text: import operator
  
  import numpy as np
  
  from . import BaseColumnProfiler
  from ..labelers.data_labelers import DataLabeler
  from .profiler_options import DataLabelerOptions
  
  
  class DataLabelerColumn(BaseColumnProfiler):
      
      col_type = "data_labeler"
      
      def __init__(self, name, data_labeler_dirpath=None, options=None):
          """
          Initialization of Data Label profiling for structured datasets.
  
          :param data_labeler_dirpath: Directory path to the data labeler
          :type data_labeler_dirpath: String
          :param options: Options for the data labeler column
          :type options: DataLabelerOptions
          """
          BaseColumnProfiler.__init__(self, name)
  
          self._max_sample_size = 1000
          if options:
              if not isinstance(options, DataLabelerOptions):
                  raise ValueError("options must be of type DataLabelerOptions.")
              if options.data_labeler_dirpath:
                  data_labeler_dirpath = options.data_labeler_dirpath
              if options.max_sample_size:
                  self._max_sample_size = options.max_sample_size
  
          self.data_labeler = DataLabeler(
              labeler_type='structured',
              dirpath=data_labeler_dirpath,
              load_options=None)
  
          reverse_label_mapping = self.data_labeler.reverse_label_mapping
          num_labels = self.data_labeler.model.num_labels
          self._possible_data_labels = list(reverse_label_mapping.values())
          self._possible_data_labels = [  # sort the data_labels based on index
              x for _, x in sorted(zip(
                  reverse_label_mapping.keys(), self._possible_data_labels)
              )
          ]
          self.rank_distribution = dict(
              [(key, 0) for key in self._possible_data_labels])
          self._sum_predictions = np.zeros(num_labels)
  
          # rank distribution variables
          self._top_k_voting = 1
          self._min_voting_prob = 0.20
  
          # data label prediction variables
          self._min_prob_differential = 0.20
          self._top_k_labels = 3
          self._min_top_label_prob = 0.35
  
          self.__calculations = {
              'is_enabled': DataLabelerColumn._update_predictions
          }
          self._filter_properties_w_options(self.__calculations, options)
  
      @staticmethod
      def assert_equal_conditions(data_labeler, data_labeler2):
          """
          Ensures data labelers have the same values. Raises error otherwise.
          
          :param data_labeler: first data_labeler
          :param data_labeler2: second data_labeler
          :type data_labeler: DataLabelerColumn
          :type data_labeler2: DataLabelerColumn
          :return: None
          """
          if data_labeler._top_k_voting != data_labeler2._top_k_voting:
              raise ValueError("Sorry, can't merge profiles: {} are not the same "
                               "in both DataLabeler Profilers being merged, "
                               "as required".format('_top_k_voting'))
          if data_labeler._min_voting_prob != data_labeler2._min_voting_prob:
              raise ValueError("Sorry, can't merge profiles: {} are not the same "
                               "in both DataLabeler Profilers being merged, "
                               "as required".format('_min_voting_prob'))
          if data_labeler._min_prob_differential != data_labeler2._min_prob_differential:
              raise ValueError("Sorry, can't merge profiles: {} are not the same "
                               "in both DataLabeler Profilers being merged, "
                               "as required".format('_min_voting_prob'))
          if data_labeler._top_k_labels != data_labeler2._top_k_labels:
              raise ValueError("Sorry, can't merge profiles: {} are not the same "
                               "in both DataLabeler Profilers being merged, "
                               "as required".format('_top_k_labels'))
          if data_labeler._min_top_label_prob != data_labeler2._min_top_label_prob:
              raise ValueError("Sorry, can't merge profiles: {} are not the same "
                               "in both DataLabeler Profilers being merged, "
                               "as required".format('_min_top_label_prob'))
          if data_labeler._possible_data_labels != data_labeler2._possible_data_labels:
              raise ValueError("Sorry, can't merge profiles: {} are not the same "
                               "in both DataLabeler Profilers being merged, "
                               "as required".format('_possible_data_labels'))
          if data_labeler.data_labeler != data_labeler2.data_labeler:
              raise ValueError("Sorry, can't merge profiles: DataLabeler1 and "
                               "DataLabeler2 have different Models for labeling.")
  
      def __add__(self, other):
          """
          Merges the properties of two DataLabelerColumn profiles
          
          :param self: first profile
          :param other: second profile
          :type self: DataLabelerColumn
          :type other: DataLabelerColumn
          :return: New DataLabelerColumn merged profile
          """
          if not isinstance(other, DataLabelerColumn):
              raise TypeError("Unsupported operand type(s) for +: "
                              "'DataLabelerColumn' and '{}'".format(
                                  other.__class__.__name__))
  
          self.assert_equal_conditions(self, other)
          merged_profile = DataLabelerColumn(None)
          BaseColumnProfiler._add_helper(merged_profile, self, other)
  
          #Set all common variables
          merged_profile.data_labeler = self.data_labeler
          merged_profile._possible_data_labels = self._possible_data_labels
          merged_profile._top_k_voting = self._top_k_voting
          merged_profile._min_voting_prob = self._min_voting_prob
          merged_profile._min_prob_differential = self._min_prob_differential
          merged_profile._top_k_labels = self._top_k_labels
          merged_profile._min_top_label_prob = self._min_top_label_prob
          merged_profile._max_sample_size = self._max_sample_size
          merged_profile._top_k_voting = self._top_k_voting
  
          #Combine rank distribution
          merged_profile.rank_distribution = {key: self.rank_distribution.get(key, 0) +
                                                   other.rank_distribution.get(key, 0) for key in
                                              set(self.rank_distribution) | set(other.rank_distribution)}
  
          #Combine Sum Predictions
          merged_profile._sum_predictions = self._sum_predictions + other._sum_predictions
  
          return merged_profile
  
      @property
      def data_label(self):
          """
          Returns the data labels which best fit the data it has seen based on
          the DataLabeler used. Data labels must be within the minimum probability
          differential of the top predicted value. If nothing is more than
          minimum top label value, it says it could not determine the data label.
          """
          if not self.sample_size:
              return None
  
          ranks_items = self.rank_distribution.items()
          ordered_top_k_rank = np.array(sorted(
              ranks_items, key=operator.itemgetter(1), reverse=True
          ))[:self._top_k_labels]
          top_k_probabilities = np.fromiter(
              map(operator.itemgetter(1), ordered_top_k_rank), dtype=float
          ) / sum(self.rank_distribution.values())
          is_value_close = top_k_probabilities - top_k_probabilities[0] >= \
                                                      -self._min_prob_differential
  
          data_label = '|'.join(map(
              operator.itemgetter(0), ordered_top_k_rank[is_value_close]
          ))
          top_label = ordered_top_k_rank[0][0]
          if self.label_representation[top_label] < self._min_top_label_prob:
              return "could not determine"
          return data_label
  
      @property
      def avg_predictions(self):
          """
          Averages all sample predictions for each data label.
          """
          if not self.sample_size:
              return None
  
          avg_predictions = self._sum_predictions / self.sample_size
          return dict(zip(self._possible_data_labels, avg_predictions))
  
      @property
      def label_representation(self):
          """
          Representation of label found within the dataset based on ranked voting.
          When top_k=1, this is simply the distribution of data labels found
          within the dataset.
          """
          if not self.sample_size:
              return None
  
          label_representation = dict([(key, 0) for key in self._possible_data_labels])
          total_votes = max(1, sum(list(self.rank_distribution.values())))
          for key in label_representation:
              label_representation[key] = \
                  self.rank_distribution[key] / total_votes
          return label_representation
  
      @property
      def profile(self):
          """
          Property for profile. Returns the profile of the column.
          """
          profile = {
              "avg_predictions": self.avg_predictions,
              "data_label_representation": self.label_representation,
              "times": self.times
          }
          return profile
  
      @BaseColumnProfiler._timeit(name='data_labeler_predict')
      def _update_predictions(self, df_series, prev_dependent_properties=None,
                              subset_properties=None):
          """
          Method for updating the column profile properties with a cleaned
          dataset and the known profile of the dataset.
  
          :param prev_dependent_properties: Contains all the previous properties
          that the calculations depend on.
          :type prev_dependent_properties: dict
          :param subset_properties: Contains the results of the properties of the
          subset before they are merged into the main data profile.
          :type subset_properties: dict
          :param df_series: Data to be profiled
          :type df_series: pandas.DataFrame
          :return: None
          """
          predictions = self.data_labeler.predict(
              df_series, predict_options=dict(show_confidences=True))
          sum_predictions = np.sum(predictions['conf'], axis=0)
          self._sum_predictions += sum_predictions
  
          label_decoder = self.data_labeler.reverse_label_mapping
          rank_predictions = np.argpartition(
              predictions['conf'], axis=1, kth=-self._top_k_voting
          )
          for i in range(rank_predictions.shape[0]):
              sorted_rank = rank_predictions[i][-self._top_k_voting:]
              sorted_rank = sorted_rank[np.argsort(predictions['conf'][i][sorted_rank])]
              for rank_position, value in enumerate(sorted_rank):
                  if predictions['conf'][i][value] > self._min_voting_prob:
                      self.rank_distribution[label_decoder[value]] += rank_position + 1
  
      def _update_helper(self, df_series_clean, profile):
          """
          Updating the column profile properties
          
          :param df_series_clean: df series with nulls removed
          :type df_series_clean: pandas.core.series.Series
          :param profile: float profile dictionary
          :type profile: dict
          :return: None
          """
          self._update_column_base_properties(profile)
  
      def update(self, df_series):
          """
          Updates the column profile.
          
          :param df_series: df series
          :type df_series: pandas.core.series.Series
          :return: None
          """
          if len(df_series) == 0:
              return
          sample_size = min(len(df_series), self._max_sample_size)
          df_series = df_series.sample(sample_size)
  
          profile = dict(sample_size=sample_size)
          BaseColumnProfiler._perform_property_calcs(
              self, self.__calculations, df_series=df_series,
              prev_dependent_properties={}, subset_properties=profile)
          self._update_helper(df_series, profile)
  