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measure_model.py

+import measures as ms
+
+
+
+class measure_model:
+    def __init__(self,clf_1,clf_2,x_test,y_test,x_1_test,y_1_test,x_2_test,y_2_test,single_classifier=True):
+        self.clf_1 = clf_1                              #in case of group-specific classifiers, the classifier of group 1, otherwise the general classifier
+        self.clf_2 = clf_2                              #in case of group-specific classifiers, the classifier of group 1, otherwise the general classifier
+        self.y_test = y_test
+        self.x_test = x_test
+        self.x_1_test = x_1_test
+        self.x_2_test = x_2_test
+        self.y_1_test = y_1_test
+        self.y_2_test=y_2_test
+        self.single_classifier=single_classifier
+    
+           
+    def get_measures(self):
+        labels,y_1,y_2,cm1,cm2=self.get_measures_by_sensitive_group()
+        IGQRs=self.get_IGQRs(y_1, y_2)
+        if self.single_classifier:
+            TN, FP, FN, TP =ms.get_confusion_matrix(self.y_test, self.clf_1.predict(self.x_test))
+            labels,y,cm = ms.get_measure_values(TN, FP, FN, TP)
+        else:
+            #Situation of one classifier per group. The overall measure values are calculated by building the weighted mean of the group-specific values to support comparisons to
+            #single classifiers
+            w_1 = self.y_1_test.shape[0]/self.y_test.shape[0]
+            w_2 = self.y_2_test.shape[0]/self.y_test.shape[0]
+            y=[y_1[i]*w_1 + y_2[i]*w_2 for i in range(len(y_1))]            
+        return labels,y,y_1,y_2,IGQRs,cm1,cm2
+    
+    def get_measures_by_sensitive_group(self):
+        TN1, FP1, FN1, TP1 =ms.get_confusion_matrix(self.y_1_test, self.clf_1.predict(self.x_1_test))
+        TN2, FP2, FN2, TP2 =ms.get_confusion_matrix(self.y_2_test, self.clf_2.predict(self.x_2_test))
+        labels,y_1,cm1 = ms.get_measure_values(TN1, FP1, FN1, TP1)
+        labels,y_2,cm2 = ms.get_measure_values(TN2, FP2, FN2, TP2)
+        return labels,y_1,y_2,cm1,cm2
+    
+    def get_IGQRs(self,y_1,y_2):
+        '''
+        Calculation of the IGQR values based on the definition from the thesis. An IGQR is further invalid (encoded by -1) if one of the group-specific quality measures is invalid.
+        '''
+        IGQRs=[]
+        for i in range(len(y_1)):
+            if y_1[i]==0 and y_2[i]==0:
+                IGQRs.append(1)
+            elif y_1[i]!=-1 and y_2[i]!=-1:
+                IGQRs.append(min(y_1[i],y_2[i])/max(y_1[i],y_2[i]))
+            else:
+                IGQRs.append(-1)
+        return IGQRs
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