Source code for tsfresh.transformers.feature_augmenter

# -*- coding: utf-8 -*-
# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)
# Maximilian Christ (, Blue Yonder Gmbh, 2016

import pandas as pd
from sklearn.base import BaseEstimator, TransformerMixin

import tsfresh.defaults
from tsfresh.feature_extraction import extract_features
from tsfresh.utilities.dataframe_functions import restrict_input_to_index

[docs] class FeatureAugmenter(BaseEstimator, TransformerMixin): """ Sklearn-compatible estimator, for calculating and adding many features calculated from a given time series to the data. It is basically a wrapper around :func:`~tsfresh.feature_extraction.extract_features`. The features include basic ones like min, max or median, and advanced features like fourier transformations or statistical tests. For a list of all possible features, see the module :mod:`~tsfresh.feature_extraction.feature_calculators`. The column name of each added feature contains the name of the function of that module, which was used for the calculation. For this estimator, two datasets play a crucial role: 1. the time series container with the timeseries data. This container (for the format see :ref:`data-formats-label`) contains the data which is used for calculating the features. It must be groupable by ids which are used to identify which feature should be attached to which row in the second dataframe. 2. the input data X, where the features will be added to. Its rows are identifies by the index and each index in X must be present as an id in the time series container. Imagine the following situation: You want to classify 10 different financial shares and you have their development in the last year as a time series. You would then start by creating features from the metainformation of the shares, e.g. how long they were on the market etc. and filling up a table - the features of one stock in one row. This is the input array X, which each row identified by e.g. the stock name as an index. >>> df = pandas.DataFrame(index=["AAA", "BBB", ...]) >>> # Fill in the information of the stocks >>> df["started_since_days"] = ... # add a feature You can then extract all the features from the time development of the shares, by using this estimator. The time series container must include a column of ids, which are the same as the index of X. >>> time_series = read_in_timeseries() # get the development of the shares >>> from tsfresh.transformers import FeatureAugmenter >>> augmenter = FeatureAugmenter(column_id="id") >>> augmenter.set_timeseries_container(time_series) >>> df_with_time_series_features = augmenter.transform(df) The settings for the feature calculation can be controlled with the settings object. If you pass ``None``, the default settings are used. Please refer to :class:`~tsfresh.feature_extraction.settings.ComprehensiveFCParameters` for more information. This estimator does not select the relevant features, but calculates and adds all of them to the DataFrame. See the :class:`~tsfresh.transformers.relevant_feature_augmenter.RelevantFeatureAugmenter` for calculating and selecting features. For a description what the parameters column_id, column_sort, column_kind and column_value mean, please see :mod:`~tsfresh.feature_extraction.extraction`. """ def __init__( self, default_fc_parameters=None, kind_to_fc_parameters=None, column_id=None, column_sort=None, column_kind=None, column_value=None, timeseries_container=None, chunksize=tsfresh.defaults.CHUNKSIZE, n_jobs=tsfresh.defaults.N_PROCESSES, show_warnings=tsfresh.defaults.SHOW_WARNINGS, disable_progressbar=tsfresh.defaults.DISABLE_PROGRESSBAR, impute_function=tsfresh.defaults.IMPUTE_FUNCTION, profile=tsfresh.defaults.PROFILING, profiling_filename=tsfresh.defaults.PROFILING_FILENAME, profiling_sorting=tsfresh.defaults.PROFILING_SORTING, ): """ Create a new FeatureAugmenter instance. :param default_fc_parameters: mapping from feature calculator names to parameters. Only those names which are keys in this dict will be calculated. See the class:`ComprehensiveFCParameters` for more information. :type default_fc_parameters: dict :param kind_to_fc_parameters: mapping from kind names to objects of the same type as the ones for default_fc_parameters. If you put a kind as a key here, the fc_parameters object (which is the value), will be used instead of the default_fc_parameters. This means that kinds, for which kind_of_fc_parameters doe not have any entries, will be ignored by the feature selection. :type kind_to_fc_parameters: dict :param column_id: The column with the id. See :mod:`~tsfresh.feature_extraction.extraction`. :type column_id: basestring :param column_sort: The column with the sort data. See :mod:`~tsfresh.feature_extraction.extraction`. :type column_sort: basestring :param column_kind: The column with the kind data. See :mod:`~tsfresh.feature_extraction.extraction`. :type column_kind: basestring :param column_value: The column with the values. See :mod:`~tsfresh.feature_extraction.extraction`. :type column_value: basestring :param n_jobs: The number of processes to use for parallelization. If zero, no parallelization is used. :type n_jobs: int :param chunksize: The size of one chunk that is submitted to the worker process for the parallelisation. Where one chunk is defined as a singular time series for one id and one kind. If you set the chunksize to 10, then it means that one task is to calculate all features for 10 time series. If it is set it to None, depending on distributor, heuristics are used to find the optimal chunksize. If you get out of memory exceptions, you can try it with the dask distributor and a smaller chunksize. :type chunksize: None or int :param show_warnings: Show warnings during the feature extraction (needed for debugging of calculators). :type show_warnings: bool :param disable_progressbar: Do not show a progressbar while doing the calculation. :type disable_progressbar: bool :param impute_function: None, if no imputing should happen or the function to call for imputing the result dataframe. Imputing will never happen on the input data. :type impute_function: None or function :param profile: Turn on profiling during feature extraction :type profile: bool :param profiling_sorting: How to sort the profiling results (see the documentation of the profiling package for more information) :type profiling_sorting: basestring :param profiling_filename: Where to save the profiling results. :type profiling_filename: basestring """ self.default_fc_parameters = default_fc_parameters self.kind_to_fc_parameters = kind_to_fc_parameters self.column_id = column_id self.column_sort = column_sort self.column_kind = column_kind self.column_value = column_value self.n_jobs = n_jobs self.chunksize = chunksize self.show_warnings = show_warnings self.disable_progressbar = disable_progressbar self.impute_function = impute_function self.profile = profile self.profiling_filename = profiling_filename self.profiling_sorting = profiling_sorting self.timeseries_container = timeseries_container
[docs] def set_timeseries_container(self, timeseries_container): """ Set the timeseries, with which the features will be calculated. For a format of the time series container, please refer to :mod:`~tsfresh.feature_extraction.extraction`. The timeseries must contain the same indices as the later DataFrame, to which the features will be added (the one you will pass to :func:`~transform`). You can call this function as often as you like, to change the timeseries later (e.g. if you want to extract for different ids). :param timeseries_container: The timeseries as a pandas.DataFrame or a dict. See :mod:`~tsfresh.feature_extraction.extraction` for the format. :type timeseries_container: pandas.DataFrame or dict :return: None :rtype: None """ self.timeseries_container = timeseries_container
[docs] def fit(self, X=None, y=None): """ The fit function is not needed for this estimator. It just does nothing and is here for compatibility reasons. :param X: Unneeded. :type X: Any :param y: Unneeded. :type y: Any :return: The estimator instance itself :rtype: FeatureAugmenter """ return self
[docs] def transform(self, X): """ Add the features calculated using the timeseries_container and add them to the corresponding rows in the input pandas.DataFrame X. To save some computing time, you should only include those time serieses in the container, that you need. You can set the timeseries container with the method :func:`set_timeseries_container`. :param X: the DataFrame to which the calculated timeseries features will be added. This is *not* the dataframe with the timeseries itself. :type X: pandas.DataFrame :return: The input DataFrame, but with added features. :rtype: pandas.DataFrame """ if self.timeseries_container is None: raise RuntimeError( "You have to provide a time series using the set_timeseries_container function before." ) # Extract only features for the IDs in X.index timeseries_container_X = restrict_input_to_index( self.timeseries_container, self.column_id, X.index ) extracted_features = extract_features( timeseries_container_X, default_fc_parameters=self.default_fc_parameters, kind_to_fc_parameters=self.kind_to_fc_parameters, column_id=self.column_id, column_sort=self.column_sort, column_kind=self.column_kind, column_value=self.column_value, chunksize=self.chunksize, n_jobs=self.n_jobs, show_warnings=self.show_warnings, disable_progressbar=self.disable_progressbar, impute_function=self.impute_function, profile=self.profile, profiling_filename=self.profiling_filename, profiling_sorting=self.profiling_sorting, ) X = pd.merge( X, extracted_features, left_index=True, right_index=True, how="left" ) return X