tsfresh includes three scikit-learn compatible transformers. You can easily add them to your existing data science pipeline. If you are not familiar with scikit-learn's pipeline we recommend you take a look at the official documentation.
The purpose of such a pipeline is to assemble several preprocessing steps that can be cross-validated together while setting different parameters. Our tsfresh transformer allows you to extract and filter the time series features during such a preprocessing sequence.
The first two estimators contained in tsfresh are the ~tsfresh.transformers.feature_augmenter.FeatureAugmenter, which extracts the features, and the ~tsfresh.transformers.feature_selector.FeatureSelector, which only performs the feature selection algorithm. It is preferable to combine extracting and filtering of the features in a single step to avoid unnecessary feature calculations. Hence, we have the ~tsfresh.transformers.feature_augmenter.RelevantFeatureAugmenter, which combines both the extraction and filtering of the features in a single step.
In the following example you see how we combine tsfresh's ~tsfresh.transformers.relevant_feature_augmenter.RelevantFeatureAugmenter and a ~sklearn.ensemble.RandomForestClassifier into a single pipeline. This pipeline can then fit both our transformer and the classifier in one step.
from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestClassifier
from tsfresh.examples import load_robot_execution_failures
from tsfresh.transformers import RelevantFeatureAugmenter
pipeline = Pipeline([('augmenter', RelevantFeatureAugmenter(column_id='id', column_sort='time')),
('classifier', RandomForestClassifier())])
df_ts, y = load_robot_execution_failures()
X = pd.DataFrame(index=y.index)
pipeline.set_params(augmenter__timeseries_container=df_ts)
pipeline.fit(X, y)The parameters of the augment transformer correspond to the parameters of the top-level convenience function ~tsfresh.convenience.relevant_extraction.extract_relevant_features. In the example, we only set the names of two columns column_id='id', column_sort='time' (see data-formats-label for an explanation of those parameters).
Because we cannot pass the time series container directly as a parameter to the augmenter step when calling fit or transform on a sklearn.pipeline.Pipeline we have to set it manually by calling pipeline.set_params(augmenter__timeseries_container=df_ts). In general, you can change the time series container from which the features are extracted by calling either the pipeline's ~sklearn.pipeline.Pipeline.set_params method or the transformers ~tsfresh.transformers.relevant_feature_augmenter.RelevantFeatureAugmenter.set_timeseries_container method.
For further examples, see the Jupyter Notebook pipeline_example.ipynb in the notebooks folder of the tsfresh package.