmind/tests.html#list_tests) to list all the data quality tests for classification:" + "We'll isolate the specific tests we want to run in `mpt`:\n", + "\n", + "- [`ClassifierPerformance`](https://docs.validmind.ai/tests/model_validation/sklearn/ClassifierPerformance.html)\n", + "- [`ConfusionMatrix`](https://docs.validmind.ai/tests/model_validation/sklearn/ConfusionMatrix.html)\n", + "- [`MinimumAccuracy`](https://docs.validmind.ai/tests/model_validation/sklearn/MinimumAccuracy.html)\n", + "- [`MinimumF1Score`](https://docs.validmind.ai/tests/model_validation/sklearn/MinimumF1Score.html)\n", + "- [`ROCCurve`](https://docs.validmind.ai/tests/model_validation/sklearn/ROCCurve.html)\n", + "\n", + "As we learned in the previous notebook [2 — Start the model validation process](2-start_validation_process.ipynb), you can use a custom `result_id` to tag the individual result with a unique identifier by appending this `result_id` to the `test_id` with a `:` separator. We'll append an identifier for our champion model here:" ] }, { @@ -523,46 +540,57 @@ "metadata": {}, "outputs": [], "source": [ - "vm.tests.list_tests(\n", - " tags=[\"data_quality\"], task=\"classification\"\n", - ")" + "mpt = [\n", + " \"validmind.model_validation.sklearn.ClassifierPerformance:logreg_champion\",\n", + " \"validmind.model_validation.sklearn.ConfusionMatrix:logreg_champion\",\n", + " \"validmind.model_validation.sklearn.MinimumAccuracy:logreg_champion\",\n", + " \"validmind.model_validation.sklearn.MinimumF1Score:logreg_champion\",\n", + " \"validmind.model_validation.sklearn.ROCCurve:logreg_champion\"\n", + "]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "    " + "#### Evaluate performance of the champion model\n", + "\n", + "Now, let's run and log our batch of model performance tests using our testing dataset (`vm_test_ds`) for our champion model:" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "We want to run all the tests that are returned, so we'll store the identified tests in `dq` in preparation for batch running these tests and logging their results to the ValidMind Platform:" + "for test in mpt:\n", + " vm.tests.run_test(\n", + " test,\n", + " inputs={\n", + " \"dataset\": vm_test_ds, \"model\" : vm_log_model,\n", + " },\n", + " ).log()" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "dq = vm.tests.list_tests(tags=[\"data_quality\"], task=\"classification\",pretty=False)\n", - "dq" + "
    Note the output returned indicating that a test-driven block doesn't currently exist in your model's documentation for this particular test ID. \n", + "

    \n", + "That's expected, as when we run individual tests the results logged need to be manually added to your documentation within the ValidMind Platform. You'll continue to see this message throughout this notebook as we run and log more tests.
    " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ + "#### Evaluate performance of challenger model\n", "\n", - "With our data quality tests identified, let's run our first batch of tests using our previously initialized preprocessed dataset (`balanced_raw_no_age_df`) and log their results.\n", + "We've now conducted similar tests as the model development team for our champion model, with the aim of verifying their test results.\n", "\n", - "- You run validation tests by calling [the `run_test` function](https://docs.validmind.ai/validmind/validmind/tests.html#run_test) provided by the `validmind.tests` module.\n", - "- Every test result returned by the `run_test()` function has a [`.log()` method](https://docs.validmind.ai/validmind/validmind/vm_models.html#TestResult.log) that can be used to send the test results to the ValidMind Platform:" + "Next, let's see how our challenger models compare. We'll use the same batch of tests here as we did in `mpt`, but append a different `result_id` to indicate that these results should be associated with our challenger model:" ] }, { @@ -571,29 +599,20 @@ "metadata": {}, "outputs": [], "source": [ - "for test in dq:\n", - " vm.tests.run_test(\n", - " test,\n", - " inputs={\n", - " \"dataset\": balanced_raw_no_age_df\n", - " }\n", - " ).log()" + "mpt_chall = [\n", + " \"validmind.model_validation.sklearn.ClassifierPerformance:champion_vs_challenger\",\n", + " \"validmind.model_validation.sklearn.ConfusionMatrix:champion_vs_challenger\",\n", + " \"validmind.model_validation.sklearn.MinimumAccuracy:champion_vs_challenger\",\n", + " \"validmind.model_validation.sklearn.MinimumF1Score:champion_vs_challenger\",\n", + " \"validmind.model_validation.sklearn.ROCCurve:champion_vs_challenger\"\n", + "]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "
    Note the output returned indicating that a test-driven block doesn't currently exist in your model's documentation for this particular test ID. \n", - "

    \n", - "That's expected, as when we run individual tests the results logged need to be manually added to your documentation within the ValidMind Platform. You'll continue to see this message throughout this notebook as we run and log more tests.
    " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, let's reuse the tests in `dq` to perform comparison tests between the raw balanced (`vm_balanced_raw_dataset`) and preprocessed (`balanced_raw_no_age_df`) dataset, again logging the results to the ValidMind Platform:" + "We'll run each test once for each model with the same `vm_test_ds` dataset to compare them:" ] }, { @@ -602,11 +621,11 @@ "metadata": {}, "outputs": [], "source": [ - "for test in dq:\n", + "for test in mpt_chall:\n", " vm.tests.run_test(\n", " test,\n", " input_grid={\n", - " \"dataset\": [vm_balanced_raw_dataset,balanced_raw_no_age_df]\n", + " \"dataset\": [vm_test_ds], \"model\" : [vm_log_model,vm_rf_model]\n", " }\n", " ).log()" ] @@ -615,7 +634,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Run model performance tests" + "### Run diagnostic tests" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run feature importance tests" ] }, { diff --git a/notebooks/tutorials/model_validation/3-perform_validation_tests_OLD.ipynb b/notebooks/tutorials/model_validation/3-perform_validation_tests_OLD.ipynb index 9721c14fe..e175fa04d 100644 --- a/notebooks/tutorials/model_validation/3-perform_validation_tests_OLD.ipynb +++ b/notebooks/tutorials/model_validation/3-perform_validation_tests_OLD.ipynb @@ -759,7 +759,7 @@ " vm.tests.run_test(\n", " test,\n", " inputs={\n", - " \"dataset\": vm_test_ds, \"model\" : vm_xgb_model, \n", + " \"dataset\": vm_test_ds, \"model\" : vm_xgb_model\n", " },\n", " ).log()" ] @@ -886,7 +886,7 @@ " vm.tests.run_test(\n", " test,\n", " input_grid={\n", - " \"dataset\": [vm_test_ds], \"model\" : [vm_xgb_model,vm_log_model,vm_rf_model],\n", + " \"dataset\": [vm_test_ds], \"model\" : [vm_xgb_model,vm_log_model,vm_rf_model]\n", " }\n", " ).log()" ] diff --git a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb index 150421c1c..f8b497db6 100644 --- a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb +++ b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb @@ -21,27 +21,6 @@ "## Setting up" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adjust a ValidMind test" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run diagnostic tests" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run feature importance tests" - ] - }, { "cell_type": "markdown", "metadata": {}, From 77bd988e997b1b1b8933b4fccd3f0adff2bdf7b8 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Fri, 4 Apr 2025 15:52:08 -0700 Subject: [PATCH 41/71] Save point --- .../model_validation/2-start_validation_process.ipynb | 4 ---- 1 file changed, 4 deletions(-) diff --git a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb index 50b31f6f5..cc210b9b8 100644 --- a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb +++ b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb @@ -274,10 +274,6 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "
    Note the output returned indicating that a test-driven block doesn't currently exist in your model's documentation for this particular test ID. \n", - "

    \n", - "That's expected, as when we run individual tests the results logged need to be manually added to your documentation within the ValidMind Platform. You'll continue to see this message throughout this notebook as we run and log more tests.
    \n", - "\n", "The output above shows that [the class imbalance test](https://docs.validmind.ai/tests/data_validation/ClassImbalance.html) did not pass according to the value we set for `min_percent_threshold` — great, this matches what was reported by the model development team.\n", "\n", "To address this issue, we'll re-run the test on some processed data. In this case let's apply a very simple rebalancing technique to the dataset:" From a8199dee9bdc53f2359fc9e22398d7b1458ad0d1 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Fri, 4 Apr 2025 15:54:56 -0700 Subject: [PATCH 42/71] Save point --- .../model_validation/2-start_validation_process.ipynb | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb index cc210b9b8..2455cd338 100644 --- a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb +++ b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb @@ -574,6 +574,15 @@ " print(f\"Error running test {t}: {str(e)}\")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
    Note the output returned indicating that a test-driven block doesn't currently exist in your model's documentation for some test IDs. \n", + "

    \n", + "That's expected, as when we run validations tests the results logged need to be manually added to your report as part of your compliance assessment process within the ValidMind Platform.
    " + ] + }, { "cell_type": "markdown", "metadata": {}, From 2cea9e3fa17f91988dbf14a7060485086da2545f Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Fri, 4 Apr 2025 15:56:17 -0700 Subject: [PATCH 43/71] Save point --- .../3-developing_challenger_model.ipynb | 13 ++----------- 1 file changed, 2 insertions(+), 11 deletions(-) diff --git a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb index ffbc1e7e4..de7388af3 100644 --- a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb +++ b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb @@ -184,15 +184,6 @@ ")" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
    Note the output returned indicating that a test-driven block doesn't currently exist in your model's documentation for this particular test ID. \n", - "

    \n", - "That's expected, as when we run individual tests the results logged need to be manually added to your documentation within the ValidMind Platform. You'll continue to see this message throughout this notebook as we run and log more tests.
    " - ] - }, { "cell_type": "code", "execution_count": null, @@ -577,9 +568,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "
    Note the output returned indicating that a test-driven block doesn't currently exist in your model's documentation for this particular test ID. \n", + "
    Note the output returned indicating that a test-driven block doesn't currently exist in your model's documentation for some test IDs. \n", "

    \n", - "That's expected, as when we run individual tests the results logged need to be manually added to your documentation within the ValidMind Platform. You'll continue to see this message throughout this notebook as we run and log more tests.
    " + "That's expected, as when we run validations tests the results logged need to be manually added to your report as part of your compliance assessment process within the ValidMind Platform.
    " ] }, { From 3e2a384b766e56780cc0309f0915bc9d98eab10f Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Fri, 4 Apr 2025 16:17:17 -0700 Subject: [PATCH 44/71] Save point --- .../3-developing_challenger_model.ipynb | 4 +- .../3-developing_challenger_model_COPY.ipynb | 668 +++++++++++++ .../3-perform_validation_tests_OLD.ipynb | 941 ------------------ 3 files changed, 671 insertions(+), 942 deletions(-) create mode 100644 notebooks/tutorials/model_validation/3-developing_challenger_model_COPY.ipynb delete mode 100644 notebooks/tutorials/model_validation/3-perform_validation_tests_OLD.ipynb diff --git a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb index de7388af3..af5ae881c 100644 --- a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb +++ b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb @@ -654,7 +654,9 @@ "source": [ "## Next steps\n", "\n", - "### Finalize validation and reporting" + "### Finalize validation and reporting\n", + "\n", + "Now that you're familiar with the basics of using the ValidMind Library to run and log validation tests, let's learn how to implement some custom tests and wrap up our validation: **[4 — Finalize validation and reporting](4-finalize_validation_reporting.ipynb)**" ] } ], diff --git a/notebooks/tutorials/model_validation/3-developing_challenger_model_COPY.ipynb b/notebooks/tutorials/model_validation/3-developing_challenger_model_COPY.ipynb new file mode 100644 index 000000000..de7388af3 --- /dev/null +++ b/notebooks/tutorials/model_validation/3-developing_challenger_model_COPY.ipynb @@ -0,0 +1,668 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ValidMind for model validation 3 — Developing a potential challenger model\n", + "\n", + "Learn how to use ValidMind for your end-to-end model validation process with our series of four introductory notebooks. In this third notebook, develop a potential challenger model and then pass your model and its predictions to ValidMind.\n", + "\n", + "A *challenger model* is an alternate model that attempt to outperform the champion model, ensuring that the best performing fit-for-purpose model is always considered for deployment. Challenger models also help avoid over-reliance on a single model, and allow testing of new features, algorithms, or data sources without disrupting the production lifecycle." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "\n", + "In order to develop potential challenger models with this notebook, you'll need to first have:\n", + "\n", + "- [x] Registered a model within the ValidMind Platform and granted yourself access to the model as a validator\n", + "- [x] Installed the ValidMind Library in your local environment, allowing you to access all its features\n", + "- [x] Learned how to import and initialize datasets for use with ValidMind\n", + "- [x] Understood the basics of how to run and log tests with ValidMind\n", + "\n", + "
    Need help with the above steps?\n", + "

    \n", + "Refer to the first two notebooks in this series:\n", + "\n", + "- 1 — Set up the ValidMind Library for validation\n", + "- 2 — Start the model validation process\n", + "\n", + "
    \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting up\n", + "\n", + "This section should be quite familiar to you — as we performed the same actions in the previous notebook, **[2 — Start the model validation process](2-start_validation_process.ipynb)**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize the ValidMind Library\n", + "\n", + "As usual, let's first connect up the ValidMind Library to our model we previously registered in the ValidMind Platform:\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 \"ValidMind for model validation\" series of notebooks.\n", + "\n", + "3. 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, + "metadata": {}, + "outputs": [], + "source": [ + "# Make sure the ValidMind Library is installed\n", + "\n", + "%pip install -q validmind\n", + "\n", + "# 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": [ + "### Import the sample dataset\n", + "\n", + "Next, we'll load in the sample [Bank Customer Churn Prediction](https://www.kaggle.com/datasets/shantanudhakadd/bank-customer-churn-prediction) dataset used to develop the champion model that we will independently preprocess:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the sample dataset\n", + "from validmind.datasets.classification import customer_churn as demo_dataset\n", + "\n", + "print(\n", + " f\"Loaded demo dataset with: \\n\\n\\t• Target column: '{demo_dataset.target_column}' \\n\\t• Class labels: {demo_dataset.class_labels}\"\n", + ")\n", + "\n", + "raw_df = demo_dataset.load_data()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Preprocess the dataset\n", + "\n", + "We’ll apply a simple rebalancing technique to the dataset before continuing:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "raw_copy_df = raw_df.sample(frac=1) # Create a copy of the raw dataset\n", + "\n", + "# Create a balanced dataset with the same number of exited and not exited customers\n", + "exited_df = raw_copy_df.loc[raw_copy_df[\"Exited\"] == 1]\n", + "not_exited_df = raw_copy_df.loc[raw_copy_df[\"Exited\"] == 0].sample(n=exited_df.shape[0])\n", + "\n", + "balanced_raw_df = pd.concat([exited_df, not_exited_df])\n", + "balanced_raw_df = balanced_raw_df.sample(frac=1, random_state=42)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let’s also quickly remove highly correlated features from the dataset using the output from a ValidMind test.\n", + "\n", + "As you know, before we can run tests you’ll need to initialize a ValidMind dataset object with the [`init_dataset` function](https://docs.validmind.ai/validmind/validmind.html#init_dataset):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Register new data and now 'balanced_raw_dataset' is the new dataset object of interest\n", + "vm_balanced_raw_dataset = vm.init_dataset(\n", + " dataset=balanced_raw_df,\n", + " input_id=\"balanced_raw_dataset\",\n", + " target_column=\"Exited\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With our balanced dataset initialized, we can then run our test and utilize the output to help us identify the features we want to remove:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run HighPearsonCorrelation test with our balanced dataset as input and return a result object\n", + "corr_result = vm.tests.run_test(\n", + " test_id=\"validmind.data_validation.HighPearsonCorrelation\",\n", + " params={\"max_threshold\": 0.3},\n", + " inputs={\"dataset\": vm_balanced_raw_dataset},\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# From result object, extract table from `corr_result.tables`\n", + "features_df = corr_result.tables[0].data\n", + "features_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Extract list of features that failed the test\n", + "high_correlation_features = features_df[features_df[\"Pass/Fail\"] == \"Fail\"][\"Columns\"].tolist()\n", + "high_correlation_features" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Extract feature names from the list of strings\n", + "high_correlation_features = [feature.split(\",\")[0].strip(\"()\") for feature in high_correlation_features]\n", + "high_correlation_features" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then re-initialize the dataset with a different `input_id` and the highly correlated features removed and re-run the test for confirmation:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Remove the highly correlated features from the dataset\n", + "balanced_raw_no_age_df = balanced_raw_df.drop(columns=high_correlation_features)\n", + "\n", + "# Re-initialize the dataset object\n", + "vm_raw_dataset_preprocessed = vm.init_dataset(\n", + " dataset=balanced_raw_no_age_df,\n", + " input_id=\"raw_dataset_preprocessed\",\n", + " target_column=\"Exited\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Re-run the test with the reduced feature set\n", + "corr_result = vm.tests.run_test(\n", + " test_id=\"validmind.data_validation.HighPearsonCorrelation\",\n", + " params={\"max_threshold\": 0.3},\n", + " inputs={\"dataset\": vm_raw_dataset_preprocessed},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import the champion model\n", + "\n", + "With our raw dataset assessed and preprocessed, let's go ahead and import the champion model submitted by the model development team in the format of a `.pkl` file: **[lr_model_champion.pkl](lr_model_champion.pkl)**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import the champion model\n", + "import pickle as pkl\n", + "\n", + "with open(\"lr_model_champion.pkl\", \"rb\") as f:\n", + " log_reg = pkl.load(f)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Split the preprocessed dataset\n", + "\n", + "With our dummy model imported, raw dataset rebalanced with highly correlated features removed, let's now **spilt our dataset into train and test** in preparation for training our potential challenger model.\n", + "\n", + "To start, let's grab the first few rows from the `balanced_raw_no_age_df` dataset we initialized earlier:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "balanced_raw_no_age_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before training the model, we need to encode the categorical features in the dataset:\n", + "\n", + "- Use the `OneHotEncoder` class from the `sklearn.preprocessing` module to encode the categorical features.\n", + "- The categorical features in the dataset are `Geography` and `Gender`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "balanced_raw_no_age_df = pd.get_dummies(\n", + " balanced_raw_no_age_df, columns=[\"Geography\", \"Gender\"], drop_first=True\n", + ")\n", + "balanced_raw_no_age_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Splitting our dataset into training and testing is essential for proper validation testing, as this helps assess how well the model generalizes to unseen data:\n", + "\n", + "- We start by dividing our `balanced_raw_no_age_df` dataset into training and test subsets using `train_test_split`, with 80% of the data allocated to training (`train_df`) and 20% to testing (`test_df`).\n", + "- From each subset, we separate the features (all columns except \"Exited\") into `X_train` and `X_test`, and the target column (\"Exited\") into `y_train` and `y_test`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "train_df, test_df = train_test_split(balanced_raw_no_age_df, test_size=0.20)\n", + "\n", + "X_train = train_df.drop(\"Exited\", axis=1)\n", + "y_train = train_df[\"Exited\"]\n", + "X_test = test_df.drop(\"Exited\", axis=1)\n", + "y_test = test_df[\"Exited\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize the split datasets\n", + "\n", + "Next, let's initialize the training and testing datasets so they are available for use:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vm_train_ds = vm.init_dataset(\n", + " input_id=\"train_dataset_final\",\n", + " dataset=train_df,\n", + " target_column=\"Exited\",\n", + ")\n", + "\n", + "vm_test_ds = vm.init_dataset(\n", + " input_id=\"test_dataset_final\",\n", + " dataset=test_df,\n", + " target_column=\"Exited\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training a potential challenger model\n", + "\n", + "We're curious how an alternate model compares to our champion model, so let's train a challenger model as a basis for our testing.\n", + "\n", + "Our champion *logistic regression model* is a simpler, parametric model that assumes a linear relationship between the independent variables and the log-odds of the outcome. While logistic regression may not capture complex patterns as effectively, it offers a high degree of interpretability and is easier to explain to stakeholders. However, model risk is not calculated in isolation from a single factor, but rather in consideration with trade-offs in predictive performance, ease of interpretability, and overall alignment with business objectives." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Random forest classification model\n", + "\n", + "A *random forest classification model* is an ensemble machine learning algorithm that uses multiple decision trees to classify data. In ensemble learning, multiple models are combined to improve prediction accuracy and robustness.\n", + "\n", + "Random forest classification models generally have higher accuracy because they capture complex, non-linear relationships, but as a result they lack transparency in their predictions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import the Random Forest Classification model\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "# Create the model instance with 50 decision trees\n", + "rf_model = RandomForestClassifier(\n", + " n_estimators=50,\n", + " random_state=42,\n", + ")\n", + "\n", + "# Train the model\n", + "rf_model.fit(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initializing the model objects" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize the model objects\n", + "\n", + "In addition to the initialized datasets, 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 each of our two models.\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, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the champion logistic regression model\n", + "vm_log_model = vm.init_model(\n", + " log_reg,\n", + " input_id=\"log_model_champion\",\n", + ")\n", + "\n", + "# Initialize the challenger random forest classification model\n", + "vm_rf_model = vm.init_model(\n", + " rf_model,\n", + " input_id=\"rf_model\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Assign predictions\n", + "\n", + "With our models registered, we'll move on to assigning both the predictive probabilities coming directly from each model's predictions, and the binary prediction after applying the cutoff threshold described in the Compute binary predictions step above.\n", + "\n", + "- The [`assign_predictions()` method](https://docs.validmind.ai/validmind/validmind/vm_models.html#VMDataset.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, + "metadata": {}, + "outputs": [], + "source": [ + "# Champion — Logistic regression model\n", + "vm_train_ds.assign_predictions(model=vm_log_model)\n", + "vm_test_ds.assign_predictions(model=vm_log_model)\n", + "\n", + "# Challenger — Random forest classification model\n", + "vm_train_ds.assign_predictions(model=vm_rf_model)\n", + "vm_test_ds.assign_predictions(model=vm_rf_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running model validation tests\n", + "\n", + "With everything ready for us, let's run the rest of our validation tests. We'll focus on comprehensive testing around model performance of both the champion and challenger models going forward as we've already verified the data quality of the datasets used to train the champion model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run model performance tests\n", + "\n", + "Let's run some performance tests, beginning with independent testing of our champion logistic regression model, then moving on to our potential challenger model.\n", + "\n", + "Use [`vm.tests.list_tests()`](https://docs.validmind.ai/validmind/validmind/tests.html#list_tests) to identify all the model performance tests for classification:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "vm.tests.list_tests(tags=[\"model_performance\"], task=\"classification\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll isolate the specific tests we want to run in `mpt`:\n", + "\n", + "- [`ClassifierPerformance`](https://docs.validmind.ai/tests/model_validation/sklearn/ClassifierPerformance.html)\n", + "- [`ConfusionMatrix`](https://docs.validmind.ai/tests/model_validation/sklearn/ConfusionMatrix.html)\n", + "- [`MinimumAccuracy`](https://docs.validmind.ai/tests/model_validation/sklearn/MinimumAccuracy.html)\n", + "- [`MinimumF1Score`](https://docs.validmind.ai/tests/model_validation/sklearn/MinimumF1Score.html)\n", + "- [`ROCCurve`](https://docs.validmind.ai/tests/model_validation/sklearn/ROCCurve.html)\n", + "\n", + "As we learned in the previous notebook [2 — Start the model validation process](2-start_validation_process.ipynb), you can use a custom `result_id` to tag the individual result with a unique identifier by appending this `result_id` to the `test_id` with a `:` separator. We'll append an identifier for our champion model here:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mpt = [\n", + " \"validmind.model_validation.sklearn.ClassifierPerformance:logreg_champion\",\n", + " \"validmind.model_validation.sklearn.ConfusionMatrix:logreg_champion\",\n", + " \"validmind.model_validation.sklearn.MinimumAccuracy:logreg_champion\",\n", + " \"validmind.model_validation.sklearn.MinimumF1Score:logreg_champion\",\n", + " \"validmind.model_validation.sklearn.ROCCurve:logreg_champion\"\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Evaluate performance of the champion model\n", + "\n", + "Now, let's run and log our batch of model performance tests using our testing dataset (`vm_test_ds`) for our champion model:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for test in mpt:\n", + " vm.tests.run_test(\n", + " test,\n", + " inputs={\n", + " \"dataset\": vm_test_ds, \"model\" : vm_log_model,\n", + " },\n", + " ).log()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
    Note the output returned indicating that a test-driven block doesn't currently exist in your model's documentation for some test IDs. \n", + "

    \n", + "That's expected, as when we run validations tests the results logged need to be manually added to your report as part of your compliance assessment process within the ValidMind Platform.
    " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Evaluate performance of challenger model\n", + "\n", + "We've now conducted similar tests as the model development team for our champion model, with the aim of verifying their test results.\n", + "\n", + "Next, let's see how our challenger models compare. We'll use the same batch of tests here as we did in `mpt`, but append a different `result_id` to indicate that these results should be associated with our challenger model:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mpt_chall = [\n", + " \"validmind.model_validation.sklearn.ClassifierPerformance:champion_vs_challenger\",\n", + " \"validmind.model_validation.sklearn.ConfusionMatrix:champion_vs_challenger\",\n", + " \"validmind.model_validation.sklearn.MinimumAccuracy:champion_vs_challenger\",\n", + " \"validmind.model_validation.sklearn.MinimumF1Score:champion_vs_challenger\",\n", + " \"validmind.model_validation.sklearn.ROCCurve:champion_vs_challenger\"\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll run each test once for each model with the same `vm_test_ds` dataset to compare them:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for test in mpt_chall:\n", + " vm.tests.run_test(\n", + " test,\n", + " input_grid={\n", + " \"dataset\": [vm_test_ds], \"model\" : [vm_log_model,vm_rf_model]\n", + " }\n", + " ).log()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run diagnostic tests" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run feature importance tests" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## In summary\n", + "\n", + "In this third notebook, you learned how to:\n", + "\n", + "- [x] Initialize ValidMind model objects\n", + "- [x] Assign predictions and probabilities to your ValidMind model objects\n", + "- [x] Use tests from ValidMind to evaluate the potential of challenger models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Next steps\n", + "\n", + "### Finalize validation and reporting" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/tutorials/model_validation/3-perform_validation_tests_OLD.ipynb b/notebooks/tutorials/model_validation/3-perform_validation_tests_OLD.ipynb deleted file mode 100644 index e175fa04d..000000000 --- a/notebooks/tutorials/model_validation/3-perform_validation_tests_OLD.ipynb +++ /dev/null @@ -1,941 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# ValidMind for model validation 3 — Perform model validation tests\n", - "\n", - "Learn how to use ValidMind for your end-to-end model validation process with our series of four introductory notebooks. In this third notebook, you'll identify relevant validation tests, run them, then add the log results to your validation report.\n", - "\n", - "You'll become familiar with the tests available in ValidMind, as well as how to run them. Running tests during model validation is crucial to the effective challenge process, as we want to independently evaluate the evidence and assessments provided by the model development team.\n", - "\n", - "While running our tests, we'll focus on:\n", - "\n", - "- Ensuring that data used for training and testing the model is of appropriate data quality\n", - "- Ensuring that the raw data has been preprocessed appropriately and that the resulting feature engineered dataset reflects this\n", - "- Comprehensive testing around model performance of both the champion ahd challenger models\n", - "\n", - "**For a full list of out-of-the-box tests,** refer to our [Test descriptions](https://docs.validmind.ai/developer/model-testing/test-descriptions.html) or try the interactive [Test sandbox](https://docs.validmind.ai/developer/model-testing/test-sandbox.html)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "In order to log tests results or evidence to your validation report with this notebook, you'll need to first have:\n", - "\n", - "- [ ] Registered a model within the ValidMind Platform and granted yourself access to the model as a validator\n", - "- [ ] Installed the ValidMind Library in your local environment, allowing you to access all its features\n", - "- [ ] Learned how to import and initialize datasets for use with ValidMind\n", - "- [ ] Understood the basics of how to enable custom context for test descriptions generated by ValidMind\n", - "\n", - "
    Need help with the above steps?\n", - "

    \n", - "Refer to the first two notebooks in this series:\n", - "\n", - "- 1 — Import the champion model\n", - "- 2 — Develop potential challenger model\n", - "\n", - "\n", - "
    " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setting up\n", - "\n", - "This section should be quite familiar to you — as we performed the same actions in the previous notebook, **[2 — Develop potential challenger model](2-develop_challenger_model.ipynb)**." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initialize the ValidMind Library\n", - "\n", - "As usual, let's first connect up the ValidMind Library to our model we previously registered in the ValidMind Platform:\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 \"ValidMind for model validation\" series of notebooks.\n", - "\n", - "3. 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, - "metadata": {}, - "outputs": [], - "source": [ - "# Make sure the ValidMind Library is installed\n", - "\n", - "%pip install -q validmind\n", - "\n", - "# 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": [ - "### Import the champion model\n", - "\n", - "Next, we'll import the champion model submitted by the model development team as we used in the last notebooks (**[xgb_model_champion.pkl](xgb_model_champion.pkl)**) and load in the sample [Lending Club](https://www.kaggle.com/datasets/devanshi23/loan-data-2007-2014/data) dataset used to develop the model, and independently preprocess and feature engineer the dataset to confirm that the model was built using appropriate and properly processed data:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import xgboost as xgb\n", - "\n", - "#Load the saved model\n", - "xgb_model = xgb.XGBClassifier()\n", - "xgb_model.load_model(\"xgb_model_champion.pkl\")\n", - "xgb_model\n", - "\n", - "# Ensure that we have to appropriate order in feature names from Champion model and dataset\n", - "cols_when_model_builds = xgb_model.get_booster().feature_names" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import the Lending Club dataset from Kaggle\n", - "from validmind.datasets.credit_risk import lending_club\n", - "\n", - "df = lending_club.load_data(source=\"offline\")\n", - "df.head()\n", - "\n", - "# Preprocess the dataset for data quality testing purposes\n", - "preprocess_df = lending_club.preprocess(df)\n", - "\n", - "# Apply feature engineering to the dataset\n", - "fe_df = lending_club.feature_engineering(preprocess_df)\n", - "fe_df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We'll also spilt our dataset into train and test to start the validation testing process:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Split the data\n", - "train_df, test_df = lending_club.split(fe_df, test_size=0.2)\n", - "\n", - "x_train = train_df.drop(lending_club.target_column, axis=1)\n", - "y_train = train_df[lending_club.target_column]\n", - "\n", - "x_test = test_df.drop(lending_club.target_column, axis=1)\n", - "y_test = test_df[lending_club.target_column]\n", - "\n", - "# Now let's apply the order of features from the champion model construction\n", - "x_train = x_train[cols_when_model_builds]\n", - "x_test = x_test[cols_when_model_builds]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cols_use = ['annual_inc_woe',\n", - " 'verification_status_woe',\n", - " 'emp_length_woe',\n", - " 'installment_woe',\n", - " 'term_woe',\n", - " 'home_ownership_woe',\n", - " 'purpose_woe',\n", - " 'open_acc_woe',\n", - " 'total_acc_woe',\n", - " 'int_rate_woe',\n", - " 'sub_grade_woe',\n", - " 'grade_woe','loan_status']\n", - "\n", - "\n", - "train_df = train_df[cols_use]\n", - "test_df = test_df[cols_use]\n", - "test_df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Train challenger models\n", - "\n", - "Just like in the last notebook, we'll train two challenger models as the basis for our testing:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import the Random Forest Classification model\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "\n", - "# Create the model instance with 50 decision trees\n", - "rf_model = RandomForestClassifier(\n", - " n_estimators=50,\n", - " random_state=42,\n", - ")\n", - "\n", - "# Train the model\n", - "rf_model.fit(x_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import the Logistic Regression model\n", - "from sklearn.linear_model import LogisticRegression\n", - "\n", - "# Logistic Regression grid params\n", - "log_reg_params = {\n", - " \"penalty\": [\"l1\", \"l2\"],\n", - " \"C\": [0.001, 0.01, 0.1, 1, 10, 100, 1000],\n", - " \"solver\": [\"liblinear\"],\n", - "}\n", - "\n", - "# Grid search for Logistic Regression\n", - "from sklearn.model_selection import GridSearchCV\n", - "\n", - "grid_log_reg = GridSearchCV(LogisticRegression(), log_reg_params)\n", - "grid_log_reg.fit(x_train, y_train)\n", - "\n", - "# Logistic Regression best estimator\n", - "log_reg = grid_log_reg.best_estimator_\n", - "log_reg" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Extract predicted probabilities\n", - "\n", - "With our challenger models trained, let's extract the predicted probabilities from our three models and convert the probability predictions into a binary:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Champion — Application scorecard model\n", - "train_xgb_prob = xgb_model.predict_proba(x_train)[:, 1]\n", - "test_xgb_prob = xgb_model.predict_proba(x_test)[:, 1]\n", - "\n", - "# Challenger — Random forest classification model\n", - "train_rf_prob = rf_model.predict_proba(x_train)[:, 1]\n", - "test_rf_prob = rf_model.predict_proba(x_test)[:, 1]\n", - "\n", - "# Challenger — Logistic regression model\n", - "train_log_prob = log_reg.predict_proba(x_train)[:, 1]\n", - "test_log_prob = log_reg.predict_proba(x_test)[:, 1]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# If probability > 0.3 = 1 (positive)\n", - "cut_off_threshold = 0.3\n", - "\n", - "# Champion — Application scorecard model\n", - "train_xgb_binary_predictions = (train_xgb_prob > cut_off_threshold).astype(int)\n", - "test_xgb_binary_predictions = (test_xgb_prob > cut_off_threshold).astype(int)\n", - "\n", - "# Challenger — Random forest classification model\n", - "train_rf_binary_predictions = (train_rf_prob > cut_off_threshold).astype(int)\n", - "test_rf_binary_predictions = (test_rf_prob > cut_off_threshold).astype(int)\n", - "\n", - "# Challenger — Logistic regression model\n", - "train_log_binary_predictions = (train_log_prob > cut_off_threshold).astype(int)\n", - "test_log_binary_predictions = (test_log_prob > cut_off_threshold).astype(int)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initialize the ValidMind objects\n", - "\n", - "Let's initialize the ValidMind `Dataset` and `Model` objects in preparation for assigning model predictions to each dataset:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize the raw dataset\n", - "vm_raw_dataset = vm.init_dataset(\n", - " dataset=df,\n", - " input_id=\"raw_dataset\",\n", - " target_column=lending_club.target_column,\n", - ")\n", - "\n", - "# Initialize the preprocessed dataset\n", - "vm_preprocess_dataset = vm.init_dataset(\n", - " dataset=preprocess_df,\n", - " input_id=\"preprocess_dataset\",\n", - " target_column=lending_club.target_column,\n", - ")\n", - "\n", - "# Initialize the feature engineered dataset\n", - "vm_fe_dataset = vm.init_dataset(\n", - " dataset=fe_df,\n", - " input_id=\"fe_dataset\",\n", - " target_column=lending_club.target_column,\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=lending_club.target_column,\n", - ")\n", - "\n", - "# Initialize the test dataset\n", - "vm_test_ds = vm.init_dataset(\n", - " dataset=test_df,\n", - " input_id=\"test_dataset\",\n", - " target_column=lending_club.target_column,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize the champion application scorecard model\n", - "vm_xgb_model = vm.init_model(\n", - " xgb_model,\n", - " input_id=\"xgb_model_developer_champion\",\n", - ")\n", - "\n", - "# Initialize the challenger random forest classification model\n", - "vm_rf_model = vm.init_model(\n", - " rf_model,\n", - " input_id=\"rf_model\",\n", - ")\n", - "\n", - "# Initialize the challenger logistic regression model\n", - "vm_log_model = vm.init_model(\n", - " log_reg,\n", - " input_id=\"log_model\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Assign predictions\n", - "\n", - "With our models registered, we'll move on to assigning both the predictive probabilities coming directly from each model's predictions, and the binary prediction after applying the cutoff threshold:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Champion — Application scorecard model\n", - "vm_train_ds.assign_predictions(\n", - " model=vm_xgb_model,\n", - " prediction_values=train_xgb_binary_predictions,\n", - " prediction_probabilities=train_xgb_prob,\n", - ")\n", - "\n", - "vm_test_ds.assign_predictions(\n", - " model=vm_xgb_model,\n", - " prediction_values=test_xgb_binary_predictions,\n", - " prediction_probabilities=test_xgb_prob,\n", - ")\n", - "\n", - "# Challenger — Random forest classification model\n", - "vm_train_ds.assign_predictions(\n", - " model=vm_rf_model,\n", - " prediction_values=train_rf_binary_predictions,\n", - " prediction_probabilities=train_rf_prob,\n", - ")\n", - "\n", - "vm_test_ds.assign_predictions(\n", - " model=vm_rf_model,\n", - " prediction_values=test_rf_binary_predictions,\n", - " prediction_probabilities=test_rf_prob,\n", - ")\n", - "\n", - "# Challenger — Logistic regression model\n", - "vm_train_ds.assign_predictions(\n", - " model=vm_log_model,\n", - " prediction_values=train_log_binary_predictions,\n", - " prediction_probabilities=train_log_prob,\n", - ")\n", - "\n", - "vm_test_ds.assign_predictions(\n", - " model=vm_log_model,\n", - " prediction_values=test_log_binary_predictions,\n", - " prediction_probabilities=test_log_prob,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we'll translate model predictions into actionable scores using probability estimates generated by our trained model:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Compute the scores\n", - "train_xgb_scores = lending_club.compute_scores(train_xgb_prob)\n", - "test_xgb_scores = lending_club.compute_scores(test_xgb_prob)\n", - "train_rf_scores = lending_club.compute_scores(train_rf_prob)\n", - "test_rf_scores = lending_club.compute_scores(test_rf_prob)\n", - "train_log_scores = lending_club.compute_scores(train_log_prob)\n", - "test_log_scores = lending_club.compute_scores(test_log_prob)\n", - "\n", - "# Assign scores to the datasets\n", - "vm_train_ds.add_extra_column(\"xgb_scores\", train_xgb_scores)\n", - "vm_test_ds.add_extra_column(\"xgb_scores\", test_xgb_scores)\n", - "vm_train_ds.add_extra_column(\"rf_scores\", train_rf_scores)\n", - "vm_test_ds.add_extra_column(\"rf_scores\", test_rf_scores)\n", - "vm_train_ds.add_extra_column(\"log_scores\", train_log_scores)\n", - "vm_test_ds.add_extra_column(\"log_scores\", test_log_scores)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Enable use case context\n", - "\n", - "Here, we'll include some initial custom use case context, improving the relevancy, insight, and format of the test descriptions returned in preparation for running our tests:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "os.environ[\"VALIDMIND_LLM_DESCRIPTIONS_CONTEXT_ENABLED\"] = \"1\"\n", - "\n", - "context = \"\"\"\n", - "FORMAT FOR THE LLM DESCRIPTIONS: \n", - " **** is designed to .\n", - "\n", - " The test operates by \n", - "\n", - " The primary advantages of this test include \n", - "\n", - " Users should be aware that \n", - "\n", - " **Key Insights:**\n", - "\n", - " The test results reveal:\n", - "\n", - " - ****: \n", - " - ****: \n", - " ...\n", - "\n", - " Based on these results, \n", - "\n", - "ADDITIONAL INSTRUCTIONS:\n", - " Present insights in order from general to specific, with each insight as a single bullet point with bold title.\n", - " You are a model validator and the goal is to identify risk and/or suggest room for improvements or recommendations on what Model Developer should do in order to improve outcomes and reduce risk\n", - "\n", - " For each metric in the test results, include in the test overview:\n", - " - The metric's purpose and what it measures\n", - " - Its mathematical formula\n", - " - The range of possible values\n", - " - What constitutes good/bad performance\n", - " - How to interpret different values\n", - "\n", - " Each insight should progressively cover:\n", - " 1. Overall scope and distribution\n", - " 2. Complete breakdown of all elements with specific values\n", - " 3. Natural groupings and patterns\n", - " 4. Comparative analysis between datasets/categories\n", - " 5. Stability and variations\n", - " 6. Notable relationships or dependencies\n", - "\n", - " Remember:\n", - " - Keep all insights at the same level (no sub-bullets or nested structures)\n", - " - Make each insight complete and self-contained\n", - " - Include specific numerical values and ranges\n", - " - Cover all elements in the results comprehensively\n", - " - Maintain clear, concise language\n", - " - Use only \"- **Title**: Description\" format for insights\n", - " - Progress naturally from general to specific observations\n", - "\n", - "\"\"\".strip()\n", - "\n", - "os.environ[\"VALIDMIND_LLM_DESCRIPTIONS_CONTEXT\"] = context" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run data quality tests\n", - "\n", - "With everything ready to go, let's explore some of ValidMind's available tests. Using ValidMind’s repository of tests streamlines your validation testing, and helps you ensure that your models are being validated appropriately." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Identify qualitative tests\n", - "\n", - "Let's first explore the list of all available tests in the ValidMind Library with [the `vm.tests.list_tests()` function](https://docs.validmind.ai/validmind/validmind/tests.html#list_tests) — we'll then narrow down the tests we want to run from this list." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vm.tests.list_tests()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "Next, we'll use the [`vm.tests.list_tasks_and_tags()` function](https://docs.validmind.ai/validmind/validmind/tests.html#list_tasks_and_tags) to list which `tags` are associated with each `task` type:\n", - "\n", - "- **`tasks`** represent the kind of modeling task associated with a test. Here we'll focus on `classification` tasks.\n", - "- **`tags`** are free-form descriptions providing more details about the test, for example, what category the test falls into. Here we'll focus on the `data_quality` tag." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vm.tests.list_tasks_and_tags()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then we'll call [`vm.tests.list_tests()`](https://docs.validmind.ai/validmind/validmind/tests.html#list_tests) to list all the data quality tests for classification:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vm.tests.list_tests(\n", - " tags=[\"data_quality\"], task=\"classification\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
    Want to learn more about navigating ValidMind tests?\n", - "

    \n", - "Refer to our notebook outlining the utilities available for viewing and understanding available ValidMind tests: Explore tests
    " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Log data quality tests\n", - "\n", - "We want to run all the tests that are returned, so we'll store the identified tests in `dq` in preparation for batch running these tests and logging their results to the ValidMind Platform:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dq = vm.tests.list_tests(tags=[\"data_quality\"], task=\"classification\",pretty=False)\n", - "dq" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "With our data quality tests identified, let's run our first batch of tests using our previously initialized preprocessed dataset (`vm_preprocess_dataset`) and log their results.\n", - "\n", - "- You run validation tests by calling [the `run_test` function](https://docs.validmind.ai/validmind/validmind/tests.html#run_test) provided by the `validmind.tests` module.\n", - "- Every test result returned by the `run_test()` function has a [`.log()` method](https://docs.validmind.ai/validmind/validmind/vm_models.html#TestResult.log) that can be used to send the test results to the ValidMind Platform:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for test in dq:\n", - " vm.tests.run_test(\n", - " test,\n", - " inputs={\n", - " \"dataset\": vm_preprocess_dataset\n", - " }\n", - " ).log()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
    Note the output returned indicating that a test-driven block doesn't currently exist in your model's documentation for some test IDs. \n", - "

    \n", - "That's expected, as when we run validations tests the results logged need to be manually added to your report as part of your compliance assessment process within the ValidMind Platform.
    " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run comparison tests\n", - "\n", - "Next, let's reuse the tests in `dq` to perform comparison tests between the raw (`vm_raw_dataset`) and preprocessed (`vm_preprocess_dataset`) dataset, again logging the results to the ValidMind Platform:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for test in dq:\n", - " vm.tests.run_test(\n", - " test,\n", - " input_grid={\n", - " \"dataset\": [vm_raw_dataset,vm_preprocess_dataset]\n", - " }\n", - " ).log()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run performance tests\n", - "\n", - "We'll also run some performance tests, beginning with independent testing of our champion application scorecard model, then moving on to our potential challenger models.\n", - "\n", - "Use `vm.tests.list_tests()` to this time identify all the model performance tests for classification:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "vm.tests.list_tests(tags=[\"model_performance\"], task=\"classification\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We'll isolate the specific tests we want to run in `mpt`:\n", - "\n", - "- [`ClassifierPerformance`](https://docs.validmind.ai/tests/model_validation/sklearn/ClassifierPerformance.html)\n", - "- [`ConfusionMatrix`](https://docs.validmind.ai/tests/model_validation/sklearn/ConfusionMatrix.html)\n", - "- [`MinimumAccuracy`](https://docs.validmind.ai/tests/model_validation/sklearn/MinimumAccuracy.html)\n", - "- [`MinimumF1Score`](https://docs.validmind.ai/tests/model_validation/sklearn/MinimumF1Score.html)\n", - "- [`ROCCurve`](https://docs.validmind.ai/tests/model_validation/sklearn/ROCCurve.html)\n", - "\n", - "When running individual tests, you can use a custom `result_id` to tag the individual result with a unique identifier by appending this `result_id` to the `test_id` with a `:` separator. We'll append an identifier for our champion model here:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mpt = [\n", - " \"validmind.model_validation.sklearn.ClassifierPerformance:xgboost_champion\",\n", - " \"validmind.model_validation.sklearn.ConfusionMatrix:xgboost_champion\",\n", - " \"validmind.model_validation.sklearn.MinimumAccuracy:xgboost_champion\",\n", - " \"validmind.model_validation.sklearn.MinimumF1Score:xgboost_champion\",\n", - " \"validmind.model_validation.sklearn.ROCCurve:xgboost_champion\"\n", - "]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Evaluate performance of the champion model\n", - "\n", - "Now, let's run and log our batch of model performance tests using our testing dataset (`vm_test_ds`) for our champion model:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for test in mpt:\n", - " vm.tests.run_test(\n", - " test,\n", - " inputs={\n", - " \"dataset\": vm_test_ds, \"model\" : vm_xgb_model\n", - " },\n", - " ).log()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Evaluate performance of challenger models\n", - "\n", - "We've now conducted similar tests as the model development team for our champion model, with the aim of verifying their test results.\n", - "\n", - "Next, let's see how our challenger models compare. We'll use the same batch of tests here as we did in `mpt`, but append a different `result_id` to indicate that these results should be associated with our challenger models:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mpt_chall = [\n", - " \"validmind.model_validation.sklearn.ClassifierPerformance:xgboost_champion_vs_challengers\",\n", - " \"validmind.model_validation.sklearn.ConfusionMatrix:xgboost_champion_vs_challengers\",\n", - " \"validmind.model_validation.sklearn.MinimumAccuracy:xgboost_champion_vs_challengers\",\n", - " \"validmind.model_validation.sklearn.MinimumF1Score:xgboost_champion_vs_challengers\",\n", - " \"validmind.model_validation.sklearn.ROCCurve:xgboost_champion_vs_challengers\"\n", - "]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We'll also adjust the use case context to focus on this comparison for tests going forward:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "os.environ[\"VALIDMIND_LLM_DESCRIPTIONS_CONTEXT_ENABLED\"] = \"1\"\n", - "\n", - "context = \"\"\"\n", - "FORMAT FOR THE LLM DESCRIPTIONS: \n", - " **** is designed to .\n", - "\n", - " The test operates by \n", - "\n", - " The primary advantages of this test include \n", - "\n", - " Users should be aware that \n", - "\n", - " **Key Insights:**\n", - "\n", - " The test results reveal:\n", - "\n", - " - ****: \n", - " - ****: \n", - " ...\n", - "\n", - " Based on these results, \n", - "\n", - "ADDITIONAL INSTRUCTIONS:\n", - "\n", - " The champion model as the basis for comparison is called \"xgb_model_developer_champion\" and emphasis should be on the following:\n", - " - The metrics for the champion model compared against the challenger models\n", - " - Which model potentially outperforms the champion model based on the metrics, this should be highlighted and emphasized\n", - "\n", - "\n", - " For each metric in the test results, include in the test overview:\n", - " - The metric's purpose and what it measures\n", - " - Its mathematical formula\n", - " - The range of possible values\n", - " - What constitutes good/bad performance\n", - " - How to interpret different values\n", - "\n", - " Each insight should progressively cover:\n", - " 1. Overall scope and distribution\n", - " 2. Complete breakdown of all elements with specific values\n", - " 3. Natural groupings and patterns\n", - " 4. Comparative analysis between datasets/categories\n", - " 5. Stability and variations\n", - " 6. Notable relationships or dependencies\n", - "\n", - " Remember:\n", - " - Champion model (xgb_model_developer_champion) is the selection and challenger models are used to challenge the selection\n", - " - Keep all insights at the same level (no sub-bullets or nested structures)\n", - " - Make each insight complete and self-contained\n", - " - Include specific numerical values and ranges\n", - " - Cover all elements in the results comprehensively\n", - " - Maintain clear, concise language\n", - " - Use only \"- **Title**: Description\" format for insights\n", - " - Progress naturally from general to specific observations\n", - "\n", - "\"\"\".strip()\n", - "\n", - "os.environ[\"VALIDMIND_LLM_DESCRIPTIONS_CONTEXT\"] = context" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We'll run each test once for each model with the same `vm_test_ds` dataset to compare them:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for test in mpt_chall:\n", - " vm.tests.run_test(\n", - " test,\n", - " input_grid={\n", - " \"dataset\": [vm_test_ds], \"model\" : [vm_xgb_model,vm_log_model,vm_rf_model]\n", - " }\n", - " ).log()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
    Based on the performance metrics, we can conclude that the random forest classification model is not a viable candidate for our use case and can be disregarded in our tests going forward.\n", - "

    \n", - "In the final notebook, we'll dive a bit deeper into some tests comparing our champion application scorecard model and our remaining challenger logistic regression model, including tests that will allow us to customize parameters and thresholds for performance standards.
    " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## In summary\n", - "\n", - "In this third notebook, you learned how to:\n", - "\n", - "- [ ] Identify and run different types of validation tests\n", - "- [ ] Log test results as evidence to the ValidMind Platform\n", - "- [ ] Use tests from ValidMind to evaluate the potential of challenger models" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Next steps\n", - "\n", - "### Finalize testing and reporting\n", - "\n", - "Now that you're familiar with the basics of using the ValidMind Library to run and log validation tests, let's learn how to implement some custom tests and wrap up our validation: **[4 — Finalize testing and reporting](4-finalize_testing_reporting.ipynb)**" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "ValidMind Library", - "language": "python", - "name": "validmind" - }, - "language_info": { - "name": "python", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From d65c263f59f1ebe9fef6277a14b59896d90d825c Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Fri, 4 Apr 2025 16:53:29 -0700 Subject: [PATCH 45/71] Validation 3 log a finding --- .../3-developing_challenger_model.ipynb | 60 +++++++++++++++++- .../model_validation/inserted-finding.png | Bin 0 -> 94575 bytes .../model_validation/link-finding.png | Bin 0 -> 287154 bytes .../model_validation/select-finding.png | Bin 0 -> 123751 bytes 4 files changed, 59 insertions(+), 1 deletion(-) create mode 100644 notebooks/tutorials/model_validation/inserted-finding.png create mode 100644 notebooks/tutorials/model_validation/link-finding.png create mode 100644 notebooks/tutorials/model_validation/select-finding.png diff --git a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb index af5ae881c..8125a7ba2 100644 --- a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb +++ b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb @@ -573,6 +573,51 @@ "That's expected, as when we run validations tests the results logged need to be manually added to your report as part of your compliance assessment process within the ValidMind Platform.
    Want to learn more about navigating ValidMind tests?\n", - "

    \n", - "Refer to our notebook outlining the utilities available for viewing and understanding available ValidMind tests:     Explore tests

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    \n", + "Learn more:     Work with content blocks

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    " ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Log a model finding\n", + "\n", + "As we can observe from the output above, our champion model doesn't pass the `MinimumAccuracy` based on the default thresholds of the out-of-the-box test, so let's log a model finding in the ValidMind Platform:\n", + "\n", + "1. From the **Inventory** in the ValidMind Platform, go to the model you connected to earlier.\n", + "\n", + "2. In the left sidebar that appears for your model, click **Validation Report**.\n", + "\n", + "3. Locate the Data Preparation section and click on **2.2.2. Model Performance** to expand that section.\n", + "\n", + "4. Under the Model Performance Metrics section, locate Findings then click **Link Finding to Report**:\n", + "\n", + " \"Screenshot\n", + "

    \n", + "\n", + "5. Click **+ Create New Finding** to add a finding.\n", + "\n", + "6. Enter in the details for your finding, for example:\n", + "\n", + " - **TITLE** — Champion Logistic Regression Model Fails Minimum Accuracy Threshold\n", + " - **RISK AREA** — Model Performance\n", + " - **DOCUMENTATION SECTION** — 3.2. Model Evaluation\n", + " - **DESCRIPTION** — The logistic regression champion model was subjected to a Minimum Accuracy test to determine whether its predictive accuracy meets the predefined performance threshold of 0.7. The model achieved an accuracy score of 0.6136, which falls below the required minimum. As a result, the test produced a Fail outcome.\n", + "\n", + "7. Click **Save**.\n", + "\n", + "8. Select the finding you just added to link to your validation report:\n", + "\n", + " \"Screenshot\n", + "

    \n", + "\n", + "9. Click **Update Linked Findings** to insert your finding.\n", + "\n", + "10. Confirm that finding you inserted has been correctly inserted into section **2.2.2. Model Performance** of the report:\n", + "\n", + " \"Screenshot\n", + "

    \n", + "\n", + "11. Click on the finding to expand the finding, where you can adjust details such as severity, owner, due date, status, etc. as well as include proposed remediation plans or supporting documentation as attachments." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -621,6 +666,18 @@ " ).log()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Adjust a default threshold\n", + "\n", + "We can see from the output of our MinimumAccuracy test that neither model passes this assessment based on the default thresholds of the out-of-the-box test.    \n", + "

    \n", + "\n", + "It's possible that the default threshold is too stringent given the problem context, and that customizing the threshold for performance standards might improve metrics. Let's adjust this threshold and log the results to the ValidMind Platform as additional evidence: " + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -645,7 +702,8 @@ "\n", "- [x] Initialize ValidMind model objects\n", "- [x] Assign predictions and probabilities to your ValidMind model objects\n", - "- [x] Use tests from ValidMind to evaluate the potential of challenger models" + "- [x] Use tests from ValidMind to evaluate the potential of models, including comparative tests between champion and challenger models\n", + "- [x] Log a model finding in the ValidMind Platform" ] }, { diff --git a/notebooks/tutorials/model_validation/inserted-finding.png b/notebooks/tutorials/model_validation/inserted-finding.png new file mode 100644 index 0000000000000000000000000000000000000000..bb784365320c89f385e1b38355d520fb76a937ea GIT binary patch literal 94575 zcmeFZWmFvB);CB3K_WmPKnU*McnA)`-MxX}?(Pmjg1bX-4Njv8+PJ$V&}bvUwc+ht z``Hv8NkifQ6@t|6%~@+k;9C` 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zj;Kw|&^qkT=ER`Hmz)Dq1e?E3=Zxvp*CL$!s=LG<-qp64ADLwxuamduE_tvS^2fzwN>q>$m)QmS)O+W^yM5MYau> z|4uhsFpDo({GwbvJCUZTY-9d67kw{wS#uWklNtS*v7$UHa0FL70Nq|eX(BtYdRD|F zUKO==nGhFb31KG|Jd*$GHvVe>S2^%VY;o@Rt9$+HDUG}e;+xmezdn!uvZ3xOc+?i- vcI?<*(|^;W5qP}+|J44yGye}+?Kel56@s3vc!RbB{23cqTr9b8 Date: Fri, 4 Apr 2025 16:56:47 -0700 Subject: [PATCH 46/71] Validation 3 Adjusted test --- .../3-developing_challenger_model.ipynb | 19 +- .../3-developing_challenger_model_COPY.ipynb | 668 ------------------ .../4-finalize_testing_reporting_OLD.ipynb | 2 +- 3 files changed, 19 insertions(+), 670 deletions(-) delete mode 100644 notebooks/tutorials/model_validation/3-developing_challenger_model_COPY.ipynb diff --git a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb index 8125a7ba2..4a74d6f34 100644 --- a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb +++ b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb @@ -678,6 +678,22 @@ "It's possible that the default threshold is too stringent given the problem context, and that customizing the threshold for performance standards might improve metrics. Let's adjust this threshold and log the results to the ValidMind Platform as additional evidence: " ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "result = vm.tests.run_test(\n", + " \"validmind.model_validation.sklearn.MinimumAccuracy:AdjThreshold\",\n", + " input_grid={\n", + " \"dataset\": [vm_test_ds],\n", + " \"model\": [vm_log_model, vm_rf_model],\n", + " \"params\": {\"min_threshold\": 0.6}\n", + " },\n", + ").log()" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -703,7 +719,8 @@ "- [x] Initialize ValidMind model objects\n", "- [x] Assign predictions and probabilities to your ValidMind model objects\n", "- [x] Use tests from ValidMind to evaluate the potential of models, including comparative tests between champion and challenger models\n", - "- [x] Log a model finding in the ValidMind Platform" + "- [x] Log a model finding in the ValidMind Platform\n", + "- [x] Adjust an out-of-the box ValidMind test" ] }, { diff --git a/notebooks/tutorials/model_validation/3-developing_challenger_model_COPY.ipynb b/notebooks/tutorials/model_validation/3-developing_challenger_model_COPY.ipynb deleted file mode 100644 index de7388af3..000000000 --- a/notebooks/tutorials/model_validation/3-developing_challenger_model_COPY.ipynb +++ /dev/null @@ -1,668 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# ValidMind for model validation 3 — Developing a potential challenger model\n", - "\n", - "Learn how to use ValidMind for your end-to-end model validation process with our series of four introductory notebooks. In this third notebook, develop a potential challenger model and then pass your model and its predictions to ValidMind.\n", - "\n", - "A *challenger model* is an alternate model that attempt to outperform the champion model, ensuring that the best performing fit-for-purpose model is always considered for deployment. Challenger models also help avoid over-reliance on a single model, and allow testing of new features, algorithms, or data sources without disrupting the production lifecycle." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "In order to develop potential challenger models with this notebook, you'll need to first have:\n", - "\n", - "- [x] Registered a model within the ValidMind Platform and granted yourself access to the model as a validator\n", - "- [x] Installed the ValidMind Library in your local environment, allowing you to access all its features\n", - "- [x] Learned how to import and initialize datasets for use with ValidMind\n", - "- [x] Understood the basics of how to run and log tests with ValidMind\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setting up\n", - "\n", - "This section should be quite familiar to you — as we performed the same actions in the previous notebook, **[2 — Start the model validation process](2-start_validation_process.ipynb)**." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initialize the ValidMind Library\n", - "\n", - "As usual, let's first connect up the ValidMind Library to our model we previously registered in the ValidMind Platform:\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 \"ValidMind for model validation\" series of notebooks.\n", - "\n", - "3. 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, - "metadata": {}, - "outputs": [], - "source": [ - "# Make sure the ValidMind Library is installed\n", - "\n", - "%pip install -q validmind\n", - "\n", - "# 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": [ - "### Import the sample dataset\n", - "\n", - "Next, we'll load in the sample [Bank Customer Churn Prediction](https://www.kaggle.com/datasets/shantanudhakadd/bank-customer-churn-prediction) dataset used to develop the champion model that we will independently preprocess:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Load the sample dataset\n", - "from validmind.datasets.classification import customer_churn as demo_dataset\n", - "\n", - "print(\n", - " f\"Loaded demo dataset with: \\n\\n\\t• Target column: '{demo_dataset.target_column}' \\n\\t• Class labels: {demo_dataset.class_labels}\"\n", - ")\n", - "\n", - "raw_df = demo_dataset.load_data()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Preprocess the dataset\n", - "\n", - "We’ll apply a simple rebalancing technique to the dataset before continuing:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "raw_copy_df = raw_df.sample(frac=1) # Create a copy of the raw dataset\n", - "\n", - "# Create a balanced dataset with the same number of exited and not exited customers\n", - "exited_df = raw_copy_df.loc[raw_copy_df[\"Exited\"] == 1]\n", - "not_exited_df = raw_copy_df.loc[raw_copy_df[\"Exited\"] == 0].sample(n=exited_df.shape[0])\n", - "\n", - "balanced_raw_df = pd.concat([exited_df, not_exited_df])\n", - "balanced_raw_df = balanced_raw_df.sample(frac=1, random_state=42)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let’s also quickly remove highly correlated features from the dataset using the output from a ValidMind test.\n", - "\n", - "As you know, before we can run tests you’ll need to initialize a ValidMind dataset object with the [`init_dataset` function](https://docs.validmind.ai/validmind/validmind.html#init_dataset):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Register new data and now 'balanced_raw_dataset' is the new dataset object of interest\n", - "vm_balanced_raw_dataset = vm.init_dataset(\n", - " dataset=balanced_raw_df,\n", - " input_id=\"balanced_raw_dataset\",\n", - " target_column=\"Exited\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With our balanced dataset initialized, we can then run our test and utilize the output to help us identify the features we want to remove:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Run HighPearsonCorrelation test with our balanced dataset as input and return a result object\n", - "corr_result = vm.tests.run_test(\n", - " test_id=\"validmind.data_validation.HighPearsonCorrelation\",\n", - " params={\"max_threshold\": 0.3},\n", - " inputs={\"dataset\": vm_balanced_raw_dataset},\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# From result object, extract table from `corr_result.tables`\n", - "features_df = corr_result.tables[0].data\n", - "features_df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Extract list of features that failed the test\n", - "high_correlation_features = features_df[features_df[\"Pass/Fail\"] == \"Fail\"][\"Columns\"].tolist()\n", - "high_correlation_features" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Extract feature names from the list of strings\n", - "high_correlation_features = [feature.split(\",\")[0].strip(\"()\") for feature in high_correlation_features]\n", - "high_correlation_features" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can then re-initialize the dataset with a different `input_id` and the highly correlated features removed and re-run the test for confirmation:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Remove the highly correlated features from the dataset\n", - "balanced_raw_no_age_df = balanced_raw_df.drop(columns=high_correlation_features)\n", - "\n", - "# Re-initialize the dataset object\n", - "vm_raw_dataset_preprocessed = vm.init_dataset(\n", - " dataset=balanced_raw_no_age_df,\n", - " input_id=\"raw_dataset_preprocessed\",\n", - " target_column=\"Exited\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Re-run the test with the reduced feature set\n", - "corr_result = vm.tests.run_test(\n", - " test_id=\"validmind.data_validation.HighPearsonCorrelation\",\n", - " params={\"max_threshold\": 0.3},\n", - " inputs={\"dataset\": vm_raw_dataset_preprocessed},\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import the champion model\n", - "\n", - "With our raw dataset assessed and preprocessed, let's go ahead and import the champion model submitted by the model development team in the format of a `.pkl` file: **[lr_model_champion.pkl](lr_model_champion.pkl)**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import the champion model\n", - "import pickle as pkl\n", - "\n", - "with open(\"lr_model_champion.pkl\", \"rb\") as f:\n", - " log_reg = pkl.load(f)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Split the preprocessed dataset\n", - "\n", - "With our dummy model imported, raw dataset rebalanced with highly correlated features removed, let's now **spilt our dataset into train and test** in preparation for training our potential challenger model.\n", - "\n", - "To start, let's grab the first few rows from the `balanced_raw_no_age_df` dataset we initialized earlier:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "balanced_raw_no_age_df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Before training the model, we need to encode the categorical features in the dataset:\n", - "\n", - "- Use the `OneHotEncoder` class from the `sklearn.preprocessing` module to encode the categorical features.\n", - "- The categorical features in the dataset are `Geography` and `Gender`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "balanced_raw_no_age_df = pd.get_dummies(\n", - " balanced_raw_no_age_df, columns=[\"Geography\", \"Gender\"], drop_first=True\n", - ")\n", - "balanced_raw_no_age_df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Splitting our dataset into training and testing is essential for proper validation testing, as this helps assess how well the model generalizes to unseen data:\n", - "\n", - "- We start by dividing our `balanced_raw_no_age_df` dataset into training and test subsets using `train_test_split`, with 80% of the data allocated to training (`train_df`) and 20% to testing (`test_df`).\n", - "- From each subset, we separate the features (all columns except \"Exited\") into `X_train` and `X_test`, and the target column (\"Exited\") into `y_train` and `y_test`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.model_selection import train_test_split\n", - "\n", - "train_df, test_df = train_test_split(balanced_raw_no_age_df, test_size=0.20)\n", - "\n", - "X_train = train_df.drop(\"Exited\", axis=1)\n", - "y_train = train_df[\"Exited\"]\n", - "X_test = test_df.drop(\"Exited\", axis=1)\n", - "y_test = test_df[\"Exited\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initialize the split datasets\n", - "\n", - "Next, let's initialize the training and testing datasets so they are available for use:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vm_train_ds = vm.init_dataset(\n", - " input_id=\"train_dataset_final\",\n", - " dataset=train_df,\n", - " target_column=\"Exited\",\n", - ")\n", - "\n", - "vm_test_ds = vm.init_dataset(\n", - " input_id=\"test_dataset_final\",\n", - " dataset=test_df,\n", - " target_column=\"Exited\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Training a potential challenger model\n", - "\n", - "We're curious how an alternate model compares to our champion model, so let's train a challenger model as a basis for our testing.\n", - "\n", - "Our champion *logistic regression model* is a simpler, parametric model that assumes a linear relationship between the independent variables and the log-odds of the outcome. While logistic regression may not capture complex patterns as effectively, it offers a high degree of interpretability and is easier to explain to stakeholders. However, model risk is not calculated in isolation from a single factor, but rather in consideration with trade-offs in predictive performance, ease of interpretability, and overall alignment with business objectives." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Random forest classification model\n", - "\n", - "A *random forest classification model* is an ensemble machine learning algorithm that uses multiple decision trees to classify data. In ensemble learning, multiple models are combined to improve prediction accuracy and robustness.\n", - "\n", - "Random forest classification models generally have higher accuracy because they capture complex, non-linear relationships, but as a result they lack transparency in their predictions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import the Random Forest Classification model\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "\n", - "# Create the model instance with 50 decision trees\n", - "rf_model = RandomForestClassifier(\n", - " n_estimators=50,\n", - " random_state=42,\n", - ")\n", - "\n", - "# Train the model\n", - "rf_model.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initializing the model objects" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initialize the model objects\n", - "\n", - "In addition to the initialized datasets, 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 each of our two models.\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, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize the champion logistic regression model\n", - "vm_log_model = vm.init_model(\n", - " log_reg,\n", - " input_id=\"log_model_champion\",\n", - ")\n", - "\n", - "# Initialize the challenger random forest classification model\n", - "vm_rf_model = vm.init_model(\n", - " rf_model,\n", - " input_id=\"rf_model\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Assign predictions\n", - "\n", - "With our models registered, we'll move on to assigning both the predictive probabilities coming directly from each model's predictions, and the binary prediction after applying the cutoff threshold described in the Compute binary predictions step above.\n", - "\n", - "- The [`assign_predictions()` method](https://docs.validmind.ai/validmind/validmind/vm_models.html#VMDataset.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, - "metadata": {}, - "outputs": [], - "source": [ - "# Champion — Logistic regression model\n", - "vm_train_ds.assign_predictions(model=vm_log_model)\n", - "vm_test_ds.assign_predictions(model=vm_log_model)\n", - "\n", - "# Challenger — Random forest classification model\n", - "vm_train_ds.assign_predictions(model=vm_rf_model)\n", - "vm_test_ds.assign_predictions(model=vm_rf_model)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Running model validation tests\n", - "\n", - "With everything ready for us, let's run the rest of our validation tests. We'll focus on comprehensive testing around model performance of both the champion and challenger models going forward as we've already verified the data quality of the datasets used to train the champion model." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run model performance tests\n", - "\n", - "Let's run some performance tests, beginning with independent testing of our champion logistic regression model, then moving on to our potential challenger model.\n", - "\n", - "Use [`vm.tests.list_tests()`](https://docs.validmind.ai/validmind/validmind/tests.html#list_tests) to identify all the model performance tests for classification:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "vm.tests.list_tests(tags=[\"model_performance\"], task=\"classification\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We'll isolate the specific tests we want to run in `mpt`:\n", - "\n", - "- [`ClassifierPerformance`](https://docs.validmind.ai/tests/model_validation/sklearn/ClassifierPerformance.html)\n", - "- [`ConfusionMatrix`](https://docs.validmind.ai/tests/model_validation/sklearn/ConfusionMatrix.html)\n", - "- [`MinimumAccuracy`](https://docs.validmind.ai/tests/model_validation/sklearn/MinimumAccuracy.html)\n", - "- [`MinimumF1Score`](https://docs.validmind.ai/tests/model_validation/sklearn/MinimumF1Score.html)\n", - "- [`ROCCurve`](https://docs.validmind.ai/tests/model_validation/sklearn/ROCCurve.html)\n", - "\n", - "As we learned in the previous notebook [2 — Start the model validation process](2-start_validation_process.ipynb), you can use a custom `result_id` to tag the individual result with a unique identifier by appending this `result_id` to the `test_id` with a `:` separator. We'll append an identifier for our champion model here:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mpt = [\n", - " \"validmind.model_validation.sklearn.ClassifierPerformance:logreg_champion\",\n", - " \"validmind.model_validation.sklearn.ConfusionMatrix:logreg_champion\",\n", - " \"validmind.model_validation.sklearn.MinimumAccuracy:logreg_champion\",\n", - " \"validmind.model_validation.sklearn.MinimumF1Score:logreg_champion\",\n", - " \"validmind.model_validation.sklearn.ROCCurve:logreg_champion\"\n", - "]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Evaluate performance of the champion model\n", - "\n", - "Now, let's run and log our batch of model performance tests using our testing dataset (`vm_test_ds`) for our champion model:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for test in mpt:\n", - " vm.tests.run_test(\n", - " test,\n", - " inputs={\n", - " \"dataset\": vm_test_ds, \"model\" : vm_log_model,\n", - " },\n", - " ).log()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
    Note the output returned indicating that a test-driven block doesn't currently exist in your model's documentation for some test IDs. \n", - "

    \n", - "That's expected, as when we run validations tests the results logged need to be manually added to your report as part of your compliance assessment process within the ValidMind Platform.
    " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Evaluate performance of challenger model\n", - "\n", - "We've now conducted similar tests as the model development team for our champion model, with the aim of verifying their test results.\n", - "\n", - "Next, let's see how our challenger models compare. We'll use the same batch of tests here as we did in `mpt`, but append a different `result_id` to indicate that these results should be associated with our challenger model:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mpt_chall = [\n", - " \"validmind.model_validation.sklearn.ClassifierPerformance:champion_vs_challenger\",\n", - " \"validmind.model_validation.sklearn.ConfusionMatrix:champion_vs_challenger\",\n", - " \"validmind.model_validation.sklearn.MinimumAccuracy:champion_vs_challenger\",\n", - " \"validmind.model_validation.sklearn.MinimumF1Score:champion_vs_challenger\",\n", - " \"validmind.model_validation.sklearn.ROCCurve:champion_vs_challenger\"\n", - "]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We'll run each test once for each model with the same `vm_test_ds` dataset to compare them:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for test in mpt_chall:\n", - " vm.tests.run_test(\n", - " test,\n", - " input_grid={\n", - " \"dataset\": [vm_test_ds], \"model\" : [vm_log_model,vm_rf_model]\n", - " }\n", - " ).log()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run diagnostic tests" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run feature importance tests" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## In summary\n", - "\n", - "In this third notebook, you learned how to:\n", - "\n", - "- [x] Initialize ValidMind model objects\n", - "- [x] Assign predictions and probabilities to your ValidMind model objects\n", - "- [x] Use tests from ValidMind to evaluate the potential of challenger models" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Next steps\n", - "\n", - "### Finalize validation and reporting" - ] - } - ], - "metadata": { - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/tutorials/model_validation/4-finalize_testing_reporting_OLD.ipynb b/notebooks/tutorials/model_validation/4-finalize_testing_reporting_OLD.ipynb index 0b312a071..c601a89e2 100644 --- a/notebooks/tutorials/model_validation/4-finalize_testing_reporting_OLD.ipynb +++ b/notebooks/tutorials/model_validation/4-finalize_testing_reporting_OLD.ipynb @@ -998,7 +998,7 @@ "\n", "In this final notebook, you learned how to:\n", "\n", - "- [ ] Adjust an out-of-the box ValidMind test\n", + "\n", "- [ ] Implement a custom inline test\n", "- [ ] Re-run tests provided by your model development team to verify that they were run and reported accurately\n", "- [ ] Link your logged test results as evidence on your validation report\n", From a95ba249c763b9111cbac6762da886ad8975319d Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Fri, 4 Apr 2025 17:11:29 -0700 Subject: [PATCH 47/71] Validation 3 finding recommendation --- .../3-developing_challenger_model.ipynb | 26 +++---------------- 1 file changed, 3 insertions(+), 23 deletions(-) diff --git a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb index 4a74d6f34..455fd64fc 100644 --- a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb +++ b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb @@ -670,28 +670,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Adjust a default threshold\n", - "\n", - "We can see from the output of our MinimumAccuracy test that neither model passes this assessment based on the default thresholds of the out-of-the-box test.
    Need help with the above steps?\n", - "

    \n", - "Refer to the first two notebooks in this series:\n", - "\n", - "-     1 — Set up the ValidMind Library for validation\n", - "- 2 — Start the model validation process\n", - "\n", - "
    \n", + "
    Based on the performance metrics, our challenger random forest classification model passes the MinimumAccuracy where our champion did not.\n", "

    \n", - "\n", - "It's possible that the default threshold is too stringent given the problem context, and that customizing the threshold for performance standards might improve metrics. Let's adjust this threshold and log the results to the ValidMind Platform as additional evidence: " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "result = vm.tests.run_test(\n", - " \"validmind.model_validation.sklearn.MinimumAccuracy:AdjThreshold\",\n", - " input_grid={\n", - " \"dataset\": [vm_test_ds],\n", - " \"model\": [vm_log_model, vm_rf_model],\n", - " \"params\": {\"min_threshold\": 0.6}\n", - " },\n", - ").log()" + "In your validation report, support your recommendation in your finding's Proposed Remediation Plan to investigate the usage of our challenger model by inserting the performance tests we logged with this notebook into the appropriate section.
    " ] }, { @@ -719,8 +700,7 @@ "- [x] Initialize ValidMind model objects\n", "- [x] Assign predictions and probabilities to your ValidMind model objects\n", "- [x] Use tests from ValidMind to evaluate the potential of models, including comparative tests between champion and challenger models\n", - "- [x] Log a model finding in the ValidMind Platform\n", - "- [x] Adjust an out-of-the box ValidMind test" + "- [x] Log a model finding in the ValidMind Platform" ] }, { From bbf7551af2120bedbcfa36e023c9cb3137f7344e Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Fri, 4 Apr 2025 17:16:34 -0700 Subject: [PATCH 48/71] Validation 3 more tests --- .../validate_application_scorecard.ipynb | 2 +- .../3-developing_challenger_model.ipynb | 96 ++++++++++++++++++- 2 files changed, 95 insertions(+), 3 deletions(-) diff --git a/notebooks/code_samples/model_validation/validate_application_scorecard.ipynb b/notebooks/code_samples/model_validation/validate_application_scorecard.ipynb index e6e7bd406..5946c78a7 100644 --- a/notebooks/code_samples/model_validation/validate_application_scorecard.ipynb +++ b/notebooks/code_samples/model_validation/validate_application_scorecard.ipynb @@ -1428,7 +1428,7 @@ "\n", "## Run feature importance tests\n", "\n", - "We want to verify the relative influence of different input features on our models' predictions, as well as inspect the differences between our champion and challenger model to see if a certain model offers more understandable or logical importance scores for features.\n", + "We also want to verify the relative influence of different input features on our models' predictions, as well as inspect the differences between our champion and challenger model to see if a certain model offers more understandable or logical importance scores for features.\n", "\n", "Use `list_tests()` to identify all the feature importance tests for classification:" ] diff --git a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb index 455fd64fc..6b9c2493c 100644 --- a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb +++ b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb @@ -679,14 +679,106 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Run diagnostic tests" + "### Run diagnostic tests\n", + "\n", + "Next we want to inspect the robustness and stability testing comparison between our champion and challenger model.\n", + "\n", + "Use `list_tests()` to identify all the model diagnosis tests for classification:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vm.tests.list_tests(tags=[\"model_diagnosis\"], task=\"classification\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's see if models suffer from any *overfit* potentials and also where there are potential sub-segments of issues with the [`OverfitDiagnosis` test](https://docs.validmind.ai/tests/model_validation/sklearn/OverfitDiagnosis.html). \n", + "\n", + "Overfitting occurs when a model learns the training data too well, capturing not only the true pattern but noise and random fluctuations resulting in excellent performance on the training dataset but poor generalization to new, unseen data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vm.tests.run_test(\n", + " test_id=\"validmind.model_validation.sklearn.OverfitDiagnosis:champion_vs_challenger\",\n", + " input_grid={\n", + " \"datasets\": [[vm_train_ds,vm_test_ds]],\n", + " \"model\" : [vm_log_model,vm_rf_model]\n", + " }\n", + ").log()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Run feature importance tests" + "Let's also conduct *robustness* and *stability* testing of the two models with the [`RobustnessDiagnosis` test](https://docs.validmind.ai/tests/model_validation/sklearn/RobustnessDiagnosis.html).\n", + "\n", + "Robustness refers to a model's ability to maintain consistent performance, and stability refers to a model's ability to produce consistent outputs over time across different data subsets." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vm.tests.run_test(\n", + " test_id=\"validmind.model_validation.sklearn.RobustnessDiagnosis:Champion_vs_LogRegression\",\n", + " input_grid={\n", + " \"datasets\": [[vm_train_ds,vm_test_ds]],\n", + " \"model\" : [vm_log_model,vm_rf_model]\n", + " },\n", + ").log()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run feature importance tests\n", + "\n", + "We also want to verify the relative influence of different input features on our models' predictions, as well as inspect the differences between our champion and challenger model to see if a certain model offers more understandable or logical importance scores for features.\n", + "\n", + "Use `list_tests()` to identify all the feature importance tests for classification:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Store the feature importance tests\n", + "FI = vm.tests.list_tests(tags=[\"feature_importance\"], task=\"classification\",pretty=False)\n", + "FI" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run and log our feature importance tests for both models for the testing dataset\n", + "for test in FI:\n", + " vm.tests.run_test(\n", + " \"\".join((test,':champion_vs_challenger')),\n", + " input_grid={\n", + " \"dataset\": [vm_test_ds], \"model\" : [vm_log_model,vm_rf_model]\n", + " },\n", + " ).log()" ] }, { From d6d0db6b9c82f4795978571c449d7e5b7bf6c991 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Fri, 4 Apr 2025 17:27:06 -0700 Subject: [PATCH 49/71] Moving split dataset --- .../2-start_validation_process.ipynb | 96 +++++++++++++++++++ .../3-developing_challenger_model.ipynb | 91 ++++++------------ 2 files changed, 123 insertions(+), 64 deletions(-) diff --git a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb index 2455cd338..699c2f97c 100644 --- a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb +++ b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb @@ -657,6 +657,102 @@ "Learn more: Work with content blocks
    " ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Split the preprocessed dataset\n", + "\n", + "With our raw dataset rebalanced with highly correlated features removed, let's now **spilt our dataset into train and test** in preparation for model evaluation testing.\n", + "\n", + "To start, let's grab the first few rows from the `balanced_raw_no_age_df` dataset we initialized earlier:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "balanced_raw_no_age_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before training the model, we need to encode the categorical features in the dataset:\n", + "\n", + "- Use the `OneHotEncoder` class from the `sklearn.preprocessing` module to encode the categorical features.\n", + "- The categorical features in the dataset are `Geography` and `Gender`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "balanced_raw_no_age_df = pd.get_dummies(\n", + " balanced_raw_no_age_df, columns=[\"Geography\", \"Gender\"], drop_first=True\n", + ")\n", + "balanced_raw_no_age_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Splitting our dataset into training and testing is essential for proper validation testing, as this helps assess how well the model generalizes to unseen data:\n", + "\n", + "- We start by dividing our `balanced_raw_no_age_df` dataset into training and test subsets using `train_test_split`, with 80% of the data allocated to training (`train_df`) and 20% to testing (`test_df`).\n", + "- From each subset, we separate the features (all columns except \"Exited\") into `X_train` and `X_test`, and the target column (\"Exited\") into `y_train` and `y_test`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "train_df, test_df = train_test_split(balanced_raw_no_age_df, test_size=0.20)\n", + "\n", + "X_train = train_df.drop(\"Exited\", axis=1)\n", + "y_train = train_df[\"Exited\"]\n", + "X_test = test_df.drop(\"Exited\", axis=1)\n", + "y_test = test_df[\"Exited\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize the split datasets\n", + "\n", + "Next, let's initialize the training and testing datasets so they are available for use:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vm_train_ds = vm.init_dataset(\n", + " input_id=\"train_dataset_final\",\n", + " dataset=train_df,\n", + " target_column=\"Exited\",\n", + ")\n", + "\n", + "vm_test_ds = vm.init_dataset(\n", + " input_id=\"test_dataset_final\",\n", + " dataset=test_df,\n", + " target_column=\"Exited\",\n", + ")" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb index 6b9c2493c..33ab922c9 100644 --- a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb +++ b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb @@ -259,52 +259,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Import the champion model\n", - "\n", - "With our raw dataset assessed and preprocessed, let's go ahead and import the champion model submitted by the model development team in the format of a `.pkl` file: **[lr_model_champion.pkl](lr_model_champion.pkl)**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import the champion model\n", - "import pickle as pkl\n", - "\n", - "with open(\"lr_model_champion.pkl\", \"rb\") as f:\n", - " log_reg = pkl.load(f)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Split the preprocessed dataset\n", + "### Split the preprocessed dataset\n", "\n", - "With our dummy model imported, raw dataset rebalanced with highly correlated features removed, let's now **spilt our dataset into train and test** in preparation for training our potential challenger model.\n", - "\n", - "To start, let's grab the first few rows from the `balanced_raw_no_age_df` dataset we initialized earlier:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "balanced_raw_no_age_df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Before training the model, we need to encode the categorical features in the dataset:\n", - "\n", - "- Use the `OneHotEncoder` class from the `sklearn.preprocessing` module to encode the categorical features.\n", - "- The categorical features in the dataset are `Geography` and `Gender`." + "With our raw dataset rebalanced with highly correlated features removed, let's now **spilt our dataset into train and test** in preparation for model evaluation testing:" ] }, { @@ -313,22 +270,13 @@ "metadata": {}, "outputs": [], "source": [ + "# Encode categorical features in the dataset\n", "balanced_raw_no_age_df = pd.get_dummies(\n", " balanced_raw_no_age_df, columns=[\"Geography\", \"Gender\"], drop_first=True\n", ")\n", "balanced_raw_no_age_df.head()" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Splitting our dataset into training and testing is essential for proper validation testing, as this helps assess how well the model generalizes to unseen data:\n", - "\n", - "- We start by dividing our `balanced_raw_no_age_df` dataset into training and test subsets using `train_test_split`, with 80% of the data allocated to training (`train_df`) and 20% to testing (`test_df`).\n", - "- From each subset, we separate the features (all columns except \"Exited\") into `X_train` and `X_test`, and the target column (\"Exited\") into `y_train` and `y_test`." - ] - }, { "cell_type": "code", "execution_count": null, @@ -337,6 +285,7 @@ "source": [ "from sklearn.model_selection import train_test_split\n", "\n", + "# Split the dataset into train and test\n", "train_df, test_df = train_test_split(balanced_raw_no_age_df, test_size=0.20)\n", "\n", "X_train = train_df.drop(\"Exited\", axis=1)\n", @@ -345,21 +294,13 @@ "y_test = test_df[\"Exited\"]" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initialize the split datasets\n", - "\n", - "Next, let's initialize the training and testing datasets so they are available for use:" - ] - }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ + "# Initialize the split datasets\n", "vm_train_ds = vm.init_dataset(\n", " input_id=\"train_dataset_final\",\n", " dataset=train_df,\n", @@ -373,6 +314,28 @@ ")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import the champion model\n", + "\n", + "With our raw dataset assessed and preprocessed, let's go ahead and import the champion model submitted by the model development team in the format of a `.pkl` file: **[lr_model_champion.pkl](lr_model_champion.pkl)**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import the champion model\n", + "import pickle as pkl\n", + "\n", + "with open(\"lr_model_champion.pkl\", \"rb\") as f:\n", + " log_reg = pkl.load(f)" + ] + }, { "cell_type": "markdown", "metadata": {}, From adfa391b61c8c2324380594ecf8e43914c67f8c2 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Fri, 4 Apr 2025 17:47:04 -0700 Subject: [PATCH 50/71] Validation 4 Setting up --- .../3-developing_challenger_model.ipynb | 4 +- .../4-finalize_validation_reporting.ipynb | 367 +++++++++++++++++- 2 files changed, 366 insertions(+), 5 deletions(-) diff --git a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb index 33ab922c9..22a4586f8 100644 --- a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb +++ b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb @@ -23,6 +23,7 @@ "- [x] Installed the ValidMind Library in your local environment, allowing you to access all its features\n", "- [x] Learned how to import and initialize datasets for use with ValidMind\n", "- [x] Understood the basics of how to run and log tests with ValidMind\n", + "- [x] Inserted your logged test results into your validation report\n", "\n", "
    Need help with the above steps?\n", "

    \n", @@ -31,8 +32,7 @@ "- 1 — Set up the ValidMind Library for validation\n", "- 2 — Start the model validation process\n", "\n", - "
    \n", - "\n" + "" ] }, { diff --git a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb index f8b497db6..a2732f61d 100644 --- a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb +++ b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb @@ -4,21 +4,382 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# ValidMind for model validation 4 — Finalize testing and reporting" + "# ValidMind for model validation 4 — Finalize testing and reporting\n", + "\n", + "Learn how to use ValidMind for your end-to-end model validation process with our series of four introductory notebooks. In this last notebook, finalize the compliance assessment process and have a complete validation report ready for review." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Prerequisites" + "## Prerequisites\n", + "\n", + "In order to finalize validation and reporting, you'll need to first have:\n", + "\n", + "- [x] Registered a model within the ValidMind Platform and granted yourself access to the model as a validator\n", + "- [x] Installed the ValidMind Library in your local environment, allowing you to access all its features\n", + "- [x] Learned how to import and initialize datasets and models for use with ValidMind\n", + "- [x] Understood the basics of how to run and log tests with ValidMind\n", + "- [x] Inserted your logged test results into your validation report\n", + "- [x] Added some preliminary findings to your validation report\n", + "\n", + "
    Need help with the above steps?\n", + "

    \n", + "Refer to the first three notebooks in this series:\n", + "\n", + "- 1 — Set up the ValidMind Library for validation\n", + "- 2 — Start the model validation process\n", + "- 2 — Developing a potential challenger model\n", + "\n", + "
    \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Setting up" + "## Setting up\n", + "\n", + "This section should be very familiar to you now — as we performed the same actions in the previous two notebooks in this series." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize the ValidMind Library\n", + "\n", + "As usual, let's first connect up the ValidMind Library to our model we previously registered in the ValidMind Platform:\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 \"ValidMind for model validation\" series of notebooks.\n", + "\n", + "3. 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, + "metadata": {}, + "outputs": [], + "source": [ + "# Make sure the ValidMind Library is installed\n", + "\n", + "%pip install -q validmind\n", + "\n", + "# 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": [ + "### Import the sample dataset\n", + "\n", + "Next, we'll load in the same sample [Bank Customer Churn Prediction](https://www.kaggle.com/datasets/shantanudhakadd/bank-customer-churn-prediction) dataset used to develop the champion model that we will independently preprocess:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the sample dataset\n", + "from validmind.datasets.classification import customer_churn as demo_dataset\n", + "\n", + "print(\n", + " f\"Loaded demo dataset with: \\n\\n\\t• Target column: '{demo_dataset.target_column}' \\n\\t• Class labels: {demo_dataset.class_labels}\"\n", + ")\n", + "\n", + "raw_df = demo_dataset.load_data()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "raw_copy_df = raw_df.sample(frac=1) # Create a copy of the raw dataset\n", + "\n", + "# Create a balanced dataset with the same number of exited and not exited customers\n", + "exited_df = raw_copy_df.loc[raw_copy_df[\"Exited\"] == 1]\n", + "not_exited_df = raw_copy_df.loc[raw_copy_df[\"Exited\"] == 0].sample(n=exited_df.shape[0])\n", + "\n", + "balanced_raw_df = pd.concat([exited_df, not_exited_df])\n", + "balanced_raw_df = balanced_raw_df.sample(frac=1, random_state=42)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let’s also quickly remove highly correlated features from the dataset using the output from a ValidMind test:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Register new data and now 'balanced_raw_dataset' is the new dataset object of interest\n", + "vm_balanced_raw_dataset = vm.init_dataset(\n", + " dataset=balanced_raw_df,\n", + " input_id=\"balanced_raw_dataset\",\n", + " target_column=\"Exited\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run HighPearsonCorrelation test with our balanced dataset as input and return a result object\n", + "corr_result = vm.tests.run_test(\n", + " test_id=\"validmind.data_validation.HighPearsonCorrelation\",\n", + " params={\"max_threshold\": 0.3},\n", + " inputs={\"dataset\": vm_balanced_raw_dataset},\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# From result object, extract table from `corr_result.tables`\n", + "features_df = corr_result.tables[0].data\n", + "features_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Extract list of features that failed the test\n", + "high_correlation_features = features_df[features_df[\"Pass/Fail\"] == \"Fail\"][\"Columns\"].tolist()\n", + "high_correlation_features" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Extract feature names from the list of strings\n", + "high_correlation_features = [feature.split(\",\")[0].strip(\"()\") for feature in high_correlation_features]\n", + "high_correlation_features" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Remove the highly correlated features from the dataset\n", + "balanced_raw_no_age_df = balanced_raw_df.drop(columns=high_correlation_features)\n", + "\n", + "# Re-initialize the dataset object\n", + "vm_raw_dataset_preprocessed = vm.init_dataset(\n", + " dataset=balanced_raw_no_age_df,\n", + " input_id=\"raw_dataset_preprocessed\",\n", + " target_column=\"Exited\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Re-run the test with the reduced feature set\n", + "corr_result = vm.tests.run_test(\n", + " test_id=\"validmind.data_validation.HighPearsonCorrelation\",\n", + " params={\"max_threshold\": 0.3},\n", + " inputs={\"dataset\": vm_raw_dataset_preprocessed},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Split the preprocessed dataset\n", + "\n", + "With our raw dataset rebalanced with highly correlated features removed, let's now **spilt our dataset into train and test** in preparation for model evaluation testing:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Encode categorical features in the dataset\n", + "balanced_raw_no_age_df = pd.get_dummies(\n", + " balanced_raw_no_age_df, columns=[\"Geography\", \"Gender\"], drop_first=True\n", + ")\n", + "balanced_raw_no_age_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "# Split the dataset into train and test\n", + "train_df, test_df = train_test_split(balanced_raw_no_age_df, test_size=0.20)\n", + "\n", + "X_train = train_df.drop(\"Exited\", axis=1)\n", + "y_train = train_df[\"Exited\"]\n", + "X_test = test_df.drop(\"Exited\", axis=1)\n", + "y_test = test_df[\"Exited\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the split datasets\n", + "vm_train_ds = vm.init_dataset(\n", + " input_id=\"train_dataset_final\",\n", + " dataset=train_df,\n", + " target_column=\"Exited\",\n", + ")\n", + "\n", + "vm_test_ds = vm.init_dataset(\n", + " input_id=\"test_dataset_final\",\n", + " dataset=test_df,\n", + " target_column=\"Exited\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import the champion model\n", + "\n", + "With our raw dataset assessed and preprocessed, let's go ahead and import the champion model submitted by the model development team in the format of a `.pkl` file: **[lr_model_champion.pkl](lr_model_champion.pkl)**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import the champion model\n", + "import pickle as pkl\n", + "\n", + "with open(\"lr_model_champion.pkl\", \"rb\") as f:\n", + " log_reg = pkl.load(f)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train potential challenger model\n", + "\n", + "We'll also train our random forest classification challenger model to see how it compares:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import the Random Forest Classification model\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "# Create the model instance with 50 decision trees\n", + "rf_model = RandomForestClassifier(\n", + " n_estimators=50,\n", + " random_state=42,\n", + ")\n", + "\n", + "# Train the model\n", + "rf_model.fit(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize the model objects\n", + "\n", + "In addition to the initialized datasets, 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 each of our two models:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the champion logistic regression model\n", + "vm_log_model = vm.init_model(\n", + " log_reg,\n", + " input_id=\"log_model_champion\",\n", + ")\n", + "\n", + "# Initialize the challenger random forest classification model\n", + "vm_rf_model = vm.init_model(\n", + " rf_model,\n", + " input_id=\"rf_model\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Assign predictions to Champion — Logistic regression model\n", + "vm_train_ds.assign_predictions(model=vm_log_model)\n", + "vm_test_ds.assign_predictions(model=vm_log_model)\n", + "\n", + "# Assign predictions to Challenger — Random forest classification model\n", + "vm_train_ds.assign_predictions(model=vm_rf_model)\n", + "vm_test_ds.assign_predictions(model=vm_rf_model)" ] }, { From f5139c12d40cddf5cf97e86055cc2f591de0de5f Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Fri, 4 Apr 2025 17:52:34 -0700 Subject: [PATCH 51/71] Validation 4 Summary & next steops --- .../4-finalize_validation_reporting.ipynb | 56 ++++++++++++++++++- 1 file changed, 54 insertions(+), 2 deletions(-) diff --git a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb index a2732f61d..106204d41 100644 --- a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb +++ b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb @@ -400,7 +400,22 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## In summary" + "## In summary\n", + "\n", + "In this final notebook, you learned how to:\n", + "\n", + "- [x] Implement a custom inline test\n", + "- [x] Run and log your custom inline tests\n", + "- [x] Use external custom test providers\n", + "- [x] Run and log tests from your custom test providers\n", + "- [x] Re-run tests provided by your model development team to verify that they were run and reported accurately\n", + "\n", + "With our ValidMind for model validation series of notebooks, you learned how to validate a model end-to-end with the ValidMind Library by running through some common scenarios in a typical model validation setting:\n", + "\n", + "- Verifying the data quality steps performed by the model development team\n", + "- Independently replicating the champion model's results and conducting additional tests to assess performance, stability, and robustness\n", + "- Setting up test inputs and a challenger model for comparative analysis\n", + "- Running validation tests, analyzing results, and logging findings to ValidMind" ] }, { @@ -414,7 +429,44 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Work with your validation report" + "### Work with your validation report\n", + "\n", + "Now that you've logged all your test results and verified the work done by the model development team, head to the ValidMind Platform to wrap up your validation report. Include risk assessment notes, insert more test results, add more findings, and assess compliance, then submit your report for review when it's ready. **Learn more:** [Preparing validation reports](https://docs.validmind.ai/guide/model-validation/preparing-validation-reports.html)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Learn more\n", + "\n", + "Now that you're familiar with the basics, you can explore the following notebooks to get a deeper understanding on how the ValidMind Library assists you in streamlining model validation:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### More how-to guides and code samples\n", + "\n", + "- [Explore available tests in detail](../../how_to/explore_tests.ipynb)\n", + "- [In-depth guide on running dataset based tests](../../how_to/run_tests/1_run_dataset_based_tests.ipynb)\n", + "- [In-depth guide for running comparison tests](../../how_to/run_tests/2_run_comparison_tests.ipynb)\n", + "- [In-depth guide for implementing custom tests](../../code_samples/custom_tests/implement_custom_tests.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Discover more learning resources\n", + "\n", + "All notebook samples can be found in the following directories of the ValidMind Library GitHub repository:\n", + "\n", + "- [Code samples](https://github.com/validmind/validmind-library/tree/main/notebooks/code_samples)\n", + "- [How-to guides](https://github.com/validmind/validmind-library/tree/main/notebooks/how_to)\n", + "\n", + "Or, visit our [documentation](https://docs.validmind.ai/) to learn more about ValidMind." ] } ], From c76c45e490add9f420034172259cd079d388c6e2 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Fri, 4 Apr 2025 17:58:57 -0700 Subject: [PATCH 52/71] Copied custom test stuff over --- .../4-finalize_validation_reporting.ipynb | 416 +++++++++++++++++- 1 file changed, 415 insertions(+), 1 deletion(-) diff --git a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb index 106204d41..b01552a36 100644 --- a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb +++ b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb @@ -386,7 +386,421 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Implement a custom test" + "## Implementing custom tests*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Implementing a custom inline test\n", + "\n", + "With the set up out of the way, let's implement a custom *inline test* that calculates the confusion matrix for a binary classification model.\n", + "\n", + "- An inline test refers to a test written and executed within the same environment as the code being tested — in this case, right in this Jupyter Notebook — without requiring a separate test file or framework.\n", + "- You'll note that the custom test function is just a regular Python function that can include and require any Python library as you see fit." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create a confusion matrix plot\n", + "\n", + "Let's first create a confusion matrix plot using the `confusion_matrix` function from the `sklearn.metrics` module:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "from sklearn import metrics\n", + "\n", + "# Get the predicted classes\n", + "y_pred = log_reg.predict(vm_test_ds.x)\n", + "\n", + "confusion_matrix = metrics.confusion_matrix(y_test, y_pred)\n", + "\n", + "cm_display = metrics.ConfusionMatrixDisplay(\n", + " confusion_matrix=confusion_matrix, display_labels=[False, True]\n", + ")\n", + "cm_display.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, create a [`@vm.test` wrapper](https://docs.validmind.ai/validmind/validmind.html#test) that will allow you to create a reusable test. **Note the following changes in the code below:**\n", + "\n", + "- The function `confusion_matrix` takes two arguments `dataset` and `model`. This is a `VMDataset` and `VMModel` object respectively.\n", + " - `VMDataset` objects allow you to access the dataset's true (target) values by accessing the `.y` attribute.\n", + " - `VMDataset` objects allow you to access the predictions for a given model by accessing the `.y_pred()` method.\n", + "- The function docstring provides a description of what the test does. This will be displayed along with the result in this notebook as well as in the ValidMind Platform.\n", + "- The function body calculates the confusion matrix using the `sklearn.metrics.confusion_matrix` function as we just did above.\n", + "- The function then returns the `ConfusionMatrixDisplay.figure_` object — this is important as the ValidMind Library expects the output of the custom test to be a plot or a table.\n", + "- The `@vm.test` decorator is doing the work of creating a wrapper around the function that will allow it to be run by the ValidMind Library. It also registers the test so it can be found by the ID `my_custom_tests.ConfusionMatrix`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@vm.test(\"my_custom_tests.ConfusionMatrix\")\n", + "def confusion_matrix(dataset, model):\n", + " \"\"\"The confusion matrix is a table that is often used to describe the performance of a classification model on a set of data for which the true values are known.\n", + "\n", + " The confusion matrix is a 2x2 table that contains 4 values:\n", + "\n", + " - True Positive (TP): the number of correct positive predictions\n", + " - True Negative (TN): the number of correct negative predictions\n", + " - False Positive (FP): the number of incorrect positive predictions\n", + " - False Negative (FN): the number of incorrect negative predictions\n", + "\n", + " The confusion matrix can be used to assess the holistic performance of a classification model by showing the accuracy, precision, recall, and F1 score of the model on a single figure.\n", + " \"\"\"\n", + " y_true = dataset.y\n", + " y_pred = dataset.y_pred(model=model)\n", + "\n", + " confusion_matrix = metrics.confusion_matrix(y_true, y_pred)\n", + "\n", + " cm_display = metrics.ConfusionMatrixDisplay(\n", + " confusion_matrix=confusion_matrix, display_labels=[False, True]\n", + " )\n", + " cm_display.plot()\n", + "\n", + " plt.close() # close the plot to avoid displaying it\n", + "\n", + " return cm_display.figure_ # return the figure object itself" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can now run the newly created custom test on both the training and test datasets using the [`run_test()` function](https://docs.validmind.ai/validmind/validmind/tests.html#run_test):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Training dataset\n", + "result = vm.tests.run_test(\n", + " \"my_custom_tests.ConfusionMatrix:training_dataset\",\n", + " inputs={\"model\": vm_model, \"dataset\": vm_train_ds},\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Test dataset\n", + "result = vm.tests.run_test(\n", + " \"my_custom_tests.ConfusionMatrix:test_dataset\",\n", + " inputs={\"model\": vm_model, \"dataset\": vm_test_ds},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Add parameters to custom tests\n", + "\n", + "Custom tests can take parameters just like any other function. To demonstrate, let's modify the `confusion_matrix` function to take an additional parameter `normalize` that will allow you to normalize the confusion matrix:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@vm.test(\"my_custom_tests.ConfusionMatrix\")\n", + "def confusion_matrix(dataset, model, normalize=False):\n", + " \"\"\"The confusion matrix is a table that is often used to describe the performance of a classification model on a set of data for which the true values are known.\n", + "\n", + " The confusion matrix is a 2x2 table that contains 4 values:\n", + "\n", + " - True Positive (TP): the number of correct positive predictions\n", + " - True Negative (TN): the number of correct negative predictions\n", + " - False Positive (FP): the number of incorrect positive predictions\n", + " - False Negative (FN): the number of incorrect negative predictions\n", + "\n", + " The confusion matrix can be used to assess the holistic performance of a classification model by showing the accuracy, precision, recall, and F1 score of the model on a single figure.\n", + " \"\"\"\n", + " y_true = dataset.y\n", + " y_pred = dataset.y_pred(model=model)\n", + "\n", + " if normalize:\n", + " confusion_matrix = metrics.confusion_matrix(y_true, y_pred, normalize=\"all\")\n", + " else:\n", + " confusion_matrix = metrics.confusion_matrix(y_true, y_pred)\n", + "\n", + " cm_display = metrics.ConfusionMatrixDisplay(\n", + " confusion_matrix=confusion_matrix, display_labels=[False, True]\n", + " )\n", + " cm_display.plot()\n", + "\n", + " plt.close() # close the plot to avoid displaying it\n", + "\n", + " return cm_display.figure_ # return the figure object itself" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Pass parameters to custom tests\n", + "\n", + "You can pass parameters to custom tests by providing a dictionary of parameters to the `run_test()` function.\n", + "\n", + "- The parameters will override any default parameters set in the custom test definition. Note that `dataset` and `model` are still passed as `inputs`.\n", + "- Since these are `VMDataset` or `VMModel` inputs, they have a special meaning.\n", + "- When declaring a `dataset`, `model`, `datasets` or `models` argument in a custom test function, the ValidMind Library will expect these get passed as `inputs` to `run_test()` or `run_documentation_tests()`.\n", + "\n", + "Re-running the confusion matrix with `normalize=True` looks like this:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Test dataset with normalize=True\n", + "result = vm.tests.run_test(\n", + " \"my_custom_tests.ConfusionMatrix:test_dataset_normalized\",\n", + " inputs={\"model\": vm_model, \"dataset\": vm_test_ds},\n", + " params={\"normalize\": True},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Log the confusion matrix results\n", + "\n", + "As we learned in **[2 — Start the model development process](2-start_development_process.ipynb)** under **Documenting results** > **Run and log an individual tests**, you can log any result to the ValidMind Platform with the [`.log()` method](https://docs.validmind.ai/validmind/validmind/vm_models.html#TestResult.log) of the result object, allowing you to then add the result to the documentation.\n", + "\n", + "You can now do the same for the confusion matrix results:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "result.log()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
    Note the output returned indicating that a test-driven block doesn't currently exist in your model's documentation for some test IDs. \n", + "

    \n", + "That's expected, as when we run validations tests the results logged need to be manually added to your report as part of your compliance assessment process within the ValidMind Platform.
    " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Use external test providers\n", + "\n", + "Creating inline custom tests with a function is a great way to customize your model documentation. However, sometimes you may want to reuse the same set of tests across multiple models and share them with others in your organization. In this case, you can create an external custom *test provider* that will allow you to load custom tests from a local folder or a Git repository.\n", + "\n", + "In this section you will learn how to declare a local filesystem test provider that allows loading tests from a local folder following these high level steps:\n", + "\n", + "1. Create a folder of custom tests from existing inline tests (tests that exist in your active Jupyter Notebook)\n", + "2. Save an inline test to a file\n", + "3. Define and register a [`LocalTestProvider`](https://docs.validmind.ai/validmind/validmind/tests.html#LocalTestProvider) that points to that folder\n", + "4. Run test provider tests\n", + "5. Add the test results to your documentation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create custom tests folder\n", + "\n", + "Let's start by creating a new folder that will contain reusable custom tests from your existing inline tests.\n", + "\n", + "The following code snippet will create a new `my_tests` directory in the current working directory if it doesn't exist:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tests_folder = \"my_tests\"\n", + "\n", + "import os\n", + "\n", + "# create tests folder\n", + "os.makedirs(tests_folder, exist_ok=True)\n", + "\n", + "# remove existing tests\n", + "for f in os.listdir(tests_folder):\n", + " # remove files and pycache\n", + " if f.endswith(\".py\") or f == \"__pycache__\":\n", + " os.system(f\"rm -rf {tests_folder}/{f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After running the command above, confirm that the new `my_tests` directory was created successfully:\n", + "\n", + "\"Screenshot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Save an inline test\n", + "\n", + "The `@vm.test` decorator we used in **Implementing a custom inline test** above to register one-off custom tests also includes a convenience method on the function object that allows you to simply call `.save()` to save the test to a Python file at a specified path.\n", + "\n", + "While `save()` will get you started by creating the file and saving the function code with the correct name, it won't automatically include any imports, or other functions or variables, outside of the functions that are needed for the test to run. To solve this, pass in an optional `imports` argument ensuring necessary imports are added to the file.\n", + "\n", + "The `confusion_matrix` test requires the following additional imports:\n", + "\n", + "```python\n", + "import matplotlib.pyplot as plt\n", + "from sklearn import metrics\n", + "```\n", + "\n", + "Let's pass these imports to the `save()` method to ensure they are included in the file with the following command:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "confusion_matrix.save(\n", + " # Save it to the custom tests folder we created\n", + " tests_folder,\n", + " imports=[\"import matplotlib.pyplot as plt\", \"from sklearn import metrics\"],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- [x] Confirm that the `save()` method saved the `confusion_matrix` function to a file named `ConfusionMatrix.py` in the `my_tests` folder.\n", + "- [x] Note that the new file provides some context on the origin of the test, which is useful for traceability:\n", + "\n", + " ```\n", + " # Saved from __main__.confusion_matrix\n", + " # Original Test ID: my_custom_tests.ConfusionMatrix\n", + " # New Test ID: .ConfusionMatrix\n", + " ```\n", + "\n", + "- [x] Additionally, the new test function has been stripped off its decorator, as it now resides in a file that will be loaded by the test provider:\n", + "\n", + " ```python\n", + " def ConfusionMatrix(dataset, model, normalize=False):\n", + " ```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Register a local test provider\n", + "\n", + "Now that your `my_tests` folder has a sample custom test, let's initialize a test provider that will tell the ValidMind Library where to find your custom tests:\n", + "\n", + "- ValidMind offers out-of-the-box test providers for local tests (tests in a folder) or a Github provider for tests in a Github repository.\n", + "- You can also create your own test provider by creating a class that has a [`load_test` method](https://docs.validmind.ai/validmind/validmind/tests.html#TestProvider.load_test) that takes a test ID and returns the test function matching that ID.\n", + "\n", + "
    Want to learn more about test providers?\n", + "

    \n", + "An extended introduction to test providers can be found in: Integrate external test providers
    " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Initialize a local test provider\n", + "\n", + "For most use cases, using a `LocalTestProvider` that allows you to load custom tests from a designated directory should be sufficient.\n", + "\n", + "**The most important attribute for a test provider is its `namespace`.** This is a string that will be used to prefix test IDs in model documentation. This allows you to have multiple test providers with tests that can even share the same ID, but are distinguished by their namespace.\n", + "\n", + "Let's go ahead and load the custom tests from our `my_tests` directory:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from validmind.tests import LocalTestProvider\n", + "\n", + "# initialize the test provider with the tests folder we created earlier\n", + "my_test_provider = LocalTestProvider(tests_folder)\n", + "\n", + "vm.tests.register_test_provider(\n", + " namespace=\"my_test_provider\",\n", + " test_provider=my_test_provider,\n", + ")\n", + "# `my_test_provider.load_test()` will be called for any test ID that starts with `my_test_provider`\n", + "# e.g. `my_test_provider.ConfusionMatrix` will look for a function named `ConfusionMatrix` in `my_tests/ConfusionMatrix.py` file" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Run test provider tests\n", + "\n", + "Now that we've set up the test provider, we can run any test that's located in the tests folder by using the `run_test()` method as with any other test:\n", + "\n", + "- For tests that reside in a test provider directory, the test ID will be the `namespace` specified when registering the provider, followed by the path to the test file relative to the tests folder.\n", + "- For example, the Confusion Matrix test we created earlier will have the test ID `my_test_provider.ConfusionMatrix`. You could organize the tests in subfolders, say `classification` and `regression`, and the test ID for the Confusion Matrix test would then be `my_test_provider.classification.ConfusionMatrix`.\n", + "\n", + "Let's go ahead and re-run the confusion matrix test by using the test ID `my_test_provider.ConfusionMatrix`. This should load the test from the test provider and run it as before." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "result = vm.tests.run_test(\n", + " \"my_test_provider.ConfusionMatrix\",\n", + " inputs={\"model\": vm_model, \"dataset\": vm_test_ds},\n", + " params={\"normalize\": True},\n", + ")\n", + "\n", + "result.log()" ] }, { From 2528d83059e46a8fa7449dcb1ceb6524e5aa5229 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Fri, 4 Apr 2025 18:09:11 -0700 Subject: [PATCH 53/71] Copied over verify test runs, adjusted next steps --- .../3-developing_challenger_model.ipynb | 2 +- .../4-finalize_validation_reporting.ipynb | 162 +++++++++++++++++- 2 files changed, 161 insertions(+), 3 deletions(-) diff --git a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb index 22a4586f8..8be145a8a 100644 --- a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb +++ b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb @@ -542,7 +542,7 @@ "source": [ "#### Log a model finding\n", "\n", - "As we can observe from the output above, our champion model doesn't pass the `MinimumAccuracy` based on the default thresholds of the out-of-the-box test, so let's log a model finding in the ValidMind Platform:\n", + "As we can observe from the output above, our champion model doesn't pass the `MinimumAccuracy` based on the default thresholds of the out-of-the-box test, so let's log a model finding in the ValidMind Platform ([Need more help?](https://docs.validmind.ai/guide/model-validation/add-manage-model-findings.html)):\n", "\n", "1. From the **Inventory** in the ValidMind Platform, go to the model you connected to earlier.\n", "\n", diff --git a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb index b01552a36..5274debcc 100644 --- a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb +++ b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb @@ -807,7 +807,160 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Verify test runs" + "## Verify test runs*\n", + "\n", + "Our final task is to verify that all the tests provided by the model development team were run and reported accurately. Note the appended `result_ids` to delineate which dataset we ran the test with for the relevant tests.\n", + "\n", + "Here, we'll specify all the tests we'd like to independently rerun in a dictionary called `test_config`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "test_config = {\n", + " # Run with the raw dataset\n", + " 'validmind.data_validation.DatasetDescription:raw_data': {\n", + " 'inputs': {'dataset': 'raw_dataset'}\n", + " },\n", + " 'validmind.data_validation.DescriptiveStatistics:raw_data': {\n", + " 'inputs': {'dataset': 'raw_dataset'}\n", + " },\n", + " 'validmind.data_validation.MissingValues:raw_data': {\n", + " 'inputs': {'dataset': 'raw_dataset'},\n", + " 'params': {'min_threshold': 1}\n", + " },\n", + " 'validmind.data_validation.ClassImbalance:raw_data': {\n", + " 'inputs': {'dataset': 'raw_dataset'},\n", + " 'params': {'min_percent_threshold': 10}\n", + " },\n", + " 'validmind.data_validation.Duplicates:raw_data': {\n", + " 'inputs': {'dataset': 'raw_dataset'},\n", + " 'params': {'min_threshold': 1}\n", + " },\n", + " 'validmind.data_validation.HighCardinality:raw_data': {\n", + " 'inputs': {'dataset': 'raw_dataset'},\n", + " 'params': {\n", + " 'num_threshold': 100,\n", + " 'percent_threshold': 0.1,\n", + " 'threshold_type': 'percent'\n", + " }\n", + " },\n", + " 'validmind.data_validation.Skewness:raw_data': {\n", + " 'inputs': {'dataset': 'raw_dataset'},\n", + " 'params': {'max_threshold': 1}\n", + " },\n", + " 'validmind.data_validation.UniqueRows:raw_data': {\n", + " 'inputs': {'dataset': 'raw_dataset'},\n", + " 'params': {'min_percent_threshold': 1}\n", + " },\n", + " 'validmind.data_validation.TooManyZeroValues:raw_data': {\n", + " 'inputs': {'dataset': 'raw_dataset'},\n", + " 'params': {'max_percent_threshold': 0.03}\n", + " },\n", + " 'validmind.data_validation.IQROutliersTable:raw_data': {\n", + " 'inputs': {'dataset': 'raw_dataset'},\n", + " 'params': {'threshold': 5}\n", + " },\n", + " # Run with the preprocessed dataset\n", + " 'validmind.data_validation.DescriptiveStatistics:preprocessed_data': {\n", + " 'inputs': {'dataset': 'preprocess_dataset'}\n", + " },\n", + " 'validmind.data_validation.TabularDescriptionTables:preprocessed_data': {\n", + " 'inputs': {'dataset': 'preprocess_dataset'}\n", + " },\n", + " 'validmind.data_validation.MissingValues:preprocessed_data': {\n", + " 'inputs': {'dataset': 'preprocess_dataset'},\n", + " 'params': {'min_threshold': 1}\n", + " },\n", + " 'validmind.data_validation.TabularNumericalHistograms:preprocessed_data': {\n", + " 'inputs': {'dataset': 'preprocess_dataset'}\n", + " },\n", + " 'validmind.data_validation.TabularCategoricalBarPlots:preprocessed_data': {\n", + " 'inputs': {'dataset': 'preprocess_dataset'}\n", + " },\n", + " 'validmind.data_validation.TargetRateBarPlots:preprocessed_data': {\n", + " 'inputs': {'dataset': 'preprocess_dataset'},\n", + " 'params': {'default_column': 'loan_status'}\n", + " },\n", + " # Run with the training and test datasets\n", + " 'validmind.data_validation.DescriptiveStatistics:development_data': {\n", + " 'input_grid': {'dataset': ['train_dataset', 'test_dataset']}\n", + " },\n", + " 'validmind.data_validation.TabularDescriptionTables:development_data': {\n", + " 'input_grid': {'dataset': ['train_dataset', 'test_dataset']}\n", + " },\n", + " 'validmind.data_validation.ClassImbalance:development_data': {\n", + " 'input_grid': {'dataset': ['train_dataset', 'test_dataset']},\n", + " 'params': {'min_percent_threshold': 10}\n", + " },\n", + " 'validmind.data_validation.UniqueRows:development_data': {\n", + " 'input_grid': {'dataset': ['train_dataset', 'test_dataset']},\n", + " 'params': {'min_percent_threshold': 1}\n", + " },\n", + " 'validmind.data_validation.TabularNumericalHistograms:development_data': {\n", + " 'input_grid': {'dataset': ['train_dataset', 'test_dataset']}\n", + " },\n", + " 'validmind.data_validation.MutualInformation:development_data': {\n", + " 'input_grid': {'dataset': ['train_dataset', 'test_dataset']},\n", + " 'params': {'min_threshold': 0.01}\n", + " },\n", + " 'validmind.data_validation.PearsonCorrelationMatrix:development_data': {\n", + " 'input_grid': {'dataset': ['train_dataset', 'test_dataset']}\n", + " },\n", + " 'validmind.data_validation.HighPearsonCorrelation:development_data': {\n", + " 'input_grid': {'dataset': ['train_dataset', 'test_dataset']},\n", + " 'params': {'max_threshold': 0.3, 'top_n_correlations': 10}\n", + " },\n", + " 'validmind.model_validation.ModelMetadata': {\n", + " 'input_grid': {'model': ['xgb_model', 'rf_model']}\n", + " },\n", + " 'validmind.model_validation.sklearn.ModelParameters': {\n", + " 'input_grid': {'model': ['xgb_model', 'rf_model']}\n", + " },\n", + " 'validmind.model_validation.sklearn.ROCCurve': {\n", + " 'input_grid': {'dataset': ['train_dataset', 'test_dataset'], 'model': ['xgb_model']}\n", + " },\n", + " 'validmind.model_validation.sklearn.MinimumROCAUCScore': {\n", + " 'input_grid': {'dataset': ['train_dataset', 'test_dataset'], 'model': ['xgb_model']},\n", + " 'params': {'min_threshold': 0.5}\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then batch run and log our tests in `test_config`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for t in test_config:\n", + " print(t)\n", + " try:\n", + " # Check if test has input_grid\n", + " if 'input_grid' in test_config[t]:\n", + " # For tests with input_grid, pass the input_grid configuration\n", + " if 'params' in test_config[t]:\n", + " vm.tests.run_test(t, input_grid=test_config[t]['input_grid'], params=test_config[t]['params']).log()\n", + " else:\n", + " vm.tests.run_test(t, input_grid=test_config[t]['input_grid']).log()\n", + " else:\n", + " # Original logic for regular inputs\n", + " if 'params' in test_config[t]:\n", + " vm.tests.run_test(t, inputs=test_config[t]['inputs'], params=test_config[t]['params']).log()\n", + " else:\n", + " vm.tests.run_test(t, inputs=test_config[t]['inputs']).log()\n", + " except Exception as e:\n", + " print(f\"Error running test {t}: {str(e)}\")" ] }, { @@ -845,7 +998,12 @@ "source": [ "### Work with your validation report\n", "\n", - "Now that you've logged all your test results and verified the work done by the model development team, head to the ValidMind Platform to wrap up your validation report. Include risk assessment notes, insert more test results, add more findings, and assess compliance, then submit your report for review when it's ready. **Learn more:** [Preparing validation reports](https://docs.validmind.ai/guide/model-validation/preparing-validation-reports.html)" + "Now that you've logged all your test results and verified the work done by the model development team, head to the ValidMind Platform to wrap up your validation report. 7. Continue to work on your validation report by:\n", + "\n", + "- **Inserting additional test results:** Click **Link Evidence to Report** under any section of 2. Validation in your validation report. (Learn more: [Link evidence to reports](https://docs.validmind.ai/guide/model-validation/assess-compliance.html#link-evidence-to-reports))\n", + "- **Adding more findings:** Click **Link Finding to Report** in any validation report section, then click **+ Create New Finding**. (Learn more: [Add and manage model findings](https://docs.validmind.ai/guide/model-validation/add-manage-model-findings.html))\n", + "- **Adding risk assessment notes:** Click under **Risk Assessment Notes** in any validation report section to access the text editor and content editing toolbar, including an option to generate a draft with AI. (Learn more: [Work with content blocks](https://docs.validmind.ai/guide/model-documentation/work-with-content-blocks.html#content-editing-toolbar))\n", + "- **Assessing compliance:** Under the Guideline for any validation report section, click **ASSESSMENT** and select the compliance status from the drop-down menu. (Learn more: [Provide compliance assessments](https://docs.validmind.ai/guide/model-validation/assess-compliance.html#provide-compliance-assessments))" ] }, { From edc424f3e71fa8752c929178099f3be2f512205f Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Fri, 4 Apr 2025 18:11:40 -0700 Subject: [PATCH 54/71] Deleting old notebook --- .../3-developing_challenger_model.ipynb | 1 + .../4-finalize_testing_reporting_OLD.ipynb | 1079 ----------------- .../4-finalize_validation_reporting.ipynb | 3 +- 3 files changed, 3 insertions(+), 1080 deletions(-) delete mode 100644 notebooks/tutorials/model_validation/4-finalize_testing_reporting_OLD.ipynb diff --git a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb index 8be145a8a..3f2c232e1 100644 --- a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb +++ b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb @@ -23,6 +23,7 @@ "- [x] Installed the ValidMind Library in your local environment, allowing you to access all its features\n", "- [x] Learned how to import and initialize datasets for use with ValidMind\n", "- [x] Understood the basics of how to run and log tests with ValidMind\n", + "- [x] Run data quality tests on the datasets used to train the champion model, and logged the results of those tests to ValidMind\n", "- [x] Inserted your logged test results into your validation report\n", "\n", "
    Need help with the above steps?\n", diff --git a/notebooks/tutorials/model_validation/4-finalize_testing_reporting_OLD.ipynb b/notebooks/tutorials/model_validation/4-finalize_testing_reporting_OLD.ipynb deleted file mode 100644 index c601a89e2..000000000 --- a/notebooks/tutorials/model_validation/4-finalize_testing_reporting_OLD.ipynb +++ /dev/null @@ -1,1079 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# ValidMind for model validation 4 — Finalize testing and reporting\n", - "\n", - "Learn how to use ValidMind for your end-to-end model validation process with our series of four introductory notebooks. In this last notebook, you'll configure and run some custom tests, then add logged test results to your validation report.\n", - "\n", - "As we concluded in [3 — Perform validation tests](3-perform_validation_tests.ipynb), our challenger random forest classification model was not a viable candidate for our use case and was eliminated as a contender. We'll finish up by comparing our champion application scorecard model against our remaining challenger logistic regression model, then use the ValidMind Platform to draft our validation report supplemented by our logged test results as evidence." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "In order to finalize the validation testing and reporting for your sample model, you'll need to first have:\n", - "\n", - "- [ ] Registered a model within the ValidMind Platform and granted yourself access to the model as a validator\n", - "- [ ] Installed the ValidMind Library in your local environment, allowing you to access all its features\n", - "- [ ] Learned how to import and initialize datasets for use with ValidMind\n", - "- [ ] Learned how to enable custom context for test descriptions generated by ValidMind\n", - "- [ ] Understood the basics of how to identify and run validation tests\n", - "- [ ] Run data quality and model performance tests for your champion and challenger models, and logged the results of those tests to the ValidMind Platform\n", - "\n", - "
    Need help with the above steps?\n", - "

    \n", - "Refer to the first three notebooks in this series:\n", - "\n", - "- 1 — Import the champion model\n", - "- 2 — Develop potential challenger model\n", - "- 3 — Perform validation tests\n", - "\n", - "
    " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setting up\n", - "\n", - "This section should be very familiar to you now — as we performed the same actions in the previous two notebooks in this series." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initialize the ValidMind Library\n", - "\n", - "As usual, let's first connect up the ValidMind Library to our model we previously registered in the ValidMind Platform:\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 \"ValidMind for model validation\" series of notebooks.\n", - "\n", - "3. 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, - "metadata": {}, - "outputs": [], - "source": [ - "# Make sure the ValidMind Library is installed\n", - "\n", - "%pip install -q validmind\n", - "\n", - "# 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": [ - "### Import the champion model\n", - "\n", - "Next, we'll import the champion model submitted by the model development team as we used in the last notebooks (**[xgb_model_champion.pkl](xgb_model_champion.pkl)**) and load in the sample [Lending Club](https://www.kaggle.com/datasets/devanshi23/loan-data-2007-2014/data) dataset used to develop the model, and independently preprocess and feature engineer the dataset to confirm that the model was built using appropriate and properly processed data:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import xgboost as xgb\n", - "\n", - "#Load the saved model\n", - "xgb_model = xgb.XGBClassifier()\n", - "xgb_model.load_model(\"xgb_model_champion.pkl\")\n", - "xgb_model\n", - "\n", - "# Ensure that we have to appropriate order in feature names from Champion model and dataset\n", - "cols_when_model_builds = xgb_model.get_booster().feature_names" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import the Lending Club dataset from Kaggle\n", - "from validmind.datasets.credit_risk import lending_club\n", - "\n", - "df = lending_club.load_data(source=\"offline\")\n", - "df.head()\n", - "\n", - "# Preprocess the dataset for data quality testing purposes\n", - "preprocess_df = lending_club.preprocess(df)\n", - "\n", - "# Apply feature engineering to the dataset\n", - "fe_df = lending_club.feature_engineering(preprocess_df)\n", - "fe_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Split our dataset into train and test to start the validation testing process\n", - "train_df, test_df = lending_club.split(fe_df, test_size=0.2)\n", - "\n", - "x_train = train_df.drop(lending_club.target_column, axis=1)\n", - "y_train = train_df[lending_club.target_column]\n", - "\n", - "x_test = test_df.drop(lending_club.target_column, axis=1)\n", - "y_test = test_df[lending_club.target_column]\n", - "\n", - "# Now let's apply the order of features from the champion model construction\n", - "x_train = x_train[cols_when_model_builds]\n", - "x_test = x_test[cols_when_model_builds]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cols_use = ['annual_inc_woe',\n", - " 'verification_status_woe',\n", - " 'emp_length_woe',\n", - " 'installment_woe',\n", - " 'term_woe',\n", - " 'home_ownership_woe',\n", - " 'purpose_woe',\n", - " 'open_acc_woe',\n", - " 'total_acc_woe',\n", - " 'int_rate_woe',\n", - " 'sub_grade_woe',\n", - " 'grade_woe','loan_status']\n", - "\n", - "\n", - "train_df = train_df[cols_use]\n", - "test_df = test_df[cols_use]\n", - "test_df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Train the challenger model\n", - "\n", - "As we eliminated the random forest classification model as a challenger, we'll only train our logistic regression model here:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import the Logistic Regression model\n", - "from sklearn.linear_model import LogisticRegression\n", - "\n", - "# Logistic Regression grid params\n", - "log_reg_params = {\n", - " \"penalty\": [\"l1\", \"l2\"],\n", - " \"C\": [0.001, 0.01, 0.1, 1, 10, 100, 1000],\n", - " \"solver\": [\"liblinear\"],\n", - "}\n", - "\n", - "# Grid search for Logistic Regression\n", - "from sklearn.model_selection import GridSearchCV\n", - "\n", - "grid_log_reg = GridSearchCV(LogisticRegression(), log_reg_params)\n", - "grid_log_reg.fit(x_train, y_train)\n", - "\n", - "# Logistic Regression best estimator\n", - "log_reg = grid_log_reg.best_estimator_\n", - "log_reg" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Extract predicted probabilities\n", - "\n", - "With our challenger model trained, let's extract the predicted probabilities from our two models and convert the probability predictions into a binary:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Champion — Application scorecard model\n", - "train_xgb_prob = xgb_model.predict_proba(x_train)[:, 1]\n", - "test_xgb_prob = xgb_model.predict_proba(x_test)[:, 1]\n", - "\n", - "# Challenger — Logistic regression model\n", - "train_log_prob = log_reg.predict_proba(x_train)[:, 1]\n", - "test_log_prob = log_reg.predict_proba(x_test)[:, 1]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# If probability > 0.3 = 1 (positive)\n", - "cut_off_threshold = 0.3\n", - "\n", - "# Champion — Application scorecard model\n", - "train_xgb_binary_predictions = (train_xgb_prob > cut_off_threshold).astype(int)\n", - "test_xgb_binary_predictions = (test_xgb_prob > cut_off_threshold).astype(int)\n", - "\n", - "# Challenger — Logistic regression model\n", - "train_log_binary_predictions = (train_log_prob > cut_off_threshold).astype(int)\n", - "test_log_binary_predictions = (test_log_prob > cut_off_threshold).astype(int)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initialize the ValidMind objects\n", - "\n", - "Let's initialize the ValidMind `Dataset` and `Model` objects in preparation for assigning model predictions to each dataset:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize the raw dataset\n", - "vm_raw_dataset = vm.init_dataset(\n", - " dataset=df,\n", - " input_id=\"raw_dataset\",\n", - " target_column=lending_club.target_column,\n", - ")\n", - "\n", - "# Initialize the preprocessed dataset\n", - "vm_preprocess_dataset = vm.init_dataset(\n", - " dataset=preprocess_df,\n", - " input_id=\"preprocess_dataset\",\n", - " target_column=lending_club.target_column,\n", - ")\n", - "\n", - "# Initialize the feature engineered dataset\n", - "vm_fe_dataset = vm.init_dataset(\n", - " dataset=fe_df,\n", - " input_id=\"fe_dataset\",\n", - " target_column=lending_club.target_column,\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=lending_club.target_column,\n", - ")\n", - "\n", - "# Initialize the test dataset\n", - "vm_test_ds = vm.init_dataset(\n", - " dataset=test_df,\n", - " input_id=\"test_dataset\",\n", - " target_column=lending_club.target_column,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize the champion application scorecard model\n", - "vm_xgb_model = vm.init_model(\n", - " xgb_model,\n", - " input_id=\"xgb_model_developer_champion\",\n", - ")\n", - "\n", - "# Initialize the challenger logistic regression model\n", - "vm_log_model = vm.init_model(\n", - " log_reg,\n", - " input_id=\"log_model\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Assign predictions\n", - "\n", - "With our models registered, we'll move on to assigning both the predictive probabilities coming directly from each model's predictions, and the binary prediction after applying the cutoff threshold:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Champion — Application scorecard model\n", - "vm_train_ds.assign_predictions(\n", - " model=vm_xgb_model,\n", - " prediction_values=train_xgb_binary_predictions,\n", - " prediction_probabilities=train_xgb_prob,\n", - ")\n", - "\n", - "vm_test_ds.assign_predictions(\n", - " model=vm_xgb_model,\n", - " prediction_values=test_xgb_binary_predictions,\n", - " prediction_probabilities=test_xgb_prob,\n", - ")\n", - "\n", - "# Challenger — Logistic regression model\n", - "vm_train_ds.assign_predictions(\n", - " model=vm_log_model,\n", - " prediction_values=train_log_binary_predictions,\n", - " prediction_probabilities=train_log_prob,\n", - ")\n", - "\n", - "vm_test_ds.assign_predictions(\n", - " model=vm_log_model,\n", - " prediction_values=test_log_binary_predictions,\n", - " prediction_probabilities=test_log_prob,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Compute the scores\n", - "train_xgb_scores = lending_club.compute_scores(train_xgb_prob)\n", - "test_xgb_scores = lending_club.compute_scores(test_xgb_prob)\n", - "train_log_scores = lending_club.compute_scores(train_log_prob)\n", - "test_log_scores = lending_club.compute_scores(test_log_prob)\n", - "\n", - "# Assign scores to the datasets\n", - "vm_train_ds.add_extra_column(\"xgb_scores\", train_xgb_scores)\n", - "vm_test_ds.add_extra_column(\"xgb_scores\", test_xgb_scores)\n", - "vm_train_ds.add_extra_column(\"log_scores\", train_log_scores)\n", - "vm_test_ds.add_extra_column(\"log_scores\", test_log_scores)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Enable use case context\n", - "\n", - "We'll also adjust the use case context to focus on comparison between our models for tests going forward:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "os.environ[\"VALIDMIND_LLM_DESCRIPTIONS_CONTEXT_ENABLED\"] = \"1\"\n", - "\n", - "context = \"\"\"\n", - "FORMAT FOR THE LLM DESCRIPTIONS: \n", - " **** is designed to .\n", - "\n", - " The test operates by \n", - "\n", - " The primary advantages of this test include \n", - "\n", - " Users should be aware that \n", - "\n", - " **Key Insights:**\n", - "\n", - " The test results reveal:\n", - "\n", - " - ****: \n", - " - ****: \n", - " ...\n", - "\n", - " Based on these results, \n", - "\n", - "ADDITIONAL INSTRUCTIONS:\n", - "\n", - " The champion model as the basis for comparison is called \"xgb_model_developer_champion\" and emphasis should be on the following:\n", - " - The metrics for the champion model compared against the challenger models\n", - " - Which model potentially outperforms the champion model based on the metrics, this should be highlighted and emphasized\n", - "\n", - "\n", - " For each metric in the test results, include in the test overview:\n", - " - The metric's purpose and what it measures\n", - " - Its mathematical formula\n", - " - The range of possible values\n", - " - What constitutes good/bad performance\n", - " - How to interpret different values\n", - "\n", - " Each insight should progressively cover:\n", - " 1. Overall scope and distribution\n", - " 2. Complete breakdown of all elements with specific values\n", - " 3. Natural groupings and patterns\n", - " 4. Comparative analysis between datasets/categories\n", - " 5. Stability and variations\n", - " 6. Notable relationships or dependencies\n", - "\n", - " Remember:\n", - " - Champion model (xgb_model_developer_champion) is the selection and challenger models are used to challenge the selection\n", - " - Keep all insights at the same level (no sub-bullets or nested structures)\n", - " - Make each insight complete and self-contained\n", - " - Include specific numerical values and ranges\n", - " - Cover all elements in the results comprehensively\n", - " - Maintain clear, concise language\n", - " - Use only \"- **Title**: Description\" format for insights\n", - " - Progress naturally from general to specific observations\n", - "\n", - "\"\"\".strip()\n", - "\n", - "os.environ[\"VALIDMIND_LLM_DESCRIPTIONS_CONTEXT\"] = context" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adjust a ValidMind test\n", - "\n", - "Let's dig deeper into the [`MinimumF1Score` test](https://docs.validmind.ai/tests/model_validation/sklearn/MinimumF1Score.html) we ran previously in [3 — Perform validation tests](3-perform_validation_tests.ipynb) to ensure that the models maintain a minimum acceptable balance between *precision* and *recall*. Precision refers to how many out of the positive predictions made by the model were actually correct, and recall refers to how many out of the actual positive cases did the model correctly identify.\n", - "\n", - "Use [`run_test()`](https://docs.validmind.ai/validmind/validmind/tests.html#run_test) with our testing dataset (`vm_test_ds`) to run the test in isolation again for our two remaining models without logging the result to have the output to compare with a subsequent iteration:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vm.tests.run_test(\n", - " \"validmind.model_validation.sklearn.MinimumF1Score:xgboost_champion_vs_challengers\",\n", - " input_grid={\n", - " \"dataset\": [vm_test_ds],\n", - " \"model\": [vm_xgb_model, vm_log_model]\n", - " },\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As `MinimumF1Score` allows us to customize parameters and thresholds for performance standards, let's adjust the threshold to see if it improves metrics:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "result = vm.tests.run_test(\n", - " \"validmind.model_validation.sklearn.MinimumF1Score:AdjThreshold\",\n", - " input_grid={\n", - " \"dataset\": [vm_test_ds],\n", - " \"model\": [vm_xgb_model, vm_log_model],\n", - " \"params\": {\"min_threshold\": 0.35}\n", - " },\n", - ").log()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
    Note the output returned indicating that a test-driven block doesn't currently exist in your model's documentation for some test IDs. \n", - "

    \n", - "That's expected, as when we run validations tests the results logged need to be manually added to your report as part of your compliance assessment process within the ValidMind Platform.
    " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run diagnostic tests\n", - "\n", - "Next we want to inspect the robustness and stability testing comparison between our champion and challenger model.\n", - "\n", - "Use [`list_tests()`](https://docs.validmind.ai/validmind/validmind/tests.html#list_tests) to identify all the model diagnosis tests for classification:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vm.tests.list_tests(tags=[\"model_diagnosis\"], task=\"classification\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
    Want to learn more about navigating ValidMind tests?\n", - "

    \n", - "Refer to our notebook outlining the utilities available for viewing and understanding available ValidMind tests: Explore tests
    " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's see if models suffer from any *overfit* potentials and also where there are potential sub-segments of issues. Overfitting occurs when a model learns the training data too well, capturing not only the true pattern but noise and random fluctuations resulting in excellent performance on the training dataset but poor generalization to new, unseen data.\n", - "\n", - "We'll select the following two tests:\n", - "\n", - "- **[`TrainingTestDegradation`](https://docs.validmind.ai/tests/model_validation/sklearn/TrainingTestDegradation.html):** Evaulates how much the models' performance degrade when moving from the training to the test datasets.\n", - "- **[`OverfitDiagnosis`](https://docs.validmind.ai/tests/model_validation/sklearn/OverfitDiagnosis.html):** Dives into detecting overfitting beyond performance degradation by examining learning curves, model complexity v.s. generalization, and cross-validation results." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Store our desired 2 tests\n", - "overfit_testing = [\n", - " \"validmind.model_validation.sklearn.TrainingTestDegradation:Champion_vs_LogRegression\",\n", - " \"validmind.model_validation.sklearn.OverfitDiagnosis:Champion_vs_LogRegression\"\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Run and log our 2 tests for both models for the training and testing datasets\n", - "for test in overfit_testing:\n", - " vm.tests.run_test(\n", - " test,\n", - " input_grid={\n", - " \"datasets\": [[vm_train_ds,vm_test_ds]],\n", - " \"model\" : [vm_xgb_model,vm_log_model]\n", - " }\n", - " ).log()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's also conduct *robustness* and *stability* testing of the two models. Robustness refers to a model's ability to maintain consistent performance, and stability refers to a model's ability to produce consistent outputs over time across different data subsets.\n", - "\n", - "We'll select the following two tests:\n", - "\n", - "- **[`RobustnessDiagnosis`](https://docs.validmind.ai/tests/model_validation/sklearn/RobustnessDiagnosis.html):** Evaluates whether the models are resilient to small *perturbations* (small, controlled modifications) or *variations* (broader subgroup-level or distributional changes) in input data.\n", - "- **[`WeakspotsDiagnosis`](https://docs.validmind.ai/tests/model_validation/sklearn/WeakspotsDiagnosis.html):** Identifies specific conditions where the models consistently underperform." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Store our desired 2 tests\n", - "stab_robust = [\n", - " \"validmind.model_validation.sklearn.RobustnessDiagnosis:Champion_vs_LogRegression\",\n", - " \"validmind.model_validation.sklearn.WeakspotsDiagnosis:Champion_vs_LogRegression\"\n", - " ]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Run and log our 2 tests for both models for the training and testing datasets\n", - "for test in stab_robust:\n", - " vm.tests.run_test(\n", - " test,\n", - " input_grid={\n", - " \"datasets\": [[vm_train_ds,vm_test_ds]],\n", - " \"model\" : [vm_xgb_model,vm_log_model]\n", - " },\n", - " ).log()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run feature importance tests\n", - "\n", - "We want to verify the relative influence of different input features on our models' predictions, as well as inspect the differences between our champion and challenger model to see if a certain model offers more understandable or logical importance scores for features.\n", - "\n", - "Use `list_tests()` to identify all the feature importance tests for classification:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the feature importance tests\n", - "FI = vm.tests.list_tests(tags=[\"feature_importance\"], task=\"classification\",pretty=False)\n", - "FI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Run and log our feature importance tests for both models for the testing dataset\n", - "for test in FI:\n", - " vm.tests.run_test(\n", - " \"\".join((test,':Champion_vs_LogisticRegression')),\n", - " input_grid={\n", - " \"dataset\": [vm_test_ds], \"model\" : [vm_xgb_model,vm_log_model]\n", - " },\n", - " ).log()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Implement a custom test\n", - "\n", - "Let's finish up testing by implementing a custom *inline test* that outputs a FICO score-type score. An inline test refers to a test written and executed within the same environment as the code being tested — in this case, right in this Jupyter Notebook — without requiring a separate test file or framework.\n", - "\n", - "The [`@vm.test` wrapper](https://docs.validmind.ai/validmind/validmind.html#test) allows you to create a reusable test:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import plotly.graph_objects as go\n", - "\n", - "@vm.test(\"my_custom_tests.ScoreToOdds\")\n", - "def score_to_odds_analysis(dataset, score_column='score', score_bands=[410, 440, 470]):\n", - " \"\"\"\n", - " Analyzes the relationship between score bands and odds (good:bad ratio).\n", - " Good odds = (1 - default_rate) / default_rate\n", - " \n", - " Higher scores should correspond to higher odds of being good.\n", - "\n", - " If there are multiple scores provided through score_column, this means that there are two different models and the scores reflect each model\n", - "\n", - " If there are more scores provided in the score_column then focus the assessment on the differences between the two scores and indicate through evidence which one is preferred.\n", - " \"\"\"\n", - " df = dataset.df\n", - " \n", - " # Create score bands\n", - " df['score_band'] = pd.cut(\n", - " df[score_column],\n", - " bins=[-np.inf] + score_bands + [np.inf],\n", - " labels=[f'<{score_bands[0]}'] + \n", - " [f'{score_bands[i]}-{score_bands[i+1]}' for i in range(len(score_bands)-1)] +\n", - " [f'>{score_bands[-1]}']\n", - " )\n", - " \n", - " # Calculate metrics per band\n", - " results = df.groupby('score_band').agg({\n", - " dataset.target_column: ['mean', 'count']\n", - " })\n", - " \n", - " results.columns = ['Default Rate', 'Total']\n", - " results['Good Count'] = results['Total'] - (results['Default Rate'] * results['Total'])\n", - " results['Bad Count'] = results['Default Rate'] * results['Total']\n", - " results['Odds'] = results['Good Count'] / results['Bad Count']\n", - " \n", - " # Create visualization\n", - " fig = go.Figure()\n", - " \n", - " # Add odds bars\n", - " fig.add_trace(go.Bar(\n", - " name='Odds (Good:Bad)',\n", - " x=results.index,\n", - " y=results['Odds'],\n", - " marker_color='blue'\n", - " ))\n", - " \n", - " fig.update_layout(\n", - " title='Score-to-Odds Analysis',\n", - " yaxis=dict(title='Odds Ratio (Good:Bad)'),\n", - " showlegend=False\n", - " )\n", - " \n", - " return fig" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With the custom test available, run and log the test for our champion and challenger models with our testing dataset (`vm_test_ds`):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "result = vm.tests.run_test(\n", - " \"my_custom_tests.ScoreToOdds:Champion_vs_Challenger\",\n", - " inputs={\n", - " \"dataset\": vm_test_ds,\n", - " },\n", - " param_grid={\n", - " \"score_column\": [\"xgb_scores\",\"log_scores\"],\n", - " \"score_bands\": [[500, 540, 570]],\n", - " },\n", - ").log()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
    Want to learn more about custom tests?\n", - "

    \n", - "Refer to our in-depth introduction to custom tests: Implement custom tests
    " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Verify test runs\n", - "\n", - "Our final task is to verify that all the tests provided by the model development team were run and reported accurately. Note the appended `result_ids` to delineate which dataset we ran the test with for the relevant tests.\n", - "\n", - "Here, we'll specify all the tests we'd like to independently rerun in a dictionary called `test_config`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "test_config = {\n", - " # Run with the raw dataset\n", - " 'validmind.data_validation.DatasetDescription:raw_data': {\n", - " 'inputs': {'dataset': 'raw_dataset'}\n", - " },\n", - " 'validmind.data_validation.DescriptiveStatistics:raw_data': {\n", - " 'inputs': {'dataset': 'raw_dataset'}\n", - " },\n", - " 'validmind.data_validation.MissingValues:raw_data': {\n", - " 'inputs': {'dataset': 'raw_dataset'},\n", - " 'params': {'min_threshold': 1}\n", - " },\n", - " 'validmind.data_validation.ClassImbalance:raw_data': {\n", - " 'inputs': {'dataset': 'raw_dataset'},\n", - " 'params': {'min_percent_threshold': 10}\n", - " },\n", - " 'validmind.data_validation.Duplicates:raw_data': {\n", - " 'inputs': {'dataset': 'raw_dataset'},\n", - " 'params': {'min_threshold': 1}\n", - " },\n", - " 'validmind.data_validation.HighCardinality:raw_data': {\n", - " 'inputs': {'dataset': 'raw_dataset'},\n", - " 'params': {\n", - " 'num_threshold': 100,\n", - " 'percent_threshold': 0.1,\n", - " 'threshold_type': 'percent'\n", - " }\n", - " },\n", - " 'validmind.data_validation.Skewness:raw_data': {\n", - " 'inputs': {'dataset': 'raw_dataset'},\n", - " 'params': {'max_threshold': 1}\n", - " },\n", - " 'validmind.data_validation.UniqueRows:raw_data': {\n", - " 'inputs': {'dataset': 'raw_dataset'},\n", - " 'params': {'min_percent_threshold': 1}\n", - " },\n", - " 'validmind.data_validation.TooManyZeroValues:raw_data': {\n", - " 'inputs': {'dataset': 'raw_dataset'},\n", - " 'params': {'max_percent_threshold': 0.03}\n", - " },\n", - " 'validmind.data_validation.IQROutliersTable:raw_data': {\n", - " 'inputs': {'dataset': 'raw_dataset'},\n", - " 'params': {'threshold': 5}\n", - " },\n", - " # Run with the preprocessed dataset\n", - " 'validmind.data_validation.DescriptiveStatistics:preprocessed_data': {\n", - " 'inputs': {'dataset': 'preprocess_dataset'}\n", - " },\n", - " 'validmind.data_validation.TabularDescriptionTables:preprocessed_data': {\n", - " 'inputs': {'dataset': 'preprocess_dataset'}\n", - " },\n", - " 'validmind.data_validation.MissingValues:preprocessed_data': {\n", - " 'inputs': {'dataset': 'preprocess_dataset'},\n", - " 'params': {'min_threshold': 1}\n", - " },\n", - " 'validmind.data_validation.TabularNumericalHistograms:preprocessed_data': {\n", - " 'inputs': {'dataset': 'preprocess_dataset'}\n", - " },\n", - " 'validmind.data_validation.TabularCategoricalBarPlots:preprocessed_data': {\n", - " 'inputs': {'dataset': 'preprocess_dataset'}\n", - " },\n", - " 'validmind.data_validation.TargetRateBarPlots:preprocessed_data': {\n", - " 'inputs': {'dataset': 'preprocess_dataset'},\n", - " 'params': {'default_column': 'loan_status'}\n", - " },\n", - " # Run with the training and test datasets\n", - " 'validmind.data_validation.DescriptiveStatistics:development_data': {\n", - " 'input_grid': {'dataset': ['train_dataset', 'test_dataset']}\n", - " },\n", - " 'validmind.data_validation.TabularDescriptionTables:development_data': {\n", - " 'input_grid': {'dataset': ['train_dataset', 'test_dataset']}\n", - " },\n", - " 'validmind.data_validation.ClassImbalance:development_data': {\n", - " 'input_grid': {'dataset': ['train_dataset', 'test_dataset']},\n", - " 'params': {'min_percent_threshold': 10}\n", - " },\n", - " 'validmind.data_validation.UniqueRows:development_data': {\n", - " 'input_grid': {'dataset': ['train_dataset', 'test_dataset']},\n", - " 'params': {'min_percent_threshold': 1}\n", - " },\n", - " 'validmind.data_validation.TabularNumericalHistograms:development_data': {\n", - " 'input_grid': {'dataset': ['train_dataset', 'test_dataset']}\n", - " },\n", - " 'validmind.data_validation.MutualInformation:development_data': {\n", - " 'input_grid': {'dataset': ['train_dataset', 'test_dataset']},\n", - " 'params': {'min_threshold': 0.01}\n", - " },\n", - " 'validmind.data_validation.PearsonCorrelationMatrix:development_data': {\n", - " 'input_grid': {'dataset': ['train_dataset', 'test_dataset']}\n", - " },\n", - " 'validmind.data_validation.HighPearsonCorrelation:development_data': {\n", - " 'input_grid': {'dataset': ['train_dataset', 'test_dataset']},\n", - " 'params': {'max_threshold': 0.3, 'top_n_correlations': 10}\n", - " },\n", - " 'validmind.model_validation.ModelMetadata': {\n", - " 'input_grid': {'model': ['xgb_model', 'rf_model']}\n", - " },\n", - " 'validmind.model_validation.sklearn.ModelParameters': {\n", - " 'input_grid': {'model': ['xgb_model', 'rf_model']}\n", - " },\n", - " 'validmind.model_validation.sklearn.ROCCurve': {\n", - " 'input_grid': {'dataset': ['train_dataset', 'test_dataset'], 'model': ['xgb_model']}\n", - " },\n", - " 'validmind.model_validation.sklearn.MinimumROCAUCScore': {\n", - " 'input_grid': {'dataset': ['train_dataset', 'test_dataset'], 'model': ['xgb_model']},\n", - " 'params': {'min_threshold': 0.5}\n", - " }\n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then batch run and log our tests in `test_config`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for t in test_config:\n", - " print(t)\n", - " try:\n", - " # Check if test has input_grid\n", - " if 'input_grid' in test_config[t]:\n", - " # For tests with input_grid, pass the input_grid configuration\n", - " if 'params' in test_config[t]:\n", - " vm.tests.run_test(t, input_grid=test_config[t]['input_grid'], params=test_config[t]['params']).log()\n", - " else:\n", - " vm.tests.run_test(t, input_grid=test_config[t]['input_grid']).log()\n", - " else:\n", - " # Original logic for regular inputs\n", - " if 'params' in test_config[t]:\n", - " vm.tests.run_test(t, inputs=test_config[t]['inputs'], params=test_config[t]['params']).log()\n", - " else:\n", - " vm.tests.run_test(t, inputs=test_config[t]['inputs']).log()\n", - " except Exception as e:\n", - " print(f\"Error running test {t}: {str(e)}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Add test results to reporting\n", - "\n", - "With all the test results logged, let's head to the model we connected to at the beginning of this notebook and insert some test results into our validation report ([Need more help?](https://docs.validmind.ai/guide/model-validation/assess-compliance.html#link-validator-evidence)):\n", - "\n", - "1. From the **Inventory** in the ValidMind Platform, go to the model you connected to earlier.\n", - "\n", - "2. In the left sidebar that appears for your model, click **Validation Report**.\n", - "\n", - "3. Locate the Data Preparation section and click on **2.2.2. Model Performance** to expand that section.\n", - "\n", - "4. Under the Model Performance Metrics section, locate Validator Evidence then click **Link Evidence to Report**:\n", - "\n", - " \"Screenshot\n", - "

    \n", - "\n", - "5. Select the Minimum F1 Score test results we logged:\n", - "\n", - " - **ValidMind Model Validation Sklearn Minimum F 1 Score Adj Threshold**\n", - " - **ValidMind Model Validation Sklearn Minimum F 1 Score Xgboost Champion**\n", - " - **ValidMind Model Validation Sklearn Minimum F 1 Score Xgboost Champion Vs Challengers**\n", - "\n", - " \"Screenshot\n", - "

    \n", - "\n", - "6. Finally, click **Update Linked Evidence** to add the test results to the validation report.\n", - "\n", - " Confirm that the results for the minimum F1 score tests have been correctly inserted into section **2.2.2. Model Performance** of the report:\n", - "\n", - " \"Screenshot\n", - "

    \n", - "\n", - "7. Continue to work on your validation report by:\n", - "\n", - " - **Adding risk assessment notes:** Click under **Risk Assessment Notes** in any validation report section to access the text editor and content editing toolbar, including an option to generate a draft with AI. (Learn more: [Work with content blocks](https://docs.validmind.ai/guide/model-documentation/work-with-content-blocks.html#content-editing-toolbar))\n", - " - **Adding findings:** Click on **Link Finding to Report** in any validation report section, then click **+ Create New Finding**. (Learn more: [Add and manage model findings](https://docs.validmind.ai/guide/model-validation/add-manage-model-findings.html))\n", - " - **Assessing compliance:** Under the Guideline for any validation report section, click **ASSESSMENT** and select the compliance status from the drop-down menu. (Learn more: [Assess compliance](https://docs.validmind.ai/guide/model-validation/assess-compliance.html#provide-compliance-assessments))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## In summary\n", - "\n", - "In this final notebook, you learned how to:\n", - "\n", - "\n", - "- [ ] Implement a custom inline test\n", - "- [ ] Re-run tests provided by your model development team to verify that they were run and reported accurately\n", - "- [ ] Link your logged test results as evidence on your validation report\n", - "\n", - "With our ValidMind for model validation series of notebooks, you learned how to validate a model end-to-end with the ValidMind Library by running through some common scenarios in a typical model validation setting:\n", - "\n", - "- Verifying the data quality steps performed by the model development team\n", - "- Independently replicating the champion model's results and conducting additional tests to assess performance, stability, and robustness\n", - "- Setting up test inputs and challenger models for comparative analysis\n", - "- Running validation tests, analyzing results, and logging findings to ValidMind" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Next steps" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Work with your validation report\n", - "\n", - "Now that you've logged all your test results and verified the work done by the model development team, head to the ValidMind Platform to wrap up your validation report. Include risk assessment notes, add findings, and assess compliance, then submit your report for review when it's ready. **Learn more:** [Preparing validation reports](https://docs.validmind.ai/guide/model-validation/preparing-validation-reports.html)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Learn more\n", - "\n", - "Now that you're familiar with the basics, you can explore the following notebooks to get a deeper understanding on how the ValidMind Library assists you in streamlining model validation:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### More how-to guides and code samples\n", - "\n", - "- [Explore available tests in detail](../../how_to/explore_tests.ipynb)\n", - "- [In-depth guide on running dataset based tests](../../how_to/run_tests/1_run_dataset_based_tests.ipynb)\n", - "- [In-depth guide for running comparison tests](../../how_to/run_tests/2_run_comparison_tests.ipynb)\n", - "- [In-depth guide for implementing custom tests](../../code_samples/custom_tests/implement_custom_tests.ipynb)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Discover more learning resources\n", - "\n", - "All notebook samples can be found in the following directories of the ValidMind Library GitHub repository:\n", - "\n", - "- [Code samples](https://github.com/validmind/validmind-library/tree/main/notebooks/code_samples)\n", - "- [How-to guides](https://github.com/validmind/validmind-library/tree/main/notebooks/how_to)\n", - "\n", - "Or, visit our [documentation](https://docs.validmind.ai/) to learn more about ValidMind." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "ValidMind Library", - "language": "python", - "name": "validmind" - }, - "language_info": { - "name": "python", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb index 5274debcc..5b05a1776 100644 --- a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb +++ b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb @@ -20,7 +20,8 @@ "- [x] Registered a model within the ValidMind Platform and granted yourself access to the model as a validator\n", "- [x] Installed the ValidMind Library in your local environment, allowing you to access all its features\n", "- [x] Learned how to import and initialize datasets and models for use with ValidMind\n", - "- [x] Understood the basics of how to run and log tests with ValidMind\n", + "- [x] Understood the basics of how to identify and run validation tests\n", + "- [x] Run validation tests for your champion and challenger models, and logged the results of those tests to the ValidMind Platform\n", "- [x] Inserted your logged test results into your validation report\n", "- [x] Added some preliminary findings to your validation report\n", "\n", From d133d6ac7a477e9595a9483dc4d46fa0901be7d2 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Mon, 7 Apr 2025 11:03:17 -0700 Subject: [PATCH 55/71] Validation 4 Editing custom tests --- .../3-developing_challenger_model.ipynb | 8 ++++- .../4-finalize_validation_reporting.ipynb | 29 +++++++++++++++---- 2 files changed, 30 insertions(+), 7 deletions(-) diff --git a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb index 3f2c232e1..9a7b24010 100644 --- a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb +++ b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb @@ -772,8 +772,14 @@ } ], "metadata": { + "kernelspec": { + "display_name": "ValidMind Library", + "language": "python", + "name": "validmind" + }, "language_info": { - "name": "python" + "name": "python", + "version": "3.10.13" } }, "nbformat": 4, diff --git a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb index 5b05a1776..6ca4ffdcd 100644 --- a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb +++ b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb @@ -387,7 +387,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Implementing custom tests*" + "## Implementing custom tests\n", + "\n", + "Thanks to the model documentation ([Learn more ...](https://docs.validmind.ai/developer/validmind-library.html#for-model-development)), we know that the model development team implemented a custom test to further evaluate the performance of the champion model. A custom test is any function that takes a set of inputs and parameters as arguments and returns one or more outputs:\n", + "\n", + "- The function can be as simple or as complex as you need it to be — it can use external libraries, make API calls, or do anything else that you can do in Python.\n", + "- The only requirement is that the function signature and return values can be \"understood\" and handled by the ValidMind Library. As such, custom tests offer added flexibility by extending the default tests provided by ValidMind, enabling you to document any type of model or use case.\n", + "\n", + "
    Want to learn more about custom tests?\n", + "

    \n", + "Refer to our in-depth introduction to custom tests: Implement custom tests
    \n", + "\n", + "In a usual model validation situation, you would load a saved custom test provided by the model development team. In the following section, we'll have you implement the same custom test and make it available for reuse, to familiarize you with the processes." ] }, { @@ -396,7 +407,7 @@ "source": [ "### Implementing a custom inline test\n", "\n", - "With the set up out of the way, let's implement a custom *inline test* that calculates the confusion matrix for a binary classification model.\n", + "Let's implement the same custom *inline test* that calculates the confusion matrix for a binary classification model that the model development team used in their performance evaluations.\n", "\n", "- An inline test refers to a test written and executed within the same environment as the code being tested — in this case, right in this Jupyter Notebook — without requiring a separate test file or framework.\n", "- You'll note that the custom test function is just a regular Python function that can include and require any Python library as you see fit." @@ -484,7 +495,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "You can now run the newly created custom test on both the training and test datasets using the [`run_test()` function](https://docs.validmind.ai/validmind/validmind/tests.html#run_test):" + "You can now run the newly created custom test on both the training and test datasets for both models using the [`run_test()` function](https://docs.validmind.ai/validmind/validmind/tests.html#run_test):" ] }, { @@ -493,7 +504,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Training dataset\n", + "# Training dataset old\n", "result = vm.tests.run_test(\n", " \"my_custom_tests.ConfusionMatrix:training_dataset\",\n", " inputs={\"model\": vm_model, \"dataset\": vm_train_ds},\n", @@ -506,7 +517,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Test dataset\n", + "# Test dataset old\n", "result = vm.tests.run_test(\n", " \"my_custom_tests.ConfusionMatrix:test_dataset\",\n", " inputs={\"model\": vm_model, \"dataset\": vm_test_ds},\n", @@ -1044,8 +1055,14 @@ } ], "metadata": { + "kernelspec": { + "display_name": "ValidMind Library", + "language": "python", + "name": "validmind" + }, "language_info": { - "name": "python" + "name": "python", + "version": "3.10.13" } }, "nbformat": 4, From 8c803ddcc501a3ce82ffa3c54aef23a898c20549 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Mon, 7 Apr 2025 11:06:15 -0700 Subject: [PATCH 56/71] Validation 4 Custom inline test pt1 --- .../4-finalize_validation_reporting.ipynb | 44 +++++++++++-------- 1 file changed, 25 insertions(+), 19 deletions(-) diff --git a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb index 6ca4ffdcd..dc067e1b4 100644 --- a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb +++ b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb @@ -504,11 +504,14 @@ "metadata": {}, "outputs": [], "source": [ - "# Training dataset old\n", - "result = vm.tests.run_test(\n", - " \"my_custom_tests.ConfusionMatrix:training_dataset\",\n", - " inputs={\"model\": vm_model, \"dataset\": vm_train_ds},\n", - ")" + "# Training dataset\n", + "vm.tests.run_test(\n", + " test_id=\"my_custom_tests.ConfusionMatrix:training_dataset\",\n", + " input_grid={\n", + " \"datasets\": [[vm_train_ds]],\n", + " \"model\" : [vm_log_model,vm_rf_model]\n", + " }\n", + ").log()" ] }, { @@ -517,11 +520,23 @@ "metadata": {}, "outputs": [], "source": [ - "# Test dataset old\n", - "result = vm.tests.run_test(\n", - " \"my_custom_tests.ConfusionMatrix:test_dataset\",\n", - " inputs={\"model\": vm_model, \"dataset\": vm_test_ds},\n", - ")" + "# Test dataset\n", + "vm.tests.run_test(\n", + " test_id=\"my_custom_tests.ConfusionMatrix:test_dataset\",\n", + " input_grid={\n", + " \"datasets\": [[vm_test_ds]],\n", + " \"model\" : [vm_log_model,vm_rf_model]\n", + " }\n", + ").log()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
    Note the output returned indicating that a test-driven block doesn't currently exist in your model's documentation for some test IDs. \n", + "

    \n", + "That's expected, as when we run validations tests the results logged need to be manually added to your report as part of your compliance assessment process within the ValidMind Platform.
    " ] }, { @@ -619,15 +634,6 @@ "result.log()" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
    Note the output returned indicating that a test-driven block doesn't currently exist in your model's documentation for some test IDs. \n", - "

    \n", - "That's expected, as when we run validations tests the results logged need to be manually added to your report as part of your compliance assessment process within the ValidMind Platform.
    " - ] - }, { "cell_type": "markdown", "metadata": {}, From 6f06b772153e92ff5a04e73cfeae6606d7371c01 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Mon, 7 Apr 2025 11:10:28 -0700 Subject: [PATCH 57/71] Validation 4 Custom inline test pt2 --- .../4-finalize_validation_reporting.ipynb | 31 ++++++++++++------- 1 file changed, 20 insertions(+), 11 deletions(-) diff --git a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb index dc067e1b4..f4a38a6b7 100644 --- a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb +++ b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb @@ -6,7 +6,14 @@ "source": [ "# ValidMind for model validation 4 — Finalize testing and reporting\n", "\n", - "Learn how to use ValidMind for your end-to-end model validation process with our series of four introductory notebooks. In this last notebook, finalize the compliance assessment process and have a complete validation report ready for review." + "Learn how to use ValidMind for your end-to-end model validation process with our series of four introductory notebooks. In this last notebook, finalize the compliance assessment process and have a complete validation report ready for review.\n", + "\n", + "This notebook will walk you through how to supplement ValidMind tests with your own custom tests and include them as additional evidence in your validation report. A custom test is any function that takes a set of inputs and parameters as arguments and returns one or more outputs:\n", + "\n", + "- The function can be as simple or as complex as you need it to be — it can use external libraries, make API calls, or do anything else that you can do in Python.\n", + "- The only requirement is that the function signature and return values can be \"understood\" and handled by the ValidMind Library. As such, custom tests offer added flexibility by extending the default tests provided by ValidMind, enabling you to document any type of model or use case.\n", + "\n", + "**For a more in-depth introduction to custom tests,** refer to our [Implement custom tests](../../code_samples/custom_tests/implement_custom_tests.ipynb) notebook." ] }, { @@ -389,14 +396,7 @@ "source": [ "## Implementing custom tests\n", "\n", - "Thanks to the model documentation ([Learn more ...](https://docs.validmind.ai/developer/validmind-library.html#for-model-development)), we know that the model development team implemented a custom test to further evaluate the performance of the champion model. A custom test is any function that takes a set of inputs and parameters as arguments and returns one or more outputs:\n", - "\n", - "- The function can be as simple or as complex as you need it to be — it can use external libraries, make API calls, or do anything else that you can do in Python.\n", - "- The only requirement is that the function signature and return values can be \"understood\" and handled by the ValidMind Library. As such, custom tests offer added flexibility by extending the default tests provided by ValidMind, enabling you to document any type of model or use case.\n", - "\n", - "
    Want to learn more about custom tests?\n", - "

    \n", - "Refer to our in-depth introduction to custom tests: Implement custom tests
    \n", + "Thanks to the model documentation ([Learn more ...](https://docs.validmind.ai/developer/validmind-library.html#for-model-development)), we know that the model development team implemented a custom test to further evaluate the performance of the champion model.\n", "\n", "In a usual model validation situation, you would load a saved custom test provided by the model development team. In the following section, we'll have you implement the same custom test and make it available for reuse, to familiarize you with the processes." ] @@ -508,7 +508,7 @@ "vm.tests.run_test(\n", " test_id=\"my_custom_tests.ConfusionMatrix:training_dataset\",\n", " input_grid={\n", - " \"datasets\": [[vm_train_ds]],\n", + " \"datasets\": [vm_train_ds],\n", " \"model\" : [vm_log_model,vm_rf_model]\n", " }\n", ").log()" @@ -524,7 +524,7 @@ "vm.tests.run_test(\n", " test_id=\"my_custom_tests.ConfusionMatrix:test_dataset\",\n", " input_grid={\n", - " \"datasets\": [[vm_test_ds]],\n", + " \"datasets\": [vm_test_ds],\n", " \"model\" : [vm_log_model,vm_rf_model]\n", " }\n", ").log()" @@ -634,6 +634,15 @@ "result.log()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
    Want to learn more about custom tests?\n", + "

    \n", + "Refer to our in-depth introduction to custom tests: Implement custom tests
    " + ] + }, { "cell_type": "markdown", "metadata": {}, From 050a2bef922d3866eb8f7b7881cbdb4826d773d0 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Mon, 7 Apr 2025 11:18:02 -0700 Subject: [PATCH 58/71] Validation 4 Custom inline test pt3 --- .../4-finalize_validation_reporting.ipynb | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb index f4a38a6b7..9595ebf01 100644 --- a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb +++ b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb @@ -504,12 +504,12 @@ "metadata": {}, "outputs": [], "source": [ - "# Training dataset\n", + "# Champion train and test\n", "vm.tests.run_test(\n", - " test_id=\"my_custom_tests.ConfusionMatrix:training_dataset\",\n", + " test_id=\"my_custom_tests.ConfusionMatrix:champion\",\n", " input_grid={\n", - " \"datasets\": [vm_train_ds],\n", - " \"model\" : [vm_log_model,vm_rf_model]\n", + " \"datasets\": [vm_train_ds,vm_test_ds],\n", + " \"model\" : [vm_log_model]\n", " }\n", ").log()" ] @@ -520,12 +520,12 @@ "metadata": {}, "outputs": [], "source": [ - "# Test dataset\n", + "# Challenger train and test\n", "vm.tests.run_test(\n", - " test_id=\"my_custom_tests.ConfusionMatrix:test_dataset\",\n", + " test_id=\"my_custom_tests.ConfusionMatrix:challenger\",\n", " input_grid={\n", - " \"datasets\": [vm_test_ds],\n", - " \"model\" : [vm_log_model,vm_rf_model]\n", + " \"datasets\": [vm_train_ds,vm_test_ds],\n", + " \"model\" : [vm_rf_model]\n", " }\n", ").log()" ] From e2d672230037e2e9376dad854af9d914509dc0d2 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Mon, 7 Apr 2025 11:19:45 -0700 Subject: [PATCH 59/71] Validation 4 Custom inline test pt4 --- .../model_validation/4-finalize_validation_reporting.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb index 9595ebf01..a5df6b10d 100644 --- a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb +++ b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb @@ -508,7 +508,7 @@ "vm.tests.run_test(\n", " test_id=\"my_custom_tests.ConfusionMatrix:champion\",\n", " input_grid={\n", - " \"datasets\": [vm_train_ds,vm_test_ds],\n", + " \"dataset\": [vm_train_ds,vm_test_ds],\n", " \"model\" : [vm_log_model]\n", " }\n", ").log()" @@ -524,7 +524,7 @@ "vm.tests.run_test(\n", " test_id=\"my_custom_tests.ConfusionMatrix:challenger\",\n", " input_grid={\n", - " \"datasets\": [vm_train_ds,vm_test_ds],\n", + " \"dataset\": [vm_train_ds,vm_test_ds],\n", " \"model\" : [vm_rf_model]\n", " }\n", ").log()" From 3172d4c0a09edbb88225cba222740ed2816b7ca8 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Mon, 7 Apr 2025 11:25:25 -0700 Subject: [PATCH 60/71] Validation 4 Custom inline test pt5 --- .../3-integrate_custom_tests.ipynb | 2 +- .../4-finalize_validation_reporting.ipynb | 38 +++++++++---------- 2 files changed, 20 insertions(+), 20 deletions(-) diff --git a/notebooks/tutorials/model_development/3-integrate_custom_tests.ipynb b/notebooks/tutorials/model_development/3-integrate_custom_tests.ipynb index 0ade31439..e5c9b4e63 100644 --- a/notebooks/tutorials/model_development/3-integrate_custom_tests.ipynb +++ b/notebooks/tutorials/model_development/3-integrate_custom_tests.ipynb @@ -639,7 +639,7 @@ "result = vm.tests.run_test(\n", " \"my_custom_tests.ConfusionMatrix:test_dataset_normalized\",\n", " inputs={\"model\": vm_model, \"dataset\": vm_test_ds},\n", - " params={\"normalize\": True},\n", + " params={\"normalize\": True}\n", ")" ] }, diff --git a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb index a5df6b10d..143183720 100644 --- a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb +++ b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb @@ -597,7 +597,7 @@ "- Since these are `VMDataset` or `VMModel` inputs, they have a special meaning.\n", "- When declaring a `dataset`, `model`, `datasets` or `models` argument in a custom test function, the ValidMind Library will expect these get passed as `inputs` to `run_test()` or `run_documentation_tests()`.\n", "\n", - "Re-running the confusion matrix with `normalize=True` looks like this:" + "Re-running and logging the custom confusion matrix with `normalize=True` looks like this:" ] }, { @@ -606,23 +606,15 @@ "metadata": {}, "outputs": [], "source": [ - "# Test dataset with normalize=True\n", - "result = vm.tests.run_test(\n", - " \"my_custom_tests.ConfusionMatrix:test_dataset_normalized\",\n", - " inputs={\"model\": vm_model, \"dataset\": vm_test_ds},\n", - " params={\"normalize\": True},\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Log the confusion matrix results\n", - "\n", - "As we learned in **[2 — Start the model development process](2-start_development_process.ipynb)** under **Documenting results** > **Run and log an individual tests**, you can log any result to the ValidMind Platform with the [`.log()` method](https://docs.validmind.ai/validmind/validmind/vm_models.html#TestResult.log) of the result object, allowing you to then add the result to the documentation.\n", - "\n", - "You can now do the same for the confusion matrix results:" + "# Champion\n", + "vm.tests.run_test(\n", + " test_id=\"my_custom_tests.ConfusionMatrix:test_normalized_champion\",\n", + " input_grid={\n", + " \"dataset\": [vm_test_ds],\n", + " \"model\" : [vm_log_model]\n", + " },\n", + " params={\"normalize\": True}\n", + ").log()" ] }, { @@ -631,7 +623,15 @@ "metadata": {}, "outputs": [], "source": [ - "result.log()" + "# Challenger\n", + "vm.tests.run_test(\n", + " test_id=\"my_custom_tests.ConfusionMatrix:test_normalized_challenger\",\n", + " input_grid={\n", + " \"dataset\": [vm_test_ds],\n", + " \"model\" : [vm_rf_model]\n", + " },\n", + " params={\"normalize\": True}\n", + ").log()" ] }, { From 9bf213b203f41c21d2e993759b6915db3a84c527 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Mon, 7 Apr 2025 11:28:29 -0700 Subject: [PATCH 61/71] Validation 4 Custom inline test pt6 --- .../model_development/3-integrate_custom_tests.ipynb | 2 +- .../model_validation/4-finalize_validation_reporting.ipynb | 7 +++---- 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/notebooks/tutorials/model_development/3-integrate_custom_tests.ipynb b/notebooks/tutorials/model_development/3-integrate_custom_tests.ipynb index e5c9b4e63..2e685a645 100644 --- a/notebooks/tutorials/model_development/3-integrate_custom_tests.ipynb +++ b/notebooks/tutorials/model_development/3-integrate_custom_tests.ipynb @@ -626,7 +626,7 @@ "- Since these are `VMDataset` or `VMModel` inputs, they have a special meaning.\n", "- When declaring a `dataset`, `model`, `datasets` or `models` argument in a custom test function, the ValidMind Library will expect these get passed as `inputs` to `run_test()` or `run_documentation_tests()`.\n", "\n", - "Re-running the confusion matrix with `normalize=True` looks like this:\n" + "Re-running the confusion matrix with `normalize=True` and our testing dataset looks like this:\n" ] }, { diff --git a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb index 143183720..d1b5904e7 100644 --- a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb +++ b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb @@ -595,9 +595,8 @@ "\n", "- The parameters will override any default parameters set in the custom test definition. Note that `dataset` and `model` are still passed as `inputs`.\n", "- Since these are `VMDataset` or `VMModel` inputs, they have a special meaning.\n", - "- When declaring a `dataset`, `model`, `datasets` or `models` argument in a custom test function, the ValidMind Library will expect these get passed as `inputs` to `run_test()` or `run_documentation_tests()`.\n", "\n", - "Re-running and logging the custom confusion matrix with `normalize=True` looks like this:" + "Re-running and logging the custom confusion matrix with `normalize=True` for both models and our testing dataset looks like this:" ] }, { @@ -606,7 +605,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Champion\n", + "# Champion with test dataset and normalize=True\n", "vm.tests.run_test(\n", " test_id=\"my_custom_tests.ConfusionMatrix:test_normalized_champion\",\n", " input_grid={\n", @@ -623,7 +622,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Challenger\n", + "# Challenger with test dataset and normalize=True\n", "vm.tests.run_test(\n", " test_id=\"my_custom_tests.ConfusionMatrix:test_normalized_challenger\",\n", " input_grid={\n", From af2ef0a9ce9db9a6d694334e0d7ebc17c3ebee4c Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Mon, 7 Apr 2025 11:36:04 -0700 Subject: [PATCH 62/71] Validation 4 Custom test provider pt1 --- .../3-integrate_custom_tests.ipynb | 6 ++-- .../model_development/my_tests_directory.png | Bin 44368 -> 0 bytes .../4-finalize_validation_reporting.ipynb | 27 ++++++++---------- 3 files changed, 16 insertions(+), 17 deletions(-) delete mode 100644 notebooks/tutorials/model_development/my_tests_directory.png diff --git a/notebooks/tutorials/model_development/3-integrate_custom_tests.ipynb b/notebooks/tutorials/model_development/3-integrate_custom_tests.ipynb index 2e685a645..56630e3a1 100644 --- a/notebooks/tutorials/model_development/3-integrate_custom_tests.ipynb +++ b/notebooks/tutorials/model_development/3-integrate_custom_tests.ipynb @@ -734,9 +734,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "After running the command above, confirm that the new `my_tests` directory was created successfully:\n", + "After running the command above, confirm that a new `my_tests` directory was created successfully. For example:\n", "\n", - "\"Screenshot" + "```\n", + "~/notebooks/tutorials/model_development/my_tests/\n", + "```" ] }, { diff --git a/notebooks/tutorials/model_development/my_tests_directory.png b/notebooks/tutorials/model_development/my_tests_directory.png deleted file mode 100644 index 47baffe80e5e0f1f05562126fc46c7150cc8ca3d..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 44368 zcmeEtRa6{b_az|^T!IF7*Wga$?(V_e-7N$S?%F_b3+|8*2yVe$g1ZxZil65D&-XMB z^Dt{ztEuX$y0@;Lv(G-e!xiNvkl=CQAs`@-q$EX^At0crAs}9`z`g>ma1pE(LO>u2 zS&E1#N{NUND>~VmS=yLFKuCrsX~1Zz^kZe~#K(eQQ9&T-9XZrEG>|kbL%;>;EUZ+3 zqA(&9CT~sgsOz>(%k*l3YOF zyfrdQh5yylOkO|s#wi3Sk63OL5 zo(0(l&B9UH2O+8l`}7i#bX~h!RP`Y-eO1W!p6fP87f-ANPpum!GBCcHUrUoo;KJjE zakdWK)a_Cc%Y+u`q(6qe*F;!H5$;$dG)jyoK?>KH&HvEG$1#L67BU+?iUb=xi@Hv% z_In1fBkp8>!A7%;UoZ`C(6$%NTwWJ^UG)S-M&My_y zF-Mj6I!90)5^qzz48?UxyD`ET;NQc&MlBOLNKL`SE3Cs&N&OoArY=s>*yL0PginSF zeMfgu9mFQ381^^_eu@&YoQysW 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z*jq!MIebK(QgY1cyV(Z&{9Ojz8$-9mLWP>N#$J?SgE@jM>`GHTowl69Dw^rExcKBb zcx9DsXD#K(7fmM97V9r^L*L$Mkw;XX!1qk%j>Tb^nkM3|pk^ES69YXIo)^2=29+55 z;7&;Erp@1#F3<*a$O@Y!I1)=j^6jz+AHM>!DO$oNIxl8|h|^^#tlO`PRp7XhCAlm? ze(b(4Do6+I+ni5zoaO4gd8{B z=!_Prqz%c8r8T`WPm{1L=4i!9v+}AhKP`|K$}{f^<(2Jw-p!+XXDAQ@lS=1wDlM2OGNgAO6&qtPq~E*xKnX!iL);S_fM#~6k1?363g&H# zu#GiX27Z(HrB3@FWNthi?!dsxyuNOGoKQU+coY$HFx(|d=f?|$RvQsID`Rz@{=75uQ#HH~Be^2pB>0I*Yo2~F-Y zJ50k47@lOZh4XV}&^Es7AT$ifV7vu{OeM49r+)S1{~eP!ptvfkbv`1g_c+duqy@c%p#m`Yj8_03JDpGVm_aCI1r@>^3C{b;#-#Gh33 z->&G-10pA#p8)i`*}a*?*|_?KEja1?!H!DLmnNf7{FUsONM;2ocRT3B?@ zm74bPPr)ayc~rTkKKF}(2XM#E5!_HkOXz09*6UIEX9q10Z$n z#b&Z-bl`>AfoSDE}ih>oq2VPhe qxSe}B`F{VifctZX2MkB|4mZ8AR*Q-DnFA!+51AUDGkR|59Q}U*i2yYK diff --git a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb index d1b5904e7..925fc5e27 100644 --- a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb +++ b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb @@ -398,14 +398,18 @@ "\n", "Thanks to the model documentation ([Learn more ...](https://docs.validmind.ai/developer/validmind-library.html#for-model-development)), we know that the model development team implemented a custom test to further evaluate the performance of the champion model.\n", "\n", - "In a usual model validation situation, you would load a saved custom test provided by the model development team. In the following section, we'll have you implement the same custom test and make it available for reuse, to familiarize you with the processes." + "In a usual model validation situation, you would load a saved custom test provided by the model development team. In the following section, we'll have you implement the same custom test and make it available for reuse, to familiarize you with the processes.\n", + "\n", + "
    Want to learn more about custom tests?\n", + "

    \n", + "Refer to our in-depth introduction to custom tests: Implement custom tests
    " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Implementing a custom inline test\n", + "### Implement a custom inline test\n", "\n", "Let's implement the same custom *inline test* that calculates the confusion matrix for a binary classification model that the model development team used in their performance evaluations.\n", "\n", @@ -633,22 +637,13 @@ ").log()" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
    Want to learn more about custom tests?\n", - "

    \n", - "Refer to our in-depth introduction to custom tests: Implement custom tests
    " - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Use external test providers\n", "\n", - "Creating inline custom tests with a function is a great way to customize your model documentation. However, sometimes you may want to reuse the same set of tests across multiple models and share them with others in your organization. In this case, you can create an external custom *test provider* that will allow you to load custom tests from a local folder or a Git repository.\n", + "Sometimes you may want to reuse the same set of custom tests across multiple models and share them with others in your organization, like the model development team would have done with you in this example workflow featured in this series of notebooks. In this case, you can create an external custom *test provider* that will allow you to load custom tests from a local folder or a Git repository.\n", "\n", "In this section you will learn how to declare a local filesystem test provider that allows loading tests from a local folder following these high level steps:\n", "\n", @@ -694,9 +689,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "After running the command above, confirm that the new `my_tests` directory was created successfully:\n", + "After running the command above, confirm that a new `my_tests` directory was created successfully. For example:\n", "\n", - "\"Screenshot" + "```\n", + "~/notebooks/tutorials/model_validation/my_tests/\n", + "```" ] }, { @@ -705,7 +702,7 @@ "source": [ "#### Save an inline test\n", "\n", - "The `@vm.test` decorator we used in **Implementing a custom inline test** above to register one-off custom tests also includes a convenience method on the function object that allows you to simply call `.save()` to save the test to a Python file at a specified path.\n", + "The `@vm.test` decorator we used in **Implement a custom inline test** above to register one-off custom tests also includes a convenience method on the function object that allows you to simply call `.save()` to save the test to a Python file at a specified path.\n", "\n", "While `save()` will get you started by creating the file and saving the function code with the correct name, it won't automatically include any imports, or other functions or variables, outside of the functions that are needed for the test to run. To solve this, pass in an optional `imports` argument ensuring necessary imports are added to the file.\n", "\n", From 15bf8722bc4a1473700a837ce04a4429ddcdd30d Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Mon, 7 Apr 2025 11:39:08 -0700 Subject: [PATCH 63/71] Validation 4 Custom test provider pt2 --- .../3-integrate_custom_tests.ipynb | 2 +- .../4-finalize_validation_reporting.ipynb | 33 ++++++++++++++----- 2 files changed, 26 insertions(+), 9 deletions(-) diff --git a/notebooks/tutorials/model_development/3-integrate_custom_tests.ipynb b/notebooks/tutorials/model_development/3-integrate_custom_tests.ipynb index 56630e3a1..ff593d6e4 100644 --- a/notebooks/tutorials/model_development/3-integrate_custom_tests.ipynb +++ b/notebooks/tutorials/model_development/3-integrate_custom_tests.ipynb @@ -863,7 +863,7 @@ "- For tests that reside in a test provider directory, the test ID will be the `namespace` specified when registering the provider, followed by the path to the test file relative to the tests folder.\n", "- For example, the Confusion Matrix test we created earlier will have the test ID `my_test_provider.ConfusionMatrix`. You could organize the tests in subfolders, say `classification` and `regression`, and the test ID for the Confusion Matrix test would then be `my_test_provider.classification.ConfusionMatrix`.\n", "\n", - "Let's go ahead and re-run the confusion matrix test by using the test ID `my_test_provider.ConfusionMatrix`. This should load the test from the test provider and run it as before.\n" + "Let's go ahead and re-run the confusion matrix test with our testing dataset by using the test ID `my_test_provider.ConfusionMatrix`. This should load the test from the test provider and run it as before.\n" ] }, { diff --git a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb index 925fc5e27..9f74e5cef 100644 --- a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb +++ b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb @@ -808,7 +808,7 @@ "- For tests that reside in a test provider directory, the test ID will be the `namespace` specified when registering the provider, followed by the path to the test file relative to the tests folder.\n", "- For example, the Confusion Matrix test we created earlier will have the test ID `my_test_provider.ConfusionMatrix`. You could organize the tests in subfolders, say `classification` and `regression`, and the test ID for the Confusion Matrix test would then be `my_test_provider.classification.ConfusionMatrix`.\n", "\n", - "Let's go ahead and re-run the confusion matrix test by using the test ID `my_test_provider.ConfusionMatrix`. This should load the test from the test provider and run it as before." + "Let's go ahead and re-run the confusion matrix test with our testing dataset for our two models by using the test ID `my_test_provider.ConfusionMatrix`. This should load the test from the test provider and run it as before." ] }, { @@ -817,13 +817,30 @@ "metadata": {}, "outputs": [], "source": [ - "result = vm.tests.run_test(\n", - " \"my_test_provider.ConfusionMatrix\",\n", - " inputs={\"model\": vm_model, \"dataset\": vm_test_ds},\n", - " params={\"normalize\": True},\n", - ")\n", - "\n", - "result.log()" + "# Champion with test dataset and test provider custom test\n", + "vm.tests.run_test(\n", + " test_id=\"my_test_provider.ConfusionMatrix:champion\",\n", + " input_grid={\n", + " \"dataset\": [vm_test_ds],\n", + " \"model\" : [vm_log_model]\n", + " }\n", + ").log()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Challenger with test dataset and test provider custom test\n", + "vm.tests.run_test(\n", + " test_id=\"my_test_provider.ConfusionMatrix:challenger\",\n", + " input_grid={\n", + " \"dataset\": [vm_test_ds],\n", + " \"model\" : [vm_rf_model]\n", + " }\n", + ").log()" ] }, { From 8d85fc33f6a11d5ca5e98bddf6ca741df32914e6 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Mon, 7 Apr 2025 11:43:42 -0700 Subject: [PATCH 64/71] Validation 4 Next steps typo --- .../model_validation/4-finalize_validation_reporting.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb index 9f74e5cef..89226db1c 100644 --- a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb +++ b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb @@ -1038,7 +1038,7 @@ "source": [ "### Work with your validation report\n", "\n", - "Now that you've logged all your test results and verified the work done by the model development team, head to the ValidMind Platform to wrap up your validation report. 7. Continue to work on your validation report by:\n", + "Now that you've logged all your test results and verified the work done by the model development team, head to the ValidMind Platform to wrap up your validation report. Continue to work on your validation report by:\n", "\n", "- **Inserting additional test results:** Click **Link Evidence to Report** under any section of 2. Validation in your validation report. (Learn more: [Link evidence to reports](https://docs.validmind.ai/guide/model-validation/assess-compliance.html#link-evidence-to-reports))\n", "- **Adding more findings:** Click **Link Finding to Report** in any validation report section, then click **+ Create New Finding**. (Learn more: [Add and manage model findings](https://docs.validmind.ai/guide/model-validation/add-manage-model-findings.html))\n", From ac81d5cad8733616bcadbb1e71b2077e676ba1e9 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Mon, 7 Apr 2025 11:50:22 -0700 Subject: [PATCH 65/71] Validation 4 Verify test runs setup --- .../4-finalize_validation_reporting.ipynb | 106 +++++++++++++++++- 1 file changed, 104 insertions(+), 2 deletions(-) diff --git a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb index 89226db1c..be3479a15 100644 --- a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb +++ b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb @@ -847,11 +847,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Verify test runs*\n", + "## Verify test runs\n", "\n", "Our final task is to verify that all the tests provided by the model development team were run and reported accurately. Note the appended `result_ids` to delineate which dataset we ran the test with for the relevant tests.\n", "\n", - "Here, we'll specify all the tests we'd like to independently rerun in a dictionary called `test_config`:" + "Here, we'll specify all the tests we'd like to independently rerun in a dictionary called `test_config`. **Note here that `inputs` and `input_grid` expect the `input_id` of the dataset or model as the value rather than the variable name we specified**:" ] }, { @@ -860,6 +860,108 @@ "metadata": {}, "outputs": [], "source": [ + "test_config = {\n", + " \"validmind.model_validation.ModelMetadata\": {\n", + " \"inputs\": {\"model\": \"log_reg_model_v1\"},\n", + " },\n", + " \"validmind.data_validation.DatasetSplit\": {\n", + " \"inputs\": {\"datasets\": [\"train_dataset_final\", \"test_dataset_final\"]},\n", + " },\n", + " \"validmind.model_validation.sklearn.PopulationStabilityIndex\": {\n", + " \"inputs\": {\n", + " \"model\": \"log_reg_model_v1\",\n", + " \"datasets\": [\"train_dataset_final\", \"test_dataset_final\"],\n", + " },\n", + " \"params\": {\"num_bins\": 10, \"mode\": \"fixed\"},\n", + " },\n", + " \"validmind.model_validation.sklearn.ConfusionMatrix\": {\n", + " \"inputs\": {\"model\": \"log_reg_model_v1\", \"dataset\": \"test_dataset_final\"},\n", + " },\n", + " \"my_test_provider.ConfusionMatrix\": {\n", + " \"inputs\": {\"dataset\": \"test_dataset_final\", \"model\": \"log_reg_model_v1\"},\n", + " },\n", + " \"my_custom_tests.ConfusionMatrix:test_dataset_normalized\": {\n", + " \"inputs\": {\"dataset\": \"test_dataset_final\", \"model\": \"log_reg_model_v1\"},\n", + " },\n", + " \"validmind.model_validation.sklearn.ClassifierPerformance:in_sample\": {\n", + " \"inputs\": {\"model\": \"log_reg_model_v1\", \"dataset\": \"train_dataset_final\"}\n", + " },\n", + " \"validmind.model_validation.sklearn.ClassifierPerformance:out_of_sample\": {\n", + " \"inputs\": {\"model\": \"log_reg_model_v1\", \"dataset\": \"test_dataset_final\"}\n", + " },\n", + " \"validmind.model_validation.sklearn.PrecisionRecallCurve\": {\n", + " \"inputs\": {\"model\": \"log_reg_model_v1\", \"dataset\": \"test_dataset_final\"},\n", + " },\n", + " \"validmind.model_validation.sklearn.ROCCurve\": {\n", + " \"inputs\": {\"model\": \"log_reg_model_v1\", \"dataset\": \"test_dataset_final\"},\n", + " },\n", + " \"validmind.model_validation.sklearn.TrainingTestDegradation\": {\n", + " \"inputs\": {\n", + " \"model\": \"log_reg_model_v1\",\n", + " \"datasets\": [\"train_dataset_final\", \"test_dataset_final\"],\n", + " },\n", + " \"params\": {\n", + " \"metrics\": [\"accuracy\", \"precision\", \"recall\", \"f1\"],\n", + " \"max_threshold\": 0.1,\n", + " },\n", + " },\n", + " \"validmind.model_validation.sklearn.MinimumAccuracy\": {\n", + " \"inputs\": {\"model\": \"log_reg_model_v1\", \"dataset\": \"test_dataset_final\"},\n", + " \"params\": {\"min_threshold\": 0.7},\n", + " },\n", + " \"validmind.model_validation.sklearn.MinimumF1Score\": {\n", + " \"inputs\": {\"model\": \"log_reg_model_v1\", \"dataset\": \"test_dataset_final\"},\n", + " \"params\": {\"min_threshold\": 0.5},\n", + " },\n", + " \"validmind.model_validation.sklearn.MinimumROCAUCScore\": {\n", + " \"inputs\": {\"model\": \"log_reg_model_v1\", \"dataset\": \"test_dataset_final\"},\n", + " \"params\": {\"min_threshold\": 0.5},\n", + " },\n", + " \"validmind.model_validation.sklearn.PermutationFeatureImportance\": {\n", + " \"inputs\": {\"model\": \"log_reg_model_v1\", \"dataset\": \"test_dataset_final\"},\n", + " },\n", + " \"validmind.model_validation.sklearn.SHAPGlobalImportance\": {\n", + " \"inputs\": {\"model\": \"log_reg_model_v1\", \"dataset\": \"test_dataset_final\"},\n", + " \"params\": {\"kernel_explainer_samples\": 10},\n", + " },\n", + " \"validmind.model_validation.sklearn.WeakspotsDiagnosis\": {\n", + " \"inputs\": {\n", + " \"model\": \"log_reg_model_v1\",\n", + " \"datasets\": [\"train_dataset_final\", \"test_dataset_final\"],\n", + " },\n", + " \"params\": {\n", + " \"thresholds\": {\"accuracy\": 0.75, \"precision\": 0.5, \"recall\": 0.5, \"f1\": 0.7}\n", + " },\n", + " },\n", + " \"validmind.model_validation.sklearn.OverfitDiagnosis\": {\n", + " \"inputs\": {\n", + " \"model\": \"log_reg_model_v1\",\n", + " \"datasets\": [\"train_dataset_final\", \"test_dataset_final\"],\n", + " },\n", + " \"params\": {\"cut_off_percentage\": 4},\n", + " },\n", + " \"validmind.model_validation.sklearn.RobustnessDiagnosis\": {\n", + " \"inputs\": {\n", + " \"model\": \"log_reg_model_v1\",\n", + " \"datasets\": [\"train_dataset_final\", \"test_dataset_final\"],\n", + " },\n", + " \"params\": {\n", + " \"scaling_factor_std_dev_list\": [0.0, 0.1, 0.2, 0.3, 0.4, 0.5],\n", + " \"accuracy_decay_threshold\": 4,\n", + " },\n", + " },\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# OLD REFERENCE\n", + "\n", + "\n", "test_config = {\n", " # Run with the raw dataset\n", " 'validmind.data_validation.DatasetDescription:raw_data': {\n", From bb90e25cf8f5d4a7c7e4a1fd0bc7656a3cfe31fc Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Mon, 7 Apr 2025 11:58:13 -0700 Subject: [PATCH 66/71] Validation 4 Verify test runs edit --- .../4-finalize_validation_reporting.ipynb | 138 +++--------------- 1 file changed, 18 insertions(+), 120 deletions(-) diff --git a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb index be3479a15..5c7a53315 100644 --- a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb +++ b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb @@ -860,108 +860,6 @@ "metadata": {}, "outputs": [], "source": [ - "test_config = {\n", - " \"validmind.model_validation.ModelMetadata\": {\n", - " \"inputs\": {\"model\": \"log_reg_model_v1\"},\n", - " },\n", - " \"validmind.data_validation.DatasetSplit\": {\n", - " \"inputs\": {\"datasets\": [\"train_dataset_final\", \"test_dataset_final\"]},\n", - " },\n", - " \"validmind.model_validation.sklearn.PopulationStabilityIndex\": {\n", - " \"inputs\": {\n", - " \"model\": \"log_reg_model_v1\",\n", - " \"datasets\": [\"train_dataset_final\", \"test_dataset_final\"],\n", - " },\n", - " \"params\": {\"num_bins\": 10, \"mode\": \"fixed\"},\n", - " },\n", - " \"validmind.model_validation.sklearn.ConfusionMatrix\": {\n", - " \"inputs\": {\"model\": \"log_reg_model_v1\", \"dataset\": \"test_dataset_final\"},\n", - " },\n", - " \"my_test_provider.ConfusionMatrix\": {\n", - " \"inputs\": {\"dataset\": \"test_dataset_final\", \"model\": \"log_reg_model_v1\"},\n", - " },\n", - " \"my_custom_tests.ConfusionMatrix:test_dataset_normalized\": {\n", - " \"inputs\": {\"dataset\": \"test_dataset_final\", \"model\": \"log_reg_model_v1\"},\n", - " },\n", - " \"validmind.model_validation.sklearn.ClassifierPerformance:in_sample\": {\n", - " \"inputs\": {\"model\": \"log_reg_model_v1\", \"dataset\": \"train_dataset_final\"}\n", - " },\n", - " \"validmind.model_validation.sklearn.ClassifierPerformance:out_of_sample\": {\n", - " \"inputs\": {\"model\": \"log_reg_model_v1\", \"dataset\": \"test_dataset_final\"}\n", - " },\n", - " \"validmind.model_validation.sklearn.PrecisionRecallCurve\": {\n", - " \"inputs\": {\"model\": \"log_reg_model_v1\", \"dataset\": \"test_dataset_final\"},\n", - " },\n", - " \"validmind.model_validation.sklearn.ROCCurve\": {\n", - " \"inputs\": {\"model\": \"log_reg_model_v1\", \"dataset\": \"test_dataset_final\"},\n", - " },\n", - " \"validmind.model_validation.sklearn.TrainingTestDegradation\": {\n", - " \"inputs\": {\n", - " \"model\": \"log_reg_model_v1\",\n", - " \"datasets\": [\"train_dataset_final\", \"test_dataset_final\"],\n", - " },\n", - " \"params\": {\n", - " \"metrics\": [\"accuracy\", \"precision\", \"recall\", \"f1\"],\n", - " \"max_threshold\": 0.1,\n", - " },\n", - " },\n", - " \"validmind.model_validation.sklearn.MinimumAccuracy\": {\n", - " \"inputs\": {\"model\": \"log_reg_model_v1\", \"dataset\": \"test_dataset_final\"},\n", - " \"params\": {\"min_threshold\": 0.7},\n", - " },\n", - " \"validmind.model_validation.sklearn.MinimumF1Score\": {\n", - " \"inputs\": {\"model\": \"log_reg_model_v1\", \"dataset\": \"test_dataset_final\"},\n", - " \"params\": {\"min_threshold\": 0.5},\n", - " },\n", - " \"validmind.model_validation.sklearn.MinimumROCAUCScore\": {\n", - " \"inputs\": {\"model\": \"log_reg_model_v1\", \"dataset\": \"test_dataset_final\"},\n", - " \"params\": {\"min_threshold\": 0.5},\n", - " },\n", - " \"validmind.model_validation.sklearn.PermutationFeatureImportance\": {\n", - " \"inputs\": {\"model\": \"log_reg_model_v1\", \"dataset\": \"test_dataset_final\"},\n", - " },\n", - " \"validmind.model_validation.sklearn.SHAPGlobalImportance\": {\n", - " \"inputs\": {\"model\": \"log_reg_model_v1\", \"dataset\": \"test_dataset_final\"},\n", - " \"params\": {\"kernel_explainer_samples\": 10},\n", - " },\n", - " \"validmind.model_validation.sklearn.WeakspotsDiagnosis\": {\n", - " \"inputs\": {\n", - " \"model\": \"log_reg_model_v1\",\n", - " \"datasets\": [\"train_dataset_final\", \"test_dataset_final\"],\n", - " },\n", - " \"params\": {\n", - " \"thresholds\": {\"accuracy\": 0.75, \"precision\": 0.5, \"recall\": 0.5, \"f1\": 0.7}\n", - " },\n", - " },\n", - " \"validmind.model_validation.sklearn.OverfitDiagnosis\": {\n", - " \"inputs\": {\n", - " \"model\": \"log_reg_model_v1\",\n", - " \"datasets\": [\"train_dataset_final\", \"test_dataset_final\"],\n", - " },\n", - " \"params\": {\"cut_off_percentage\": 4},\n", - " },\n", - " \"validmind.model_validation.sklearn.RobustnessDiagnosis\": {\n", - " \"inputs\": {\n", - " \"model\": \"log_reg_model_v1\",\n", - " \"datasets\": [\"train_dataset_final\", \"test_dataset_final\"],\n", - " },\n", - " \"params\": {\n", - " \"scaling_factor_std_dev_list\": [0.0, 0.1, 0.2, 0.3, 0.4, 0.5],\n", - " \"accuracy_decay_threshold\": 4,\n", - " },\n", - " },\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# OLD REFERENCE\n", - "\n", - "\n", "test_config = {\n", " # Run with the raw dataset\n", " 'validmind.data_validation.DatasetDescription:raw_data': {\n", @@ -1008,65 +906,65 @@ " },\n", " # Run with the preprocessed dataset\n", " 'validmind.data_validation.DescriptiveStatistics:preprocessed_data': {\n", - " 'inputs': {'dataset': 'preprocess_dataset'}\n", + " 'inputs': {'dataset': 'raw_dataset_preprocessed'}\n", " },\n", " 'validmind.data_validation.TabularDescriptionTables:preprocessed_data': {\n", - " 'inputs': {'dataset': 'preprocess_dataset'}\n", + " 'inputs': {'dataset': 'raw_dataset_preprocessed'}\n", " },\n", " 'validmind.data_validation.MissingValues:preprocessed_data': {\n", - " 'inputs': {'dataset': 'preprocess_dataset'},\n", + " 'inputs': {'dataset': 'raw_dataset_preprocessed'},\n", " 'params': {'min_threshold': 1}\n", " },\n", " 'validmind.data_validation.TabularNumericalHistograms:preprocessed_data': {\n", - " 'inputs': {'dataset': 'preprocess_dataset'}\n", + " 'inputs': {'dataset': 'raw_dataset_preprocessed'}\n", " },\n", " 'validmind.data_validation.TabularCategoricalBarPlots:preprocessed_data': {\n", - " 'inputs': {'dataset': 'preprocess_dataset'}\n", + " 'inputs': {'dataset': 'raw_dataset_preprocessed'}\n", " },\n", " 'validmind.data_validation.TargetRateBarPlots:preprocessed_data': {\n", - " 'inputs': {'dataset': 'preprocess_dataset'},\n", + " 'inputs': {'dataset': 'raw_dataset_preprocessed'},\n", " 'params': {'default_column': 'loan_status'}\n", " },\n", " # Run with the training and test datasets\n", " 'validmind.data_validation.DescriptiveStatistics:development_data': {\n", - " 'input_grid': {'dataset': ['train_dataset', 'test_dataset']}\n", + " 'input_grid': {'dataset': ['train_dataset_final', 'test_dataset_final']}\n", " },\n", " 'validmind.data_validation.TabularDescriptionTables:development_data': {\n", - " 'input_grid': {'dataset': ['train_dataset', 'test_dataset']}\n", + " 'input_grid': {'dataset': ['train_dataset_final', 'test_dataset_final']}\n", " },\n", " 'validmind.data_validation.ClassImbalance:development_data': {\n", - " 'input_grid': {'dataset': ['train_dataset', 'test_dataset']},\n", + " 'input_grid': {'dataset': ['train_dataset_final', 'test_dataset_final']},\n", " 'params': {'min_percent_threshold': 10}\n", " },\n", " 'validmind.data_validation.UniqueRows:development_data': {\n", - " 'input_grid': {'dataset': ['train_dataset', 'test_dataset']},\n", + " 'input_grid': {'dataset': ['train_dataset_final', 'test_dataset_final']},\n", " 'params': {'min_percent_threshold': 1}\n", " },\n", " 'validmind.data_validation.TabularNumericalHistograms:development_data': {\n", - " 'input_grid': {'dataset': ['train_dataset', 'test_dataset']}\n", + " 'input_grid': {'dataset': ['train_dataset_final', 'test_dataset_final']}\n", " },\n", " 'validmind.data_validation.MutualInformation:development_data': {\n", - " 'input_grid': {'dataset': ['train_dataset', 'test_dataset']},\n", + " 'input_grid': {'dataset': ['train_dataset_final', 'test_dataset_final']},\n", " 'params': {'min_threshold': 0.01}\n", " },\n", " 'validmind.data_validation.PearsonCorrelationMatrix:development_data': {\n", - " 'input_grid': {'dataset': ['train_dataset', 'test_dataset']}\n", + " 'input_grid': {'dataset': ['train_dataset_final', 'test_dataset_final']}\n", " },\n", " 'validmind.data_validation.HighPearsonCorrelation:development_data': {\n", - " 'input_grid': {'dataset': ['train_dataset', 'test_dataset']},\n", + " 'input_grid': {'dataset': ['train_dataset_final', 'test_dataset_final']},\n", " 'params': {'max_threshold': 0.3, 'top_n_correlations': 10}\n", " },\n", " 'validmind.model_validation.ModelMetadata': {\n", - " 'input_grid': {'model': ['xgb_model', 'rf_model']}\n", + " 'input_grid': {'model': ['log_model_champion', 'rf_model']}\n", " },\n", " 'validmind.model_validation.sklearn.ModelParameters': {\n", - " 'input_grid': {'model': ['xgb_model', 'rf_model']}\n", + " 'input_grid': {'model': ['log_model_champion', 'rf_model']}\n", " },\n", " 'validmind.model_validation.sklearn.ROCCurve': {\n", - " 'input_grid': {'dataset': ['train_dataset', 'test_dataset'], 'model': ['xgb_model']}\n", + " 'input_grid': {'dataset': ['train_dataset_final', 'test_dataset_final'], 'model': ['log_model_champion']}\n", " },\n", " 'validmind.model_validation.sklearn.MinimumROCAUCScore': {\n", - " 'input_grid': {'dataset': ['train_dataset', 'test_dataset'], 'model': ['xgb_model']},\n", + " 'input_grid': {'dataset': ['train_dataset_final', 'test_dataset_final'], 'model': ['log_model_champion']},\n", " 'params': {'min_threshold': 0.5}\n", " }\n", "}" From 8508dbf9b4e6d917a0c102f1cd00b0c4876063bb Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Mon, 7 Apr 2025 12:06:04 -0700 Subject: [PATCH 67/71] Forgot the raw dataset --- .../4-finalize_validation_reporting.ipynb | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb index 5c7a53315..4a10cf483 100644 --- a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb +++ b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb @@ -121,6 +121,20 @@ "raw_df = demo_dataset.load_data()" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the raw dataset for use in ValidMind tests\n", + "vm_raw_dataset = vm.init_dataset(\n", + " dataset=raw_df,\n", + " input_id=\"raw_dataset\",\n", + " target_column=\"Exited\",\n", + ")" + ] + }, { "cell_type": "code", "execution_count": null, From 4ce581104e0b50da2eb910dacec38c70891dbe6a Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Mon, 7 Apr 2025 12:52:01 -0700 Subject: [PATCH 68/71] Fixing broken API links --- .../model_development/2-start_development_process.ipynb | 2 +- .../tutorials/model_development/3-integrate_custom_tests.ipynb | 2 +- .../model_validation/3-developing_challenger_model.ipynb | 2 +- .../model_validation/4-finalize_validation_reporting.ipynb | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/notebooks/tutorials/model_development/2-start_development_process.ipynb b/notebooks/tutorials/model_development/2-start_development_process.ipynb index 28ce3ea1e..74bec6960 100644 --- a/notebooks/tutorials/model_development/2-start_development_process.ipynb +++ b/notebooks/tutorials/model_development/2-start_development_process.ipynb @@ -884,7 +884,7 @@ "\n", "Once the model has been registered you can assign model predictions to the training and test datasets.\n", "\n", - "- The [`assign_predictions()` method](https://docs.validmind.ai/validmind/validmind/vm_models.html#VMDataset.assign_predictions) from the `Dataset` object can link existing predictions to any number of models.\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:\n" diff --git a/notebooks/tutorials/model_development/3-integrate_custom_tests.ipynb b/notebooks/tutorials/model_development/3-integrate_custom_tests.ipynb index ff593d6e4..038f45c38 100644 --- a/notebooks/tutorials/model_development/3-integrate_custom_tests.ipynb +++ b/notebooks/tutorials/model_development/3-integrate_custom_tests.ipynb @@ -809,7 +809,7 @@ "Now that your `my_tests` folder has a sample custom test, let's initialize a test provider that will tell the ValidMind Library where to find your custom tests:\n", "\n", "- ValidMind offers out-of-the-box test providers for local tests (tests in a folder) or a Github provider for tests in a Github repository.\n", - "- You can also create your own test provider by creating a class that has a [`load_test` method](https://docs.validmind.ai/validmind/validmind/tests.html#TestProvider.load_test) that takes a test ID and returns the test function matching that ID.\n", + "- You can also create your own test provider by creating a class that has a [`load_test` method](https://docs.validmind.ai/validmind/validmind/tests.html#load_test) that takes a test ID and returns the test function matching that ID.\n", "\n", "
    Want to learn more about test providers?\n", "

    \n", diff --git a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb index 9a7b24010..0c26cd615 100644 --- a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb +++ b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb @@ -423,7 +423,7 @@ "\n", "With our models registered, we'll move on to assigning both the predictive probabilities coming directly from each model's predictions, and the binary prediction after applying the cutoff threshold described in the Compute binary predictions step above.\n", "\n", - "- The [`assign_predictions()` method](https://docs.validmind.ai/validmind/validmind/vm_models.html#VMDataset.assign_predictions) from the `Dataset` object can link existing predictions to any number of models.\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:" diff --git a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb index 4a10cf483..fa6fbb3b3 100644 --- a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb +++ b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb @@ -772,7 +772,7 @@ "Now that your `my_tests` folder has a sample custom test, let's initialize a test provider that will tell the ValidMind Library where to find your custom tests:\n", "\n", "- ValidMind offers out-of-the-box test providers for local tests (tests in a folder) or a Github provider for tests in a Github repository.\n", - "- You can also create your own test provider by creating a class that has a [`load_test` method](https://docs.validmind.ai/validmind/validmind/tests.html#TestProvider.load_test) that takes a test ID and returns the test function matching that ID.\n", + "- You can also create your own test provider by creating a class that has a [`load_test` method](https://docs.validmind.ai/validmind/validmind/tests.html#load_test) that takes a test ID and returns the test function matching that ID.\n", "\n", "
    Want to learn more about test providers?\n", "

    \n", From 1d0396b3a32ecac6fa389dd1df285a457092b7e8 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Mon, 7 Apr 2025 12:59:33 -0700 Subject: [PATCH 69/71] Adding ToCs --- .../1-set_up_validmind_for_validation.ipynb | 113 +++++++++++++++--- .../2-start_validation_process.ipynb | 75 ++++++++++++ .../3-developing_challenger_model.ipynb | 84 +++++++++++++ .../4-finalize_validation_reporting.ipynb | 90 ++++++++++++++ 4 files changed, 343 insertions(+), 19 deletions(-) diff --git a/notebooks/tutorials/model_validation/1-set_up_validmind_for_validation.ipynb b/notebooks/tutorials/model_validation/1-set_up_validmind_for_validation.ipynb index 769149cfa..2f85b39d7 100644 --- a/notebooks/tutorials/model_validation/1-set_up_validmind_for_validation.ipynb +++ b/notebooks/tutorials/model_validation/1-set_up_validmind_for_validation.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "248883b1", + "id": "a9d0996f", "metadata": {}, "source": [ "# ValidMind for model validation 1 — Set up the ValidMind Library for validation\n", @@ -14,9 +14,49 @@ }, { "cell_type": "markdown", - "id": "e1657d54", + "id": "c747db34", "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", + " - [Register a sample model](#toc3_1_) \n", + " - [Assign validator credentials](#toc3_1_1_) \n", + " - [Install the ValidMind Library](#toc3_2_) \n", + " - [Initialize the ValidMind Library](#toc3_3_) \n", + " - [Get your code snippet](#toc3_3_1_) \n", + "- [Getting to know ValidMind](#toc4_) \n", + " - [Preview the validation report template](#toc4_1_) \n", + " - [View validation report in the ValidMind Platform](#toc4_1_1_) \n", + " - [Explore available tests](#toc4_2_) \n", + "- [Upgrade ValidMind](#toc5_) \n", + "- [In summary](#toc6_) \n", + "- [Next steps](#toc7_) \n", + " - [Start the model validation process](#toc7_1_) \n", + "\n", + ":::\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "f1d4715f", + "metadata": {}, + "source": [ + "\n", + "\n", "## Introduction\n", "\n", "Model validation aims to independently assess the compliance of *champion models* created by model developers with regulatory guidance by conducting thorough testing and analysis, potentially including the use of challenger models to benchmark performance. Assessments, presented in the form of a validation report, typically include *model findings* and recommendations to address those issues.\n", @@ -29,9 +69,11 @@ }, { "cell_type": "markdown", - "id": "434bea70", + "id": "14c2d80d", "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", @@ -41,9 +83,11 @@ }, { "cell_type": "markdown", - "id": "662d6027", + "id": "151a4ca5", "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", @@ -53,9 +97,11 @@ }, { "cell_type": "markdown", - "id": "5a1a2fa8", + "id": "089c960e", "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", @@ -67,9 +113,11 @@ }, { "cell_type": "markdown", - "id": "aa9b1d8e", + "id": "5f307177", "metadata": {}, "source": [ + "\n", + "\n", "### Key concepts\n", "\n", "**Validation report**: A comprehensive and structured assessment of a model’s development and performance, focusing on verifying its integrity, appropriateness, and alignment with its intended use. It includes analyses of model assumptions, data quality, performance metrics, outcomes of testing procedures, and risk considerations. The validation report supports transparency, regulatory compliance, and informed decision-making by documenting the validator’s independent review and conclusions.\n", @@ -96,17 +144,21 @@ }, { "cell_type": "markdown", - "id": "057c1417", + "id": "c42665b8", "metadata": {}, "source": [ + "\n", + "\n", "## Setting up" ] }, { "cell_type": "markdown", - "id": "ef1f5ce4", + "id": "0faed42c", "metadata": {}, "source": [ + "\n", + "\n", "### Register a sample model\n", "\n", "In a usual model lifecycle, a champion model will have been independently registered in your model inventory and submitted to you for validation by your model development team as part of the effective challenge process. (**Learn more:** [Submit for approval](https://docs.validmind.ai/guide/model-documentation/submit-for-approval.html))\n", @@ -129,9 +181,11 @@ }, { "cell_type": "markdown", - "id": "9743927c", + "id": "0c350e0d", "metadata": {}, "source": [ + "\n", + "\n", "#### Assign validator credentials\n", "\n", "In order to log tests as a validator instead of as a developer, on the model details page that appears after you've successfully registered your sample model:\n", @@ -151,9 +205,10 @@ }, { "cell_type": "markdown", - "id": "37a94e48", "metadata": {}, "source": [ + "\n", + "\n", "### Install the ValidMind Library\n", "\n", "
    Recommended Python versions\n", @@ -166,7 +221,7 @@ { "cell_type": "code", "execution_count": null, - "id": "cef99db1", + "id": "931d8f7f", "metadata": {}, "outputs": [], "source": [ @@ -175,8 +230,11 @@ }, { "cell_type": "markdown", + "id": "5ec7fcb7", "metadata": {}, "source": [ + "\n", + "\n", "### Initialize the ValidMind Library\n", "\n", "ValidMind generates a unique _code snippet_ for each registered model to connect with your validation environment. You initialize the ValidMind Library with this code snippet, which ensures that your test results are uploaded to the correct model when you run the notebook." @@ -184,9 +242,10 @@ }, { "cell_type": "markdown", - "id": "30803184", "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", @@ -201,7 +260,7 @@ { "cell_type": "code", "execution_count": null, - "id": "d5de450a", + "id": "d5d87e2d", "metadata": {}, "outputs": [], "source": [ @@ -224,16 +283,20 @@ }, { "cell_type": "markdown", + "id": "b4b5a00f", "metadata": {}, "source": [ + "\n", + "\n", "## Getting to know ValidMind" ] }, { "cell_type": "markdown", - "id": "d22ef356", "metadata": {}, "source": [ + "\n", + "\n", "### Preview the validation report 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 model validation. A template predefines sections for your validation report and provides a general outline to follow, making the validation process much easier.\n", @@ -244,7 +307,7 @@ { "cell_type": "code", "execution_count": null, - "id": "38592086", + "id": "13d34bbb", "metadata": {}, "outputs": [], "source": [ @@ -253,8 +316,11 @@ }, { "cell_type": "markdown", + "id": "a2e86bc8", "metadata": {}, "source": [ + "\n", + "\n", "#### View validation report in the ValidMind Platform\n", "\n", "Next, let's head to the ValidMind Platform to see the template in action:\n", @@ -274,9 +340,10 @@ }, { "cell_type": "markdown", - "id": "3fa2883d", "metadata": {}, "source": [ + "\n", + "\n", "### Explore available tests\n", "\n", "Next, let's explore the list of all available tests in the ValidMind Library with [the `vm.tests.list_tests()` function](https://docs.validmind.ai/validmind/validmind/tests.html#list_tests) — we'll later narrow down the tests we want to run from this list when we learn to run tests." @@ -285,7 +352,7 @@ { "cell_type": "code", "execution_count": null, - "id": "c1107ca4", + "id": "de6abc2a", "metadata": {}, "outputs": [], "source": [ @@ -296,6 +363,8 @@ "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", @@ -327,7 +396,7 @@ }, { "cell_type": "markdown", - "id": "6e12584a", + "id": "44657dea", "metadata": {}, "source": [ "You may need to restart your kernel after running the upgrade package for changes to be applied." @@ -335,8 +404,11 @@ }, { "cell_type": "markdown", + "id": "39f45f58", "metadata": {}, "source": [ + "\n", + "\n", "## In summary\n", "\n", "In this first notebook, you learned how to:\n", @@ -349,11 +421,14 @@ }, { "cell_type": "markdown", - "id": "78f8161b", "metadata": {}, "source": [ + "\n", + "\n", "## Next steps\n", "\n", + "\n", + "\n", "### Start the model validation process\n", "\n", "Now that the ValidMind Library is connected to your model in the ValidMind Library with the correct template applied, we can go ahead and start the model validation process: **[2 — Start the model validation process](2-start_validation_process.ipynb)**" diff --git a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb index 699c2f97c..5493f1f9c 100644 --- a/notebooks/tutorials/model_validation/2-start_validation_process.ipynb +++ b/notebooks/tutorials/model_validation/2-start_validation_process.ipynb @@ -22,6 +22,45 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "::: {.content-hidden when-format=\"html\"}\n", + "## Contents \n", + "- [Prerequisites](#toc1_) \n", + "- [Setting up](#toc2_) \n", + " - [Initialize the ValidMind Library](#toc2_1_) \n", + "- [Load the sample dataset](#toc3_) \n", + "- [Verifying data quality adjustments](#toc4_) \n", + " - [Identify qualitative tests](#toc4_1_) \n", + " - [Initialize the ValidMind datasets](#toc4_2_) \n", + " - [Run data quality tests](#toc4_3_) \n", + " - [Run tabular data tests](#toc4_3_1_) \n", + " - [Remove highly correlated features](#toc4_4_) \n", + "- [Documenting test results](#toc5_) \n", + " - [Configure and run comparison tests](#toc5_1_) \n", + " - [Log tests with a unique identifiers](#toc5_2_) \n", + " - [Add test results to reporting](#toc5_3_) \n", + "- [Split the preprocessed dataset](#toc6_) \n", + " - [Initialize the split datasets](#toc6_1_) \n", + "- [In summary](#toc7_) \n", + "- [Next steps](#toc8_) \n", + " - [Develop potential challenger models](#toc8_1_) \n", + "\n", + ":::\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", "## Prerequisites\n", "\n", "In order to independently assess the quality of your datasets with notebook, you'll need to first have:\n", @@ -38,6 +77,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "## Setting up" ] }, @@ -45,6 +86,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "### Initialize the ValidMind Library\n", "\n", "First, let's connect up the ValidMind Library to our model we previously registered in the ValidMind Platform:\n", @@ -89,6 +132,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "## Load the sample dataset\n", "\n", "Let's first import the public [Bank Customer Churn Prediction](https://www.kaggle.com/datasets/shantanudhakadd/bank-customer-churn-prediction) dataset from Kaggle, which was used to develop the dummy champion model.\n", @@ -121,6 +166,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "## Verifying data quality adjustments\n", "\n", "Let's say that thanks to the documentation submitted by the model development team ([Learn more ...](https://docs.validmind.ai/developer/validmind-library.html#for-model-development)), we know that the sample dataset was first modified before being used to train the champion model. After performing some data quality assessments on the raw dataset, it was determined that the dataset required rebalancing, and highly correlated features were also removed." @@ -130,6 +177,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "### Identify qualitative tests\n", "\n", "During model validation, we use the same data processing logic and training procedure to confirm that the model's results can be reproduced independently, so let's start by doing some data quality assessments by running a few individual tests just like the development team did.\n", @@ -191,6 +240,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "### Initialize the ValidMind datasets\n", "\n", "With the individual tests we want to run identified, the next step is to connect your data with a ValidMind `Dataset` object. **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", @@ -220,6 +271,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "### Run data quality tests\n", "\n", "Now that we know how to initialize a ValidMind `dataset` object, we're ready to run some tests!\n", @@ -234,6 +287,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "#### Run tabular data tests\n", "\n", "The inputs expected by a test can also be found in the test definition — let's take [`validmind.data_validation.DescriptiveStatistics`](https://docs.validmind.ai/tests/data_validation/DescriptiveStatistics.html) as an example.\n", @@ -338,6 +393,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "### Remove highly correlated features\n", "\n", "Next, let's also remove highly correlated features from our dataset as outlined by the development team. Removing highly correlated features helps make the model simpler, more stable, and easier to understand.\n", @@ -499,6 +556,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "## Documenting test results\n", "\n", "Now that we've done some analysis on two different datasets, we can use ValidMind to easily document why certain things were done to our raw data with testing to support it. As we learned above, every test result returned by the `run_test()` function has a `.log()` method that can be used to send the test results to the ValidMind Platform.\n", @@ -510,6 +569,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "### Configure and run comparison tests\n", "\n", "Below, we'll perform comparison tests between the original raw dataset (`raw_dataset`) and the final preprocessed (`raw_dataset_preprocessed`) dataset, again logging the results to the ValidMind Platform. \n", @@ -587,6 +648,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "### Log tests with a unique identifiers\n", "\n", "Next, we'll use the previously initialized `vm_balanced_raw_dataset` (that still has a highly correlated `Age` column) as input to run an individual test, then log the result to the ValidMind Platform.\n", @@ -615,6 +678,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "### Add test results to reporting\n", "\n", "With some test results logged, let's head to the model we connected to at the beginning of this notebook and learn how to insert a test result into our validation report ([Need more help?](https://docs.validmind.ai/guide/model-validation/assess-compliance.html#link-validator-evidence)).\n", @@ -661,6 +726,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "## Split the preprocessed dataset\n", "\n", "With our raw dataset rebalanced with highly correlated features removed, let's now **spilt our dataset into train and test** in preparation for model evaluation testing.\n", @@ -729,6 +796,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "### Initialize the split datasets\n", "\n", "Next, let's initialize the training and testing datasets so they are available for use:" @@ -757,6 +826,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "## In summary\n", "\n", "In this second notebook, you learned how to:\n", @@ -774,8 +845,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "## Next steps\n", "\n", + "\n", + "\n", "### Develop potential challenger models\n", "\n", "Now that you're familiar with the basics of using the ValidMind Library, let's use it to develop a challenger model: **[3 — Developing a potential challenger model](3-developing_challenger_model.ipynb)**" diff --git a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb index 0c26cd615..b0d226012 100644 --- a/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb +++ b/notebooks/tutorials/model_validation/3-developing_challenger_model.ipynb @@ -15,6 +15,48 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "::: {.content-hidden when-format=\"html\"}\n", + "## Contents \n", + "- [Prerequisites](#toc1_) \n", + "- [Setting up](#toc2_) \n", + " - [Initialize the ValidMind Library](#toc2_1_) \n", + " - [Import the sample dataset](#toc2_2_) \n", + " - [Preprocess the dataset](#toc2_2_1_) \n", + " - [Split the preprocessed dataset](#toc2_3_) \n", + "- [Import the champion model](#toc3_) \n", + "- [Training a potential challenger model](#toc4_) \n", + " - [Random forest classification model](#toc4_1_) \n", + "- [Initializing the model objects](#toc5_) \n", + " - [Initialize the model objects](#toc5_1_) \n", + " - [Assign predictions](#toc5_2_) \n", + "- [Running model validation tests](#toc6_) \n", + " - [Run model performance tests](#toc6_1_) \n", + " - [Evaluate performance of the champion model](#toc6_1_1_) \n", + " - [Log a model finding](#toc6_1_2_) \n", + " - [Evaluate performance of challenger model](#toc6_1_3_) \n", + " - [Run diagnostic tests](#toc6_2_) \n", + " - [Run feature importance tests](#toc6_3_) \n", + "- [In summary](#toc7_) \n", + "- [Next steps](#toc8_) \n", + " - [Finalize validation and reporting](#toc8_1_) \n", + "\n", + ":::\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", "## Prerequisites\n", "\n", "In order to develop potential challenger models with this notebook, you'll need to first have:\n", @@ -40,6 +82,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "## Setting up\n", "\n", "This section should be quite familiar to you — as we performed the same actions in the previous notebook, **[2 — Start the model validation process](2-start_validation_process.ipynb)**." @@ -49,6 +93,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "### Initialize the ValidMind Library\n", "\n", "As usual, let's first connect up the ValidMind Library to our model we previously registered in the ValidMind Platform:\n", @@ -93,6 +139,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "### Import the sample dataset\n", "\n", "Next, we'll load in the sample [Bank Customer Churn Prediction](https://www.kaggle.com/datasets/shantanudhakadd/bank-customer-churn-prediction) dataset used to develop the champion model that we will independently preprocess:" @@ -118,6 +166,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "#### Preprocess the dataset\n", "\n", "We’ll apply a simple rebalancing technique to the dataset before continuing:" @@ -260,6 +310,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "### Split the preprocessed dataset\n", "\n", "With our raw dataset rebalanced with highly correlated features removed, let's now **spilt our dataset into train and test** in preparation for model evaluation testing:" @@ -319,6 +371,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "## Import the champion model\n", "\n", "With our raw dataset assessed and preprocessed, let's go ahead and import the champion model submitted by the model development team in the format of a `.pkl` file: **[lr_model_champion.pkl](lr_model_champion.pkl)**" @@ -341,6 +395,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "## Training a potential challenger model\n", "\n", "We're curious how an alternate model compares to our champion model, so let's train a challenger model as a basis for our testing.\n", @@ -352,6 +408,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "### Random forest classification model\n", "\n", "A *random forest classification model* is an ensemble machine learning algorithm that uses multiple decision trees to classify data. In ensemble learning, multiple models are combined to improve prediction accuracy and robustness.\n", @@ -382,6 +440,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "## Initializing the model objects" ] }, @@ -389,6 +449,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "### Initialize the model objects\n", "\n", "In addition to the initialized datasets, 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 each of our two models.\n", @@ -419,6 +481,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "### Assign predictions\n", "\n", "With our models registered, we'll move on to assigning both the predictive probabilities coming directly from each model's predictions, and the binary prediction after applying the cutoff threshold described in the Compute binary predictions step above.\n", @@ -448,6 +512,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "## Running model validation tests\n", "\n", "With everything ready for us, let's run the rest of our validation tests. We'll focus on comprehensive testing around model performance of both the champion and challenger models going forward as we've already verified the data quality of the datasets used to train the champion model." @@ -457,6 +523,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "### Run model performance tests\n", "\n", "Let's run some performance tests, beginning with independent testing of our champion logistic regression model, then moving on to our potential challenger model.\n", @@ -508,6 +576,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "#### Evaluate performance of the champion model\n", "\n", "Now, let's run and log our batch of model performance tests using our testing dataset (`vm_test_ds`) for our champion model:" @@ -541,6 +611,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "#### Log a model finding\n", "\n", "As we can observe from the output above, our champion model doesn't pass the `MinimumAccuracy` based on the default thresholds of the out-of-the-box test, so let's log a model finding in the ValidMind Platform ([Need more help?](https://docs.validmind.ai/guide/model-validation/add-manage-model-findings.html)):\n", @@ -586,6 +658,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "#### Evaluate performance of challenger model\n", "\n", "We've now conducted similar tests as the model development team for our champion model, with the aim of verifying their test results.\n", @@ -643,6 +717,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "### Run diagnostic tests\n", "\n", "Next we want to inspect the robustness and stability testing comparison between our champion and challenger model.\n", @@ -711,6 +787,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "### Run feature importance tests\n", "\n", "We also want to verify the relative influence of different input features on our models' predictions, as well as inspect the differences between our champion and challenger model to see if a certain model offers more understandable or logical importance scores for features.\n", @@ -749,6 +827,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "## In summary\n", "\n", "In this third notebook, you learned how to:\n", @@ -763,8 +843,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "## Next steps\n", "\n", + "\n", + "\n", "### Finalize validation and reporting\n", "\n", "Now that you're familiar with the basics of using the ValidMind Library to run and log validation tests, let's learn how to implement some custom tests and wrap up our validation: **[4 — Finalize validation and reporting](4-finalize_validation_reporting.ipynb)**" diff --git a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb index fa6fbb3b3..4c2363eb6 100644 --- a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb +++ b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb @@ -20,6 +20,50 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "::: {.content-hidden when-format=\"html\"}\n", + "## Contents \n", + "- [Prerequisites](#toc1_) \n", + "- [Setting up](#toc2_) \n", + " - [Initialize the ValidMind Library](#toc2_1_) \n", + " - [Import the sample dataset](#toc2_2_) \n", + " - [Split the preprocessed dataset](#toc2_3_) \n", + " - [Import the champion model](#toc2_4_) \n", + " - [Train potential challenger model](#toc2_5_) \n", + " - [Initialize the model objects](#toc2_6_) \n", + "- [Implementing custom tests](#toc3_) \n", + " - [Implement a custom inline test](#toc3_1_) \n", + " - [Create a confusion matrix plot](#toc3_1_1_) \n", + " - [Add parameters to custom tests](#toc3_1_2_) \n", + " - [Pass parameters to custom tests](#toc3_1_3_) \n", + " - [Use external test providers](#toc3_2_) \n", + " - [Create custom tests folder](#toc3_2_1_) \n", + " - [Save an inline test](#toc3_2_2_) \n", + " - [Register a local test provider](#toc3_2_3_) \n", + "- [Verify test runs](#toc4_) \n", + "- [In summary](#toc5_) \n", + "- [Next steps](#toc6_) \n", + " - [Work with your validation report](#toc6_1_) \n", + " - [Learn more](#toc6_2_) \n", + " - [More how-to guides and code samples](#toc6_2_1_) \n", + " - [Discover more learning resources](#toc6_2_2_) \n", + "\n", + ":::\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", "## Prerequisites\n", "\n", "In order to finalize validation and reporting, you'll need to first have:\n", @@ -47,6 +91,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "## Setting up\n", "\n", "This section should be very familiar to you now — as we performed the same actions in the previous two notebooks in this series." @@ -56,6 +102,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "### Initialize the ValidMind Library\n", "\n", "As usual, let's first connect up the ValidMind Library to our model we previously registered in the ValidMind Platform:\n", @@ -100,6 +148,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "### Import the sample dataset\n", "\n", "Next, we'll load in the same sample [Bank Customer Churn Prediction](https://www.kaggle.com/datasets/shantanudhakadd/bank-customer-churn-prediction) dataset used to develop the champion model that we will independently preprocess:" @@ -256,6 +306,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "### Split the preprocessed dataset\n", "\n", "With our raw dataset rebalanced with highly correlated features removed, let's now **spilt our dataset into train and test** in preparation for model evaluation testing:" @@ -315,6 +367,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "### Import the champion model\n", "\n", "With our raw dataset assessed and preprocessed, let's go ahead and import the champion model submitted by the model development team in the format of a `.pkl` file: **[lr_model_champion.pkl](lr_model_champion.pkl)**" @@ -337,6 +391,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "### Train potential challenger model\n", "\n", "We'll also train our random forest classification challenger model to see how it compares:" @@ -365,6 +421,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "### Initialize the model objects\n", "\n", "In addition to the initialized datasets, 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 each of our two models:" @@ -408,6 +466,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "## Implementing custom tests\n", "\n", "Thanks to the model documentation ([Learn more ...](https://docs.validmind.ai/developer/validmind-library.html#for-model-development)), we know that the model development team implemented a custom test to further evaluate the performance of the champion model.\n", @@ -423,6 +483,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "### Implement a custom inline test\n", "\n", "Let's implement the same custom *inline test* that calculates the confusion matrix for a binary classification model that the model development team used in their performance evaluations.\n", @@ -435,6 +497,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "#### Create a confusion matrix plot\n", "\n", "Let's first create a confusion matrix plot using the `confusion_matrix` function from the `sklearn.metrics` module:" @@ -561,6 +625,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "#### Add parameters to custom tests\n", "\n", "Custom tests can take parameters just like any other function. To demonstrate, let's modify the `confusion_matrix` function to take an additional parameter `normalize` that will allow you to normalize the confusion matrix:" @@ -607,6 +673,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "#### Pass parameters to custom tests\n", "\n", "You can pass parameters to custom tests by providing a dictionary of parameters to the `run_test()` function.\n", @@ -655,6 +723,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "### Use external test providers\n", "\n", "Sometimes you may want to reuse the same set of custom tests across multiple models and share them with others in your organization, like the model development team would have done with you in this example workflow featured in this series of notebooks. In this case, you can create an external custom *test provider* that will allow you to load custom tests from a local folder or a Git repository.\n", @@ -672,6 +742,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "#### Create custom tests folder\n", "\n", "Let's start by creating a new folder that will contain reusable custom tests from your existing inline tests.\n", @@ -714,6 +786,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "#### Save an inline test\n", "\n", "The `@vm.test` decorator we used in **Implement a custom inline test** above to register one-off custom tests also includes a convenience method on the function object that allows you to simply call `.save()` to save the test to a Python file at a specified path.\n", @@ -767,6 +841,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "#### Register a local test provider\n", "\n", "Now that your `my_tests` folder has a sample custom test, let's initialize a test provider that will tell the ValidMind Library where to find your custom tests:\n", @@ -861,6 +937,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "## Verify test runs\n", "\n", "Our final task is to verify that all the tests provided by the model development team were run and reported accurately. Note the appended `result_ids` to delineate which dataset we ran the test with for the relevant tests.\n", @@ -1021,6 +1099,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "## In summary\n", "\n", "In this final notebook, you learned how to:\n", @@ -1043,6 +1123,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "## Next steps" ] }, @@ -1050,6 +1132,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "### Work with your validation report\n", "\n", "Now that you've logged all your test results and verified the work done by the model development team, head to the ValidMind Platform to wrap up your validation report. Continue to work on your validation report by:\n", @@ -1064,6 +1148,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "### Learn more\n", "\n", "Now that you're familiar with the basics, you can explore the following notebooks to get a deeper understanding on how the ValidMind Library assists you in streamlining model validation:" @@ -1073,6 +1159,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "#### More how-to guides and code samples\n", "\n", "- [Explore available tests in detail](../../how_to/explore_tests.ipynb)\n", @@ -1085,6 +1173,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "#### Discover more learning resources\n", "\n", "All notebook samples can be found in the following directories of the ValidMind Library GitHub repository:\n", From 8318dba97ad7546b83117a23dd209c8ca9e7a0cc Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Mon, 7 Apr 2025 13:32:37 -0700 Subject: [PATCH 70/71] Next steps - Work with your validation report --- .../model_validation/4-finalize_validation_reporting.ipynb | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb index 4c2363eb6..1e5561c51 100644 --- a/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb +++ b/notebooks/tutorials/model_validation/4-finalize_validation_reporting.ipynb @@ -1139,8 +1139,13 @@ "Now that you've logged all your test results and verified the work done by the model development team, head to the ValidMind Platform to wrap up your validation report. Continue to work on your validation report by:\n", "\n", "- **Inserting additional test results:** Click **Link Evidence to Report** under any section of 2. Validation in your validation report. (Learn more: [Link evidence to reports](https://docs.validmind.ai/guide/model-validation/assess-compliance.html#link-evidence-to-reports))\n", + "\n", + "- **Making qualitative edits to your test descriptions:** Expand any linked evidence under Validator Evidence and click **See evidence details** to review and edit the ValidMind-generated test descriptions for quality and accuracy.\n", + "\n", "- **Adding more findings:** Click **Link Finding to Report** in any validation report section, then click **+ Create New Finding**. (Learn more: [Add and manage model findings](https://docs.validmind.ai/guide/model-validation/add-manage-model-findings.html))\n", - "- **Adding risk assessment notes:** Click under **Risk Assessment Notes** in any validation report section to access the text editor and content editing toolbar, including an option to generate a draft with AI. (Learn more: [Work with content blocks](https://docs.validmind.ai/guide/model-documentation/work-with-content-blocks.html#content-editing-toolbar))\n", + "\n", + "- **Adding risk assessment notes:** Click under **Risk Assessment Notes** in any validation report section to access the text editor and content editing toolbar, including an option to generate a draft with AI. Edit your ValidMind-generated test descriptions (Learn more: [Work with content blocks](https://docs.validmind.ai/guide/model-documentation/work-with-content-blocks.html#content-editing-toolbar))\n", + "\n", "- **Assessing compliance:** Under the Guideline for any validation report section, click **ASSESSMENT** and select the compliance status from the drop-down menu. (Learn more: [Provide compliance assessments](https://docs.validmind.ai/guide/model-validation/assess-compliance.html#provide-compliance-assessments))" ] }, From 7bc19970aebe709630aae4415bbf4a3ebfa97bb0 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Wed, 9 Apr 2025 09:31:10 -0700 Subject: [PATCH 71/71] 2.8.18 --- pyproject.toml | 2 +- validmind/__version__.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 94e353999..b8a4f62cb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -10,7 +10,7 @@ description = "ValidMind Library" license = "Commercial License" name = "validmind" readme = "README.pypi.md" -version = "2.8.17" +version = "2.8.18" [tool.poetry.dependencies] aiohttp = {extras = ["speedups"], version = "*"} diff --git a/validmind/__version__.py b/validmind/__version__.py index 42e92e36d..c2cb868ca 100644 --- a/validmind/__version__.py +++ b/validmind/__version__.py @@ -1 +1 @@ -__version__ = "2.8.17" +__version__ = "2.8.18"