class_docstr: 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`.
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docstr: 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
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code: 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
  
file_path: tsfresh/tsfresh/transformers/feature_augmenter.py
imports: import pandas
  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
label_desc: Use image node_id 1 to create a new FeatureAugmenter object from inherited base classes: BaseEstimator, TransformerMixin with example: obj = FeatureAugmenter(default_fc_parameters, kind_to_fc_parameters, column_id, column_sort, column_kind, column_value, timeseries_container, chunksize, n_jobs, show_warnings, disable_progressbar, impute_function, profile, profiling_filename, profiling_sorting)