pypi i deepchecks-core


Test Suites for Validating ML Models & Data. Deepchecks is a Python package for comprehensively validating your machine learning models and data with minimal effort.

by deepchecks

0.0.1 (see all)
pypi i deepchecks-core

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Testing and Validating ML Models & Data

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🧐 What is Deepchecks?

Deepchecks is a Python package for comprehensively validating your machine learning models and data with minimal effort. This includes checks related to various types of issues, such as model performance, data integrity, distribution mismatches, and more.

🖼️ Computer Vision & 🔢 Tabular Support

This README refers to the Tabular version of deepchecks.

Check out the Deepchecks for Computer Vision & Images subpackage <deepchecks/vision>__ for more details about deepchecks for CV, currently in beta release.

💻 Installation

Using pip

.. code:: bash

pip install deepchecks -U --user


Note: Computer Vision Install

To install deepchecks together with the Computer Vision Submodule that is currently in beta release, replace deepchecks with "deepchecks[vision]" as follows.

.. code:: bash

  pip install "deepchecks[vision]" -U --user

Using conda

.. code:: bash

conda install -c conda-forge deepchecks

⏩ Try it Out!

🏃‍♀️ See It in Action

Head over to one of our following quickstart tutorials, and have deepchecks running on your environment in less than 5 min:

  • Train-Test Validation Quickstart (loans data) < auto_quickstarts/plot_quick_train_test_validation.html?>__

  • Data Integrity Quickstart (avocado sales data) < auto_quickstarts/plot_quick_data_integrity.html?>__

  • Full Suite (many checks) Quickstart (iris data) < auto_quickstarts/plot_quickstart_in_5_minutes.html?>__

    Recommended - download the code and run it locally on the built-in dataset and (optional) model, or replace them with your own.

🚀 See Our Checks Demo

Play with some of the existing checks in our Interactive Checks Demo < &>__, and see how they work on various datasets with custom corruptions injected.

📊 Usage Examples

Running a Suite

A Suite <#suite> runs a collection of Checks <#check> with optional Conditions <#condition>_ added to them.

Example for running a suite on given datasets and with a supported model:

.. code:: python

from deepchecks.tabular.suites import model_evaluation suite = model_evaluation() result =, test_dataset=test_dataset, model=model) result.save_as_html() # replace this with or result.show_in_window() to see results inline or in window

Which will result in a report that looks like this:

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  • Results can also displayed inline, saved as an html report, saved as JSON, or exported to other tools (e.g Weights & Biases - wandb)
  • Other suites that run only on the data (data_integrity, train_test_validation) don't require a model as part of the input.

See the full code tutorials here_.

.. _full code tutorials here:

.. _datasets: user-guide/tabular/dataset_object.html ? utm_campaign=readme&utm_content=running_a_suite

.. _supported model: user-guide/supported_models.html ? utm_campaign=readme&utm_content=running_a_suite

In the following section you can see an example of how the output of a single check without a condition may look.

Running a Check

To run a specific single check, all you need to do is import it and then to run it with the required (check-dependent) input parameters. More details about the existing checks and the parameters they can receive can be found in our API Reference_.

.. _API Reference: api/index.html? utm_campaign=readme&utm_content=running_a_check

.. code:: python

from deepchecks.tabular.checks import TrainTestFeatureDrift import pandas as pd

train_df = pd.read_csv('train_data.csv') test_df = pd.read_csv('test_data.csv')

Initialize and run desired check

TrainTestFeatureDrift().run(train_df, test_df)

Will produce output of the type:

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  <h4>Train Test Drift</h4>
  <p>The Drift score is a measure for the difference between two distributions,
  in this check - the test and train distributions. <br>
  The check shows the drift score and distributions for the features,
  sorted by feature importance and showing only the top 5 features, according to feature importance.
  If available, the plot titles also show the feature importance (FI) rank.</p>
  <p align="left">
    <img src="docs/source/_static/images/general/train-test-drift-output.png">

🙋🏼 When Should You Use Deepchecks?

While you’re in the research phase, and want to validate your data, find potential methodological problems, and/or validate your model and evaluate it.

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See more about typical usage scenarios and the built-in suites in the docs <>__.

🗝️ Key Concepts


Each check enables you to inspect a specific aspect of your data and models. They are the basic building block of the deepchecks package, covering all kinds of common issues, such as:

  • Model Error Analysis
  • Label Ambiguity
  • Data Sample Leakage and many more checks_.

.. _many more checks: checks_gallery/tabular.html ? utm_campaign=readme&utm_content=key_concepts__check

Each check can have two types of results:

  1. A visual result meant for display (e.g. a figure or a table).
  2. A return value that can be used for validating the expected check results (validations are typically done by adding a "condition" to the check, as explained below).


A condition is a function that can be added to a Check, which returns a pass ✓, fail ✖ or warning ! result, intended for validating the Check's return value. An example for adding a condition would be:

.. code:: python

from deepchecks.tabular.checks import BoostingOverfit BoostingOverfit().add_condition_test_score_percent_decline_not_greater_than(threshold=0.05)

which will return a check failure when running it if there is a difference of more than 5% between the best score achieved on the test set during the boosting iterations and the score achieved in the last iteration (the model's "original" score on the test set).


An ordered collection of checks, that can have conditions added to them. The Suite enables displaying a concluding report for all of the Checks that ran.

See the list of predefined existing suites_ for tabular data to learn more about the suites you can work with directly and also to see a code example demonstrating how to build your own custom suite.

The existing suites include default conditions added for most of the checks. You can edit the preconfigured suites or build a suite of your own with a collection of checks and optional conditions.

.. _predefined existing suites: deepchecks/tabular/suites

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🤔 What Do You Need in Order to Start Validating?


  • The deepchecks package installed

  • JupyterLab or Jupyter Notebook or any Python IDE

Data / Model

Depending on your phase and what you wish to validate, you'll need a subset of the following:

  • Raw data (before pre-processing such as OHE, string processing, etc.), with optional labels

  • The model's training data with labels

  • Test data (which the model isn't exposed to) with labels

  • A supported model_ (e.g. scikit-learn models, XGBoost, any model implementing the predict method in the required format)

Supported Data Types

The package currently supports tabular data and is in beta release for the Computer Vision subpackage <deepchecks/vision>__.

📖 Documentation

  • <>__
    • HTML documentation (stable release)
  • <>__
    • HTML documentation (latest release)

👭 Community

  • Join our Slack Community <>__ to connect with the maintainers and follow users and interesting discussions
  • Post a Github Issue <>__ to suggest improvements, open an issue, or share feedback.

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