text: """Weight of Evidence"""
  import numpy as np
  import pandas as pd
  from sklearn.base import BaseEstimator, TransformerMixin
  import category_encoders.utils as util
  from sklearn.utils.random import check_random_state
  
  __author__ = 'Jan Motl'
  
  class WOEEncoder(BaseEstimator, TransformerMixin):
      """Weight of Evidence coding for categorical features.
  
      Parameters
      ----------
  
      verbose: int
          integer indicating verbosity of output. 0 for none.
      cols: list
          a list of columns to encode, if None, all string columns will be encoded.
      drop_invariant: bool
          boolean for whether or not to drop columns with 0 variance.
      return_df: bool
          boolean for whether to return a pandas DataFrame from transform (otherwise it will be a numpy array).
      impute_missing: bool
          boolean for whether or not to apply the logic for handle_unknown, will be deprecated in the future.
      handle_unknown: str
          options are 'ignore', 'error' and 'impute', defaults to 'impute', which will assume WOE=0.
      randomized: bool,
          adds normal (Gaussian) distribution noise into training data in order to decrease overfitting (testing data are untouched).
      sigma: float
          standard deviation (spread or "width") of the normal distribution.
      regularization: float
          the purpose of regularization is mostly to prevent division by zero.
          When regularization is 0, you may encounter division by zero.
  
      Example
      -------
      >>> from category_encoders import *
      >>> import pandas as pd
      >>> from sklearn.datasets import load_boston
      >>> bunch = load_boston()
      >>> y = bunch.target > 22.5
      >>> X = pd.DataFrame(bunch.data, columns=bunch.feature_names)
      >>> enc = WOEEncoder(cols=['CHAS', 'RAD']).fit(X, y)
      >>> numeric_dataset = enc.transform(X)
      >>> print(numeric_dataset.info())
      <class 'pandas.core.frame.DataFrame'>
      RangeIndex: 506 entries, 0 to 505
      Data columns (total 13 columns):
      CRIM       506 non-null float64
      ZN         506 non-null float64
      INDUS      506 non-null float64
      CHAS       506 non-null float64
      NOX        506 non-null float64
      RM         506 non-null float64
      AGE        506 non-null float64
      DIS        506 non-null float64
      RAD        506 non-null float64
      TAX        506 non-null float64
      PTRATIO    506 non-null float64
      B          506 non-null float64
      LSTAT      506 non-null float64
      dtypes: float64(13)
      memory usage: 51.5 KB
      None
  
      References
      ----------
  
      .. [1] Weight of Evidence (WOE) and Information Value Explained. from
      https://www.listendata.com/2015/03/weight-of-evidence-woe-and-information.html.
  
  
      """
  
      def __init__(self, verbose=0, cols=None, drop_invariant=False, return_df=True, impute_missing=True,
                   handle_unknown='impute', random_state=None, randomized=False, sigma=0.05, regularization=1.0):
          self.verbose = verbose
          self.return_df = return_df
          self.drop_invariant = drop_invariant
          self.drop_cols = []
          self.cols = cols
          self._dim = None
          self.mapping = None
          self.impute_missing = impute_missing
          self.handle_unknown = handle_unknown
          self._sum = None
          self._count = None
          self.random_state = random_state
          self.randomized = randomized
          self.sigma = sigma
          self.regularization = regularization
  
      # noinspection PyUnusedLocal
      def fit(self, X, y, **kwargs):
          """Fit encoder according to X and binary y.
  
          Parameters
          ----------
  
          X : array-like, shape = [n_samples, n_features]
              Training vectors, where n_samples is the number of samples
              and n_features is the number of features.
          y : array-like, shape = [n_samples]
              Binary target values.
  
          Returns
          -------
  
          self : encoder
              Returns self.
  
          """
  
          # Unite parameters into pandas types
          X = util.convert_input(X)
          if isinstance(y, pd.DataFrame):
              y = y.iloc[:,0]
          else:
              y = pd.Series(y, name='target')
  
          # The lengths must be equal
          if X.shape[0] != y.shape[0]:
              raise ValueError("The length of X is " + str(X.shape[0]) + " but length of y is " + str(y.shape[0]) + ".")
  
          # The label must be binary with values {0,1}
          unique = y.unique()
          if len(unique) != 2:
              raise ValueError("The target column y must be binary. But the target contains " + str(len(unique)) + " unique value(s).")
          if y.isnull().any():
              raise ValueError("The target column y must not contain missing values.")
          if np.max(unique) < 1:
              raise ValueError("The target column y must be binary with values {0, 1}. Value 1 was not found in the target.")
          if np.min(unique) > 0:
              raise ValueError("The target column y must be binary with values {0, 1}. Value 0 was not found in the target.")
  
          self._dim = X.shape[1]
  
          # If columns aren't passed, just use every string column
          if self.cols is None:
              self.cols = util.get_obj_cols(X)
          else:
              self.cols = util.convert_cols_to_list(self.cols)
  
          # Training
          self.mapping = self._train(X, y, cols=self.cols)
  
          # Store column names with approximately constant variance on the training data
          if self.drop_invariant:
              self.drop_cols = []
              X_temp = self.transform(X)
              generated_cols = util.get_generated_cols(X, X_temp, self.cols)
              self.drop_cols = [x for x in generated_cols if X_temp[x].var() <= 10e-5]
  
          return self
  
      def transform(self, X, y=None):
          """Perform the transformation to new categorical data. When the data are used for model training,
          it is important to also pass the target in order to apply leave one out.
  
          Parameters
          ----------
  
          X : array-like, shape = [n_samples, n_features]
          y : array-like, shape = [n_samples] when transform by leave one out
              None, when transform without target information (such as transform test set)
  
  
  
          Returns
          -------
  
          p : array, shape = [n_samples, n_numeric + N]
              Transformed values with encoding applied.
  
          """
  
          if self._dim is None:
              raise ValueError('Must train encoder before it can be used to transform data.')
  
          # Unite the input into pandas DataFrame
          X = util.convert_input(X)
  
          # Then make sure that it is the right size
          if X.shape[1] != self._dim:
              raise ValueError('Unexpected input dimension %d, expected %d' % (X.shape[1], self._dim,))
  
          # If we are encoding the training data, we have to check the target
          if y is not None:
              if isinstance(y, pd.DataFrame):
                  y = y.iloc[:, 0]
              else:
                  y = pd.Series(y, name='target')
              if X.shape[0] != y.shape[0]:
                  raise ValueError("The length of X is " + str(X.shape[0]) + " but length of y is " + str(y.shape[0]) + ".")
  
          if not self.cols:
              return X
  
          # Do not modify the input argument
          X = X.copy(deep=True)
  
          # Loop over columns and replace nominal values with WOE
          X = self._score(X, y)
  
          # Postprocessing
          # Note: We should not even convert these columns.
          if self.drop_invariant:
              for col in self.drop_cols:
                  X.drop(col, 1, inplace=True)
  
          if self.return_df:
              return X
          else:
              return X.values
  
      def fit_transform(self, X, y=None, **fit_params):
          """
          Encoders that utilize the target must make sure that the training data are transformed with:
              transform(X, y)
          and not with:
              transform(X)
          """
          return self.fit(X, y, **fit_params).transform(X, y)
  
      def _train(self, X, y, cols=None):
          # Initialize the output
          mapping = {}
  
          # Calculate global statistics
          self._sum  = y.sum()
          self._count = y.count()
  
          for col in cols:
              # Calculate sum and count of the target for each unique value in the feature col
              stats = y.groupby(X[col]).agg(['sum', 'count']) # Count of x_{i,+} and x_i
  
              # Create a new column with regularized WOE.
              # Regularization helps to avoid division by zero.
              # Pre-calculate WOEs because logarithms are slow.
              nominator = (stats['sum'] + self.regularization) / (self._sum + 2*self.regularization)
              denominator = ((stats['count'] - stats['sum']) + self.regularization) / (self._count - self._sum + 2*self.regularization)
              woe = np.log(nominator / denominator)
  
              # Ignore unique values. This helps to prevent overfitting on id-like columns.
              woe[stats['count'] == 1] = 0
  
              # Store WOE for transform() function
              mapping[col] = woe
  
          return mapping
  
      def _score(self, X, y):
          for col in self.cols:
              # Score the column
              X[col] = X[col].map(self.mapping[col])
  
              # Replace missing values only in the computed columns
              if self.impute_missing:
                  if self.handle_unknown == 'impute':
                      X[col].fillna(0, inplace=True)
                  elif self.handle_unknown == 'error':
                      if X[col].isnull().any():
                          raise ValueError('Unexpected categories found in column %s' % col)
  
              # Randomization is meaningful only for training data -> we do it only if y is present
              if self.randomized and y is not None:
                  random_state_generator = check_random_state(self.random_state)
                  X[col] = (X[col] * random_state_generator.normal(1., self.sigma, X[col].shape[0]))
  
          return X
  