class_docstr: Defines the top-level Model Analysis API.
  Use RAIInsights to analyze errors, explain the most important
  features, compute counterfactuals and run causal analysis in a
  single API.
class_docstr_tok: ["Defines","the","top-level","Model","Analysis","API",".","Use","RAIInsights","to","analyze","errors",",","explain","the","most","important","features",",","compute","counterfactuals","and","run","causal","analysis","in","a","single","API","."]
docstr: Returns the test dataset after dropping
  features if any were configured to be dropped.
  
  :return: The test dataset after dropping features.
  :rtype: pandas.DataFrame
docstr_tok: ["Returns","the","test","dataset","after","dropping","features","if","any","were","configured","to","be","dropped",".",":","return",":","The","test","dataset","after","dropping","features",".",":","rtype",":","pandas.DataFrame"]
code: def get_test_data(self, test_data=None):
      """Returns the test dataset after dropping
      features if any were configured to be dropped.
  
      :return: The test dataset after dropping features.
      :rtype: pandas.DataFrame
      """
      if self._feature_metadata is None:
          return test_data if test_data is not None else self.test
      else:
          if (
              self._feature_metadata.dropped_features is None
              or len(self._feature_metadata.dropped_features) == 0
          ):
              return test_data if test_data is not None else self.test
          else:
              if test_data is None:
                  return self.test.drop(
                      columns=self._feature_metadata.dropped_features,
                      axis=1,
                  )
              else:
                  return test_data.drop(
                      columns=self._feature_metadata.dropped_features,
                      axis=1,
                  )
  
imports: import json
  import pickle
  import warnings
  from enum import Enum
  from pathlib import Path
  from typing import Any, List, Optional, Union
  import numpy
  import pandas
  from erroranalysis._internal.cohort_filter import FilterDataWithCohortFilters
  from erroranalysis._internal.process_categoricals import process_categoricals
  from raiutils.data_processing import convert_to_list
  from raiutils.exceptions import SystemErrorException, UserConfigValidationException
  from raiutils.models import Forecasting, ModelTask, SKLearn
  from responsibleai._interfaces import Dataset, RAIInsightsData, TabularDatasetMetadata
  from responsibleai._internal._forecasting_wrappers import _wrap_model
  from responsibleai._internal.constants import FileFormats, ManagerNames, Metadata, SerializationAttributes
  from responsibleai.feature_metadata import FeatureMetadata
  from responsibleai.managers.causal_manager import CausalManager
  from responsibleai.managers.counterfactual_manager import CounterfactualManager
  from responsibleai.managers.data_balance_manager import DataBalanceManager
  from responsibleai.managers.error_analysis_manager import ErrorAnalysisManager
  from responsibleai.managers.explainer_manager import ExplainerManager
  from responsibleai.rai_insights.rai_base_insights import RAIBaseInsights
label_desc: Use image node_id 4 for calling the RAIInsights obj's underlying member method code with example usage: obj.get_test_data(test_data) and returns: unknown, unknown, self, test_data