Source code for dice_ml.diverse_counterfactuals

import numpy as np
import pandas as pd
import copy
from IPython.display import display

[docs]class CounterfactualExamples: """A class to store and visualize the resulting counterfactual explanations.""" def __init__(self, data_interface, test_instance, test_pred, final_cfs, final_cfs_preds, final_cfs_sparse=None, cfs_preds_sparse=None, posthoc_sparsity_param=0, desired_class="opposite"): self.data_interface = data_interface self.test_instance = test_instance self.test_pred = test_pred self.final_cfs = final_cfs self.final_cfs_preds = final_cfs_preds self.final_cfs_sparse = final_cfs_sparse self.cfs_preds_sparse = cfs_preds_sparse self.posthoc_sparsity_param = posthoc_sparsity_param # might be useful for future additions if desired_class == "opposite": self.new_outcome = 1.0 - round(self.test_pred) else: self.new_outcome = desired_class self.convert_to_dataframe() # transforming the test input from numpy to pandas dataframe if self.final_cfs_sparse is not None: self.convert_to_dataframe_sparse()
[docs] def convert_to_dataframe(self): test_instance_updated = pd.DataFrame(np.array([np.append(self.test_instance, self.test_pred)]), columns = self.data_interface.encoded_feature_names+[self.data_interface.outcome_name]) org_instance = self.data_interface.from_dummies(test_instance_updated) org_instance = org_instance[self.data_interface.feature_names + [self.data_interface.outcome_name]] self.org_instance = self.data_interface.de_normalize_data(org_instance) precisions = self.data_interface.get_decimal_precisions() # to display the values with the same precision as the original data for ix, feature in enumerate(self.data_interface.continuous_feature_names): self.org_instance[feature] = self.org_instance[feature].astype(float).round(precisions[ix]) cfs = np.array([self.final_cfs[i][0] for i in range(len(self.final_cfs))]) result = self.data_interface.get_decoded_data(cfs) result = self.data_interface.de_normalize_data(result) for ix, feature in enumerate(self.data_interface.continuous_feature_names): result[feature] = result[feature].astype(float).round(precisions[ix]) # predictions for CFs test_preds = [np.round(preds.flatten().tolist(), 3) for preds in self.final_cfs_preds] test_preds = [item for sublist in test_preds for item in sublist] test_preds = np.array(test_preds) result[self.data_interface.outcome_name] = test_preds self.final_cfs_df = result[self.data_interface.feature_names + [self.data_interface.outcome_name]] self.final_cfs_list = self.final_cfs_df.values.tolist()
[docs] def convert_to_dataframe_sparse(self): test_instance_updated = pd.DataFrame(np.array([np.append(self.test_instance, self.test_pred)]), columns = self.data_interface.encoded_feature_names+[self.data_interface.outcome_name]) org_instance = self.data_interface.from_dummies(test_instance_updated) org_instance = org_instance[self.data_interface.feature_names + [self.data_interface.outcome_name]] self.org_instance = self.data_interface.de_normalize_data(org_instance) precisions = self.data_interface.get_decimal_precisions() # to display the values with the same precision as the original data for ix, feature in enumerate(self.data_interface.continuous_feature_names): self.org_instance[feature] = self.org_instance[feature].astype(float).round(precisions[ix]) cfs = np.array([self.final_cfs_sparse[i][0] for i in range(len(self.final_cfs_sparse))]) result = self.data_interface.get_decoded_data(cfs) result = self.data_interface.de_normalize_data(result) for ix, feature in enumerate(self.data_interface.continuous_feature_names): result[feature] = result[feature].astype(float).round(precisions[ix]) # predictions for CFs test_preds = [np.round(preds.flatten().tolist(), 3) for preds in self.cfs_preds_sparse] test_preds = [item for sublist in test_preds for item in sublist] test_preds = np.array(test_preds) result[self.data_interface.outcome_name] = test_preds self.final_cfs_df_sparse = result[self.data_interface.feature_names + [self.data_interface.outcome_name]] self.final_cfs_list_sparse = self.final_cfs_df_sparse.values.tolist()
[docs] def visualize_as_dataframe(self, display_sparse_df=True, show_only_changes=False): # original instance print('Query instance (original outcome : %i)' %round(self.test_pred)) display(self.org_instance) # works only in Jupyter notebook if self.posthoc_sparsity_param == None: print('\nCounterfactual set (new outcome : %i)' %(self.new_outcome)) self.display_df(self.final_cfs_df, show_only_changes) elif 'data_df' in self.data_interface.__dict__ and display_sparse_df==True and self.final_cfs_sparse is not None: # CFs print('\nDiverse Counterfactual set (new outcome : %i)' %(self.new_outcome)) self.display_df(self.final_cfs_df_sparse, show_only_changes) elif 'data_df' in self.data_interface.__dict__ and display_sparse_df==True and self.final_cfs_sparse is None: print('\nPlease specify a valid posthoc_sparsity_param to perform sparsity correction.. displaying Diverse Counterfactual set without sparsity correction (new outcome : %i)' %(self.new_outcome)) self.display_df(self.final_cfs_df, show_only_changes) elif 'data_df' not in self.data_interface.__dict__: # for private data print('\nDiverse Counterfactual set without sparsity correction since only metadata about each feature is available (new outcome : %i)' %(self.new_outcome)) self.display_df(self.final_cfs_df, show_only_changes) else: # CFs print('\nDiverse Counterfactual set without sparsity correction (new outcome : %i)' %(self.new_outcome)) self.display_df(self.final_cfs_df, show_only_changes)
[docs] def display_df(self, df, show_only_changes): if show_only_changes is False: display(df) # works only in Jupyter notebook else: newdf = df.values.tolist() org = self.org_instance.values.tolist()[0] for ix in range(df.shape[0]): for jx in range(len(org)): if newdf[ix][jx] == org[jx]: newdf[ix][jx] = '-' else: newdf[ix][jx] = str(newdf[ix][jx]) display(pd.DataFrame(newdf, columns=df.columns)) # works only in Jupyter notebook
[docs] def visualize_as_list(self, display_sparse_df=True, show_only_changes=False): # original instance print('Query instance (original outcome : %i)' %round(self.test_pred)) print(self.org_instance.values.tolist()[0]) if self.posthoc_sparsity_param == None: print('\nCounterfactual set (new outcome : %i)' %(self.new_outcome)) self.print_list(self.final_cfs_df, show_only_changes) elif 'data_df' in self.data_interface.__dict__ and display_sparse_df==True and self.final_cfs_sparse is not None: # CFs print('\nDiverse Counterfactual set (new outcome : %i)' %(self.new_outcome)) self.print_list(self.final_cfs_list_sparse, show_only_changes) elif 'data_df' in self.data_interface.__dict__ and display_sparse_df==True and self.final_cfs_sparse is None: print('\nPlease specify a valid posthoc_sparsity_param to perform sparsity correction.. displaying Diverse Counterfactual set without sparsity correction (new outcome : %i)' %(self.new_outcome)) self.print_list(self.final_cfs_list_sparse, show_only_changes) elif 'data_df' not in self.data_interface.__dict__: # for private data print('\nDiverse Counterfactual set without sparsity correction since only metadata about each feature is available (new outcome : %i)' %(self.new_outcome)) self.print_list(self.final_cfs_list, show_only_changes) else: # CFs print('\nDiverse Counterfactual set without sparsity correction (new outcome : %i)' %(self.new_outcome)) self.print_list(self.final_cfs_list, show_only_changes)
[docs] def print_list(self, li, show_only_changes): if show_only_changes is False: for ix in range(len(li)): print(li[ix]) else: newli = copy.deepcopy(li) org = self.org_instance.values.tolist()[0] for ix in range(len(newli)): for jx in range(len(newli[ix])): if newli[ix][jx] == org[jx]: newli[ix][jx] = '-' print(newli[ix])