code: def target_binary_feature_binary_test(x, y):
      """
      Calculate the feature significance of a binary feature to a binary target as a p-value.
      Use the two-sided univariate fisher test from :func:`~scipy.stats.fisher_exact` for this.
  
      :param x: the binary feature vector
      :type x: pandas.Series
  
      :param y: the binary target vector
      :type y: pandas.Series
  
      :return: the p-value of the feature significance test. Lower p-values indicate a higher feature significance
      :rtype: float
  
      :raise: ``ValueError`` if the target or the feature is not binary.
      """
      __check_if_pandas_series(x, y)
      _check_for_nans(x, y)
  
      # Check for correct value range
      __check_for_binary_feature(x)
      __check_for_binary_target(y)
  
      # Extract the unique values
      x0, x1 = np.unique(x.values)
      y0, y1 = np.unique(y.values)
  
      # Calculate contingency table
      n_y1_x0 = np.sum(y[x == x0] == y1)
      n_y0_x0 = len(y[x == x0]) - n_y1_x0
      n_y1_x1 = np.sum(y[x == x1] == y1)
      n_y0_x1 = len(y[x == x1]) - n_y1_x1
  
      table = np.array([[n_y1_x1, n_y1_x0], [n_y0_x1, n_y0_x0]])
  
      # Perform the Fisher test
      oddsratio, p_value = stats.fisher_exact(
          table, alternative="two-sided"
      )
  
      return p_value
  
file_path: tsfresh/tsfresh/feature_selection/significance_tests.py
imports: import warnings
  from builtins import str
  import numpy
  import pandas
  from scipy import stats
label_desc: Use image node_id 1 for calling a global function with example usage: target_binary_feature_binary_test(x, y) and returns: p_value