Source code for dice_ml.diverse_counterfactuals

import numpy as np
import pandas as pd
import copy
from IPython.display import display
import json
from dice_ml.utils.serialize import DummyDataInterface


[docs]def json_converter(obj): """ Helper function to convert object to json. """ try: return obj.to_json() except AttributeError: return obj.__dict__
[docs]class CounterfactualExamples: """A class to store and visualize the resulting counterfactual explanations.""" def __init__(self, data_interface=None, final_cfs_df=None, test_instance_df=None, final_cfs_df_sparse=None, posthoc_sparsity_param=0, desired_range=None, desired_class="opposite", model_type='classifier'): self.data_interface = data_interface self.final_cfs_df = final_cfs_df self.test_instance_df = test_instance_df self.final_cfs_df_sparse = final_cfs_df_sparse self.model_type = model_type self.desired_class = desired_class self.desired_range = desired_range self.final_cfs_list = None self.posthoc_sparsity_param = posthoc_sparsity_param # might be useful for future additions self.test_pred = self.test_instance_df[self.data_interface.outcome_name].iloc[0] if model_type == 'classifier': if desired_class == "opposite": self.new_outcome = 1.0 - round(self.test_pred) else: self.new_outcome = desired_class elif model_type == 'regressor': self.new_outcome = desired_range
[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.test_instance_df) # works only in Jupyter notebook if self.final_cfs_df is not None and len(self.final_cfs_df) > 0: if self.posthoc_sparsity_param == None: print('\nCounterfactual set (new outcome: {0})'.format(self.new_outcome)) self.display_df(self.final_cfs_df, show_only_changes) elif hasattr(self.data_interface, 'data_df') and display_sparse_df==True and self.final_cfs_df_sparse is not None: # CFs print('\nDiverse Counterfactual set (new outcome: {0})'.format(self.new_outcome)) self.display_df(self.final_cfs_df_sparse, show_only_changes) elif hasattr(self.data_interface, 'data_df') and display_sparse_df==True and self.final_cfs_df_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 not hasattr(self.data_interface, 'data_df'):# for private data print('\nDiverse Counterfactual set without sparsity correction since only metadata about each feature is available (new outcome: ', self.new_outcome) self.display_df(self.final_cfs_df, show_only_changes) else: # CFs print('\nDiverse Counterfactual set without sparsity correction (new outcome: ', self.new_outcome) self.display_df(self.final_cfs_df, show_only_changes) else: print('\nNo counterfactuals found!')
[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.test_instance_df.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.test_instance_df.values.tolist()[0]) if len(self.final_cfs) > 0: if self.posthoc_sparsity_param == None: print('\nCounterfactual set (new outcome : %i)' %(self.new_outcome)) self.print_list(self.final_cfs_df.values.tolist(), show_only_changes) elif hasattr(self.data_interface, 'data_df') and display_sparse_df==True and self.final_cfs_df_sparse is not None: # CFs print('\nDiverse Counterfactual set (new outcome : %i)' %(self.new_outcome)) self.print_list(self.final_cfs_df_sparse.values.tolist(), show_only_changes) elif hasattr(self.data_interface, 'data_df') and display_sparse_df==True and self.final_cfs_df_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_df.values.tolist(), show_only_changes) elif not hasattr(self.data_interface, 'data_df'): # 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_df.values.tolist(), show_only_changes) else: # CFs print('\nDiverse Counterfactual set without sparsity correction (new outcome : %i)' %(self.new_outcome)) self.print_list(self.final_cfs_df.values.tolist(), show_only_changes) else: print('\n0 counterfactuals found!')
[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.test_instance_df.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])
[docs] def to_json(self): if self.final_cfs_df_sparse is not None: df = self.final_cfs_df_sparse else: df = self.final_cfs_df dummy_data_interface = None if hasattr(self.data_interface, 'data_df'): dummy_data_interface = DummyDataInterface( self.data_interface.outcome_name, "dummy_data") else: dummy_data_interface = DummyDataInterface( self.data_interface.outcome_name) obj = {'data_interface': dummy_data_interface, 'model_type': self.model_type, 'desired_class': self.desired_class, 'desired_range': self.desired_range, 'test_instance_df': self.test_instance_df, 'final_cfs_df': df} return json.dumps(obj, default=json_converter)