Metadata-Version: 2.1
Name: featuretools-tsfresh-primitives
Version: 0.3.0
Summary: TSFresh primitives for featuretools
Home-page: UNKNOWN
Author: Feature Labs, Inc.
Author-email: support@featurelabs.com
License: MIT
Description: # Featuretools TSFresh Primitives
        ### Installation
        ```python
        pip install 'featuretools[tsfresh]'
        ```
        ## Calculating Features
        In `tsfresh`, this is how to calculate a feature.
        ```python
        from tsfresh.feature_extraction.feature_calculators import agg_autocorrelation
        
        data = list(range(10))
        param = [{'f_agg': 'mean', 'maxlag': 5}]
        agg_autocorrelation(data, param=param)
        ```
        ```
        [('f_agg_"mean"__maxlag_5', 0.1717171717171717)]
        ```
        With tsfresh primtives in `featuretools`, this is how to calculate the same feature.
        ```python
        from featuretools.tsfresh import AggAutocorrelation
        
        data = list(range(10))
        AggAutocorrelation(f_agg='mean', maxlag=5)(data)
        ```
        ```
        0.1717171717171717
        ```
        ## Combining Primitives
        In `featuretools`, this is how to combine tsfresh primitives with built-in or other installed primitives.
        ```python
        import featuretools as ft
        from featuretools.tsfresh import AggAutocorrelation, Mean
        
        entityset = ft.demo.load_mock_customer(return_entityset=True)
        agg_primitives = [Mean, AggAutocorrelation(f_agg='mean', maxlag=5)]
        feature_matrix, features = ft.dfs(entityset=entityset, target_entity='sessions', agg_primitives=agg_primitives)
        
        feature_matrix[[
            'MEAN(transactions.amount)',
            'AGG_AUTOCORRELATION(transactions.amount, f_agg=mean, maxlag=5)',
        ]].head()
        ```
        ```
                    MEAN(transactions.amount)  AGG_AUTOCORRELATION(transactions.amount, f_agg=mean, maxlag=5)
        session_id
        1                           76.813125                                           0.044268
        2                           74.696000                                          -0.053110
        3                           88.600000                                           0.007520
        4                           64.557200                                          -0.034542
        5                           70.638182                                          -0.100571
        ```
        Notice that tsfresh primtives are applied across relationships in an entityset generating many features that are otherwise not possible.
        
        ```python
        feature_matrix[['customers.AGG_AUTOCORRELATION(transactions.amount, f_agg=mean, maxlag=5)']].head()
        ```
        ```
                    customers.AGG_AUTOCORRELATION(transactions.amount, f_agg=mean, maxlag=5)
        session_id
        1                                                    0.011102
        2                                                   -0.001686
        3                                                   -0.010679
        4                                                    0.011204
        5                                                   -0.010679
        ```
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
