Metadata-Version: 2.1
Name: featuretools-sklearn-transformer
Version: 0.1.1
Summary: Featuretools Transformer for Scikit-Learn Pipeline use.
Home-page: http://www.featurelabs.com/
Author: Feature Labs, Inc.
Author-email: support@featurelabs.com
License: BSD 3-clause
Description: # featuretools-sklearn-transformer
        
        [![CircleCI](https://circleci.com/gh/FeatureLabs/featuretools-sklearn-transformer/tree/master.svg?style=shield)](https://circleci.com/gh/FeatureLabs/featuretools-sklearn-transformer/tree/master)
        [![codecov](https://codecov.io/gh/FeatureLabs/featuretools-sklearn-transformer/branch/master/graph/badge.svg)](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/).
        
Platform: UNKNOWN
Requires-Python: >=3.6, <4
Description-Content-Type: text/markdown
