pr curve

cnn.py

 import models as m
 import models as m
 import roc_auc as ra
 import roc_auc as ra
+from prec_recall import pr, avg_pr
 import sys
 import sys
 from sklearn.metrics import confusion_matrix
 from sklearn.metrics import confusion_matrix
 
 
@@ -9,6 +10,7 @@ from imblearn.over_sampling import SMOTE
 from sklearn.model_selection import KFold
 from sklearn.model_selection import KFold
 from tensorflow.keras.utils import to_categorical
 from tensorflow.keras.utils import to_categorical
 
 
+
 ## global variables
 ## global variables
 N_SPLITS = 5 # for the kfold
 N_SPLITS = 5 # for the kfold
 N_CLASSES = 4
 N_CLASSES = 4
@@ -124,13 +126,12 @@ def run_nn(input_, output_, n_experiences, params):
 	print("Average accuracy: ", total_acc)
 	print("Average accuracy: ", total_acc)
 	print("Average area under the curve: ", total_auc)
 	print("Average area under the curve: ", total_auc)
 
 
-	return total_acc, total_auc, X_train_kfold, X_test_kfold, train_Y_one_hot, validation_Y_one_hot
+	return total_acc, total_auc, X_train_kfold, X_test_kfold, y_train_kfold, y_test_kfold
 
 
 
 
 def run_kfold(X_train, X_test, y_train, y_test, params):
 def run_kfold(X_train, X_test, y_train, y_test, params):
 	c, b, e = params
 	c, b, e = params
 
 
-
 	for i in range(N_SPLITS):
 	for i in range(N_SPLITS):
 		# change the labels from categorical to one-hot encoding
 		# change the labels from categorical to one-hot encoding
 	    y_train[i] = to_categorical(y_train[i], num_classes = 4)
 	    y_train[i] = to_categorical(y_train[i], num_classes = 4)
@@ -156,8 +157,9 @@ def run_kfold(X_train, X_test, y_train, y_test, params):
 
 
 	total_acc = 0
 	total_acc = 0
 	total_auc = 0
 	total_auc = 0
-	cm_tab = []
-	pr_tab = []
+	precs_k = [] 	#it will contain the average pr curve for each class
+	recs_k = []
+	avgs_k = []
 	bs, ep = m.choose_batch_epochs(b,e)
 	bs, ep = m.choose_batch_epochs(b,e)
 
 
 
 
@@ -179,19 +181,23 @@ def run_kfold(X_train, X_test, y_train, y_test, params):
 		print("Area under the curve:", auc)
 		print("Area under the curve:", auc)
 
 
 		# confusion matrix
 		# confusion matrix
-		cm_tab.append(confusion_matrix(y_test[i].argmax(axis=1), y_pred.argmax(axis=1)))
+		cm+=confusion_matrix(y_test[i].argmax(axis=1), y_pred.argmax(axis=1))
+
+		#pr curve (contains 4 pr curves: one for each class)
+		recall, precision, average_prec = pr(N_CLASSES, y_test[i], y_pred)
+		recs_k.append(recall)
+		precs_k.append(precision)
+		avgs_k.append(average_prec)
 
 
-		#pr curve (1 for each class)
 
 
 	#average of acc, auc, cm, pr
 	#average of acc, auc, cm, pr
 	total_acc = total_acc/(N_SPLITS)
 	total_acc = total_acc/(N_SPLITS)
 	total_auc = total_acc/(N_SPLITS)
 	total_auc = total_acc/(N_SPLITS)
-	cm = sum([cm_tab[j] for j in range(N_SPLITS)])/5
+	cm = cm/N_SPLITS
+	pr = avg_pr(N_SPLITS, N_CLASSES, recs_k, precs_k, avgs_k)
 
 
 	print("Average accuracy: ", total_acc)
 	print("Average accuracy: ", total_acc)
 	print("Average area under the curve: ", total_auc)
 	print("Average area under the curve: ", total_auc)
 
 
 
 
-
-
-	return total_acc, total_auc, cm_tab, pr_tab
+	return total_acc, total_auc, cm, pr