Sklearn interface #91
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This sounds really nice, thanks a lot! I can review this PR as I initially proposed the feature (without doing the hard work of implementing it ;-) Most likely end of this month. |
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The sklearn API implementation of MODNet can be found under modnet.sklearn. It enables integration with scikit-learn methods such as pipelines, model selection functions (e.g. gridsearch), and integration with other sklearn models.
The main classes are:
MODNetFeaturizer: A transformer that converts a list of compositions or structures to a dataframe of shape (n_samples, n_features)
RR: A transformer based on Relevance-Redundancy (RR) feature selection. Given an input array or dataframe it will keep n_feat features with the highest RR-score.
MODNetRegressor: A regressor based on the MODNetModel for fitting, allowing multiple properties.
MODNetClassifier: A classifier based on the MODNetModel.
Tests: modnet/tests/test_sklearn.py
Example notebook: here