class_docstr: Sklearn-compatible estimator to calculate relevant features out of a time series and add them to a data sample.
  
  As many other sklearn estimators, this estimator works in two steps:
  
  In the fit phase, all possible time series features are calculated using the time series, that is set by the
  set_timeseries_container function (if the features are not manually changed by handing in a
  feature_extraction_settings object). Then, their significance and relevance to the target is computed using
  statistical methods and only the relevant ones are selected using the Benjamini Hochberg procedure. These features
  are stored internally.
  
  In the transform step, the information on which features are relevant from the fit step is used and those features
  are extracted from the time series. These extracted features are then added to the input data sample.
  
  This estimator is a wrapper around most of the functionality in the tsfresh package. For more information on the
  subtasks, please refer to the single modules and functions, which are:
  
  * Settings for the feature extraction: :class:`~tsfresh.feature_extraction.settings.ComprehensiveFCParameters`
  * Feature extraction method: :func:`~tsfresh.feature_extraction.extraction.extract_features`
  * Extracted features: :mod:`~tsfresh.feature_extraction.feature_calculators`
  * Feature selection: :func:`~tsfresh.feature_selection.feature_selector.check_fs_sig_bh`
  
  This estimator works analogue to the :class:`~tsfresh.transformers.feature_augmenter.FeatureAugmenter` with
  the difference that this estimator does only output and calculate the relevant features,
  whereas the other outputs all features.
  
  Also for this estimator, two datasets play a crucial role:
  
  1. the time series container with the timeseries data. This container (for the format see
     :mod:`~tsfresh.feature_extraction.extraction`) 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, where the features will be added to.
  
  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.
  
  >>> # Fill in the information of the stocks and the target
  >>> X_train, X_test, y_train = pd.DataFrame(), pd.DataFrame(), pd.Series()
  
  You can then extract all the relevant features from the time development of the shares, by using this estimator:
  
  >>> train_time_series, test_time_series = read_in_timeseries() # get the development of the shares
  >>> from tsfresh.transformers import RelevantFeatureAugmenter
  >>> augmenter = RelevantFeatureAugmenter()
  >>> augmenter.set_timeseries_container(train_time_series)
  >>> augmenter.fit(X_train, y_train)
  >>> augmenter.set_timeseries_container(test_time_series)
  >>> X_test_with_features = augmenter.transform(X_test)
  
  X_test_with_features will then contain the same information as X_test (with all the meta information you have
  probably added) plus some relevant time series features calculated on the time series you handed in.
  
  Please keep in mind that the time series you hand in before fit or transform must contain data for the rows that are
  present in X.
  
  If your set filter_only_tsfresh_features to True, your manually-created features that were present in X_train (or
  X_test) before using this estimator are not touched. Otherwise, also those features are evaluated and may be
  rejected from the data sample, because they are irrelevant.
  
  For a description what the parameters column_id, column_sort, column_kind and column_value mean, please see
  :mod:`~tsfresh.feature_extraction.extraction`.
  
  You can control the feature extraction in the fit step (the feature extraction in the transform step is done
  automatically) as well as the feature selection in the fit step by handing in settings.
  However, the default settings which are used if you pass no flags are often quite sensible.
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docstr: Create a new RelevantFeatureAugmenter instance.
  
  
  :param filter_only_tsfresh_features: Whether to touch the manually-created features during feature selection or
                                       not.
  :type filter_only_tsfresh_features: bool
  
  :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 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 n_jobs: The number of processes to use for parallelization. If zero, no parallelization is used.
  :type n_jobs: 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 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
  
  :param test_for_binary_target_binary_feature: Which test to be used for binary target, binary feature
                                                (currently unused)
  :type test_for_binary_target_binary_feature: str
  
  :param test_for_binary_target_real_feature: Which test to be used for binary target, real feature
  :type test_for_binary_target_real_feature: str
  
  :param test_for_real_target_binary_feature: Which test to be used for real target, binary feature
                                              (currently unused)
  :type test_for_real_target_binary_feature: str
  
  :param test_for_real_target_real_feature: Which test to be used for real target, real feature (currently unused)
  :type test_for_real_target_real_feature: str
  
  :param fdr_level: The FDR level that should be respected, this is the theoretical expected percentage
                    of irrelevant features among all created features.
  :type fdr_level: float
  
  :param hypotheses_independent: Can the significance of the features be assumed to be independent?
                                 Normally, this should be set to False as the features are never
                                 independent (e.g. mean and median)
  :type hypotheses_independent: bool
  
  :param ml_task: The intended machine learning task. Either `'classification'`, `'regression'` or `'auto'`.
              Defaults to `'auto'`, meaning the intended task is inferred from `y`.
              If `y` has a boolean, integer or object dtype, the task is assumed to be classification,
              else regression.
  :type ml_task: str
  
  :param multiclass: Whether the problem is multiclass classification. This modifies the way in which features
                 are selected. Multiclass requires the features to be statistically significant for
                 predicting n_significant classes.
  :type multiclass: bool
  
  :param n_significant: The number of classes for which features should be statistically significant predictors
                      to be regarded as 'relevant'
  :type n_significant: int
  
  :param multiclass_p_values: The desired method for choosing how to display multiclass p-values for each feature.
                              Either `'avg'`, `'max'`, `'min'`, `'all'`. Defaults to `'min'`, meaning the p-value
                              with the highest significance is chosen. When set to `'all'`, the attributes
                              `self.feature_importances_` and `self.p_values` are of type pandas.DataFrame, where
                              each column corresponds to a target class.
  :type multiclass_p_values: str
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code: def __init__(
      self,
      filter_only_tsfresh_features=True,
      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=defaults.CHUNKSIZE,
      n_jobs=defaults.N_PROCESSES,
      show_warnings=defaults.SHOW_WARNINGS,
      disable_progressbar=defaults.DISABLE_PROGRESSBAR,
      profile=defaults.PROFILING,
      profiling_filename=defaults.PROFILING_FILENAME,
      profiling_sorting=defaults.PROFILING_SORTING,
      test_for_binary_target_binary_feature=defaults.TEST_FOR_BINARY_TARGET_BINARY_FEATURE,
      test_for_binary_target_real_feature=defaults.TEST_FOR_BINARY_TARGET_REAL_FEATURE,
      test_for_real_target_binary_feature=defaults.TEST_FOR_REAL_TARGET_BINARY_FEATURE,
      test_for_real_target_real_feature=defaults.TEST_FOR_REAL_TARGET_REAL_FEATURE,
      fdr_level=defaults.FDR_LEVEL,
      hypotheses_independent=defaults.HYPOTHESES_INDEPENDENT,
      ml_task="auto",
      multiclass=False,
      n_significant=1,
      multiclass_p_values="min",
  ):
      """
      Create a new RelevantFeatureAugmenter instance.
  
  
      :param filter_only_tsfresh_features: Whether to touch the manually-created features during feature selection or
                                           not.
      :type filter_only_tsfresh_features: bool
  
      :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 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 n_jobs: The number of processes to use for parallelization. If zero, no parallelization is used.
      :type n_jobs: 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 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
  
      :param test_for_binary_target_binary_feature: Which test to be used for binary target, binary feature
                                                    (currently unused)
      :type test_for_binary_target_binary_feature: str
  
      :param test_for_binary_target_real_feature: Which test to be used for binary target, real feature
      :type test_for_binary_target_real_feature: str
  
      :param test_for_real_target_binary_feature: Which test to be used for real target, binary feature
                                                  (currently unused)
      :type test_for_real_target_binary_feature: str
  
      :param test_for_real_target_real_feature: Which test to be used for real target, real feature (currently unused)
      :type test_for_real_target_real_feature: str
  
      :param fdr_level: The FDR level that should be respected, this is the theoretical expected percentage
                        of irrelevant features among all created features.
      :type fdr_level: float
  
      :param hypotheses_independent: Can the significance of the features be assumed to be independent?
                                     Normally, this should be set to False as the features are never
                                     independent (e.g. mean and median)
      :type hypotheses_independent: bool
  
      :param ml_task: The intended machine learning task. Either `'classification'`, `'regression'` or `'auto'`.
                  Defaults to `'auto'`, meaning the intended task is inferred from `y`.
                  If `y` has a boolean, integer or object dtype, the task is assumed to be classification,
                  else regression.
      :type ml_task: str
  
      :param multiclass: Whether the problem is multiclass classification. This modifies the way in which features
                     are selected. Multiclass requires the features to be statistically significant for
                     predicting n_significant classes.
      :type multiclass: bool
  
      :param n_significant: The number of classes for which features should be statistically significant predictors
                          to be regarded as 'relevant'
      :type n_significant: int
  
      :param multiclass_p_values: The desired method for choosing how to display multiclass p-values for each feature.
                                  Either `'avg'`, `'max'`, `'min'`, `'all'`. Defaults to `'min'`, meaning the p-value
                                  with the highest significance is chosen. When set to `'all'`, the attributes
                                  `self.feature_importances_` and `self.p_values` are of type pandas.DataFrame, where
                                  each column corresponds to a target class.
      :type multiclass_p_values: str
      """
      self.filter_only_tsfresh_features = filter_only_tsfresh_features
      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.timeseries_container = timeseries_container
      self.chunksize = chunksize
      self.n_jobs = n_jobs
      self.show_warnings = show_warnings
      self.disable_progressbar = disable_progressbar
      self.profile = profile
      self.profiling_filename = profiling_filename
      self.profiling_sorting = profiling_sorting
      self.test_for_binary_target_binary_feature = (
          test_for_binary_target_binary_feature
      )
      self.test_for_binary_target_real_feature = (
          test_for_binary_target_real_feature
      )
      self.test_for_real_target_binary_feature = (
          test_for_real_target_binary_feature
      )
      self.test_for_real_target_real_feature = (
          test_for_real_target_real_feature
      )
      self.fdr_level = fdr_level
      self.hypotheses_independent = hypotheses_independent
      self.ml_task = ml_task
      self.multiclass = multiclass
      self.n_significant = n_significant
      self.multiclass_p_values = multiclass_p_values
  
      # attributes
      self.feature_extractor = None
      self.feature_selector = None
  
file_path: tsfresh/tsfresh/transformers/relevant_feature_augmenter.py
imports: from functools import partial
  import pandas
  from sklearn.base import BaseEstimator, TransformerMixin
  from tsfresh import defaults
  from tsfresh.feature_extraction.settings import from_columns
  from tsfresh.transformers.feature_augmenter import FeatureAugmenter
  from tsfresh.transformers.feature_selector import FeatureSelector
  from tsfresh.utilities.dataframe_functions import get_range_values_per_column, impute_dataframe_range
label_desc: Use image node_id 1 to create a new RelevantFeatureAugmenter object from inherited base classes: BaseEstimator, TransformerMixin with example: obj = RelevantFeatureAugmenter(filter_only_tsfresh_features, default_fc_parameters, kind_to_fc_parameters, column_id, column_sort, column_kind, column_value, timeseries_container, chunksize, n_jobs, show_warnings, disable_progressbar, profile, profiling_filename, profiling_sorting, test_for_binary_target_binary_feature, test_for_binary_target_real_feature, test_for_real_target_binary_feature, test_for_real_target_real_feature, fdr_level, hypotheses_independent, ml_task, multiclass, n_significant, multiclass_p_values)