class_docstr: Sklearn-compatible estimator, for reducing the number of features in a dataset to only those,
  that are relevant and significant to a given target. It is basically a wrapper around
  :func:`~tsfresh.feature_selection.feature_selector.check_fs_sig_bh`.
  
  The check is done by testing the hypothesis
  
      :math:`H_0` = the Feature is not relevant and can not be added`
  
  against
  
      :math:`H_1` = the Feature is relevant and should be kept
  
  using several statistical tests (depending on whether the feature or/and the target is binary
  or not). Using the Benjamini Hochberg procedure, only features in :math:`H_0` are rejected.
  
  This estimator - as most of the sklearn estimators - works in a two step procedure. First, it is fitted
  on training data, where the target is known:
  
  >>> import pandas as pd
  >>> X_train, y_train = pd.DataFrame(), pd.Series() # fill in with your features and target
  >>> from tsfresh.transformers import FeatureSelector
  >>> selector = FeatureSelector()
  >>> selector.fit(X_train, y_train)
  
  In this example the list of relevant features is empty:
  
  >>> selector.relevant_features
  >>> []
  
  The same holds for the feature importance:
  
  >>> selector.feature_importances_
  >>> array([], dtype=float64)
  
  The estimator keeps track on those features, that were relevant in the training step. If you
  apply the estimator after the training, it will delete all other features in the testing
  data sample:
  
  >>> X_test = pd.DataFrame()
  >>> X_selected = selector.transform(X_test)
  
  After that, X_selected will only contain the features that were relevant during the training.
  
  If you are interested in more information on the features, you can look into the member
  ``relevant_features`` after the fit.
docstr: Delete all features, which were not relevant in the fit phase.
  
  :param X: data sample with all features, which will be reduced to only those that are relevant
  :type X: pandas.DataSeries or numpy.array
  
  :return: same data sample as X, but with only the relevant features
  :rtype: pandas.DataFrame or numpy.array
code: def transform(self, X):
      """
      Delete all features, which were not relevant in the fit phase.
  
      :param X: data sample with all features, which will be reduced to only those that are relevant
      :type X: pandas.DataSeries or numpy.array
  
      :return: same data sample as X, but with only the relevant features
      :rtype: pandas.DataFrame or numpy.array
      """
      if self.relevant_features is None:
          raise RuntimeError("You have to call fit before.")
  
      if isinstance(X, pd.DataFrame):
          return X.copy().loc[:, self.relevant_features]
      else:
          return X[:, self.relevant_features]
  
file_path: tsfresh/tsfresh/transformers/feature_selector.py
imports: import pandas
  from sklearn.base import BaseEstimator, TransformerMixin
  from tsfresh import defaults
  from tsfresh.feature_selection.relevance import calculate_relevance_table
label_desc: Use image node_id 3 for calling the FeatureSelector obj's underlying member method code with example usage: obj.transform(X) and returns: X, X