sherpan/pycaret

An open-source, low-code machine learning library in Python

An open-source, low-code machine learning library in Python
🚀 PyCaret 3.0-rc is now out. pip install --pre pycaret

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Welcome to PyCaret

PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows. It is an end-to-end machine learning and model management tool that speeds up the experiment cycle exponentially and makes you more productive.

In comparison with the other open-source machine learning libraries, PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few lines only. This makes experiments exponentially fast and efficient. PyCaret is essentially a Python wrapper around several machine learning libraries and frameworks such as scikit-learn, XGBoost, LightGBM, CatBoost, spaCy, Optuna, Hyperopt, Ray, and few more.

The design and simplicity of PyCaret are inspired by the emerging role of citizen data scientists, a term first used by Gartner. Citizen Data Scientists are power users who can perform both simple and moderately sophisticated analytical tasks that would previously have required more technical expertise. PyCaret was inspired by the caret library in R programming language.

Important Links
⭐ Tutorials	New to PyCaret? Checkout our official notebooks!
📋 Example Notebooks	Example notebooks created by community.
📙 Official Blog	Tutorials and articles by contributors.
📚 Documentation	The detailed API docs of PyCaret
📺 Video Tutorials	Our video tutorial from various events.
✈️ Cheat sheet	Cheat sheet for all functions across modules.
📢 Discussions	Have questions? Engage with community and contributors.
🛠️ Changelog	Changes and version history.
🌳 Roadmap	PyCaret's software and community development plan.

Installation

PyCaret's default installation only installs hard dependencies as listed in the requirements.txt file.

pip install pycaret

To install the full version:

pip install pycaret[full]

Supervised Workflow

Classification	Regression

Unsupervised Workflow

Clustering	Anomaly Detection

⚡ PyCaret Time Series Module

PyCaret time series module is now available with the main pycaret installation. Staying true to simplicity of PyCaret, it is consistent with our existing API and fully loaded with functionalities. Statistical testing, model training and selection (30+ algorithms), model analysis, automated hyperparameter tuning, experiment logging, deployment on cloud, and more. All of this with only few lines of code (just like the other modules of pycaret).

Important Links
⭐ Time Series Quickstart	Get started with Time Series Analysis
📚 Time Series Notebooks	New to Time Series? Checkout our official (detailed) notebooks!
📺 Time Series Video Tutorials	Our video tutorial from various events.
❓ Time Series FAQs	Have questions? Queck out the FAQ's
🛠️ Time Series API Interface	The detailed API interface for the Time Series Module
🌳 Time Series Features and Roadmap	PyCaret's software and community development plan.

Installation

pip install --pre pycaret

Who should use PyCaret?

PyCaret is an open source library that anybody can use. In our view the ideal target audience of PyCaret is:

• Experienced Data Scientists who want to increase productivity.
• Citizen Data Scientists who prefer a low code machine learning solution.
• Data Science Professionals who want to build rapid prototypes.
• Data Science and Machine Learning students and enthusiasts.

PyCaret GPU support

With PyCaret >= 2.2, you can train models on GPU and speed up your workflow by 10x. To train models on GPU simply pass use_gpu = True in the setup function. There is no change in the use of the API, however, in some cases, additional libraries have to be installed as they are not installed with the default version or the full version. As of the latest release, the following models can be trained on GPU:

• Extreme Gradient Boosting (requires no further installation)
• CatBoost (requires no further installation)
• Light Gradient Boosting Machine requires GPU installation
• Logistic Regression, Ridge Classifier, Random Forest, K Neighbors Classifier, K Neighbors Regressor, Support Vector Machine, Linear Regression, Ridge Regression, Lasso Regression requires cuML >= 0.15

PyCaret Intel sklearnex support

You can apply Intel optimizations for machine learning algorithms and speed up your workflow. To train models with Intel optimizations use sklearnex engine. There is no change in the use of the API, however, installation of Intel sklearnex is required:

pip install scikit-learn-intelex

License

PyCaret is completely free and open-source and licensed under the MIT license.

Contributors