PyPI 官网下载 | featuretools_sklearn_transformer-0.1.1.tar.gz

# featuretools-sklearn-transformer [](https://circleci.com/gh/FeatureLabs/featuretools-sklearn-transformer/tree/master) [](https://codecov.io/gh/FeatureLabs/featuretools-sklearn-transformer) [Featuretools](https://github.com/FeatureLabs/featuretools)' DFS as a scikit-learn transformer ### Install ```shell pip install featuretools_sklearn_transformer ``` ### Use ```python import featuretools as ft import pandas as pd from featuretools.wrappers import DFSTransformer from sklearn.pipeline import Pipeline from sklearn.ensemble import ExtraTreesClassifier # Get examle data n_customers = 3 es = ft.demo.load_mock_customer(return_entityset=True, n_customers=5) y = [True, False, True] # Build pipeline pipeline = Pipeline(steps=[ ('ft', DFSTransformer(entityset=es, target_entity="customers", max_features=2)), ('et', ExtraTreesClassifier(n_estimators=100)) ]) # Fit and predict pipeline.fit([1, 2, 3], y=y) # fit on first 3 customers pipeline.predict_proba([4,5]) # predict probability of each class on last 2 pipeline.predict([4,5]) # predict on last 2 # Same as above, but using cutoff times ct = pd.DataFrame() ct['customer_id'] = [1, 2, 3, 4, 5] ct['time'] = pd.to_datetime(['2014-1-1 04:00', '2014-1-2 17:20', '2014-1-4 09:53', '2014-1-4 13:48', '2014-1-5 15:32']) pipeline.fit(ct.head(3), y=y) pipeline.predict_proba(ct.tail(2)) pipeline.predict(ct.tail(2)) ``` ## Feature Labs <a href="https://www.featurelabs.com/"> <img src="http://www.featurelabs.com/wp-content/uploads/2017/12/logo.png" alt="Featuretools" /> </a> featuretools-sklearn-transformer is an open source project created by [Feature Labs](https://www.featurelabs.com/). To see the other open source projects we're working on visit Feature Labs [Open Source](https://www.featurelabs.com/open). If building impactful data science pipelines is important to you or your business, please [get in touch](https://www.featurelabs.com/contact/).