This is the experimental PyCaret Fork of the Algorithmic Business Research Team at the Lucerne University of Applied Sciences and Arts.
So far we have implemented custom residual plots to judge the performance of a regression model. Those plots were merged back to the original PyCaret repository (see PR 980). Additionally two new methods to calculate feature importance (Drop & Relearn, Permute & Relearn) were integrated for classification as well as regression models.
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 words 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 many more.
The design and simplicity of PyCaret is 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 expertise. Seasoned data scientists are often difficult to find and expensive to hire but citizen data scientists can be an effective way to mitigate this gap and address data-related challenges in the business setting.
PyCaret is a great library which not only simplifies the machine learning tasks for citizen data scientists but also helps new startups to reduce the cost of investing in a team of data scientists. Therefore, this library has not only helped the citizen data scientists but has also helped individuals who want to start exploring the field of data science, having no prior knowledge in this field. Iniitial idea of PyCaret was inspired by Caret library in R.
- Official Website: https://www.pycaret.org
- Documentation: https://pycaret.readthedocs.io/en/latest/
PyCaret 2.3.3 is now available. See 2.3.3 release notes. The easiest way to install pycaret is using pip.
pip install pycaretPyCaret's default installation is a slim version of pycaret which only installs hard dependencies that are listed in requirements.txt. To install the full version of pycaret, use the following command:
pip install pycaret[full]PyCaret >= 2.2 provides the option to use GPU for select model training and hyperparameter tuning. 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 slim version or the full version. The following estimators can be trained on GPU.
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Extreme Gradient Boosting (requires no further installation)
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CatBoost (requires no further installation)
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Light Gradient Boosting Machine (requires GPU installation: https://lightgbm.readthedocs.io/en/latest/GPU-Tutorial.html)
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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 https://github.com/rapidsai/cuml)
If you are using Google Colab you can install Light Gradient Boosting Machine for GPU but first you have to uninstall LightGBM on CPU. Use the below command to do that:
pip uninstall lightgbm -y
# install lightgbm GPU
pip install lightgbm --install-option=--gpu --install-option="--opencl-include-dir=/usr/local/cuda/include/" --install-option="--opencl-library=/usr/local/cuda/lib64/libOpenCL.so"CatBoost is only enabled on GPU when dataset has > 50,000 rows.
cuML >= 0.15 cannot be installed on Google Colab. Instead use blazingSQL (https://blazingsql.com/) which comes pre-installed with cuML 0.15. Use following command to install pycaret:
# install pycaret on blazingSQL
!/opt/conda-environments/rapids-stable/bin/python -m pip install --upgrade pycaret- Release notes: https://github.com/pycaret/pycaret/releases
- Docs: https://pycaret.readthedocs.io/en/latest/
- Tutorials: https://pycaret.readthedocs.io/en/latest/tutorials.html
- Example Notebooks: https://github.com/pycaret/pycaret/tree/master/examples
- Other Resources: https://github.com/pycaret/pycaret/tree/master/resources
- Issue Logs: https://github.com/pycaret/pycaret/issues
- Contribute: https://pycaret.readthedocs.io/en/latest/contribute.html
- Join Slack Community: https://join.slack.com/t/pycaret/shared_invite/zt-row9phbm-BoJdEVPYnGf7_NxNBP307w
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 Students.
- Data Science Professionals who wants to build rapid prototypes.
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