From 84a6446040a7b8369e4e476210e8d5b5fcca8a98 Mon Sep 17 00:00:00 2001
From: Sefik Ilkin Serengil <serengil@gmail.com>
Date: Thu, 24 Mar 2022 20:40:43 +0000
Subject: [PATCH] ensemble bug

---
 tests/Ensemble-Face-Recognition.py | 47 +++++++++++++++++++-----------
 1 file changed, 30 insertions(+), 17 deletions(-)

diff --git a/tests/Ensemble-Face-Recognition.py b/tests/Ensemble-Face-Recognition.py
index 3d5a688..9c41ae0 100644
--- a/tests/Ensemble-Face-Recognition.py
+++ b/tests/Ensemble-Face-Recognition.py
@@ -25,7 +25,6 @@ idendities = {
     "Matt": ["img29.jpg", "img30.jpg", "img31.jpg", "img32.jpg", "img33.jpg"],
     "Leonardo": ["img34.jpg", "img35.jpg", "img36.jpg", "img37.jpg"],
     "George": ["img38.jpg", "img39.jpg", "img40.jpg", "img41.jpg"]
-    
 }
 #--------------------------
 #Positives
@@ -33,7 +32,7 @@ idendities = {
 positives = []
 
 for key, values in idendities.items():
-    
+
     #print(key)
     for i in range(0, len(values)-1):
         for j in range(i+1, len(values)):
@@ -45,6 +44,7 @@ for key, values in idendities.items():
 
 positives = pd.DataFrame(positives, columns = ["file_x", "file_y"])
 positives["decision"] = "Yes"
+
 print(positives.shape)
 #--------------------------
 #Negatives
@@ -55,18 +55,18 @@ negatives = []
 
 for i in range(0, len(idendities) - 1):
     for j in range(i+1, len(idendities)):
-        #print(samples_list[i], " vs ",samples_list[j]) 
+        #print(samples_list[i], " vs ",samples_list[j])
         cross_product = itertools.product(samples_list[i], samples_list[j])
         cross_product = list(cross_product)
         #print(cross_product)
-        
+
         for cross_sample in cross_product:
             #print(cross_sample[0], " vs ", cross_sample[1])
             negative = []
             negative.append(cross_sample[0])
             negative.append(cross_sample[1])
             negatives.append(negative)
-			
+
 negatives = pd.DataFrame(negatives, columns = ["file_x", "file_y"])
 negatives["decision"] = "No"
 
@@ -80,8 +80,11 @@ df = pd.concat([positives, negatives]).reset_index(drop = True)
 
 print(df.decision.value_counts())
 
-df.file_x = "deepface/tests/dataset/"+df.file_x
-df.file_y = "deepface/tests/dataset/"+df.file_y
+df.file_x = "dataset/"+df.file_x
+df.file_y = "dataset/"+df.file_y
+
+print(df.head())
+
 #--------------------------
 #DeepFace
 
@@ -94,7 +97,7 @@ pretrained_models["VGG-Face"] = VGGFace.loadModel()
 print("VGG-Face loaded")
 pretrained_models["Facenet"] = Facenet.loadModel()
 print("Facenet loaded")
-pretrained_models["OpenFace"] = OpenFace.loadModel() 
+pretrained_models["OpenFace"] = OpenFace.loadModel()
 print("OpenFace loaded")
 pretrained_models["DeepFace"] = FbDeepFace.loadModel()
 print("FbDeepFace loaded")
@@ -111,7 +114,8 @@ if True:
             resp_obj = DeepFace.verify(instances
                                        , model_name = model
                                        , model = pretrained_models[model]
-                                       , distance_metric = metric)
+                                       , distance_metric = metric
+                                       , enforce_detection = False)
 
             distances = []
 
@@ -120,7 +124,7 @@ if True:
                 distances.append(distance)
 
             df['%s_%s' % (model, metric)] = distances
-    
+
     df.to_csv("face-recognition-pivot.csv", index = False)
 else:
     df = pd.read_csv("face-recognition-pivot.csv")
@@ -135,14 +139,14 @@ fig = plt.figure(figsize=(15, 15))
 figure_idx = 1
 for model in models:
     for metric in metrics:
-        
+
         feature = '%s_%s' % (model, metric)
-        
-        ax1 = fig.add_subplot(4, 2, figure_idx)
-        
+
+        ax1 = fig.add_subplot(len(models) * len(metrics), len(metrics), figure_idx)
+
         df[df.decision == "Yes"][feature].plot(kind='kde', title = feature, label = 'Yes', legend = True)
         df[df.decision == "No"][feature].plot(kind='kde', title = feature, label = 'No', legend = True)
-        
+
         figure_idx = figure_idx + 1
 
 plt.show()
@@ -204,6 +208,15 @@ gbm.save_model("face-recognition-ensemble-model.txt")
 
 predictions = gbm.predict(x_test)
 
+prediction_classes = []
+for prediction in predictions:
+    prediction_class = np.argmax(prediction)
+    prediction_classes.append(prediction_class)
+
+print(y_test)
+print("------")
+print(prediction_classes)
+
 cm = confusion_matrix(y_test, prediction_classes)
 print(cm)
 
@@ -232,7 +245,7 @@ plt.rcParams["figure.figsize"] = [20, 20]
 for i in range(0, gbm.num_trees()):
     ax = lgb.plot_tree(gbm, tree_index = i)
     plt.show()
-    
+
     if i == 2:
         break
 #--------------------------
@@ -246,4 +259,4 @@ auc = metrics.roc_auc_score(y_test, y_pred_proba)
 plt.figure(figsize=(7,3))
 plt.plot(fpr,tpr,label="data 1, auc="+str(auc))
 
-#--------------------------
\ No newline at end of file
+#--------------------------