New argument added to indicate which explainer should be employed

README.md

@@ -2,6 +2,23 @@
 This project is an extension of LimeOut[1]. It aims at tackle process fairness for classification, while keeping the accuracy level (or improving). 
 More precisely, ExpOut incorporates different explainers.
 
+Classifiers available: 
+* Multilayer Perceptron
+* Logistic Regression
+* Random Forest
+* Bagging
+* AdaBoost
+* Gaussian Mixture
+* Gradiente Boosting
+
+
+Explainers
+* LIME
+* Anchors
+
+# Example
+`runner --data german.data --trainsize 0.8 --algo mlp --cat_features 0 2 3 5 6 8 9 11 13 14 16 18 19 --drop 8 18 19`
+
 # References 
 [1] Vaishnavi Bhargava, Miguel Couceiro, Amedeo Napoli. LimeOut: An Ensemble Approach To Improve Process Fairness. 2020. ⟨hal-02864059v2⟩
 

anchor_global.py

@@ -3,22 +3,43 @@ from collections import Counter
 
 from anchor import anchor_tabular
 import lime_global
+import pandas as pd
+import numpy as np
 
 
-def features_contributions(model, train, feature_names, class_names, categorical_features, categorical_names, kernel_width=3):
+def fairness_eval(model, train, max_features, sensitive_features, feature_names, class_names, categorical_features, categorical_names):
     
-    _, sp_obj = lime_global.features_contributions(model.prob, train, feature_names, class_names, categorical_features, categorical_names, kernel_width)
+    _, sp_obj = lime_global.features_contributions(model.prob, train, feature_names, max_features, class_names, categorical_features, categorical_names)
     
     indices = sp_obj.indices
     a_explainer = anchor_tabular.AnchorTabularExplainer(class_names,feature_names,train,categorical_names=categorical_names)
     
+    non_empty_anchors = 0
     counter = Counter()
     for i in indices:
         exp = a_explainer.explain_instance(train[i], model.predict, threshold=0.95)
-        print(i,'%.2f' % exp.precision(),' %.2f' % exp.coverage(), '%s' % (' AND '.join(exp.names())))
-        a1 = Counter(exp.exp_map['feature'])
-        counter.update(a1)
+        print(i,'%.2f' % exp.precision(),' %.2f' % exp.coverage(), ' (class %s)' % exp.exp_map['prediction'], '%s' % (' AND '.join(exp.names())))
+        features = exp.exp_map['feature']
+        if len(features) > 0:
+            a1 = Counter(features)
+            non_empty_anchors += 1
+            counter.update(a1)
     
+    
+    is_fair = True
+    i = 0
+    ans_data = []
     for key, value in sorted(counter.items(), key=lambda x: x[1], reverse=True):
-        print(feature_names[key],"\t",value)
+        ans_data1 = [feature_names[key],value/non_empty_anchors]
+        ans_data.append(ans_data1)
+        if i < max_features and key in sensitive_features:
+            is_fair = False
+        i += 1
+        
+    df = pd.DataFrame(ans_data, columns = ["Feature", "Frequency"])
+    print(df.iloc[(-np.abs(df['Frequency'].values)).argsort()])
+    
+    
+    return is_fair, ans_data    
+
     

core.py

@@ -107,8 +107,6 @@ class Model:
         self.encoders = encoders
         self.features_to_remove = features_to_remove
         
-        # TODO :  
-        
     """
     Args:
         models: a list of trained classifiers

german.py

@@ -7,7 +7,7 @@ from sklearn.model_selection import train_test_split
 from sklearn.neural_network._multilayer_perceptron import MLPClassifier
 from sklearn.tree import DecisionTreeClassifier
 
-from anchor_global import features_contributions
+from anchor_global import fairness_eval
 from core import load_data, Model, evaluation, \
     find_threshold, remove, ensemble_out, train_classifier
 
@@ -20,6 +20,7 @@ from core import load_data, Model, evaluation, \
 def main():
     train_size = 0.8
     test_size = 0.2
+    max_features = 10
     
     algo = MLPClassifier
     print(algo.__name__)
@@ -39,7 +40,7 @@ def main():
     accuracy = evaluation(original_model.prob(test), labels_test, threshold_1)
     print(accuracy)
 
-    features_contributions(original_model, train, feature_names, class_names, all_categorical_features, categorical_names)
+    fairness_eval(original_model, train, max_features, to_drop, feature_names, class_names, all_categorical_features, categorical_names)
     
     print("###########\nExpOut ensemble's model \n###########")
     ensemble = ensemble_out(algo,to_drop,train,labels_train, all_categorical_features)
@@ -49,7 +50,7 @@ def main():
     accuracy = evaluation(ensemble.prob(test), labels_test, threshold_2)
     print(accuracy)
     
-    features_contributions(ensemble, train, feature_names, class_names, all_categorical_features, categorical_names)
+    fairness_eval(ensemble, train, max_features, to_drop, feature_names, class_names, all_categorical_features, categorical_names)
 
     
 if __name__ == "__main__":

lime_global.py

@@ -8,11 +8,17 @@ import numpy as np
 from lime import lime_tabular, submodular_pick
 
 
-def features_contributions(model, train, feature_names, class_names, categorical_features, categorical_names, kernel_width=3):
+def features_contributions(predict_fn, train, feature_names, max_features, class_names, categorical_features, categorical_names, kernel_width=3):
         
     explainer = lime_tabular.LimeTabularExplainer(train,feature_names=feature_names,class_names=class_names,categorical_features=categorical_features,categorical_names=categorical_names,kernel_width=kernel_width)
     
-    sp_obj = submodular_pick.SubmodularPick(explainer, train, model, sample_size=5, num_features=5, num_exps_desired=5)
+    sp_obj = submodular_pick.SubmodularPick(explainer, train, predict_fn, sample_size=500, num_features=max_features, num_exps_desired=10)
+    
+    return explainer, sp_obj
+
+def fairness_eval(model, train, max_features, sensitive_features, feature_names, class_names, categorical_features, categorical_names):
+    
+    _, sp_obj = features_contributions(model.prob, train, feature_names, max_features, class_names, categorical_features, categorical_names)
     
     a = Counter()
     for i in sp_obj.V:
@@ -20,16 +26,22 @@ def features_contributions(model, train, feature_names, class_names, categorical
         a1 = Counter(dict(exp.local_exp[1]))
         a.update(a1)
     
+    is_fair = True
+    counter = 0
     ans_data = []
     for key in a:
-        ans_data1 = []
-        ans_data1.append(feature_names[key])
-        ans_data1.append(a[key])
+        ans_data1 = [feature_names[key],a[key]]
         ans_data.append(ans_data1)
+        if counter < max_features and key in sensitive_features:
+            is_fair = False
+        counter += 1
+        
     #     print(feature_names[key] )
     df = pd.DataFrame(ans_data, columns = ["Feature", "Contribution"])
-    sumdf = df['Contribution'].sum()
-    df['Contribution'] = df['Contribution']
+#     sumdf = df['Contribution'].sum()
+#     df['Contribution'] = df['Contribution']
     print(df.iloc[(-np.abs(df['Contribution'].values)).argsort()])
     
-    return explainer, sp_obj
\ No newline at end of file
+    
+    return is_fair, ans_data
+    
\ No newline at end of file

runner.py

@@ -7,16 +7,18 @@ from sklearn.ensemble._forest import RandomForestClassifier
 from sklearn.ensemble._gb import GradientBoostingClassifier
 from sklearn.linear_model._logistic import LogisticRegression
 from sklearn.mixture._gaussian_mixture import GaussianMixture
-from sklearn.model_selection import train_test_split
 from sklearn.neural_network._multilayer_perceptron import MLPClassifier
 from sklearn.svm._classes import SVC
+from sklearn.model_selection import train_test_split
+import anchor_global
+import lime_global
 
-from anchor_global import features_contributions
+# from anchor_global import features_contributions
 from core import load_data, train_classifier, Model, evaluation, \
-    find_threshold, remove, ensemble_out
+    find_threshold, ensemble_out
 
     
-def main(source_name, train_size, to_drop, all_categorical_features, algo):
+def main(source_name, train_size, to_drop, all_categorical_features, max_features, algo, exp):
        
     data, labels, class_names, feature_names, categorical_names = load_data(source_name, all_categorical_features)
     train, test, labels_train, labels_test = train_test_split(data, labels, train_size=train_size, test_size=1-train_size, random_state=2)
@@ -30,17 +32,19 @@ def main(source_name, train_size, to_drop, all_categorical_features, algo):
     accuracy = evaluation(original_model.prob(test), labels_test, threshold_1)
     print(accuracy)
 
-    features_contributions(original_model, train, feature_names, class_names, all_categorical_features, categorical_names)
-    
-    print("###########\nExpOut ensemble's model \n###########")
-    ensemble = ensemble_out(algo,to_drop,train,labels_train, all_categorical_features)
-    
-    
-    threshold_2 = find_threshold(ensemble.prob(train), labels_train)
-    accuracy = evaluation(ensemble.prob(test), labels_test, threshold_2)
-    print(accuracy)
-    
-    features_contributions(ensemble, train, feature_names, class_names, all_categorical_features, categorical_names)
+    is_fair ,_ = exp(original_model, train, max_features, to_drop, feature_names, class_names, all_categorical_features, categorical_names)
+    
+    if not is_fair:
+    
+        print("###########\nExpOut ensemble's model \n###########")
+        ensemble = ensemble_out(algo,to_drop,train,labels_train, all_categorical_features)
+        
+        
+        threshold_2 = find_threshold(ensemble.prob(train), labels_train)
+        accuracy = evaluation(ensemble.prob(test), labels_test, threshold_2)
+        print(accuracy)
+        
+        is_fair ,_ = exp(ensemble, train, max_features, to_drop, feature_names, class_names, all_categorical_features, categorical_names)
 
 
 def algo_parser(algo_str):
@@ -65,61 +69,35 @@ def algo_parser(algo_str):
         return SVC
     else:
         return None
-
-# AdaBoostClassifier
-# BaggingClassifier
-# BayesianGaussianMixture
-# BernoulliNB
-# CalibratedClassifierCV
-# CategoricalNB
-# ClassifierChain
-# ComplementNB
-# DecisionTreeClassifier
-# DummyClassifier
-# ExtraTreeClassifier
-# ExtraTreesClassifier
-# GaussianMixture
-# GaussianNB
-# GaussianProcessClassifier
-# GradientBoostingClassifier
-# GridSearchCV
-# HistGradientBoostingClassifier
-# KNeighborsClassifier
-# LabelPropagation
-# LabelSpreading
-# LinearDiscriminantAnalysis
-# LogisticRegression
-# LogisticRegressionCV
-# MLPClassifier
-# MultiOutputClassifier
-# MultinomialNB
-# NuSVC
-# OneVsRestClassifier
-# Pipeline
-# QuadraticDiscriminantAnalysis
-# RFE
-# RFECV
-# RadiusNeighborsClassifier
-# RandomForestClassifier
-# RandomizedSearchCV
-# SGDClassifier
-# SVC
-# StackingClassifier
-# VotingClassifier
     
+def exp_parser(algo_str):
+    
+    algo = algo_str.lower()
     
+    if algo == "lime":
+        return lime_global.fairness_eval
+    elif algo == "anchors":
+        return anchor_global.fairness_eval
+    elif algo == "shap":
+        return None
+    else:
+        return None
+
+   
 if __name__ == "__main__":
     
-    parser = argparse.ArgumentParser(description='Description')
+    parser = argparse.ArgumentParser(description='ExpOut')
     parser.add_argument('--data')  
     parser.add_argument('--trainsize', type=float)
     parser.add_argument('--algo')  
+    parser.add_argument('--exp')  
+    parser.add_argument('--max_features', type=int)  
     parser.add_argument('-cat_features', '--cat_features', action='store', dest='cat_features_list', type=int, nargs='*', default=[], help="Examples: -i ")
     parser.add_argument('-drop', '--drop', action='store', dest='drop_list', type=int, nargs='*', default=[], help="Examples: -i ")
     
     args = parser.parse_args() 
     
     now = datetime.datetime.now()
-    print(now.year,'-', now.month,'-', now.day,',', now.hour,':', now.minute,':', now.second)
+    print(now.year,'-', now.month,'-', now.day,',', now.hour,':', now.minute,':', now.second,sep='')
     
-    main(args.data, args.trainsize, args.drop_list, args.cat_features_list, algo_parser(args.algo))
\ No newline at end of file
+    main(args.data, args.trainsize, args.drop_list, args.cat_features_list, args.max_features, algo_parser(args.algo), exp_parser(args.exp))   
\ No newline at end of file