text: # Authors: Soledad Galli <solegalli@protonmail.com>
  # License: BSD 3 clause
  
  from typing import List, Optional, Union
  
  import numpy as np
  import pandas as pd
  
  from feature_engine.encoding.base_encoder import BaseCategoricalTransformer
  from feature_engine.variable_manipulation import _check_input_parameter_variables
  
  
  class OneHotEncoder(BaseCategoricalTransformer):
      """
      One hot encoding consists in replacing the categorical variable by a
      combination of binary variables which take value 0 or 1, to indicate if
      a certain category is present in an observation. The binary variables are also
      known as dummy variables.
  
      For example, from the categorical variable "Gender" with categories "female" and
      "male", we can generate the boolean variable "female", which takes 1 if the
      observation is female or 0 otherwise. We can also generate the variable "male",
      which takes 1 if the observation is "male" and 0 otherwise.
  
      The encoder can create k binary variables per categorical variable, k being the
      number of unique categories, or alternatively k-1 to avoid redundant information.
      This behaviour can be specified using the parameter `drop_last`.
  
      The encoder has the additional option to generate binary variables only for the
      top n most popular categories, that is, the categories that are shared by the
      majority of the observations in the dataset. This behaviour can be specified with
      the parameter `top_categories`.
  
      **Note**
  
      Only when creating binary variables for all categories of the variable, we
      can specify if we want to encode into k or k-1 binary variables, where k is the
      number if unique categories. If we encode only the top n most popular categories,
      the encoder will create only n binary variables per categorical variable.
      Observations that do not show any of these popular categories, will have 0 in all
      the binary variables.
  
      The encoder will encode only categorical variables by default (type 'object' or
      'categorical'). You can pass a list of variables to encode. Alternatively, the
      encoder will find and encode all categorical variables (type 'object' or
      'categorical').
  
      With `ignore_format=True` you have the option to encode numerical variables as well.
      The procedure is identical, you can either enter the list of variables to encode, or
      the transformer will automatically select all variables.
  
      The encoder first finds the categories to be encoded for each variable (fit). The
      encoder then creates one dummy variable per category for each variable
      (transform).
  
      **Note**
  
      New categories in the data to transform, that is, those that did not appear
      in the training set, will be ignored (no binary variable will be created for them).
      This means that observations with categories not present in the train set, will be
      encoded as 0 in all the binary variables.
  
      **Also Note**
  
      The original categorical variables are removed from the returned dataset when we
      apply the transform() method. In their place, the binary variables are returned.
  
      Parameters
      ----------
      top_categories: int, default=None
          If None, a dummy variable will be created for each category of the variable.
          Alternatively, we can indicate in `top_categories` the number of most frequent
          categories to encode. In this case, dummy variables will be created only for
          those popular categories and the rest will be ignored, i.e., they will show the
          value 0 in all the binary variables.
  
      drop_last: boolean, default=False
          Only used if `top_categories = None`. It indicates whether to create dummy
          variables for all the categories (k dummies), or if set to `True`, it will
          ignore the last binary variable and return k-1 dummies.
  
      drop_last_binary: boolean, default=False
          Whether to return 1 or 2 dummy variables for binary categorical variables. When
          a categorical variable has only 2 categories, then the second dummy variable
          created by one hot encoding can be completely redundant. Setting this parameter
          to `True`, will ensure that for every binary variable in the dataset, only 1
          dummy is created.
  
      variables: list, default=None
          The list of categorical variables that will be encoded. If None, the
          encoder will find and transform all variables of type object or categorical by
          default. You can also make the transformer accept numerical variables, see the
          next parameter.
  
      ignore_format: bool, default=False
          Whether the format in which the categorical variables are cast should be
          ignored. If false, the encoder will automatically select variables of type
          object or categorical, or check that the variables entered by the user are of
          type object or categorical. If True, the encoder will select all variables or
          accept all variables entered by the user, including those cast as numeric.
  
      Attributes
      ----------
      encoder_dict_:
          Dictionary with the categories for which dummy variables will be created.
  
      variables_:
          The group of variables that will be transformed.
  
      variables_binary_:
          A list with binary variables identified from the data. That is, variables with
          only 2 categories.
  
      n_features_in_:
          The number of features in the train set used in fit.
  
      Methods
      -------
      fit:
          Learn the unique categories per variable
      transform:
          Replace the categorical variables by the binary variables.
      fit_transform:
          Fit to the data, then transform it.
  
      Notes
      -----
      If the variables are intended for linear models, it is recommended to encode into
      k-1 or top categories. If the variables are intended for tree based algorithms,
      it is recommended to encode into k or top n categories. If feature selection
      will be performed, then also encode into k or top n categories. Linear models
      evaluate all features during fit, while tree based models and many feature
      selection algorithms evaluate variables or groups of variables separately. Thus, if
      encoding into k-1, the last variable / category will not be examined.
  
      References
      ----------
      One hot encoding of top categories was described in the following article:
  
      .. [1] Niculescu-Mizil, et al. "Winning the KDD Cup Orange Challenge with Ensemble
          Selection". JMLR: Workshop and Conference Proceedings 7: 23-34. KDD 2009
          http://proceedings.mlr.press/v7/niculescu09/niculescu09.pdf
      """
  
      def __init__(
          self,
          top_categories: Optional[int] = None,
          drop_last: bool = False,
          drop_last_binary: bool = False,
          variables: Union[None, int, str, List[Union[str, int]]] = None,
          ignore_format: bool = False,
      ) -> None:
  
          if top_categories and not isinstance(top_categories, int):
              raise ValueError("top_categories takes only integer numbers, 1, 2, 3, etc.")
  
          if not isinstance(drop_last, bool):
              raise ValueError("drop_last takes only True or False")
  
          if not isinstance(drop_last_binary, bool):
              raise ValueError("drop_last_binary takes only True or False")
  
          if not isinstance(ignore_format, bool):
              raise ValueError("ignore_format takes only booleans True and False")
  
          self.top_categories = top_categories
          self.drop_last = drop_last
          self.drop_last_binary = drop_last_binary
          self.variables = _check_input_parameter_variables(variables)
          self.ignore_format = ignore_format
  
      def fit(self, X: pd.DataFrame, y: Optional[pd.Series] = None):
          """
          Learns the unique categories per variable. If top_categories is indicated,
          it will learn the most popular categories. Alternatively, it learns all
          unique categories per variable.
  
          Parameters
          ----------
  
          X: pandas dataframe of shape = [n_samples, n_features]
              The training input samples.
              Can be the entire dataframe, not just seleted variables.
  
          y: pandas series, default=None
              Target. It is not needed in this encoded. You can pass y or
              None.
  
          Raises
          ------
          TypeError
              - If the input is not a Pandas DataFrame.
              - f user enters non-categorical variables (unless ignore_format is True)
          ValueError
              - If there are no categorical variables in the df or the df is empty
              - If the variable(s) contain null values
  
          Returns
          -------
          self
          """
  
          X = self._check_fit_input_and_variables(X)
  
          self.encoder_dict_ = {}
  
          # make dummies only for the most popular categories
          if self.top_categories:
              for var in self.variables_:
                  self.encoder_dict_[var] = [
                      x
                      for x in X[var]
                      .value_counts()
                      .sort_values(ascending=False)
                      .head(self.top_categories)
                      .index
                  ]
  
          else:
              # return k-1 dummies
              if self.drop_last:
                  for var in self.variables_:
                      category_ls = [x for x in X[var].unique()]
                      self.encoder_dict_[var] = category_ls[:-1]
  
              # return k dummies
              else:
                  for var in self.variables_:
                      self.encoder_dict_[var] = [x for x in X[var].unique()]
  
          self.variables_binary_ = [
              var for var in self.variables_ if X[var].nunique() == 2
          ]
  
          # automatically encode binary variables as 1 dummy
          if self.drop_last_binary:
              for var in self.variables_binary_:
                  category = X[var].unique()[0]
                  self.encoder_dict_[var] = [category]
  
          self._check_encoding_dictionary()
  
          self.n_features_in_ = X.shape[1]
  
          return self
  
      def transform(self, X: pd.DataFrame) -> pd.DataFrame:
          """
          Replaces the categorical variables by the binary variables.
  
          Parameters
          ----------
          X: pandas dataframe of shape = [n_samples, n_features]
              The data to transform.
  
          Raises
          ------
          TypeError
              If the input is not a Pandas DataFrame
          ValueError
              - If the variable(s) contain null values.
              - If dataframe has different number of features than the df used in fit()
  
          Returns
          -------
          X: pandas dataframe.
              The transformed dataframe. The shape of the dataframe will be different from
              the original as it includes the dummy variables in place of the of the
              original categorical ones.
          """
  
          X = self._check_transform_input_and_state(X)
  
          for feature in self.variables_:
              for category in self.encoder_dict_[feature]:
                  X[str(feature) + "_" + str(category)] = np.where(
                      X[feature] == category, 1, 0
                  )
  
          # drop the original non-encoded variables.
          X.drop(labels=self.variables_, axis=1, inplace=True)
  
          return X
  
      def inverse_transform(self, X: pd.DataFrame):
          """inverse_transform is not implemented for this transformer."""
          return self
  