diff --git a/.github/workflows/dependency-testing.yaml b/.github/workflows/dependency-testing.yaml
index cf37f33ed..357f87a76 100644
--- a/.github/workflows/dependency-testing.yaml
+++ b/.github/workflows/dependency-testing.yaml
@@ -42,7 +42,7 @@ jobs:
strategy:
fail-fast: false
matrix:
- python-version: ['3.9', '3.10', '3.11', '3.12']
+ python-version: ['3.9', '3.10', '3.11', '3.12', '3.13', '3.14']
deps-type: ['max', 'default']
steps:
@@ -88,7 +88,7 @@ jobs:
else
# Generate latest-compatible constraints (max)
OUT=constraints-max.txt
- uv pip compile pyproject.toml --all-extras --no-emit-index-url --no-annotate --output-file "$OUT" --upgrade
+ uv pip compile pyproject.toml -p "${{ matrix.python-version }}" --all-extras --no-emit-index-url --no-annotate --no-strip-markers --output-file "$OUT" --upgrade
# Install constraints then the wheel without reinstalling deps
pip install -r "$OUT"
WHEEL=$(ls dist/*.whl | head -n 1)
diff --git a/.github/workflows/python.yaml b/.github/workflows/python.yaml
index ba5848e52..7add5171e 100644
--- a/.github/workflows/python.yaml
+++ b/.github/workflows/python.yaml
@@ -59,7 +59,6 @@ jobs:
- name: Install Dependencies
run: |
poetry env use python3.9
- poetry run pip install pyarrow==17.0.0
poetry install --all-extras
poetry run pip install torch==2.0.1 --extra-index-url https://download.pytorch.org/whl/cpu
poetry run pip install aequitas fairlearn vl-convert-python
diff --git a/notebooks/code_sharing/clustering/quickstart_cluster_demo.ipynb b/notebooks/code_sharing/clustering/quickstart_cluster_demo.ipynb
index f257c4d2e..9858047b8 100644
--- a/notebooks/code_sharing/clustering/quickstart_cluster_demo.ipynb
+++ b/notebooks/code_sharing/clustering/quickstart_cluster_demo.ipynb
@@ -50,7 +50,7 @@
"\n",
"Recommended Python versions\n",
"
\n",
- "Python 3.8 <= x <= 3.11
\n",
+ "Python 3.8 <= x <= 3.14\n",
"\n",
"To install the library:"
]
diff --git a/notebooks/code_sharing/credit_risk/assign_prediction_probabilities.ipynb b/notebooks/code_sharing/credit_risk/assign_prediction_probabilities.ipynb
index db5579508..640f986d3 100644
--- a/notebooks/code_sharing/credit_risk/assign_prediction_probabilities.ipynb
+++ b/notebooks/code_sharing/credit_risk/assign_prediction_probabilities.ipynb
@@ -70,7 +70,7 @@
"\n",
"Recommended Python versions\n",
"
\n",
- "Python 3.8 <= x <= 3.11
\n",
+ "Python 3.8 <= x <= 3.14\n",
"\n",
"To install the library:"
]
diff --git a/notebooks/code_sharing/deepeval_integration_demo.ipynb b/notebooks/code_sharing/deepeval_integration_demo.ipynb
index 001d60e0e..5ccc5f89c 100644
--- a/notebooks/code_sharing/deepeval_integration_demo.ipynb
+++ b/notebooks/code_sharing/deepeval_integration_demo.ipynb
@@ -152,7 +152,7 @@
"\n",
"Recommended Python versions\n",
"
\n",
- "Python 3.8 <= x <= 3.11
\n",
+ "Python 3.8 <= x <= 3.14\n",
"\n",
"To install the library:"
]
diff --git a/notebooks/code_sharing/embeddings/quickstart_embeddings_demo.ipynb b/notebooks/code_sharing/embeddings/quickstart_embeddings_demo.ipynb
index 841f04726..38b80634a 100644
--- a/notebooks/code_sharing/embeddings/quickstart_embeddings_demo.ipynb
+++ b/notebooks/code_sharing/embeddings/quickstart_embeddings_demo.ipynb
@@ -42,7 +42,7 @@
"\n",
"Recommended Python versions\n",
"
\n",
- "Python 3.8 <= x <= 3.11
\n",
+ "Python 3.8 <= x <= 3.14\n",
"\n",
"To install the library:"
]
diff --git a/notebooks/code_sharing/geval_deepeval_integration_demo.ipynb b/notebooks/code_sharing/geval_deepeval_integration_demo.ipynb
index 560d8e772..8194e7823 100644
--- a/notebooks/code_sharing/geval_deepeval_integration_demo.ipynb
+++ b/notebooks/code_sharing/geval_deepeval_integration_demo.ipynb
@@ -118,7 +118,7 @@
"\n",
"Recommended Python versions\n",
"
\n",
- "Python 3.8 <= x <= 3.11
\n",
+ "Python 3.8 <= x <= 3.14\n",
"\n",
"To install the library:"
]
diff --git a/notebooks/code_sharing/insurance_mortality/insurance_validation_demo.ipynb b/notebooks/code_sharing/insurance_mortality/insurance_validation_demo.ipynb
index ef335260f..7e04c9e03 100644
--- a/notebooks/code_sharing/insurance_mortality/insurance_validation_demo.ipynb
+++ b/notebooks/code_sharing/insurance_mortality/insurance_validation_demo.ipynb
@@ -80,7 +80,7 @@
"\n",
"Recommended Python versions\n",
"
\n",
- "Python 3.8 <= x <= 3.11
\n",
+ "Python 3.8 <= x <= 3.14\n",
"\n",
"To install the library:"
]
diff --git a/notebooks/code_sharing/market_basket_analysis/mba_poc.ipynb b/notebooks/code_sharing/market_basket_analysis/mba_poc.ipynb
index e9cfbd2f4..7a19f19bd 100644
--- a/notebooks/code_sharing/market_basket_analysis/mba_poc.ipynb
+++ b/notebooks/code_sharing/market_basket_analysis/mba_poc.ipynb
@@ -40,7 +40,7 @@
"\n",
"Recommended Python versions\n",
"
\n",
- "Python 3.8 <= x <= 3.11
\n",
+ "Python 3.8 <= x <= 3.14\n",
"\n",
"To install the library:"
]
diff --git a/notebooks/code_sharing/operational_deposit/operational_deposit_poc.ipynb b/notebooks/code_sharing/operational_deposit/operational_deposit_poc.ipynb
index 650e164a1..92ac04460 100644
--- a/notebooks/code_sharing/operational_deposit/operational_deposit_poc.ipynb
+++ b/notebooks/code_sharing/operational_deposit/operational_deposit_poc.ipynb
@@ -152,7 +152,7 @@
"\n",
"Recommended Python versions\n",
"
\n",
- "Python 3.8 <= x <= 3.11
\n",
+ "Python 3.8 <= x <= 3.14\n",
"\n",
"To install the library:"
]
diff --git a/notebooks/code_sharing/output_templates/customizing_tests_with_output_templates.ipynb b/notebooks/code_sharing/output_templates/customizing_tests_with_output_templates.ipynb
index 10e1c7cfb..496e34bdc 100644
--- a/notebooks/code_sharing/output_templates/customizing_tests_with_output_templates.ipynb
+++ b/notebooks/code_sharing/output_templates/customizing_tests_with_output_templates.ipynb
@@ -239,7 +239,7 @@
"\n",
"Recommended Python versions\n",
"
\n",
- "Python 3.8 <= x <= 3.11
\n",
+ "Python 3.8 <= x <= 3.14\n",
"\n",
"To install the library:"
]
diff --git a/notebooks/code_sharing/plots_and_stats_demo.ipynb b/notebooks/code_sharing/plots_and_stats_demo.ipynb
index 0b490e359..bd92417da 100644
--- a/notebooks/code_sharing/plots_and_stats_demo.ipynb
+++ b/notebooks/code_sharing/plots_and_stats_demo.ipynb
@@ -88,7 +88,7 @@
"\n",
"Recommended Python versions\n",
"
\n",
- "Python 3.8 <= x <= 3.11
\n",
+ "Python 3.8 <= x <= 3.14\n",
"\n",
"To install the library:"
]
diff --git a/notebooks/code_sharing/regression/regression_unit_metrics.ipynb b/notebooks/code_sharing/regression/regression_unit_metrics.ipynb
index 0a14a5b6f..df615c0a7 100644
--- a/notebooks/code_sharing/regression/regression_unit_metrics.ipynb
+++ b/notebooks/code_sharing/regression/regression_unit_metrics.ipynb
@@ -116,7 +116,7 @@
"\n",
"Recommended Python versions\n",
"
\n",
- "Python 3.8 <= x <= 3.11
\n",
+ "Python 3.8 <= x <= 3.14\n",
"\n",
"To install the library:"
]
diff --git a/notebooks/code_sharing/test_configuration_updates_demo.ipynb b/notebooks/code_sharing/test_configuration_updates_demo.ipynb
index 0bda1550d..5ff18d74e 100644
--- a/notebooks/code_sharing/test_configuration_updates_demo.ipynb
+++ b/notebooks/code_sharing/test_configuration_updates_demo.ipynb
@@ -17,7 +17,7 @@
"\n",
"Recommended Python versions\n",
"
\n",
- "Python 3.8 <= x <= 3.11
\n",
+ "Python 3.8 <= x <= 3.14\n",
"\n",
"To install the library:"
]
diff --git a/notebooks/how_to/tests/explore_tests/explore_test_suites.ipynb b/notebooks/how_to/tests/explore_tests/explore_test_suites.ipynb
index 6eb1e5ef7..976eaedef 100644
--- a/notebooks/how_to/tests/explore_tests/explore_test_suites.ipynb
+++ b/notebooks/how_to/tests/explore_tests/explore_test_suites.ipynb
@@ -137,7 +137,7 @@
"\n",
"Recommended Python versions\n",
"
\n",
- "Python 3.8 <= x <= 3.11
\n",
+ "Python 3.8 <= x <= 3.14\n",
"\n",
"To install the library:"
]
diff --git a/notebooks/how_to/tests/explore_tests/explore_tests.ipynb b/notebooks/how_to/tests/explore_tests/explore_tests.ipynb
index 56c58b62c..3bfda3032 100644
--- a/notebooks/how_to/tests/explore_tests/explore_tests.ipynb
+++ b/notebooks/how_to/tests/explore_tests/explore_tests.ipynb
@@ -107,7 +107,7 @@
"\n",
"Recommended Python versions\n",
"
\n",
- "Python 3.8 <= x <= 3.11
\n",
+ "Python 3.8 <= x <= 3.14\n",
"\n",
"To install the library:"
]
diff --git a/notebooks/how_to/tests/run_tests/1_run_dataset_based_tests.ipynb b/notebooks/how_to/tests/run_tests/1_run_dataset_based_tests.ipynb
index 9af05b3b3..c4937af21 100644
--- a/notebooks/how_to/tests/run_tests/1_run_dataset_based_tests.ipynb
+++ b/notebooks/how_to/tests/run_tests/1_run_dataset_based_tests.ipynb
@@ -153,7 +153,7 @@
"\n",
"Recommended Python versions\n",
"
\n",
- "Python 3.8 <= x <= 3.11
\n",
+ "Python 3.8 <= x <= 3.14\n",
"\n",
"To install the library:"
]
diff --git a/notebooks/how_to/tests/run_tests/2_run_comparison_tests.ipynb b/notebooks/how_to/tests/run_tests/2_run_comparison_tests.ipynb
index 1ba4627bc..ffcd999fc 100644
--- a/notebooks/how_to/tests/run_tests/2_run_comparison_tests.ipynb
+++ b/notebooks/how_to/tests/run_tests/2_run_comparison_tests.ipynb
@@ -1,1094 +1,1095 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "1d29276f",
- "metadata": {},
- "source": [
- "# Run comparison tests\n",
- "\n",
- "Learn how to use the ValidMind Library to run comparison tests that take any datasets or models as inputs. Identify comparison tests to run, initialize ValidMind dataset and model objects in preparation for passing them to tests, and then run tests — generating outputs automatically logged to your model's documentation in the ValidMind Platform.\n",
- "\n",
- "We recommend that you first complete our introductory notebook on running tests.\n",
- "
\n",
- "
Run dataset-based tests "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "61065444",
- "metadata": {},
- "source": [
- "::: {.content-hidden when-format=\"html\"}\n",
- "## Contents \n",
- "- [About ValidMind](#toc1__) \n",
- " - [Before you begin](#toc1_1__) \n",
- " - [New to ValidMind?](#toc1_2__) \n",
- " - [Key concepts](#toc1_3__) \n",
- "- [Setting up](#toc2__) \n",
- " - [Install the ValidMind Library](#toc2_1__) \n",
- " - [Initialize the ValidMind Library](#toc2_2__) \n",
- " - [Register sample model](#toc2_2_1__) \n",
- " - [Apply documentation template](#toc2_2_2__) \n",
- " - [Get your code snippet](#toc2_2_3__) \n",
- " - [Preview the documentation template](#toc2_3__) \n",
- " - [Initialize the Python environment](#toc2_4__) \n",
- "- [Explore a ValidMind test](#toc3__) \n",
- "- [Working with ValidMind datasets](#toc4__) \n",
- " - [Import the sample dataset](#toc4_1__) \n",
- " - [Split the dataset](#toc4_2__) \n",
- " - [Initialize the ValidMind dataset](#toc4_3__) \n",
- "- [Working with ValidMind models](#toc5__) \n",
- " - [Train a sample model](#toc5_1__) \n",
- " - [Initialize the ValidMind model](#toc5_2__) \n",
- " - [Assign predictions](#toc5_3__) \n",
- "- [Running ValidMind tests](#toc6__) \n",
- " - [Run classifier performance test with one model](#toc6_1__) \n",
- " - [Run comparison tests](#toc6_2__) \n",
- " - [Run classifier performance test with multiple models](#toc6_2_1__) \n",
- " - [Run classifier performance test with multiple parameter values](#toc6_2_2__) \n",
- " - [Run comparison test with multiple datasets](#toc6_2_3__) \n",
- "- [Work with test results](#toc7__) \n",
- "- [Next steps](#toc8__) \n",
- " - [Discover more learning resources](#toc8_1__) \n",
- "- [Upgrade ValidMind](#toc9__) \n",
- "\n",
- ":::\n",
- "\n",
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "id": "67a4d9dc",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "## About ValidMind\n",
- "\n",
- "ValidMind is a suite of tools for managing model risk, including risk associated with AI and statistical models. \n",
- "\n",
- "You use the ValidMind Library to automate documentation and validation tests, and then use the ValidMind Platform to collaborate on model documentation. Together, these products simplify model risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and model validators."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "eeb30df8",
- "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",
- "\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": "293c3f98",
- "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",
- "\n",
- "For access to all features available in this notebook, you'll need access to a ValidMind account.\n",
- "
\n",
- "
Register with ValidMind "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "4fc836d0",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Key concepts\n",
- "\n",
- "**Model documentation**: A structured and detailed record pertaining to a model, encompassing key components such as its underlying assumptions, methodologies, data sources, inputs, performance metrics, evaluations, limitations, and intended uses. It serves to ensure transparency, adherence to regulatory requirements, and a clear understanding of potential risks associated with the model’s application.\n",
- "\n",
- "**Documentation template**: Functions as a test suite and lays out the structure of model documentation, segmented into various sections and sub-sections. Documentation templates define the structure of your model documentation, specifying the tests that should be run, and how the results should be displayed.\n",
- "\n",
- "**Tests**: A function contained in the ValidMind Library, designed to run a specific quantitative test on the dataset or model. Tests are the building blocks of ValidMind, used to evaluate and document models and datasets, and can be run individually or as part of a suite defined by your model documentation template.\n",
- "\n",
- "**Metrics**: A subset of tests that do not have thresholds. In the context of this notebook, metrics and tests can be thought of as interchangeable concepts.\n",
- "\n",
- "**Custom metrics**: Custom metrics are functions that you define to evaluate your model or dataset. These functions can be registered with the ValidMind Library to be used in the ValidMind Platform.\n",
- "\n",
- "**Inputs**: Objects to be evaluated and documented in the ValidMind Library. They can be any of the following:\n",
- "\n",
- " - **model**: A single model that has been initialized in ValidMind with [`vm.init_model()`](https://docs.validmind.ai/validmind/validmind.html#init_model).\n",
- " - **dataset**: Single dataset that has been initialized in ValidMind with [`vm.init_dataset()`](https://docs.validmind.ai/validmind/validmind.html#init_dataset).\n",
- " - **models**: A list of ValidMind models - usually this is used when you want to compare multiple models in your custom metric.\n",
- " - **datasets**: A list of ValidMind datasets - usually this is used when you want to compare multiple datasets in your custom metric. (Learn more: [Run tests with multiple datasets](https://docs.validmind.ai/notebooks/how_to/tests/run_tests/configure_tests/run_tests_that_require_multiple_datasets.html))\n",
- "\n",
- "**Parameters**: Additional arguments that can be passed when running a ValidMind test, used to pass additional information to a metric, customize its behavior, or provide additional context.\n",
- "\n",
- "**Outputs**: Custom metrics can return elements like tables or plots. Tables may be a list of dictionaries (each representing a row) or a pandas DataFrame. Plots may be matplotlib or plotly figures.\n",
- "\n",
- "**Test suites**: Collections of tests designed to run together to automate and generate model documentation end-to-end for specific use-cases.\n",
- "\n",
- "Example: the [`classifier_full_suite`](https://docs.validmind.ai/validmind/validmind/test_suites/classifier.html#ClassifierFullSuite) test suite runs tests from the [`tabular_dataset`](https://docs.validmind.ai/validmind/validmind/test_suites/tabular_datasets.html) and [`classifier`](https://docs.validmind.ai/validmind/validmind/test_suites/classifier.html) test suites to fully document the data and model sections for binary classification model use-cases."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "8d52b6e0",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "## Setting up"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e0d2daaf",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### 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": "fc97888f",
- "metadata": {},
- "outputs": [],
- "source": [
- "%pip install -q validmind"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1ff56571",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Initialize the ValidMind Library"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c4d9f164",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "#### Register sample model\n",
- "\n",
- "Let's first register a sample model for use with this notebook.\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 **Next >** to continue to assignment of model stakeholders. ([Need more help?](https://docs.validmind.ai/guide/model-inventory/register-models-in-inventory.html))\n",
- "\n",
- "4. Select your own name under the **MODEL OWNER** drop-down.\n",
- "\n",
- "5. Click **Register Model** to add the model to your inventory."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "852392e5",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "#### Apply documentation template\n",
- "\n",
- "Once you've registered your model, let's select a documentation template. A template predefines sections for your model documentation and provides a general outline to follow, making the documentation process much easier.\n",
- "\n",
- "1. In the left sidebar that appears for your model, click **Documents** and select **Development**.\n",
- "\n",
- "2. Under **TEMPLATE**, select `Binary classification`.\n",
- "\n",
- "3. Click **Use Template** to apply the template."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "6490e991",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "#### Get your code snippet\n",
- "\n",
- "Initialize the ValidMind Library with the *code snippet* unique to each model per document, ensuring your test results are uploaded to the correct model and automatically populated in the right document in the ValidMind Platform when you run this notebook.\n",
- "\n",
- "1. On the left sidebar that appears for your model, select **Getting Started** and select `Development` from the **DOCUMENT** drop-down menu.\n",
- "2. Click **Copy snippet to clipboard**.\n",
- "3. 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": "c51ae01c",
- "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",
- " document=\"documentation\",\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "99e9d14f",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Preview the documentation template\n",
- "\n",
- "Let's verify that you have connected the ValidMind Library to the ValidMind Platform and that the appropriate *template* is selected for your model.\n",
- "\n",
- "You will upload documentation and test results unique to your model based on this template later on. For now, **take a look at the default structure that the template provides with [the `vm.preview_template()` function](https://docs.validmind.ai/validmind/validmind.html#preview_template)** from the ValidMind library and note the empty sections:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "fd332a9d",
- "metadata": {},
- "outputs": [],
- "source": [
- "vm.preview_template()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f805ec38",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Initialize the Python environment\n",
- "\n",
- "Next, let's import the necessary libraries and set up your Python environment for data analysis:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8e2127cd",
- "metadata": {},
- "outputs": [],
- "source": [
- "import xgboost as xgb\n",
- "\n",
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1783e13c",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "## Explore a ValidMind test\n",
- "\n",
- "Before we run a test, use [the `vm.tests.list_tests()` function](https://docs.validmind.ai/validmind/validmind/tests.html#list_tests) to return information on out-of-the-box tests available in the ValidMind Library.\n",
- "\n",
- "Let's assume you want to evaluate *classifier performance* for a model. Classifier performance measures how well a classification model correctly predicts outcomes, using metrics like [precision, recall, and F1 score](https://en.wikipedia.org/wiki/Precision_and_recall).\n",
- "\n",
- "We'll pass in a `filter` to the `list_tests` function to find the test ID for classifier performance:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a6a6f715",
- "metadata": {},
- "outputs": [],
- "source": [
- "vm.tests.list_tests(filter=\"ClassifierPerformance\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "96a56e4b",
- "metadata": {},
- "source": [
- "We've identified from the output that the test ID for the classifier performance test is `validmind.model_validation.ClassifierPerformance`.\n",
- "\n",
- "Use this ID combined with [the `describe_test()` function](https://docs.validmind.ai/validmind/validmind/tests.html#describe_test) to retrieve more information about the test, including its **Required Inputs**:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f8a46c7d",
- "metadata": {},
- "outputs": [],
- "source": [
- "test_id = \"validmind.model_validation.sklearn.ClassifierPerformance\"\n",
- "vm.tests.describe_test(test_id)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "97053f50",
- "metadata": {},
- "source": [
- "Since this test requires a dataset and a model, you can expect it to throw an error when we run it without passing in either as input:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f853c272",
- "metadata": {},
- "outputs": [],
- "source": [
- "try:\n",
- " vm.tests.run_test(test_id)\n",
- "except Exception as e:\n",
- " print(e)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1a3115ed",
- "metadata": {},
- "source": [
- "Learn more about the individual tests available in the ValidMind Library\n",
- "
\n",
- "Check out our
Explore tests notebook for more code examples and usage of key functions.
"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "89da851b",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "## Working with ValidMind datasets"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "50bfdb1b",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Import the sample dataset\n",
- "\n",
- "Since we need a dataset to run tests, let's import the public [Bank Customer Churn Prediction](https://www.kaggle.com/datasets/shantanudhakadd/bank-customer-churn-prediction) dataset from Kaggle so that we have something to work with.\n",
- "\n",
- "In our below example, note that:\n",
- "\n",
- "- The target column, `Exited` has a value of `1` when a customer has churned and `0` otherwise.\n",
- "- The ValidMind Library provides a wrapper to automatically load the dataset as a [Pandas DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) object. A Pandas Dataframe is a two-dimensional tabular data structure that makes use of rows and columns."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3ef2dfbb",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Import the sample dataset from the library\n",
- "\n",
- "from validmind.datasets.classification import customer_churn\n",
- "\n",
- "print(\n",
- " f\"Loaded demo dataset with: \\n\\n\\t• Target column: '{customer_churn.target_column}' \\n\\t• Class labels: {customer_churn.class_labels}\"\n",
- ")\n",
- "\n",
- "raw_df = customer_churn.load_data()\n",
- "raw_df.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a5a8212f",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Split the dataset\n",
- "\n",
- "Let's first split our dataset to help assess how well the model generalizes to unseen data.\n",
- "\n",
- "Use [`preprocess()`](https://docs.validmind.ai/validmind/validmind/datasets/classification/customer_churn.html#preprocess) to split our dataset into three subsets:\n",
- "\n",
- "1. **train_df** — Used to train the model.\n",
- "2. **validation_df** — Used to evaluate the model's performance during training.\n",
- "3. **test_df** — Used later on to asses the model's performance on new, unseen data."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "88c87d4a",
- "metadata": {},
- "outputs": [],
- "source": [
- "train_df, validation_df, test_df = customer_churn.preprocess(raw_df)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "2ae225d7",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Initialize the ValidMind dataset\n",
- "\n",
- "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",
- "ValidMind dataset objects provide a wrapper to any type of dataset (NumPy, Pandas, Polars, etc.) so that tests can run transparently regardless of the underlying library.\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,
- "id": "bf0ec747",
- "metadata": {},
- "outputs": [],
- "source": [
- "vm_train_ds = vm.init_dataset(\n",
- " dataset=train_df,\n",
- " input_id=\"train_dataset\",\n",
- " target_column=customer_churn.target_column,\n",
- ")\n",
- "\n",
- "vm_test_ds = vm.init_dataset(\n",
- " dataset=test_df,\n",
- " input_id=\"test_dataset\",\n",
- " target_column=customer_churn.target_column,\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "6d26f65b",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "## Working with ValidMind models"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "6d1677f6",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Train a sample model\n",
- "\n",
- "To train the model, we need to provide it with:\n",
- "\n",
- "1. **Inputs** — Features such as customer age, usage, etc.\n",
- "2. **Outputs (Expected answers/labels)** — in our case, we would like to know whether the customer churned or not.\n",
- "\n",
- "Here, we'll use `x_train` and `x_val` to hold the input data (features), and `y_train` and `y_val` to hold the answers (the target we want to predict):"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "39e8c7ea",
- "metadata": {},
- "outputs": [],
- "source": [
- "x_train = train_df.drop(customer_churn.target_column, axis=1)\n",
- "y_train = train_df[customer_churn.target_column]\n",
- "x_val = validation_df.drop(customer_churn.target_column, axis=1)\n",
- "y_val = validation_df[customer_churn.target_column]"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "4ac628eb",
- "metadata": {},
- "source": [
- "Next, let's create an *XGBoost classifier model* that will automatically stop training if it doesn't improve after 10 tries. XGBoost is a gradient-boosted tree ensemble that builds trees sequentially, with each tree correcting the errors of the previous ones — typically known for strong predictive performance and built-in regularization to reduce overfitting.\n",
- "\n",
- "Setting an explicit threshold avoids wasting time and helps prevent further overfitting by stopping training when further improvement isn't happening. We'll also set three evaluation metrics to get a more complete picture of model performance:\n",
- "\n",
- "1. **error** — Measures how often the model makes incorrect predictions.\n",
- "2. **logloss** — Indicates how confident the predictions are.\n",
- "3. **auc** — Evaluates how well the model distinguishes between churn and not churn."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "255e3583",
- "metadata": {},
- "outputs": [],
- "source": [
- "model = xgb.XGBClassifier(early_stopping_rounds=10)\n",
- "model.set_params(\n",
- " eval_metric=[\"error\", \"logloss\", \"auc\"],\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f6430312",
- "metadata": {},
- "source": [
- "Finally, our actual training step — where the model learns patterns from the data, so it can make predictions later:\n",
- "\n",
- "- The model is trained on `x_train` and `y_train`, and evaluates its performance using `x_val` and `y_val` to check if it’s learning well.\n",
- "- To turn off printed output while training, we'll set `verbose` to `False`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e3aa3657",
- "metadata": {},
- "outputs": [],
- "source": [
- "model.fit(\n",
- " x_train,\n",
- " y_train,\n",
- " eval_set=[(x_val, y_val)],\n",
- " verbose=False,\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c303a046",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Initialize the ValidMind model\n",
- "\n",
- "You'll also need to initialize a ValidMind model object (`vm_model`) that can be passed to other functions for analysis and tests on the data for our model.\n",
- "\n",
- "You simply initialize this model object with [`vm.init_model()`](https://docs.validmind.ai/validmind/validmind.html#init_model):"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4b2be11f",
- "metadata": {},
- "outputs": [],
- "source": [
- "vm_model_xgb = vm.init_model(\n",
- " model,\n",
- " input_id=\"xgboost\",\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "2fa83857",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Assign predictions\n",
- "\n",
- "Once the model has been registered, you can assign model predictions to the training and testing datasets.\n",
- "\n",
- "- The [`assign_predictions()` method](https://docs.validmind.ai/validmind/validmind/vm_models.html#assign_predictions) from the `Dataset` object can link existing predictions to any number of models.\n",
- "- This method links the model's class prediction values and probabilities to our `vm_train_ds` and `vm_test_ds` datasets.\n",
- "\n",
- "If no prediction values are passed, the method will compute predictions automatically:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "229185fd",
- "metadata": {},
- "outputs": [],
- "source": [
- "vm_train_ds.assign_predictions(model=vm_model_xgb)\n",
- "vm_test_ds.assign_predictions(model=vm_model_xgb)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d0b3312e",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "## Running ValidMind tests\n",
- "\n",
- "Now that we know how to initialize ValidMind `dataset` and `model` objects, 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",
- "- **`inputs`** — A dictionary of test inputs, such as `dataset`, `model`, `datasets`, or `models`. These are ValidMind objects initialized with [`vm.init_dataset()`](https://docs.validmind.ai/validmind/validmind.html#init_dataset) or [`vm.init_model()`](https://docs.validmind.ai/validmind/validmind.html#init_model)."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "96c89f32",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Run classifier performance test with one model\n",
- "\n",
- "Run `validmind.data_validation.ClassifierPerformance` test with the testing dataset (`vm_test_ds`) and model (`vm_model_xgb`) as inputs:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "85189af9",
- "metadata": {},
- "outputs": [],
- "source": [
- "result = vm.tests.run_test(\n",
- " \"validmind.model_validation.sklearn.ClassifierPerformance\",\n",
- " inputs={\n",
- " \"dataset\": vm_test_ds,\n",
- " \"model\": vm_model_xgb,\n",
- " },\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "676dff89",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Run comparison tests\n",
- "\n",
- "To evaluate which models might be a better fit for a use case based on their performance on selected criteria, we can run the same test with multiple models. We'll train three additional models and run the classifier performance test with for all four models using a single `run_test()` call.\n",
- "\n",
- "ValidMind helps streamline your documentation and testing.\n",
- "
\n",
- "You could call run_test() multiple times passing in different inputs, but you can also pass an input_grid object — a dictionary of test input keys and values that allow you to run a single test for a combination of models and datasets.\n",
- "
\n",
- "With input_grid, run comparison tests for multiple datasets, or even multiple datasets and models simultaneously — input_grid can be used with run_test() for all possible combinations of inputs, generating a cohesive and comprehensive single output.\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "3d9912dc",
- "metadata": {},
- "source": [
- "*Random forest classifier* models use an ensemble method that builds multiple decision trees and averages their predictions. Random forest is robust to overfitting and handles non-linear relations well, but is typically less interpretable than simpler models:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1976b7e8",
- "metadata": {},
- "outputs": [],
- "source": [
- "from sklearn.ensemble import RandomForestClassifier\n",
- "\n",
- "# Train the random forest classifer model\n",
- "model_rf = RandomForestClassifier()\n",
- "model_rf.fit(x_train, y_train)\n",
- "\n",
- "# Initialize the ValidMind model object for the random forest classifer model\n",
- "vm_model_rf = vm.init_model(\n",
- " model_rf,\n",
- " input_id=\"random_forest\",\n",
- ")\n",
- "\n",
- "# Assign predictions to the test dataset for the random forest classifer model\n",
- "vm_test_ds.assign_predictions(model=vm_model_rf)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a259927c",
- "metadata": {},
- "source": [
- "*Logistic regression* models are linear models that estimate class probabilities via a logistic (sigmoid) function. Logistic regression is highly interpretable with fast training, establishing a strong baseline — however, they struggle when relationships are non-linear as real-world relationships often are:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "90bbf148",
- "metadata": {},
- "outputs": [],
- "source": [
- "from sklearn.linear_model import LogisticRegression\n",
- "from sklearn.preprocessing import StandardScaler\n",
- "from sklearn.pipeline import Pipeline\n",
- "\n",
- "# Scaling features ensures the lbfgs solver converges reliably\n",
- "model_lr = Pipeline([\n",
- " (\"scaler\", StandardScaler()),\n",
- " (\"lr\", LogisticRegression()),\n",
- "])\n",
- "model_lr.fit(x_train, y_train)\n",
- "\n",
- "# Initialize the ValidMind model object for the logistic regression model\n",
- "vm_model_lr = vm.init_model(\n",
- " model_lr,\n",
- " input_id=\"logistic_regression\",\n",
- ")\n",
- "\n",
- "# Assign predictions to the test dataset for the logistic regression model\n",
- "vm_test_ds.assign_predictions(model=vm_model_lr)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9a666b41",
- "metadata": {},
- "source": [
- "*Decision tree classifier* models are a single tree with data split on feature thresholds. Useful as an explanability benchmark, decision trees are easy to visualize and interpret — but are prone to overfitting without pruning or ensemble techniques:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "bfa1e17d",
- "metadata": {},
- "outputs": [],
- "source": [
- "from sklearn.tree import DecisionTreeClassifier\n",
- "\n",
- "# Train the decision tree classifer model\n",
- "model_dt = DecisionTreeClassifier()\n",
- "model_dt.fit(x_train, y_train)\n",
- "\n",
- "# Initialize the ValidMind model object for the decision tree classifier model\n",
- "vm_model_dt = vm.init_model(\n",
- " model_dt,\n",
- " input_id=\"decision_tree\",\n",
- ")\n",
- "\n",
- "# Assign predictions to the test dataset for the decision tree classifiermodel\n",
- "vm_test_ds.assign_predictions(model=vm_model_dt)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "2c8f3268",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "#### Run classifier performance test with multiple models\n",
- "\n",
- "Now, we'll use the `input_grid` to run the [`ClassifierPerformance` test](https://docs.validmind.ai/tests/model_validation/sklearn/ClassifierPerformance.html) on all four models using the testing dataset (`vm_test_ds`).\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 to signify that this test was run on `all_models` to differentiate this test run from other runs:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "2e48ce1e",
- "metadata": {},
- "outputs": [],
- "source": [
- "perf_comparison_result = vm.tests.run_test(\n",
- " \"validmind.model_validation.sklearn.ClassifierPerformance:all_models\",\n",
- " input_grid={\n",
- " \"dataset\": [vm_test_ds],\n",
- " \"model\": [vm_model_xgb, vm_model_rf, vm_model_lr, vm_model_dt],\n",
- " },\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "81cbf144",
- "metadata": {},
- "source": [
- "Our output indicates that the XGBoost and random forest classification models provide the strongest overall classification performance, so we'll continue our testing with those two models as input only."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "3d3fb6ec",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "#### Run classifier performance test with multiple parameter values\n",
- "\n",
- "Next, let's run the classifier performance test with the `param_grid` object, which runs the same test multiple times with different parameter values. We'll append an identifier to signify that this test was run with our `parameter_grid` configuration:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d0ad94c9",
- "metadata": {},
- "outputs": [],
- "source": [
- "parameter_comparison_result = vm.tests.run_test(\n",
- " \"validmind.model_validation.sklearn.ClassifierPerformance:parameter_grid\",\n",
- " input_grid={\n",
- " \"dataset\": [vm_test_ds],\n",
- " \"model\": [vm_model_xgb,vm_model_rf]\n",
- " },\n",
- " param_grid={\n",
- " \"average\": [\"macro\", \"micro\"]\n",
- " },\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "508c7546",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "#### Run comparison test with multiple datasets\n",
- "\n",
- "Let's also run the [ROCCurve test](https://docs.validmind.ai/tests/model_validation/sklearn/ROCCurve.html) using `input_grid` to iterate through multiple datasets, which plots the ROC curves for the training (`vm_train_ds`) and test (`vm_test_ds`) datasets side by side — a common scenario when you want to compare the performance of a model on the training and test datasets and visually assess how much performance is lost in the test dataset.\n",
- "\n",
- "We'll also need to assign predictions to the training dataset for the random forest classifier model, since we didn't do that in our earlier setup:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "96c3b426",
- "metadata": {},
- "outputs": [],
- "source": [
- "vm_train_ds.assign_predictions(model=vm_model_rf)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "2be82bae",
- "metadata": {},
- "source": [
- "We'll append an identifier to signify that this test was run with our `train_vs_test` dataset comparison configuration:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4056aa1e",
- "metadata": {},
- "outputs": [],
- "source": [
- "roc_curve_result = vm.tests.run_test(\n",
- " \"validmind.model_validation.sklearn.ROCCurve:train_vs_test\",\n",
- " input_grid={\n",
- " \"dataset\": [vm_train_ds, vm_test_ds],\n",
- " \"model\": [vm_model_xgb,vm_model_rf],\n",
- " },\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a05570d5",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "## Work with test 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. When logging individual test results to the platform, you'll need to manually add those results to the desired section of the model documentation.\n",
- "\n",
- "You can do this through the ValidMind Platform interface after logging your test results ([Learn more ...](https://docs.validmind.ai/developer/model-documentation/work-with-test-results.html)), or directly via the ValidMind Library when calling `.log()` by providing an optional `section_id`. The `section_id` should be a string that matches the title of a section in the documentation template in `snake_case`.\n",
- "\n",
- "Let's log the results of the classifier performance test (`perf_comparison_result`) and the ROCCurve (`roc_curve_result`) test in the `model_evaluation` section of the documentation — present in the template we previewed in the beginning of this notebook:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e119bf1e",
- "metadata": {},
- "outputs": [],
- "source": [
- "perf_comparison_result.log(section_id=\"model_evaluation\")\n",
- "roc_curve_result.log(section_id=\"model_evaluation\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ab5205ee",
- "metadata": {},
- "source": [
- "Finally, let's head to the model we connected to at the beginning of this notebook and view our inserted test results in the updated documentation ([Need more help?](https://docs.validmind.ai/guide/model-documentation/working-with-model-documentation.html)):\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 **Development** under Documents.\n",
- "\n",
- "3. Expand the **3.2. Model Evaluation** section.\n",
- "\n",
- "4. Confirm that `perf_comparison_result` and `roc_curve_result` display in this section as expected."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "eb196aac",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "## Next steps\n",
- "\n",
- "Now that you know how to run comparison tests with the ValidMind Library, you’re ready to take the next step. Extend the functionality of `run_test()` with your own custom test functions that can be incorporated into documentation templates just like any default out-of-the-box ValidMind test.\n",
- "\n",
- "Learn how to implement custom tests with the ValidMind Library.\n",
- "
\n",
- "Check out our
Implement comparison tests notebook for code examples and usage of key functions.
"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "083c1d8d",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Discover more learning resources\n",
- "\n",
- "We offer many interactive notebooks to help you automate testing, documenting, validating, and more:\n",
- "\n",
- "- [Run tests & test suites](https://docs.validmind.ai/developer/how-to/testing-overview.html)\n",
- "- [Use ValidMind Library features](https://docs.validmind.ai/developer/how-to/feature-overview.html)\n",
- "- [Code samples by use case](https://docs.validmind.ai/guide/samples-jupyter-notebooks.html)\n",
- "\n",
- "Or, visit our [documentation](https://docs.validmind.ai/) to learn more about ValidMind."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "efba0f57",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "## Upgrade ValidMind\n",
- "\n",
- "After installing ValidMind, you’ll want to periodically make sure you are on the latest version to access any new features and other enhancements.
\n",
- "\n",
- "Retrieve the information for the currently installed version of ValidMind:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "0d35972c",
- "metadata": {
- "vscode": {
- "languageId": "plaintext"
- }
- },
- "outputs": [],
- "source": [
- "%pip show validmind"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "abcd07ef",
- "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": "5fe70b90",
- "metadata": {},
- "source": [
- "You may need to restart your kernel after running the upgrade package for changes to be applied."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "\n",
- "\n",
- "***\n",
- "\n",
- "Copyright © 2023-2026 ValidMind Inc. All rights reserved.
\n",
- "Refer to [LICENSE](https://github.com/validmind/validmind-library/blob/main/LICENSE) for details.
\n",
- "SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "name": "python",
- "version": "3.10"
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "1d29276f",
+ "metadata": {},
+ "source": [
+ "# Run comparison tests\n",
+ "\n",
+ "Learn how to use the ValidMind Library to run comparison tests that take any datasets or models as inputs. Identify comparison tests to run, initialize ValidMind dataset and model objects in preparation for passing them to tests, and then run tests — generating outputs automatically logged to your model's documentation in the ValidMind Platform.\n",
+ "\n",
+ "We recommend that you first complete our introductory notebook on running tests.\n",
+ "
\n",
+ "
Run dataset-based tests "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "61065444",
+ "metadata": {},
+ "source": [
+ "::: {.content-hidden when-format=\"html\"}\n",
+ "## Contents \n",
+ "- [About ValidMind](#toc1__) \n",
+ " - [Before you begin](#toc1_1__) \n",
+ " - [New to ValidMind?](#toc1_2__) \n",
+ " - [Key concepts](#toc1_3__) \n",
+ "- [Setting up](#toc2__) \n",
+ " - [Install the ValidMind Library](#toc2_1__) \n",
+ " - [Initialize the ValidMind Library](#toc2_2__) \n",
+ " - [Register sample model](#toc2_2_1__) \n",
+ " - [Apply documentation template](#toc2_2_2__) \n",
+ " - [Get your code snippet](#toc2_2_3__) \n",
+ " - [Preview the documentation template](#toc2_3__) \n",
+ " - [Initialize the Python environment](#toc2_4__) \n",
+ "- [Explore a ValidMind test](#toc3__) \n",
+ "- [Working with ValidMind datasets](#toc4__) \n",
+ " - [Import the sample dataset](#toc4_1__) \n",
+ " - [Split the dataset](#toc4_2__) \n",
+ " - [Initialize the ValidMind dataset](#toc4_3__) \n",
+ "- [Working with ValidMind models](#toc5__) \n",
+ " - [Train a sample model](#toc5_1__) \n",
+ " - [Initialize the ValidMind model](#toc5_2__) \n",
+ " - [Assign predictions](#toc5_3__) \n",
+ "- [Running ValidMind tests](#toc6__) \n",
+ " - [Run classifier performance test with one model](#toc6_1__) \n",
+ " - [Run comparison tests](#toc6_2__) \n",
+ " - [Run classifier performance test with multiple models](#toc6_2_1__) \n",
+ " - [Run classifier performance test with multiple parameter values](#toc6_2_2__) \n",
+ " - [Run comparison test with multiple datasets](#toc6_2_3__) \n",
+ "- [Work with test results](#toc7__) \n",
+ "- [Next steps](#toc8__) \n",
+ " - [Discover more learning resources](#toc8_1__) \n",
+ "- [Upgrade ValidMind](#toc9__) \n",
+ "\n",
+ ":::\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "67a4d9dc",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "## About ValidMind\n",
+ "\n",
+ "ValidMind is a suite of tools for managing model risk, including risk associated with AI and statistical models. \n",
+ "\n",
+ "You use the ValidMind Library to automate documentation and validation tests, and then use the ValidMind Platform to collaborate on model documentation. Together, these products simplify model risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and model validators."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "eeb30df8",
+ "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",
+ "\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": "293c3f98",
+ "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",
+ "\n",
+ "For access to all features available in this notebook, you'll need access to a ValidMind account.\n",
+ "
\n",
+ "
Register with ValidMind "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4fc836d0",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### Key concepts\n",
+ "\n",
+ "**Model documentation**: A structured and detailed record pertaining to a model, encompassing key components such as its underlying assumptions, methodologies, data sources, inputs, performance metrics, evaluations, limitations, and intended uses. It serves to ensure transparency, adherence to regulatory requirements, and a clear understanding of potential risks associated with the model’s application.\n",
+ "\n",
+ "**Documentation template**: Functions as a test suite and lays out the structure of model documentation, segmented into various sections and sub-sections. Documentation templates define the structure of your model documentation, specifying the tests that should be run, and how the results should be displayed.\n",
+ "\n",
+ "**Tests**: A function contained in the ValidMind Library, designed to run a specific quantitative test on the dataset or model. Tests are the building blocks of ValidMind, used to evaluate and document models and datasets, and can be run individually or as part of a suite defined by your model documentation template.\n",
+ "\n",
+ "**Metrics**: A subset of tests that do not have thresholds. In the context of this notebook, metrics and tests can be thought of as interchangeable concepts.\n",
+ "\n",
+ "**Custom metrics**: Custom metrics are functions that you define to evaluate your model or dataset. These functions can be registered with the ValidMind Library to be used in the ValidMind Platform.\n",
+ "\n",
+ "**Inputs**: Objects to be evaluated and documented in the ValidMind Library. They can be any of the following:\n",
+ "\n",
+ " - **model**: A single model that has been initialized in ValidMind with [`vm.init_model()`](https://docs.validmind.ai/validmind/validmind.html#init_model).\n",
+ " - **dataset**: Single dataset that has been initialized in ValidMind with [`vm.init_dataset()`](https://docs.validmind.ai/validmind/validmind.html#init_dataset).\n",
+ " - **models**: A list of ValidMind models - usually this is used when you want to compare multiple models in your custom metric.\n",
+ " - **datasets**: A list of ValidMind datasets - usually this is used when you want to compare multiple datasets in your custom metric. (Learn more: [Run tests with multiple datasets](https://docs.validmind.ai/notebooks/how_to/tests/run_tests/configure_tests/run_tests_that_require_multiple_datasets.html))\n",
+ "\n",
+ "**Parameters**: Additional arguments that can be passed when running a ValidMind test, used to pass additional information to a metric, customize its behavior, or provide additional context.\n",
+ "\n",
+ "**Outputs**: Custom metrics can return elements like tables or plots. Tables may be a list of dictionaries (each representing a row) or a pandas DataFrame. Plots may be matplotlib or plotly figures.\n",
+ "\n",
+ "**Test suites**: Collections of tests designed to run together to automate and generate model documentation end-to-end for specific use-cases.\n",
+ "\n",
+ "Example: the [`classifier_full_suite`](https://docs.validmind.ai/validmind/validmind/test_suites/classifier.html#ClassifierFullSuite) test suite runs tests from the [`tabular_dataset`](https://docs.validmind.ai/validmind/validmind/test_suites/tabular_datasets.html) and [`classifier`](https://docs.validmind.ai/validmind/validmind/test_suites/classifier.html) test suites to fully document the data and model sections for binary classification model use-cases."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8d52b6e0",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "## Setting up"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e0d2daaf",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### Install the ValidMind Library\n",
+ "\n",
+ "Recommended Python versions\n",
+ "
\n",
+ "Python 3.8 <= x <= 3.14
\n",
+ "\n",
+ "To install the library:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fc97888f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%pip install -q validmind"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1ff56571",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### Initialize the ValidMind Library"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c4d9f164",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "#### Register sample model\n",
+ "\n",
+ "Let's first register a sample model for use with this notebook.\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 **Next >** to continue to assignment of model stakeholders. ([Need more help?](https://docs.validmind.ai/guide/model-inventory/register-models-in-inventory.html))\n",
+ "\n",
+ "4. Select your own name under the **MODEL OWNER** drop-down.\n",
+ "\n",
+ "5. Click **Register Model** to add the model to your inventory."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "852392e5",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "#### Apply documentation template\n",
+ "\n",
+ "Once you've registered your model, let's select a documentation template. A template predefines sections for your model documentation and provides a general outline to follow, making the documentation process much easier.\n",
+ "\n",
+ "1. In the left sidebar that appears for your model, click **Documents** and select **Development**.\n",
+ "\n",
+ "2. Under **TEMPLATE**, select `Binary classification`.\n",
+ "\n",
+ "3. Click **Use Template** to apply the template."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6490e991",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "#### Get your code snippet\n",
+ "\n",
+ "Initialize the ValidMind Library with the *code snippet* unique to each model per document, ensuring your test results are uploaded to the correct model and automatically populated in the right document in the ValidMind Platform when you run this notebook.\n",
+ "\n",
+ "1. On the left sidebar that appears for your model, select **Getting Started** and select `Development` from the **DOCUMENT** drop-down menu.\n",
+ "2. Click **Copy snippet to clipboard**.\n",
+ "3. 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": "c51ae01c",
+ "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",
+ " document=\"documentation\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "99e9d14f",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### Preview the documentation template\n",
+ "\n",
+ "Let's verify that you have connected the ValidMind Library to the ValidMind Platform and that the appropriate *template* is selected for your model.\n",
+ "\n",
+ "You will upload documentation and test results unique to your model based on this template later on. For now, **take a look at the default structure that the template provides with [the `vm.preview_template()` function](https://docs.validmind.ai/validmind/validmind.html#preview_template)** from the ValidMind library and note the empty sections:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fd332a9d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "vm.preview_template()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f805ec38",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### Initialize the Python environment\n",
+ "\n",
+ "Next, let's import the necessary libraries and set up your Python environment for data analysis:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8e2127cd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import xgboost as xgb\n",
+ "\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1783e13c",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "## Explore a ValidMind test\n",
+ "\n",
+ "Before we run a test, use [the `vm.tests.list_tests()` function](https://docs.validmind.ai/validmind/validmind/tests.html#list_tests) to return information on out-of-the-box tests available in the ValidMind Library.\n",
+ "\n",
+ "Let's assume you want to evaluate *classifier performance* for a model. Classifier performance measures how well a classification model correctly predicts outcomes, using metrics like [precision, recall, and F1 score](https://en.wikipedia.org/wiki/Precision_and_recall).\n",
+ "\n",
+ "We'll pass in a `filter` to the `list_tests` function to find the test ID for classifier performance:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a6a6f715",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "vm.tests.list_tests(filter=\"ClassifierPerformance\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "96a56e4b",
+ "metadata": {},
+ "source": [
+ "We've identified from the output that the test ID for the classifier performance test is `validmind.model_validation.ClassifierPerformance`.\n",
+ "\n",
+ "Use this ID combined with [the `describe_test()` function](https://docs.validmind.ai/validmind/validmind/tests.html#describe_test) to retrieve more information about the test, including its **Required Inputs**:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f8a46c7d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "test_id = \"validmind.model_validation.sklearn.ClassifierPerformance\"\n",
+ "vm.tests.describe_test(test_id)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "97053f50",
+ "metadata": {},
+ "source": [
+ "Since this test requires a dataset and a model, you can expect it to throw an error when we run it without passing in either as input:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f853c272",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " vm.tests.run_test(test_id)\n",
+ "except Exception as e:\n",
+ " print(e)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1a3115ed",
+ "metadata": {},
+ "source": [
+ "Learn more about the individual tests available in the ValidMind Library\n",
+ "
\n",
+ "Check out our
Explore tests notebook for more code examples and usage of key functions.
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "89da851b",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "## Working with ValidMind datasets"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "50bfdb1b",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### Import the sample dataset\n",
+ "\n",
+ "Since we need a dataset to run tests, let's import the public [Bank Customer Churn Prediction](https://www.kaggle.com/datasets/shantanudhakadd/bank-customer-churn-prediction) dataset from Kaggle so that we have something to work with.\n",
+ "\n",
+ "In our below example, note that:\n",
+ "\n",
+ "- The target column, `Exited` has a value of `1` when a customer has churned and `0` otherwise.\n",
+ "- The ValidMind Library provides a wrapper to automatically load the dataset as a [Pandas DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) object. A Pandas Dataframe is a two-dimensional tabular data structure that makes use of rows and columns."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3ef2dfbb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Import the sample dataset from the library\n",
+ "\n",
+ "from validmind.datasets.classification import customer_churn\n",
+ "\n",
+ "print(\n",
+ " f\"Loaded demo dataset with: \\n\\n\\t• Target column: '{customer_churn.target_column}' \\n\\t• Class labels: {customer_churn.class_labels}\"\n",
+ ")\n",
+ "\n",
+ "raw_df = customer_churn.load_data()\n",
+ "raw_df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a5a8212f",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### Split the dataset\n",
+ "\n",
+ "Let's first split our dataset to help assess how well the model generalizes to unseen data.\n",
+ "\n",
+ "Use [`preprocess()`](https://docs.validmind.ai/validmind/validmind/datasets/classification/customer_churn.html#preprocess) to split our dataset into three subsets:\n",
+ "\n",
+ "1. **train_df** — Used to train the model.\n",
+ "2. **validation_df** — Used to evaluate the model's performance during training.\n",
+ "3. **test_df** — Used later on to asses the model's performance on new, unseen data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "88c87d4a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "train_df, validation_df, test_df = customer_churn.preprocess(raw_df)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2ae225d7",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### Initialize the ValidMind dataset\n",
+ "\n",
+ "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",
+ "ValidMind dataset objects provide a wrapper to any type of dataset (NumPy, Pandas, Polars, etc.) so that tests can run transparently regardless of the underlying library.\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,
+ "id": "bf0ec747",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "vm_train_ds = vm.init_dataset(\n",
+ " dataset=train_df,\n",
+ " input_id=\"train_dataset\",\n",
+ " target_column=customer_churn.target_column,\n",
+ ")\n",
+ "\n",
+ "vm_test_ds = vm.init_dataset(\n",
+ " dataset=test_df,\n",
+ " input_id=\"test_dataset\",\n",
+ " target_column=customer_churn.target_column,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6d26f65b",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "## Working with ValidMind models"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6d1677f6",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### Train a sample model\n",
+ "\n",
+ "To train the model, we need to provide it with:\n",
+ "\n",
+ "1. **Inputs** — Features such as customer age, usage, etc.\n",
+ "2. **Outputs (Expected answers/labels)** — in our case, we would like to know whether the customer churned or not.\n",
+ "\n",
+ "Here, we'll use `x_train` and `x_val` to hold the input data (features), and `y_train` and `y_val` to hold the answers (the target we want to predict):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "39e8c7ea",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_train = train_df.drop(customer_churn.target_column, axis=1)\n",
+ "y_train = train_df[customer_churn.target_column]\n",
+ "x_val = validation_df.drop(customer_churn.target_column, axis=1)\n",
+ "y_val = validation_df[customer_churn.target_column]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4ac628eb",
+ "metadata": {},
+ "source": [
+ "Next, let's create an *XGBoost classifier model* that will automatically stop training if it doesn't improve after 10 tries. XGBoost is a gradient-boosted tree ensemble that builds trees sequentially, with each tree correcting the errors of the previous ones — typically known for strong predictive performance and built-in regularization to reduce overfitting.\n",
+ "\n",
+ "Setting an explicit threshold avoids wasting time and helps prevent further overfitting by stopping training when further improvement isn't happening. We'll also set three evaluation metrics to get a more complete picture of model performance:\n",
+ "\n",
+ "1. **error** — Measures how often the model makes incorrect predictions.\n",
+ "2. **logloss** — Indicates how confident the predictions are.\n",
+ "3. **auc** — Evaluates how well the model distinguishes between churn and not churn."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "255e3583",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = xgb.XGBClassifier(early_stopping_rounds=10)\n",
+ "model.set_params(\n",
+ " eval_metric=[\"error\", \"logloss\", \"auc\"],\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f6430312",
+ "metadata": {},
+ "source": [
+ "Finally, our actual training step — where the model learns patterns from the data, so it can make predictions later:\n",
+ "\n",
+ "- The model is trained on `x_train` and `y_train`, and evaluates its performance using `x_val` and `y_val` to check if it’s learning well.\n",
+ "- To turn off printed output while training, we'll set `verbose` to `False`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e3aa3657",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model.fit(\n",
+ " x_train,\n",
+ " y_train,\n",
+ " eval_set=[(x_val, y_val)],\n",
+ " verbose=False,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c303a046",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### Initialize the ValidMind model\n",
+ "\n",
+ "You'll also need to initialize a ValidMind model object (`vm_model`) that can be passed to other functions for analysis and tests on the data for our model.\n",
+ "\n",
+ "You simply initialize this model object with [`vm.init_model()`](https://docs.validmind.ai/validmind/validmind.html#init_model):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4b2be11f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "vm_model_xgb = vm.init_model(\n",
+ " model,\n",
+ " input_id=\"xgboost\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2fa83857",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### Assign predictions\n",
+ "\n",
+ "Once the model has been registered, you can assign model predictions to the training and testing datasets.\n",
+ "\n",
+ "- The [`assign_predictions()` method](https://docs.validmind.ai/validmind/validmind/vm_models.html#assign_predictions) from the `Dataset` object can link existing predictions to any number of models.\n",
+ "- This method links the model's class prediction values and probabilities to our `vm_train_ds` and `vm_test_ds` datasets.\n",
+ "\n",
+ "If no prediction values are passed, the method will compute predictions automatically:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "229185fd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "vm_train_ds.assign_predictions(model=vm_model_xgb)\n",
+ "vm_test_ds.assign_predictions(model=vm_model_xgb)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d0b3312e",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "## Running ValidMind tests\n",
+ "\n",
+ "Now that we know how to initialize ValidMind `dataset` and `model` objects, 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",
+ "- **`inputs`** — A dictionary of test inputs, such as `dataset`, `model`, `datasets`, or `models`. These are ValidMind objects initialized with [`vm.init_dataset()`](https://docs.validmind.ai/validmind/validmind.html#init_dataset) or [`vm.init_model()`](https://docs.validmind.ai/validmind/validmind.html#init_model)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "96c89f32",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### Run classifier performance test with one model\n",
+ "\n",
+ "Run `validmind.data_validation.ClassifierPerformance` test with the testing dataset (`vm_test_ds`) and model (`vm_model_xgb`) as inputs:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "85189af9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "result = vm.tests.run_test(\n",
+ " \"validmind.model_validation.sklearn.ClassifierPerformance\",\n",
+ " inputs={\n",
+ " \"dataset\": vm_test_ds,\n",
+ " \"model\": vm_model_xgb,\n",
+ " },\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "676dff89",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### Run comparison tests\n",
+ "\n",
+ "To evaluate which models might be a better fit for a use case based on their performance on selected criteria, we can run the same test with multiple models. We'll train three additional models and run the classifier performance test with for all four models using a single `run_test()` call.\n",
+ "\n",
+ "ValidMind helps streamline your documentation and testing.\n",
+ "
\n",
+ "You could call run_test() multiple times passing in different inputs, but you can also pass an input_grid object — a dictionary of test input keys and values that allow you to run a single test for a combination of models and datasets.\n",
+ "
\n",
+ "With input_grid, run comparison tests for multiple datasets, or even multiple datasets and models simultaneously — input_grid can be used with run_test() for all possible combinations of inputs, generating a cohesive and comprehensive single output.\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3d9912dc",
+ "metadata": {},
+ "source": [
+ "*Random forest classifier* models use an ensemble method that builds multiple decision trees and averages their predictions. Random forest is robust to overfitting and handles non-linear relations well, but is typically less interpretable than simpler models:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1976b7e8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.ensemble import RandomForestClassifier\n",
+ "\n",
+ "# Train the random forest classifer model\n",
+ "model_rf = RandomForestClassifier()\n",
+ "model_rf.fit(x_train, y_train)\n",
+ "\n",
+ "# Initialize the ValidMind model object for the random forest classifer model\n",
+ "vm_model_rf = vm.init_model(\n",
+ " model_rf,\n",
+ " input_id=\"random_forest\",\n",
+ ")\n",
+ "\n",
+ "# Assign predictions to the test dataset for the random forest classifer model\n",
+ "vm_test_ds.assign_predictions(model=vm_model_rf)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a259927c",
+ "metadata": {},
+ "source": [
+ "*Logistic regression* models are linear models that estimate class probabilities via a logistic (sigmoid) function. Logistic regression is highly interpretable with fast training, establishing a strong baseline — however, they struggle when relationships are non-linear as real-world relationships often are:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "90bbf148",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.linear_model import LogisticRegression\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "from sklearn.pipeline import Pipeline\n",
+ "\n",
+ "# Scaling features ensures the lbfgs solver converges reliably\n",
+ "model_lr = Pipeline([\n",
+ " (\"scaler\", StandardScaler()),\n",
+ " (\"lr\", LogisticRegression()),\n",
+ "])\n",
+ "model_lr.fit(x_train, y_train)\n",
+ "\n",
+ "# Initialize the ValidMind model object for the logistic regression model\n",
+ "vm_model_lr = vm.init_model(\n",
+ " model_lr,\n",
+ " input_id=\"logistic_regression\",\n",
+ ")\n",
+ "\n",
+ "# Assign predictions to the test dataset for the logistic regression model\n",
+ "vm_test_ds.assign_predictions(model=vm_model_lr)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9a666b41",
+ "metadata": {},
+ "source": [
+ "*Decision tree classifier* models are a single tree with data split on feature thresholds. Useful as an explanability benchmark, decision trees are easy to visualize and interpret — but are prone to overfitting without pruning or ensemble techniques:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bfa1e17d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.tree import DecisionTreeClassifier\n",
+ "\n",
+ "# Train the decision tree classifer model\n",
+ "model_dt = DecisionTreeClassifier()\n",
+ "model_dt.fit(x_train, y_train)\n",
+ "\n",
+ "# Initialize the ValidMind model object for the decision tree classifier model\n",
+ "vm_model_dt = vm.init_model(\n",
+ " model_dt,\n",
+ " input_id=\"decision_tree\",\n",
+ ")\n",
+ "\n",
+ "# Assign predictions to the test dataset for the decision tree classifiermodel\n",
+ "vm_test_ds.assign_predictions(model=vm_model_dt)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2c8f3268",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "#### Run classifier performance test with multiple models\n",
+ "\n",
+ "Now, we'll use the `input_grid` to run the [`ClassifierPerformance` test](https://docs.validmind.ai/tests/model_validation/sklearn/ClassifierPerformance.html) on all four models using the testing dataset (`vm_test_ds`).\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 to signify that this test was run on `all_models` to differentiate this test run from other runs:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2e48ce1e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "perf_comparison_result = vm.tests.run_test(\n",
+ " \"validmind.model_validation.sklearn.ClassifierPerformance:all_models\",\n",
+ " input_grid={\n",
+ " \"dataset\": [vm_test_ds],\n",
+ " \"model\": [vm_model_xgb, vm_model_rf, vm_model_lr, vm_model_dt],\n",
+ " },\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "81cbf144",
+ "metadata": {},
+ "source": [
+ "Our output indicates that the XGBoost and random forest classification models provide the strongest overall classification performance, so we'll continue our testing with those two models as input only."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3d3fb6ec",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "#### Run classifier performance test with multiple parameter values\n",
+ "\n",
+ "Next, let's run the classifier performance test with the `param_grid` object, which runs the same test multiple times with different parameter values. We'll append an identifier to signify that this test was run with our `parameter_grid` configuration:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d0ad94c9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "parameter_comparison_result = vm.tests.run_test(\n",
+ " \"validmind.model_validation.sklearn.ClassifierPerformance:parameter_grid\",\n",
+ " input_grid={\n",
+ " \"dataset\": [vm_test_ds],\n",
+ " \"model\": [vm_model_xgb,vm_model_rf]\n",
+ " },\n",
+ " param_grid={\n",
+ " \"average\": [\"macro\", \"micro\"]\n",
+ " },\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "508c7546",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "#### Run comparison test with multiple datasets\n",
+ "\n",
+ "Let's also run the [ROCCurve test](https://docs.validmind.ai/tests/model_validation/sklearn/ROCCurve.html) using `input_grid` to iterate through multiple datasets, which plots the ROC curves for the training (`vm_train_ds`) and test (`vm_test_ds`) datasets side by side — a common scenario when you want to compare the performance of a model on the training and test datasets and visually assess how much performance is lost in the test dataset.\n",
+ "\n",
+ "We'll also need to assign predictions to the training dataset for the random forest classifier model, since we didn't do that in our earlier setup:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "96c3b426",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "vm_train_ds.assign_predictions(model=vm_model_rf)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2be82bae",
+ "metadata": {},
+ "source": [
+ "We'll append an identifier to signify that this test was run with our `train_vs_test` dataset comparison configuration:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4056aa1e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "roc_curve_result = vm.tests.run_test(\n",
+ " \"validmind.model_validation.sklearn.ROCCurve:train_vs_test\",\n",
+ " input_grid={\n",
+ " \"dataset\": [vm_train_ds, vm_test_ds],\n",
+ " \"model\": [vm_model_xgb,vm_model_rf],\n",
+ " },\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a05570d5",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "## Work with test 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. When logging individual test results to the platform, you'll need to manually add those results to the desired section of the model documentation.\n",
+ "\n",
+ "You can do this through the ValidMind Platform interface after logging your test results ([Learn more ...](https://docs.validmind.ai/developer/model-documentation/work-with-test-results.html)), or directly via the ValidMind Library when calling `.log()` by providing an optional `section_id`. The `section_id` should be a string that matches the title of a section in the documentation template in `snake_case`.\n",
+ "\n",
+ "Let's log the results of the classifier performance test (`perf_comparison_result`) and the ROCCurve (`roc_curve_result`) test in the `model_evaluation` section of the documentation — present in the template we previewed in the beginning of this notebook:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e119bf1e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "perf_comparison_result.log(section_id=\"model_evaluation\")\n",
+ "roc_curve_result.log(section_id=\"model_evaluation\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ab5205ee",
+ "metadata": {},
+ "source": [
+ "Finally, let's head to the model we connected to at the beginning of this notebook and view our inserted test results in the updated documentation ([Need more help?](https://docs.validmind.ai/guide/model-documentation/working-with-model-documentation.html)):\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 **Development** under Documents.\n",
+ "\n",
+ "3. Expand the **3.2. Model Evaluation** section.\n",
+ "\n",
+ "4. Confirm that `perf_comparison_result` and `roc_curve_result` display in this section as expected."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "eb196aac",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "## Next steps\n",
+ "\n",
+ "Now that you know how to run comparison tests with the ValidMind Library, you’re ready to take the next step. Extend the functionality of `run_test()` with your own custom test functions that can be incorporated into documentation templates just like any default out-of-the-box ValidMind test.\n",
+ "\n",
+ "Learn how to implement custom tests with the ValidMind Library.\n",
+ "
\n",
+ "Check out our
Implement comparison tests notebook for code examples and usage of key functions.
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "083c1d8d",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### Discover more learning resources\n",
+ "\n",
+ "We offer many interactive notebooks to help you automate testing, documenting, validating, and more:\n",
+ "\n",
+ "- [Run tests & test suites](https://docs.validmind.ai/developer/how-to/testing-overview.html)\n",
+ "- [Use ValidMind Library features](https://docs.validmind.ai/developer/how-to/feature-overview.html)\n",
+ "- [Code samples by use case](https://docs.validmind.ai/guide/samples-jupyter-notebooks.html)\n",
+ "\n",
+ "Or, visit our [documentation](https://docs.validmind.ai/) to learn more about ValidMind."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "efba0f57",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "## Upgrade ValidMind\n",
+ "\n",
+ "After installing ValidMind, you’ll want to periodically make sure you are on the latest version to access any new features and other enhancements.
\n",
+ "\n",
+ "Retrieve the information for the currently installed version of ValidMind:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0d35972c",
+ "metadata": {
+ "vscode": {
+ "languageId": "plaintext"
}
+ },
+ "outputs": [],
+ "source": [
+ "%pip show validmind"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "abcd07ef",
+ "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": "5fe70b90",
+ "metadata": {},
+ "source": [
+ "You may need to restart your kernel after running the upgrade package for changes to be applied."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "copyright-54faffd51a5a4717a02b6be426d6b441",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "***\n",
+ "\n",
+ "Copyright © 2023-2026 ValidMind Inc. All rights reserved.
\n",
+ "Refer to [LICENSE](https://github.com/validmind/validmind-library/blob/main/LICENSE) for details.
\n",
+ "SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
},
- "nbformat": 4,
- "nbformat_minor": 5
+ "language_info": {
+ "name": "python",
+ "version": "3.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/notebooks/how_to/tests/run_tests/configure_tests/enable_pii_detection.ipynb b/notebooks/how_to/tests/run_tests/configure_tests/enable_pii_detection.ipynb
index e0ae28fff..ddbd734e9 100644
--- a/notebooks/how_to/tests/run_tests/configure_tests/enable_pii_detection.ipynb
+++ b/notebooks/how_to/tests/run_tests/configure_tests/enable_pii_detection.ipynb
@@ -157,7 +157,7 @@
"\n",
"Recommended Python versions\n",
"
\n",
- "Python 3.8 <= x <= 3.11
\n",
+ "Python 3.8 <= x <= 3.14\n",
"\n",
"To use PII detection powered by [Microsoft Presidio](https://microsoft.github.io/presidio/), install the library with the explicit `[pii-detection]` extra specifier:"
]
diff --git a/notebooks/quickstart/quickstart_model_documentation.ipynb b/notebooks/quickstart/quickstart_model_documentation.ipynb
index e7c28cf33..40287aa57 100644
--- a/notebooks/quickstart/quickstart_model_documentation.ipynb
+++ b/notebooks/quickstart/quickstart_model_documentation.ipynb
@@ -184,7 +184,7 @@
"\n",
"Recommended Python versions\n",
"
\n",
- "Python 3.8 <= x <= 3.11
\n",
+ "Python 3.8 <= x <= 3.14\n",
"\n",
"To install the library:"
]
diff --git a/notebooks/quickstart/quickstart_model_validation.ipynb b/notebooks/quickstart/quickstart_model_validation.ipynb
index 640e64015..63e17f2a8 100644
--- a/notebooks/quickstart/quickstart_model_validation.ipynb
+++ b/notebooks/quickstart/quickstart_model_validation.ipynb
@@ -259,7 +259,7 @@
"\n",
"Recommended Python versions\n",
"
\n",
- "Python 3.8 <= x <= 3.11
\n",
+ "Python 3.8 <= x <= 3.14\n",
"\n",
"To install the library:"
]
diff --git a/notebooks/templates/install-validmind/_install-initialize-with-development.ipynb b/notebooks/templates/install-validmind/_install-initialize-with-development.ipynb
index c3bea6d6e..80ebcaa70 100644
--- a/notebooks/templates/install-validmind/_install-initialize-with-development.ipynb
+++ b/notebooks/templates/install-validmind/_install-initialize-with-development.ipynb
@@ -17,7 +17,7 @@
"\n",
"Recommended Python versions\n",
"
\n",
- "Python 3.8 <= x <= 3.11
\n",
+ "Python 3.8 <= x <= 3.14\n",
"\n",
"To install the library:"
]
diff --git a/notebooks/templates/install-validmind/_install-initialize-with-monitoring.ipynb b/notebooks/templates/install-validmind/_install-initialize-with-monitoring.ipynb
index 33b31663c..7f2cf6d72 100644
--- a/notebooks/templates/install-validmind/_install-initialize-with-monitoring.ipynb
+++ b/notebooks/templates/install-validmind/_install-initialize-with-monitoring.ipynb
@@ -17,7 +17,7 @@
"\n",
"Recommended Python versions\n",
"
\n",
- "Python 3.8 <= x <= 3.11
\n",
+ "Python 3.8 <= x <= 3.14\n",
"\n",
"To install the library:"
]
diff --git a/notebooks/templates/install-validmind/_install-initialize-with-validation.ipynb b/notebooks/templates/install-validmind/_install-initialize-with-validation.ipynb
index 342a6aa35..25441c333 100644
--- a/notebooks/templates/install-validmind/_install-initialize-with-validation.ipynb
+++ b/notebooks/templates/install-validmind/_install-initialize-with-validation.ipynb
@@ -83,7 +83,7 @@
"\n",
"Recommended Python versions\n",
"
\n",
- "Python 3.8 <= x <= 3.11
\n",
+ "Python 3.8 <= x <= 3.14\n",
"\n",
"To install the library:"
]
diff --git a/notebooks/templates/install-validmind/_install-only.ipynb b/notebooks/templates/install-validmind/_install-only.ipynb
index 3d5bf8ea0..ba3e10281 100644
--- a/notebooks/templates/install-validmind/_install-only.ipynb
+++ b/notebooks/templates/install-validmind/_install-only.ipynb
@@ -17,7 +17,7 @@
"\n",
"Recommended Python versions\n",
"
\n",
- "Python 3.8 <= x <= 3.11
\n",
+ "Python 3.8 <= x <= 3.14\n",
"\n",
"To install the library:"
]
diff --git a/notebooks/tutorials/model_development/1-set_up_validmind.ipynb b/notebooks/tutorials/model_development/1-set_up_validmind.ipynb
index f82f57eaa..4244924b9 100644
--- a/notebooks/tutorials/model_development/1-set_up_validmind.ipynb
+++ b/notebooks/tutorials/model_development/1-set_up_validmind.ipynb
@@ -171,7 +171,7 @@
"\n",
"Recommended Python versions\n",
"
\n",
- "Python 3.8 <= x <= 3.11
\n",
+ "Python 3.8 <= x <= 3.14\n",
"\n",
"To install the library:"
]
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 c5dc1fb39..05ad11c2c 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
@@ -261,7 +261,7 @@
"\n",
"Recommended Python versions\n",
"
\n",
- "Python 3.8 <= x <= 3.11
\n",
+ "Python 3.8 <= x <= 3.14\n",
"\n",
"To install the library:"
]
diff --git a/notebooks/use_cases/agents/document_agentic_ai.ipynb b/notebooks/use_cases/agents/document_agentic_ai.ipynb
index 89e815221..3c3b6817b 100644
--- a/notebooks/use_cases/agents/document_agentic_ai.ipynb
+++ b/notebooks/use_cases/agents/document_agentic_ai.ipynb
@@ -194,7 +194,7 @@
"\n",
"Recommended Python versions\n",
"
\n",
- "Python 3.9 <= x <= 3.11
\n",
+ "Python 3.9 <= x <= 3.14\n",
"\n",
"Let's begin by installing the ValidMind Library with large language model (LLM) support:"
]
diff --git a/notebooks/use_cases/model_validation/validate_application_scorecard.ipynb b/notebooks/use_cases/model_validation/validate_application_scorecard.ipynb
index f3df8a617..7857d42e0 100644
--- a/notebooks/use_cases/model_validation/validate_application_scorecard.ipynb
+++ b/notebooks/use_cases/model_validation/validate_application_scorecard.ipynb
@@ -247,7 +247,7 @@
"\n",
"Recommended Python versions\n",
"
\n",
- "Python 3.8 <= x <= 3.11
\n",
+ "Python 3.8 <= x <= 3.14\n",
"\n",
"To install the library:"
]
diff --git a/notebooks/use_cases/nlp_and_llm/rag_benchmark_demo.ipynb b/notebooks/use_cases/nlp_and_llm/rag_benchmark_demo.ipynb
index 1e3eb07b6..1b56fa1b0 100644
--- a/notebooks/use_cases/nlp_and_llm/rag_benchmark_demo.ipynb
+++ b/notebooks/use_cases/nlp_and_llm/rag_benchmark_demo.ipynb
@@ -159,7 +159,7 @@
"\n",
"Recommended Python versions\n",
"
\n",
- "Python 3.8 <= x <= 3.11
\n",
+ "Python 3.8 <= x <= 3.14\n",
"\n",
"To install the library:"
]
diff --git a/poetry.lock b/poetry.lock
index b661b86e7..b9a61669b 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2,18 +2,35 @@
[[package]]
name = "aiodns"
-version = "3.5.0"
+version = "3.6.1"
description = "Simple DNS resolver for asyncio"
optional = false
python-versions = ">=3.9"
groups = ["main"]
+markers = "python_version == \"3.9\""
+files = [
+ {file = "aiodns-3.6.1-py3-none-any.whl", hash = "sha256:46233ccad25f2037903828c5d05b64590eaa756e51d12b4a5616e2defcbc98c7"},
+ {file = "aiodns-3.6.1.tar.gz", hash = "sha256:b0e9ce98718a5b8f7ca8cd16fc393163374bc2412236b91f6c851d066e3324b6"},
+]
+
+[package.dependencies]
+pycares = ">=4.9.0,<5"
+
+[[package]]
+name = "aiodns"
+version = "4.0.0"
+description = "Simple DNS resolver for asyncio"
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+markers = "python_version >= \"3.10\""
files = [
- {file = "aiodns-3.5.0-py3-none-any.whl", hash = "sha256:6d0404f7d5215849233f6ee44854f2bb2481adf71b336b2279016ea5990ca5c5"},
- {file = "aiodns-3.5.0.tar.gz", hash = "sha256:11264edbab51896ecf546c18eb0dd56dff0428c6aa6d2cd87e643e07300eb310"},
+ {file = "aiodns-4.0.0-py3-none-any.whl", hash = "sha256:a188a75fb8b2b7862ac8f84811a231402fb74f5b4e6f10766dc8a4544b0cf989"},
+ {file = "aiodns-4.0.0.tar.gz", hash = "sha256:17be26a936ba788c849ba5fd20e0ba69d8c46e6273e846eb5430eae2630ce5b1"},
]
[package.dependencies]
-pycares = ">=4.9.0"
+pycares = ">=5.0.0,<6"
[[package]]
name = "aiohappyeyeballs"
@@ -205,6 +222,19 @@ files = [
{file = "alabaster-0.7.16.tar.gz", hash = "sha256:75a8b99c28a5dad50dd7f8ccdd447a121ddb3892da9e53d1ca5cca3106d58d65"},
]
+[[package]]
+name = "annotated-doc"
+version = "0.0.4"
+description = "Document parameters, class attributes, return types, and variables inline, with Annotated."
+optional = true
+python-versions = ">=3.8"
+groups = ["main"]
+markers = "extra == \"llm\" or extra == \"pii-detection\""
+files = [
+ {file = "annotated_doc-0.0.4-py3-none-any.whl", hash = "sha256:571ac1dc6991c450b25a9c2d84a3705e2ae7a53467b5d111c24fa8baabbed320"},
+ {file = "annotated_doc-0.0.4.tar.gz", hash = "sha256:fbcda96e87e9c92ad167c2e53839e57503ecfda18804ea28102353485033faa4"},
+]
+
[[package]]
name = "annotated-types"
version = "0.7.0"
@@ -224,6 +254,7 @@ description = "ANSI colors for Python"
optional = false
python-versions = "*"
groups = ["dev"]
+markers = "python_version == \"3.9\""
files = [
{file = "ansicolors-1.1.8-py2.py3-none-any.whl", hash = "sha256:00d2dde5a675579325902536738dd27e4fac1fd68f773fe36c21044eb559e187"},
{file = "ansicolors-1.1.8.zip", hash = "sha256:99f94f5e3348a0bcd43c82e5fc4414013ccc19d70bd939ad71e0133ce9c372e0"},
@@ -231,35 +262,34 @@ files = [
[[package]]
name = "anyio"
-version = "4.10.0"
+version = "4.12.1"
description = "High-level concurrency and networking framework on top of asyncio or Trio"
optional = false
python-versions = ">=3.9"
groups = ["main", "dev"]
files = [
- {file = "anyio-4.10.0-py3-none-any.whl", hash = "sha256:60e474ac86736bbfd6f210f7a61218939c318f43f9972497381f1c5e930ed3d1"},
- {file = "anyio-4.10.0.tar.gz", hash = "sha256:3f3fae35c96039744587aa5b8371e7e8e603c0702999535961dd336026973ba6"},
+ {file = "anyio-4.12.1-py3-none-any.whl", hash = "sha256:d405828884fc140aa80a3c667b8beed277f1dfedec42ba031bd6ac3db606ab6c"},
+ {file = "anyio-4.12.1.tar.gz", hash = "sha256:41cfcc3a4c85d3f05c932da7c26d0201ac36f72abd4435ba90d0464a3ffed703"},
]
[package.dependencies]
exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
idna = ">=2.8"
-sniffio = ">=1.1"
typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
[package.extras]
-trio = ["trio (>=0.26.1)"]
+trio = ["trio (>=0.31.0) ; python_version < \"3.10\"", "trio (>=0.32.0) ; python_version >= \"3.10\""]
[[package]]
name = "anywidget"
-version = "0.9.18"
+version = "0.9.21"
description = "custom jupyter widgets made easy"
optional = false
python-versions = ">=3.8"
groups = ["main"]
files = [
- {file = "anywidget-0.9.18-py3-none-any.whl", hash = "sha256:944b82ef1dd17b8ff0fb6d1f199f613caf9111338e6e2857da478f6e73770cb8"},
- {file = "anywidget-0.9.18.tar.gz", hash = "sha256:262cf459b517a7d044d6fbc84b953e9c83f026790b2dd3ce90f21a7f8eded00f"},
+ {file = "anywidget-0.9.21-py3-none-any.whl", hash = "sha256:78c268e0fbdb1dfd15da37fb578f9cf0a0df58a430e68d9156942b7a9391a761"},
+ {file = "anywidget-0.9.21.tar.gz", hash = "sha256:b8d0172029ac426573053c416c6a587838661612208bb390fa0607862e594b27"},
]
[package.dependencies]
@@ -303,7 +333,7 @@ description = "ARCH for Python"
optional = true
python-versions = ">=3.9"
groups = ["main"]
-markers = "extra == \"all\" or extra == \"stats\""
+markers = "python_version == \"3.9\" and (extra == \"all\" or extra == \"stats\")"
files = [
{file = "arch-7.2.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ae119847fae37bbbf7273b4aa1f709f49d4637497ab6a4d7e76417eb94cae4af"},
{file = "arch-7.2.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:cb3cb8bcf7f28e3a18d4e2b6cb661a8dd883af844277633ebe533ee1df2cc12a"},
@@ -343,6 +373,60 @@ pandas = ">=1.4"
scipy = ">=1.8"
statsmodels = ">=0.12"
+[[package]]
+name = "arch"
+version = "8.0.0"
+description = "ARCH for Python"
+optional = true
+python-versions = ">=3.10"
+groups = ["main"]
+markers = "(extra == \"all\" or extra == \"stats\") and python_version >= \"3.10\""
+files = [
+ {file = "arch-8.0.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:fb60acaa9caa1353b75f19db910ac88e4238f4ca40282418e728174b1d3fe297"},
+ {file = "arch-8.0.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:51641954adb35f969b5c3333f6527fb090720fc0c973b6d2c4d927c8cc5dfe58"},
+ {file = "arch-8.0.0-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:aee1099e0d12538d8d1ff391ddb8061c42e4864d199ca6887487bedb58b1dc9c"},
+ {file = "arch-8.0.0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ec799301d904318d067d8d919069efc2825e8cea1654b1ffe623ea8c591fff5e"},
+ {file = "arch-8.0.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:8ebe43e390e2df5e07cb1a5abbbdf545a31d858d2e504e9fa379f011ba4715ab"},
+ {file = "arch-8.0.0-cp310-cp310-win_amd64.whl", hash = "sha256:b56c3994069532c6621d211a333c62ae6b3cfeb3eef8089806bd4698062137ca"},
+ {file = "arch-8.0.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:94262bef94dda3f72182a8dfc21cab1a8a79750cf168f3cf2aec02d7217bee55"},
+ {file = "arch-8.0.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:1d9cb343f15e71e9cee2415bffa1e3458aeb674a538118de71f1124b6c5b755a"},
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+ {file = "arch-8.0.0.tar.gz", hash = "sha256:5e9895c2354b9475aff50797ff2191dc64dc5f79602baf0c9321310fb864b637"},
+]
+
+[package.dependencies]
+numpy = ">=1.22.3,<3"
+packaging = "*"
+pandas = ">=1.4.0"
+scipy = ">=1.8"
+statsmodels = ">=0.13.0"
+
+[package.extras]
+dev = ["black[jupyter] (>=25.9.0,<25.10.0)", "colorama", "cython (>=3.0.10)", "flake8", "flake8-bugbear", "isort (>=6.0,<7.0)", "jupyterlab (>=4.4.8)", "jupyterlab-code-formatter", "matplotlib (>=3)", "mypy (>=1.3)", "numba (>=0.55)", "packaging", "pytest (>=8.4.1,<9)", "pytest-cov", "pytest-randomly", "pytest-xdist", "pyupgrade (>=3.4.0)", "ruff (>=0.8.6)", "seaborn", "setuptools_scm[toml] (>=9.2.0,<10)", "zipp (>=3.19.1)"]
+doc = ["cython (>=3.0.13,<4)", "fonttools (>=4.43.0)", "ipython (>=7)", "ipython (>=8.0.1)", "jinja2", "jupyter", "jupyterlab (>=4.4.8)", "matplotlib (>=3.10)", "nbconvert", "nbsphinx", "notebook", "numpy (>=2.3.4)", "numpydoc (>=1.0.0)", "pandas (>=2.3.3,<2.4.0)", "patsy (>=0.5.3)", "pickleshare", "scipy (>=1.16.0)", "seaborn", "setuptools (>=61)", "setuptools_scm[toml] (>=9.2.0,<10)", "sphinx (>=7)", "sphinx-autodoc-typehints", "sphinx-immaterial", "sphinx_immaterial", "statsmodels (>=0.13.5)", "wheel"]
+test = ["coverage[toml]", "meson", "ninja", "pytest (>=8.4.1,<9)", "pytest-cov", "pytest-randomly", "pytest-xdist"]
+
[[package]]
name = "argon2-cffi"
version = "25.1.0"
@@ -395,43 +479,46 @@ files = [
]
[package.dependencies]
-cffi = {version = ">=1.0.1", markers = "python_version < \"3.14\""}
+cffi = [
+ {version = ">=1.0.1", markers = "python_version < \"3.14\""},
+ {version = ">=2.0.0b1", markers = "python_version >= \"3.14\""},
+]
[[package]]
name = "arrow"
-version = "1.3.0"
+version = "1.4.0"
description = "Better dates & times for Python"
optional = false
python-versions = ">=3.8"
groups = ["dev"]
files = [
- {file = "arrow-1.3.0-py3-none-any.whl", hash = "sha256:c728b120ebc00eb84e01882a6f5e7927a53960aa990ce7dd2b10f39005a67f80"},
- {file = "arrow-1.3.0.tar.gz", hash = "sha256:d4540617648cb5f895730f1ad8c82a65f2dad0166f57b75f3ca54759c4d67a85"},
+ {file = "arrow-1.4.0-py3-none-any.whl", hash = "sha256:749f0769958ebdc79c173ff0b0670d59051a535fa26e8eba02953dc19eb43205"},
+ {file = "arrow-1.4.0.tar.gz", hash = "sha256:ed0cc050e98001b8779e84d461b0098c4ac597e88704a655582b21d116e526d7"},
]
[package.dependencies]
python-dateutil = ">=2.7.0"
-types-python-dateutil = ">=2.8.10"
+tzdata = {version = "*", markers = "python_version >= \"3.9\""}
[package.extras]
doc = ["doc8", "sphinx (>=7.0.0)", "sphinx-autobuild", "sphinx-autodoc-typehints", "sphinx_rtd_theme (>=1.3.0)"]
-test = ["dateparser (==1.*)", "pre-commit", "pytest", "pytest-cov", "pytest-mock", "pytz (==2021.1)", "simplejson (==3.*)"]
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name = "asttokens"
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optional = false
python-versions = ">=3.8"
groups = ["main", "dev"]
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+test = ["astroid (>=2,<5)", "pytest (<9.0)", "pytest-cov", "pytest-xdist"]
[[package]]
name = "async-lru"
@@ -440,6 +527,7 @@ description = "Simple LRU cache for asyncio"
optional = false
python-versions = ">=3.9"
groups = ["dev"]
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optional = false
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-
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name = "babel"
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description = "Internationalization utilities"
optional = false
python-versions = ">=3.8"
groups = ["dev"]
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optional = true
python-versions = "<3.11,>=3.8"
groups = ["main"]
-markers = "python_version < \"3.11\" and extra == \"llm\""
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groups = ["dev"]
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optional = false
python-versions = ">=3.8"
groups = ["dev"]
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test-no-images = ["pytest", "pytest-cov", "pytest-rerunfailures", "pytest-xdist", "wurlitzer"]
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groups = ["main"]
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@@ -2206,7 +2703,8 @@ abfs = ["adlfs"]
adl = ["adlfs"]
arrow = ["pyarrow (>=1)"]
dask = ["dask", "distributed"]
-devel = ["pytest", "pytest-cov"]
+dev = ["pre-commit", "ruff"]
+doc = ["numpydoc", "sphinx", "sphinx-design", "sphinx-rtd-theme", "yarl"]
dropbox = ["dropbox", "dropboxdrivefs", "requests"]
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fuse = ["fusepy"]
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sftp = ["paramiko"]
smb = ["smbprotocol"]
ssh = ["paramiko"]
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tqdm = ["tqdm"]
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python-versions = ">=3.7"
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torch = ["safetensors[torch]", "torch"]
typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
[[package]]
name = "identify"
-version = "2.6.13"
+version = "2.6.15"
description = "File identification library for Python"
optional = false
python-versions = ">=3.9"
groups = ["dev"]
+markers = "python_version == \"3.9\""
+files = [
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+
+[package.extras]
+license = ["ukkonen"]
+
+[[package]]
+name = "identify"
+version = "2.6.18"
+description = "File identification library for Python"
+optional = false
+python-versions = ">=3.10"
+groups = ["dev"]
+markers = "python_version >= \"3.10\""
files = [
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@@ -2568,14 +3216,14 @@ license = ["ukkonen"]
[[package]]
name = "idna"
-version = "3.10"
+version = "3.11"
description = "Internationalized Domain Names in Applications (IDNA)"
optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
groups = ["main", "dev"]
files = [
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@@ -2583,28 +3231,42 @@ all = ["flake8 (>=7.1.1)", "mypy (>=1.11.2)", "pytest (>=8.3.2)", "ruff (>=0.6.2
[[package]]
name = "imagesize"
-version = "1.4.1"
+version = "1.5.0"
description = "Getting image size from png/jpeg/jpeg2000/gif file"
optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7"
+groups = ["dev"]
+markers = "python_version == \"3.9\""
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+[[package]]
+name = "imagesize"
+version = "2.0.0"
+description = "Get image size from headers (BMP/PNG/JPEG/JPEG2000/GIF/TIFF/SVG/Netpbm/WebP/AVIF/HEIC/HEIF)"
+optional = false
+python-versions = "<3.15,>=3.10"
groups = ["dev"]
+markers = "python_version >= \"3.10\""
files = [
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[[package]]
name = "importlib-metadata"
-version = "8.7.0"
+version = "8.7.1"
description = "Read metadata from Python packages"
optional = false
python-versions = ">=3.9"
groups = ["main", "dev"]
files = [
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+markers = {main = "extra == \"llm\" or python_version == \"3.9\" and platform_system == \"Linux\" and platform_machine == \"x86_64\" and (extra == \"llm\" or extra == \"all\" or extra == \"pytorch\" or extra == \"nlp\")"}
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zipp = ">=3.20"
@@ -2613,10 +3275,10 @@ zipp = ">=3.20"
check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\""]
cover = ["pytest-cov"]
doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-enabler = ["pytest-enabler (>=2.2)"]
+enabler = ["pytest-enabler (>=3.4)"]
perf = ["ipython"]
-test = ["flufl.flake8", "importlib_resources (>=1.3) ; python_version < \"3.9\"", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-perf (>=0.9.2)"]
-type = ["pytest-mypy"]
+test = ["flufl.flake8", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-perf (>=0.9.2)"]
+type = ["mypy (<1.19) ; platform_python_implementation == \"PyPy\"", "pytest-mypy (>=1.0.1)"]
[[package]]
name = "importlib-resources"
@@ -2649,22 +3311,35 @@ description = "brain-dead simple config-ini parsing"
optional = true
python-versions = ">=3.8"
groups = ["main"]
-markers = "extra == \"llm\""
+markers = "python_version == \"3.9\" and extra == \"llm\""
files = [
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]
+[[package]]
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+version = "2.3.0"
+description = "brain-dead simple config-ini parsing"
+optional = true
+python-versions = ">=3.10"
+groups = ["main"]
+markers = "extra == \"llm\" and python_version >= \"3.10\""
+files = [
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+
[[package]]
name = "ipykernel"
-version = "6.30.1"
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description = "IPython Kernel for Jupyter"
optional = false
python-versions = ">=3.9"
groups = ["dev"]
files = [
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[package.dependencies]
@@ -2696,7 +3371,7 @@ description = "IPython: Productive Interactive Computing"
optional = false
python-versions = ">=3.9"
groups = ["main", "dev"]
-markers = "python_version < \"3.11\""
+markers = "python_version == \"3.9\""
files = [
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@@ -2730,21 +3405,21 @@ test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.22)", "pa
[[package]]
name = "ipython"
-version = "9.4.0"
+version = "8.38.0"
description = "IPython: Productive Interactive Computing"
optional = false
-python-versions = ">=3.11"
+python-versions = ">=3.10"
groups = ["main", "dev"]
-markers = "python_version >= \"3.11\""
+markers = "python_version == \"3.10\""
files = [
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colorama = {version = "*", markers = "sys_platform == \"win32\""}
decorator = "*"
-ipython-pygments-lexers = "*"
+exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
jedi = ">=0.16"
matplotlib-inline = "*"
pexpect = {version = ">4.3", markers = "sys_platform != \"win32\" and sys_platform != \"emscripten\""}
@@ -2755,12 +3430,85 @@ traitlets = ">=5.13.0"
typing_extensions = {version = ">=4.6", markers = "python_version < \"3.12\""}
[package.extras]
-all = ["ipython[doc,matplotlib,test,test-extra]"]
+all = ["ipython[black,doc,kernel,matplotlib,nbconvert,nbformat,notebook,parallel,qtconsole]", "ipython[test,test-extra]"]
black = ["black"]
-doc = ["docrepr", "exceptiongroup", "intersphinx_registry", "ipykernel", "ipython[test]", "matplotlib", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "sphinx_toml (==0.0.4)", "typing_extensions"]
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+kernel = ["ipykernel"]
matplotlib = ["matplotlib"]
-test = ["packaging", "pytest", "pytest-asyncio (<0.22)", "testpath"]
-test-extra = ["curio", "ipykernel", "ipython[test]", "jupyter_ai", "matplotlib (!=3.2.0)", "nbclient", "nbformat", "numpy (>=1.23)", "pandas", "trio"]
+nbconvert = ["nbconvert"]
+nbformat = ["nbformat"]
+notebook = ["ipywidgets", "notebook"]
+parallel = ["ipyparallel"]
+qtconsole = ["qtconsole"]
+test = ["packaging", "pickleshare", "pytest", "pytest-asyncio (<0.22)", "testpath"]
+test-extra = ["curio", "ipython[test]", "jupyter_ai", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.23)", "pandas", "trio"]
+
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+version = "9.10.0"
+description = "IPython: Productive Interactive Computing"
+optional = false
+python-versions = ">=3.11"
+groups = ["main", "dev"]
+markers = "python_version == \"3.11\""
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[[package]]
name = "ipython-pygments-lexers"
@@ -2780,14 +3528,14 @@ pygments = "*"
[[package]]
name = "ipywidgets"
-version = "8.1.7"
+version = "8.1.8"
description = "Jupyter interactive widgets"
optional = false
python-versions = ">=3.7"
groups = ["main", "dev"]
files = [
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@@ -2851,33 +3599,62 @@ testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-ena
[[package]]
name = "jaraco-context"
-version = "6.0.1"
+version = "6.1.1"
description = "Useful decorators and context managers"
optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
+groups = ["dev"]
+markers = "python_version == \"3.9\""
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+
+[package.extras]
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+cover = ["pytest-cov"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+enabler = ["pytest-enabler (>=3.4)"]
+test = ["jaraco.test (>=5.6.0)", "portend", "pytest (>=6,!=8.1.*)"]
+type = ["mypy (<1.19) ; platform_python_implementation == \"PyPy\"", "pytest-mypy (>=1.0.1)"]
+
+[[package]]
+name = "jaraco-context"
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+description = "Useful decorators and context managers"
+optional = false
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groups = ["dev"]
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]
[[package]]
name = "joblib"
-version = "1.5.1"
+version = "1.5.3"
description = "Lightweight pipelining with Python functions"
optional = false
python-versions = ">=3.9"
groups = ["main"]
files = [
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]
[[package]]
name = "json5"
-version = "0.12.1"
+version = "0.13.0"
description = "A Python implementation of the JSON5 data format."
optional = false
python-versions = ">=3.8.0"
groups = ["dev"]
files = [
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-dev = ["build (==1.2.2.post1)", "coverage (==7.5.4) ; python_version < \"3.9\"", "coverage (==7.8.0) ; python_version >= \"3.9\"", "mypy (==1.14.1) ; python_version < \"3.9\"", "mypy (==1.15.0) ; python_version >= \"3.9\"", "pip (==25.0.1)", "pylint (==3.2.7) ; python_version < \"3.9\"", "pylint (==3.3.6) ; python_version >= \"3.9\"", "ruff (==0.11.2)", "twine (==6.1.0)", "uv (==0.6.11)"]
-
[[package]]
name = "jsonpatch"
version = "1.33"
@@ -3088,7 +3887,20 @@ files = [
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{file = "jsonpointer-3.0.0.tar.gz", hash = "sha256:2b2d729f2091522d61c3b31f82e11870f60b68f43fbc705cb76bf4b832af59ef"},
]
-markers = {main = "extra == \"all\" or extra == \"llm\""}
+markers = {main = "python_version == \"3.9\" and (extra == \"all\" or extra == \"llm\")", dev = "python_version == \"3.9\""}
+
+[[package]]
+name = "jsonpointer"
+version = "3.1.0"
+description = "Identify specific nodes in a JSON document (RFC 6901) "
+optional = false
+python-versions = ">=3.10"
+groups = ["main", "dev"]
+files = [
+ {file = "jsonpointer-3.1.0-py3-none-any.whl", hash = "sha256:f82aa0f745001f169b96473348370b43c3f581446889c41c807bab1db11c8e7b"},
+ {file = "jsonpointer-3.1.0.tar.gz", hash = "sha256:f9b39abd59ba8c1de8a4ff16141605d2a8dacc4dd6cf399672cf237bfe47c211"},
+]
+markers = {main = "(extra == \"all\" or extra == \"llm\") and python_version >= \"3.10\"", dev = "python_version >= \"3.10\""}
[[package]]
name = "jsonschema"
@@ -3097,6 +3909,7 @@ description = "An implementation of JSON Schema validation for Python"
optional = false
python-versions = ">=3.9"
groups = ["dev"]
+markers = "python_version == \"3.9\""
files = [
{file = "jsonschema-4.25.1-py3-none-any.whl", hash = "sha256:3fba0169e345c7175110351d456342c364814cfcf3b964ba4587f22915230a63"},
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@@ -3121,16 +3934,48 @@ webcolors = {version = ">=24.6.0", optional = true, markers = "extra == \"format
format = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3987", "uri-template", "webcolors (>=1.11)"]
format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "rfc3987-syntax (>=1.1.0)", "uri-template", "webcolors (>=24.6.0)"]
+[[package]]
+name = "jsonschema"
+version = "4.26.0"
+description = "An implementation of JSON Schema validation for Python"
+optional = false
+python-versions = ">=3.10"
+groups = ["dev"]
+markers = "python_version >= \"3.10\""
+files = [
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+ {file = "jsonschema-4.26.0.tar.gz", hash = "sha256:0c26707e2efad8aa1bfc5b7ce170f3fccc2e4918ff85989ba9ffa9facb2be326"},
+]
+
+[package.dependencies]
+attrs = ">=22.2.0"
+fqdn = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+idna = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+isoduration = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+jsonpointer = {version = ">1.13", optional = true, markers = "extra == \"format-nongpl\""}
+jsonschema-specifications = ">=2023.3.6"
+referencing = ">=0.28.4"
+rfc3339-validator = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+rfc3986-validator = {version = ">0.1.0", optional = true, markers = "extra == \"format-nongpl\""}
+rfc3987-syntax = {version = ">=1.1.0", optional = true, markers = "extra == \"format-nongpl\""}
+rpds-py = ">=0.25.0"
+uri-template = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+webcolors = {version = ">=24.6.0", optional = true, markers = "extra == \"format-nongpl\""}
+
+[package.extras]
+format = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3987", "uri-template", "webcolors (>=1.11)"]
+format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "rfc3987-syntax (>=1.1.0)", "uri-template", "webcolors (>=24.6.0)"]
+
[[package]]
name = "jsonschema-specifications"
-version = "2025.4.1"
+version = "2025.9.1"
description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry"
optional = false
python-versions = ">=3.9"
groups = ["dev"]
files = [
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+ {file = "jsonschema_specifications-2025.9.1.tar.gz", hash = "sha256:b540987f239e745613c7a9176f3edb72b832a4ac465cf02712288397832b5e8d"},
]
[package.dependencies]
@@ -3163,6 +4008,7 @@ description = "Jupyter protocol implementation and client libraries"
optional = false
python-versions = ">=3.8"
groups = ["dev"]
+markers = "python_version == \"3.9\""
files = [
{file = "jupyter_client-8.6.3-py3-none-any.whl", hash = "sha256:e8a19cc986cc45905ac3362915f410f3af85424b4c0905e94fa5f2cb08e8f23f"},
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@@ -3180,6 +4026,31 @@ traitlets = ">=5.3"
docs = ["ipykernel", "myst-parser", "pydata-sphinx-theme", "sphinx (>=4)", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"]
test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko ; sys_platform == \"win32\"", "pre-commit", "pytest (<8.2.0)", "pytest-cov", "pytest-jupyter[client] (>=0.4.1)", "pytest-timeout"]
+[[package]]
+name = "jupyter-client"
+version = "8.8.0"
+description = "Jupyter protocol implementation and client libraries"
+optional = false
+python-versions = ">=3.10"
+groups = ["dev"]
+markers = "python_version >= \"3.10\""
+files = [
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+ {file = "jupyter_client-8.8.0.tar.gz", hash = "sha256:d556811419a4f2d96c869af34e854e3f059b7cc2d6d01a9cd9c85c267691be3e"},
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+
+[package.dependencies]
+jupyter-core = ">=5.1"
+python-dateutil = ">=2.8.2"
+pyzmq = ">=25.0"
+tornado = ">=6.4.1"
+traitlets = ">=5.3"
+
+[package.extras]
+docs = ["ipykernel", "myst-parser", "pydata-sphinx-theme", "sphinx (>=4)", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"]
+orjson = ["orjson"]
+test = ["anyio", "coverage", "ipykernel (>=6.14)", "msgpack", "mypy ; platform_python_implementation != \"PyPy\"", "paramiko ; sys_platform == \"win32\"", "pre-commit", "pytest", "pytest-cov", "pytest-jupyter[client] (>=0.6.2)", "pytest-timeout"]
+
[[package]]
name = "jupyter-console"
version = "6.6.3"
@@ -3212,6 +4083,7 @@ description = "Jupyter core package. A base package on which Jupyter projects re
optional = false
python-versions = ">=3.8"
groups = ["dev"]
+markers = "python_version == \"3.9\""
files = [
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@@ -3226,6 +4098,27 @@ traitlets = ">=5.3"
docs = ["intersphinx-registry", "myst-parser", "pydata-sphinx-theme", "sphinx-autodoc-typehints", "sphinxcontrib-spelling", "traitlets"]
test = ["ipykernel", "pre-commit", "pytest (<9)", "pytest-cov", "pytest-timeout"]
+[[package]]
+name = "jupyter-core"
+version = "5.9.1"
+description = "Jupyter core package. A base package on which Jupyter projects rely."
+optional = false
+python-versions = ">=3.10"
+groups = ["dev"]
+markers = "python_version >= \"3.10\""
+files = [
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+ {file = "jupyter_core-5.9.1.tar.gz", hash = "sha256:4d09aaff303b9566c3ce657f580bd089ff5c91f5f89cf7d8846c3cdf465b5508"},
+]
+
+[package.dependencies]
+platformdirs = ">=2.5"
+traitlets = ">=5.3"
+
+[package.extras]
+docs = ["intersphinx-registry", "myst-parser", "pydata-sphinx-theme", "sphinx-autodoc-typehints", "sphinxcontrib-spelling", "traitlets"]
+test = ["ipykernel", "pre-commit", "pytest (<9)", "pytest-cov", "pytest-timeout"]
+
[[package]]
name = "jupyter-events"
version = "0.12.0"
@@ -3255,14 +4148,14 @@ test = ["click", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.19.0)", "p
[[package]]
name = "jupyter-lsp"
-version = "2.2.6"
+version = "2.3.0"
description = "Multi-Language Server WebSocket proxy for Jupyter Notebook/Lab server"
optional = false
python-versions = ">=3.8"
groups = ["dev"]
files = [
- {file = "jupyter_lsp-2.2.6-py3-none-any.whl", hash = "sha256:283783752bf0b459ee7fa88effa72104d87dd343b82d5c06cf113ef755b15b6d"},
- {file = "jupyter_lsp-2.2.6.tar.gz", hash = "sha256:0566bd9bb04fd9e6774a937ed01522b555ba78be37bebef787c8ab22de4c0361"},
+ {file = "jupyter_lsp-2.3.0-py3-none-any.whl", hash = "sha256:e914a3cb2addf48b1c7710914771aaf1819d46b2e5a79b0f917b5478ec93f34f"},
+ {file = "jupyter_lsp-2.3.0.tar.gz", hash = "sha256:458aa59339dc868fb784d73364f17dbce8836e906cd75fd471a325cba02e0245"},
]
[package.dependencies]
@@ -3308,14 +4201,14 @@ test = ["flaky", "ipykernel", "pre-commit", "pytest (>=7.0,<9)", "pytest-console
[[package]]
name = "jupyter-server-terminals"
-version = "0.5.3"
+version = "0.5.4"
description = "A Jupyter Server Extension Providing Terminals."
optional = false
python-versions = ">=3.8"
groups = ["dev"]
files = [
- {file = "jupyter_server_terminals-0.5.3-py3-none-any.whl", hash = "sha256:41ee0d7dc0ebf2809c668e0fc726dfaf258fcd3e769568996ca731b6194ae9aa"},
- {file = "jupyter_server_terminals-0.5.3.tar.gz", hash = "sha256:5ae0295167220e9ace0edcfdb212afd2b01ee8d179fe6f23c899590e9b8a5269"},
+ {file = "jupyter_server_terminals-0.5.4-py3-none-any.whl", hash = "sha256:55be353fc74a80bc7f3b20e6be50a55a61cd525626f578dcb66a5708e2007d14"},
+ {file = "jupyter_server_terminals-0.5.4.tar.gz", hash = "sha256:bbda128ed41d0be9020349f9f1f2a4ab9952a73ed5f5ac9f1419794761fb87f5"},
]
[package.dependencies]
@@ -3328,14 +4221,14 @@ test = ["jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-jupyter[server] (>
[[package]]
name = "jupyterlab"
-version = "4.4.6"
+version = "4.5.6"
description = "JupyterLab computational environment"
optional = false
python-versions = ">=3.9"
groups = ["dev"]
files = [
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- {file = "jupyterlab-4.4.6.tar.gz", hash = "sha256:e0b720ff5392846bdbc01745f32f29f4d001c071a4bff94d8b516ba89b5a4157"},
+ {file = "jupyterlab-4.5.6-py3-none-any.whl", hash = "sha256:d6b3dac883aa4d9993348e0f8e95b24624f75099aed64eab6a4351a9cdd1e580"},
+ {file = "jupyterlab-4.5.6.tar.gz", hash = "sha256:642fe2cfe7f0f5922a8a558ba7a0d246c7bc133b708dfe43f7b3a826d163cf42"},
]
[package.dependencies]
@@ -3347,7 +4240,7 @@ jinja2 = ">=3.0.3"
jupyter-core = "*"
jupyter-lsp = ">=2.0.0"
jupyter-server = ">=2.4.0,<3"
-jupyterlab-server = ">=2.27.1,<3"
+jupyterlab-server = ">=2.28.0,<3"
notebook-shim = ">=0.2"
packaging = "*"
setuptools = ">=41.1.0"
@@ -3356,9 +4249,9 @@ tornado = ">=6.2.0"
traitlets = "*"
[package.extras]
-dev = ["build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov", "ruff (==0.11.4)"]
+dev = ["build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov", "ruff (==0.11.12)"]
docs = ["jsx-lexer", "myst-parser", "pydata-sphinx-theme (>=0.13.0)", "pytest", "pytest-check-links", "pytest-jupyter", "sphinx (>=1.8,<8.2.0)", "sphinx-copybutton"]
-docs-screenshots = ["altair (==5.5.0)", "ipython (==8.16.1)", "ipywidgets (==8.1.5)", "jupyterlab-geojson (==3.4.0)", "jupyterlab-language-pack-zh-cn (==4.3.post1)", "matplotlib (==3.10.0)", "nbconvert (>=7.0.0)", "pandas (==2.2.3)", "scipy (==1.15.1)", "vega-datasets (==0.9.0)"]
+docs-screenshots = ["altair (==6.0.0)", "ipython (==8.16.1)", "ipywidgets (==8.1.5)", "jupyterlab-geojson (==3.4.0)", "jupyterlab-language-pack-zh-cn (==4.3.post1)", "matplotlib (==3.10.0)", "nbconvert (>=7.0.0)", "pandas (==2.2.3)", "scipy (==1.15.1)"]
test = ["coverage", "pytest (>=7.0)", "pytest-check-links (>=0.7)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter (>=0.5.3)", "pytest-timeout", "pytest-tornasync", "requests", "requests-cache", "virtualenv"]
upgrade-extension = ["copier (>=9,<10)", "jinja2-time (<0.3)", "pydantic (<3.0)", "pyyaml-include (<3.0)", "tomli-w (<2.0)"]
@@ -3376,14 +4269,14 @@ files = [
[[package]]
name = "jupyterlab-server"
-version = "2.27.3"
+version = "2.28.0"
description = "A set of server components for JupyterLab and JupyterLab like applications."
optional = false
python-versions = ">=3.8"
groups = ["dev"]
files = [
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- {file = "jupyterlab_server-2.27.3.tar.gz", hash = "sha256:eb36caca59e74471988f0ae25c77945610b887f777255aa21f8065def9e51ed4"},
+ {file = "jupyterlab_server-2.28.0-py3-none-any.whl", hash = "sha256:e4355b148fdcf34d312bbbc80f22467d6d20460e8b8736bf235577dd18506968"},
+ {file = "jupyterlab_server-2.28.0.tar.gz", hash = "sha256:35baa81898b15f93573e2deca50d11ac0ae407ebb688299d3a5213265033712c"},
]
[package.dependencies]
@@ -3403,14 +4296,14 @@ test = ["hatch", "ipykernel", "openapi-core (>=0.18.0,<0.19.0)", "openapi-spec-v
[[package]]
name = "jupyterlab-widgets"
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+version = "3.0.16"
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optional = false
python-versions = ">=3.9"
groups = ["main"]
-markers = "python_version < \"3.11\""
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[[package]]
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duckdb = ["duckdb (>=1.1)"]
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pyspark = ["pyspark (>=3.5.0)"]
pyspark-connect = ["pyspark[connect] (>=3.5.0)"]
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sqlframe = ["sqlframe (>=3.22.0,!=3.39.3)"]
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optional = false
python-versions = ">=3.9.0"
groups = ["dev"]
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optional = false
python-versions = ">=3.6"
groups = ["dev"]
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optional = false
python-versions = ">=3.8"
groups = ["dev"]
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xmp = ["defusedxml"]
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optional = true
python-versions = "<4.0,>=3.9"
groups = ["main"]
-markers = "extra == \"pii-detection\""
+markers = "python_version == \"3.14\" and extra == \"pii-detection\""
files = [
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@@ -6132,40 +7765,99 @@ server = ["flask (>=1.1)", "gunicorn"]
stanza = ["stanza (>=1.10.1,<2.0.0)"]
transformers = ["accelerate", "huggingface_hub", "spacy_huggingface_pipelines", "transformers"]
+[[package]]
+name = "presidio-analyzer"
+version = "2.2.360"
+description = "Presidio Analyzer package"
+optional = true
+python-versions = "<3.14,>=3.9"
+groups = ["main"]
+markers = "python_version == \"3.9\" and extra == \"pii-detection\""
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+spacy = ">=3.4.4,<3.7.0 || >3.7.0"
+tldextract = "*"
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+azure-ai-language = ["azure-ai-textanalytics", "azure-core"]
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+transformers = ["accelerate", "huggingface_hub", "spacy_huggingface_pipelines", "transformers"]
+
+[[package]]
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+version = "2.2.362"
+description = "Presidio Analyzer package"
+optional = true
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+groups = ["main"]
+markers = "extra == \"pii-detection\" and python_version >= \"3.10\" and python_version < \"3.14\""
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+transformers = ["accelerate", "huggingface_hub", "spacy_huggingface_pipelines", "transformers"]
+
[[package]]
name = "presidio-anonymizer"
-version = "2.2.359"
+version = "2.2.360"
description = "Presidio Anonymizer package - replaces analyzed text with desired values."
optional = true
python-versions = "<4.0,>=3.9"
groups = ["main"]
-markers = "extra == \"pii-detection\""
+markers = "python_version == \"3.9\" and extra == \"pii-detection\""
files = [
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cryptography = "<44.1"
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server = ["flask (>=1.1)", "gunicorn"]
[[package]]
-name = "presidio-structured"
-version = "0.0.4a0"
-description = "Presidio structured package - analyzes and anonymizes structured and semi-structured data."
+name = "presidio-anonymizer"
+version = "2.2.362"
+description = "Presidio Anonymizer package - replaces analyzed text with desired values."
optional = true
-python-versions = "<4.0,>=3.9"
+python-versions = "<4.0,>=3.10"
groups = ["main"]
-markers = "python_version < \"3.11\" and extra == \"pii-detection\""
+markers = "extra == \"pii-detection\" and python_version >= \"3.10\""
files = [
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-presidio-analyzer = ">=2.2"
-presidio-anonymizer = ">=2.2"
+cryptography = ">=46.0.4"
+
+[package.extras]
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+server = ["flask (>=1.1)", "gunicorn ; platform_system != \"Windows\"", "waitress ; platform_system == \"Windows\""]
[[package]]
name = "presidio-structured"
@@ -6174,7 +7866,7 @@ description = "Presidio structured package - analyzes and anonymizes structured
optional = true
python-versions = "<4.0,>=3.9"
groups = ["main"]
-markers = "python_version >= \"3.11\" and extra == \"pii-detection\""
+markers = "extra == \"pii-detection\""
files = [
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@@ -6183,32 +7875,35 @@ files = [
pandas = ">=1.5.2"
presidio-analyzer = ">=2.2"
presidio-anonymizer = ">=2.2"
+spacy = {version = "<3.8.4", markers = "python_version < \"3.10\""}
[[package]]
name = "prometheus-client"
-version = "0.22.1"
+version = "0.24.1"
description = "Python client for the Prometheus monitoring system."
optional = false
python-versions = ">=3.9"
groups = ["dev"]
files = [
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+django = ["django"]
twisted = ["twisted"]
[[package]]
name = "prompt-toolkit"
-version = "3.0.51"
+version = "3.0.52"
description = "Library for building powerful interactive command lines in Python"
optional = false
python-versions = ">=3.8"
groups = ["main", "dev"]
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description = "Accelerated property cache"
optional = false
python-versions = ">=3.9"
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name = "pyparsing"
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python-versions = ">=3.9"
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+version = "8.4.2"
description = "pytest: simple powerful testing with Python"
optional = true
python-versions = ">=3.9"
groups = ["main"]
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@@ -6983,20 +9121,37 @@ pytest = "*"
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name = "pytest-rerunfailures"
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description = "pytest plugin to re-run tests to eliminate flaky failures"
optional = true
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groups = ["main"]
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name = "pytest-xdist"
@@ -7035,16 +9190,53 @@ files = [
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six = ">=1.5"
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python-versions = ">=3.9"
groups = ["main"]
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groups = ["dev"]
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[[package]]
name = "pytz"
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description = "World timezone definitions, modern and historical"
optional = false
python-versions = "*"
groups = ["main"]
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python-versions = "*"
groups = ["main", "dev"]
files = [
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@@ -7126,186 +9318,215 @@ files = [
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markers = "os_name == \"nt\""
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@@ -8106,7 +10790,7 @@ description = "Python bindings to FreeDesktop.org Secret Service API"
optional = false
python-versions = ">=3.6"
groups = ["dev"]
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cryptography = ">=2.0"
jeepney = ">=0.6"
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name = "sentencepiece"
@@ -8215,23 +10915,24 @@ testpaths = ["test"]
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falcon = ["falcon (>=1.4)"]
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flask = ["blinker (>=1.1)", "flask (>=0.11)", "markupsafe"]
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quart = ["blinker (>=1.1)", "quart (>=0.16.1)"]
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sqlalchemy = ["sqlalchemy (>=1.2)"]
starlette = ["starlette (>=0.19.1)"]
starlite = ["starlite (>=1.48)"]
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@@ -8340,9 +11055,11 @@ typing-extensions = "*"
[package.extras]
docs = ["ipython", "ipywidgets", "matplotlib", "myst-parser", "nbsphinx", "numpydoc", "requests", "sphinx", "sphinx_github_changelog", "sphinx_rtd_theme"]
+download-datasets = ["scikit-learn"]
+nbtest = ["nbtest-gen", "nbtest-lab-extension", "nbtest-plugin"]
others = ["lime"]
plots = ["ipython", "matplotlib"]
-test = ["catboost ; python_version < \"3.13\"", "causalml", "gpboost", "lightgbm", "ngboost", "numpy (<2.0)", "opencv-python", "protobuf (==3.20.3)", "pyod", "pyspark", "pytest", "pytest-cov", "pytest-mpl", "scikit-learn (<=1.6.1)", "selenium", "sentencepiece", "tensorflow ; python_version < \"3.13\"", "tf-keras ; python_version < \"3.13\"", "torch ; python_version < \"3.13\"", "torchvision ; python_version < \"3.13\"", "transformers ; python_version < \"3.13\"", "xgboost"]
+test = ["catboost ; python_version < \"3.13\"", "causalml", "gpboost (<=1.6.1)", "lightgbm", "ngboost", "numpy (<2.0)", "opencv-python", "protobuf (==3.20.3)", "pyod", "pyspark", "pytest", "pytest-cov", "pytest-mpl", "scikit-learn (<=1.6.1)", "selenium", "sentencepiece", "tensorflow ; python_version < \"3.13\"", "tf-keras ; python_version < \"3.13\"", "torch ; python_version < \"3.13\"", "torchvision ; python_version < \"3.13\"", "transformers (<4.54.0) ; python_version < \"3.13\"", "xgboost"]
test-core = ["mypy", "pytest", "pytest-cov", "pytest-mpl"]
test-notebooks = ["datasets", "jupyter", "keras", "nbconvert", "nbformat", "nlp", "transformers"]
@@ -8386,15 +11103,15 @@ files = [
[[package]]
name = "smart-open"
-version = "7.3.0.post1"
+version = "7.5.0"
description = "Utils for streaming large files (S3, HDFS, GCS, SFTP, Azure Blob Storage, gzip, bz2, zst...)"
optional = true
-python-versions = "<4.0,>=3.8"
+python-versions = "<4.0,>=3.9"
groups = ["main"]
-markers = "extra == \"pii-detection\""
+markers = "python_version == \"3.9\" and extra == \"pii-detection\""
files = [
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[package.extras]
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azure = ["azure-common", "azure-core", "azure-storage-blob"]
-gcs = ["google-cloud-storage (>=2.6.0)"]
+gcs = ["google-api-core (<2.28) ; python_version < \"3.10\"", "google-cloud-storage (>=2.6.0)"]
http = ["requests"]
-s3 = ["boto3"]
+s3 = ["boto3 (>=1.9.17)"]
ssh = ["paramiko"]
-test = ["awscli", "moto[server]", "numpy", "pyopenssl", "pytest", "pytest-rerunfailures", "pytest_benchmark", "responses", "smart_open[all]"]
+test = ["awscli", "flake8", "moto[server]", "numpy", "pyopenssl", "pytest", "pytest-rerunfailures", "pytest-timeout", "pytest-xdist[psutil]", "pytest_benchmark", "responses", "smart_open[all]"]
webhdfs = ["requests"]
-zst = ["zstandard"]
+zst = ["backports.zstd (>=1.0.0) ; python_version < \"3.14\""]
+
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+name = "smart-open"
+version = "7.5.1"
+description = "Utils for streaming large files (S3, HDFS, GCS, SFTP, Azure Blob Storage, gzip, bz2, zst...)"
+optional = true
+python-versions = "<4.0,>=3.10"
+groups = ["main"]
+markers = "extra == \"pii-detection\" and python_version >= \"3.10\""
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+
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+azure = ["azure-common", "azure-core", "azure-storage-blob"]
+gcs = ["google-api-core (<2.28) ; python_version < \"3.10\"", "google-cloud-storage (>=2.6.0)"]
+http = ["requests"]
+s3 = ["boto3 (>=1.9.17)"]
+ssh = ["paramiko"]
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+zst = ["backports.zstd (>=1.0.0) ; python_version < \"3.14\""]
[[package]]
name = "sniffio"
@@ -8417,7 +11161,7 @@ version = "1.3.1"
description = "Sniff out which async library your code is running under"
optional = false
python-versions = ">=3.7"
-groups = ["main", "dev"]
+groups = ["main"]
files = [
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[[package]]
name = "soupsieve"
-version = "2.7"
+version = "2.8.3"
description = "A modern CSS selector implementation for Beautiful Soup."
optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
groups = ["main", "dev"]
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+
+[[package]]
+name = "spacy"
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optional = true
python-versions = ">=3.9"
groups = ["main"]
-markers = "extra == \"pii-detection\""
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testing = ["mypy", "pytest", "pytest-gitignore", "pytest-mock", "responses", "ruff", "syrupy", "tox", "tox-uv", "types-filelock", "types-requests"]
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@@ -9504,27 +12633,27 @@ onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1
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[[package]]
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optional = false
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+
+[package.extras]
+cffi = ["cffi (>=1.17,<2.0) ; platform_python_implementation != \"PyPy\" and python_version < \"3.14\"", "cffi (>=2.0.0b0) ; platform_python_implementation != \"PyPy\" and python_version >= \"3.14\""]
[extras]
-all = ["arch", "bert-score", "evaluate", "langchain-openai", "langdetect", "nltk", "pycocoevalcap", "ragas", "rouge", "scipy", "scorecardpy", "sentencepiece", "shap", "statsmodels", "textblob", "torch", "transformers", "xgboost"]
-credit-risk = ["scorecardpy"]
-datasets = ["datasets"]
+all = ["arch", "bert-score", "datasets", "datasets", "evaluate", "langchain", "langchain-community", "langchain-openai", "langdetect", "nltk", "pyarrow", "pyarrow", "pyarrow", "pycocoevalcap", "ragas", "rouge", "scipy", "scorecardpy", "sentencepiece", "setuptools", "shap", "statsmodels", "textblob", "torch", "transformers", "xgboost"]
+credit-risk = ["scorecardpy", "setuptools"]
+datasets = ["datasets", "datasets", "pyarrow", "pyarrow", "pyarrow"]
explainability = ["shap"]
huggingface = ["sentencepiece", "transformers"]
-llm = ["deepeval", "langchain-openai", "pycocoevalcap", "ragas", "sentencepiece", "torch", "transformers"]
-nlp = ["bert-score", "evaluate", "langdetect", "nltk", "pyarrow", "rouge", "textblob"]
-pii-detection = ["presidio-analyzer", "presidio-structured"]
+llm = ["datasets", "datasets", "deepeval", "langchain", "langchain-community", "langchain-openai", "pyarrow", "pyarrow", "pyarrow", "pycocoevalcap", "ragas", "sentencepiece", "torch", "transformers"]
+nlp = ["bert-score", "datasets", "datasets", "evaluate", "langdetect", "nltk", "pyarrow", "pyarrow", "pyarrow", "rouge", "textblob", "transformers"]
+pii-detection = ["presidio-analyzer", "presidio-analyzer", "presidio-structured"]
pytorch = ["torch"]
stats = ["arch", "scipy", "statsmodels"]
xgboost = ["xgboost"]
[metadata]
lock-version = "2.1"
-python-versions = ">=3.9,<3.13"
-content-hash = "003c831ae251049b5e75e935ddb8979b9e53afbe2b23d09cc9335911f4016bdd"
+python-versions = ">=3.9,<3.15"
+content-hash = "aecd99f24cfcfcf3c1eb088a634b9688f77bedccfb7526f2eeb7d71875397c78"
diff --git a/pyproject.toml b/pyproject.toml
index d9543162e..7fd360c68 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,9 +1,9 @@
[project]
name = "validmind"
-version = "2.12.2"
+version = "2.12.3"
description = "ValidMind Library"
readme = "README.pypi.md"
-requires-python = ">=3.9,<3.13"
+requires-python = ">=3.9,<3.15"
license = { file = "LICENSE" }
authors = [
{ name = "Andres Rodriguez", email = "andres@validmind.ai" },
@@ -19,7 +19,8 @@ dependencies = [
"matplotlib",
"mistune (>=3.0.2,<4.0.0)",
"nest-asyncio (>=1.6.0,<2.0.0)",
- "numpy (>=1.23,<2.0.0)",
+ "numpy (>=1.23,<2.0.0) ; python_version < '3.14'",
+ "numpy (>=2.3,<3.0.0) ; python_version >= '3.14'",
"openai (>=1)",
"pandas (>=2.0.3,<3.0.0)",
"plotly (>=6.0.0)",
@@ -41,6 +42,8 @@ all = [
"transformers (>=4.32.0,<5.0.0)",
"pycocoevalcap",
"ragas (>=0.2.3,<=0.2.7)",
+ "langchain (>=0.2.0,<0.4.0)",
+ "langchain-community (>=0.2.0,<0.4.0)",
"sentencepiece (>=0.2.0,<0.3.0)",
"langchain-openai (>=0.1.8)",
"scipy",
@@ -48,12 +51,18 @@ all = [
"langdetect",
"nltk (>=3.8.1,<4.0.0)",
"textblob (>=0.18.0.post0,<0.19.0)",
+ "datasets (>=2.10.0,<3.0.0) ; python_version < '3.14'",
+ "datasets (>=4.4,<5.0.0) ; python_version >= '3.14'",
"evaluate",
"rouge (>=1)",
"bert-score (>=0.3.13)",
- "arch",
+ "pyarrow (<16) ; python_version < '3.13'",
+ "pyarrow (>=20,<21) ; python_version == '3.13'",
+ "pyarrow (>=22,<23) ; python_version >= '3.14'",
+ "arch (>=7.0.0)",
"shap (>=0.46.0)",
"scorecardpy==0.1.9.6",
+ "setuptools (<82)",
]
huggingface = [
"transformers (>=4.32.0,<5.0.0)",
@@ -64,26 +73,48 @@ llm = [
"transformers (>=4.32.0,<5.0.0)",
"pycocoevalcap",
"ragas (>=0.2.3,<=0.2.7)",
+ "langchain (>=0.2.0,<0.4.0)",
+ "langchain-community (>=0.2.0,<0.4.0)",
+ "datasets (>=2.10.0,<3.0.0) ; python_version < '3.14'",
+ "datasets (>=4.4,<5.0.0) ; python_version >= '3.14'",
"sentencepiece (>=0.2.0,<0.3.0)",
"langchain-openai (>=0.1.8)",
- "deepeval (>=3.7.0)",
+ "pyarrow (<16) ; python_version < '3.13'",
+ "pyarrow (>=20,<21) ; python_version == '3.13'",
+ "pyarrow (>=22,<23) ; python_version >= '3.14'",
+ "deepeval (>=3.7.0,<=3.7.5)",
]
nlp = [
"langdetect",
"nltk (>=3.8.1,<4.0.0)",
"textblob (>=0.18.0.post0,<0.19.0)",
+ "datasets (>=2.10.0,<3.0.0) ; python_version < '3.14'",
+ "datasets (>=4.4,<5.0.0) ; python_version >= '3.14'",
+ "transformers (>=4.32.0,<5.0.0)",
"evaluate",
"rouge (>=1)",
"bert-score (>=0.3.13)",
- "pyarrow (<16)",
+ "pyarrow (<16) ; python_version < '3.13'",
+ "pyarrow (>=20,<21) ; python_version == '3.13'",
+ "pyarrow (>=22,<23) ; python_version >= '3.14'",
]
pytorch = ["torch (>=2.0.0)"]
-stats = ["scipy", "statsmodels (>=0.14.2,<0.15.0)", "arch"]
+stats = ["scipy", "statsmodels (>=0.14.2,<0.15.0)", "arch (>=7.0.0)"]
xgboost = ["xgboost (>=1.5.2,<3)"]
explainability = ["shap (>=0.46.0)"]
-credit_risk = ["scorecardpy==0.1.9.6"]
-datasets = ["datasets (>=2.10.0,<3.0.0)"]
-pii-detection = ["presidio-analyzer", "presidio-structured"]
+credit_risk = ["scorecardpy==0.1.9.6", "setuptools (<82)"]
+datasets = [
+ "datasets (>=2.10.0,<3.0.0) ; python_version < '3.14'",
+ "datasets (>=4.4,<5.0.0) ; python_version >= '3.14'",
+ "pyarrow (<16) ; python_version < '3.13'",
+ "pyarrow (>=20,<21) ; python_version == '3.13'",
+ "pyarrow (>=22,<23) ; python_version >= '3.14'",
+]
+pii-detection = [
+ "presidio-analyzer ; python_version < '3.14'",
+ "presidio-analyzer (<2.2.360) ; python_version >= '3.14'",
+ "presidio-structured",
+]
[tool.poetry.group.dev.dependencies]
black = "^22.1.0"
diff --git a/validmind/__version__.py b/validmind/__version__.py
index 0f9026021..521faacaf 100644
--- a/validmind/__version__.py
+++ b/validmind/__version__.py
@@ -1 +1 @@
-__version__ = "2.12.2"
+__version__ = "2.12.3"