✨ add analysis

README.md

@@ -1,2 +1,4 @@
 # PlotaFakeNews
 
+Run : 
+``pip install -r requirements.txt``

requirements.txt

@@ -0,0 +1,4 @@
+scikit-learn
+matplotlib
+numpy
+pandas

src/analysis.py

@@ -1,5 +1,5 @@
-from sklearn import metrics
-from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, ConfusionMatrixDisplay
+#from sklearn import metrics
+from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, ConfusionMatrixDisplay, roc_curve, auc, RocCurveDisplay
 import matplotlib.pyplot as plt
 from sklearn.model_selection import learning_curve
 import numpy as np
@@ -10,3 +10,31 @@ def metrics(y_test, y_pred):
     class_report = classification_report(y_test, y_pred)
 
     return accuracy, conf_matrix, class_report
+
+def confusion_matrix_view(confusion_matrix, knn_classes):
+    cmap = plt.cm.Blues
+    disp = ConfusionMatrixDisplay(confusion_matrix=confusion_matrix, display_labels=knn_classes)
+    disp.plot(cmap=cmap)
+    plt.show()
+
+def roc_curve_view(y_test, y_pred):
+    fpr, tpr, thresholds = roc_curve(y_test, y_pred)
+    roc_auc = auc(fpr, tpr)
+    display = RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc, estimator_name='estimator')
+    display.plot()
+    plt.show()
+
+def learning_curve_view(classifier, X_train, y_train, learning_reps):
+    print('Training in progress, wait please...')
+    train_sizes, train_scores, test_scores = learning_curve(classifier, X_train, y_train, cv=learning_reps, scoring='accuracy', n_jobs=-1)
+
+    train_scores_mean = np.mean(train_scores, axis=1)
+    test_scores_mean = np.mean(test_scores, axis=1)
+
+    plt.plot(train_sizes, train_scores_mean, label='Train')
+    plt.plot(train_sizes, test_scores_mean, label='Test')
+    plt.xlabel("Training set size")
+    plt.ylabel('Score')
+    plt.title('Learning curve')
+    plt.legend()
+    plt.show()

src/classifier.py

@@ -7,22 +7,22 @@ def logistic_regression(X_train, y_train, X_test):
     logistic = LogisticRegression(max_iter = 100000)
     logistic.fit(X_train,y_train)
 
-    return logistic.predict(X_test)
+    return logistic.predict(X_test), logistic
 
 def decision_tree(X_train, y_train, X_test):
     decisionTree = DecisionTreeClassifier()
     decisionTree = decisionTree.fit(X_train,y_train)
 
-    return decisionTree.predict(X_test)
+    return decisionTree.predict(X_test), decisionTree
 
 def knn_classifier(X_train, y_train, X_test):
     knn = KNeighborsClassifier(n_neighbors=5)
     knn.fit(X_train, y_train)
 
-    return knn.predict(X_test)
+    return knn.predict(X_test), knn
 
 def sgd_classifier(X_train, y_train, X_test):
     sgd = SGDClassifier(loss="hinge", penalty="l2")
     sgd.fit(X_train, y_train)
 
-    return sgd.predict(X_test)
+    return sgd.predict(X_test), sgd

src/main.py

@@ -13,7 +13,7 @@ if __name__ == '__main__':
 
     print("\nTraining models...")
     
-    y_pred_knn = classifier.knn_classifier(X_train, y_train, X_test)
+    y_pred_knn, knn = classifier.knn_classifier(X_train, y_train, X_test)
     print("Knn... OK")
 
     y_pred_dt = classifier.decision_tree(X_train, y_train, X_test)
@@ -33,4 +33,6 @@ if __name__ == '__main__':
     print(f'Confusion Matrix:\n{knn_conf_matrix}')
     print(f'Classification Report:\n{knn_class_report}')
 
-    print("\nTODO()")
+    analysis.confusion_matrix_view(knn_conf_matrix, knn.classes_)
+    analysis.roc_curve_view(y_test, y_pred_knn)
+    analysis.learning_curve_view(knn, X_train, y_train, 10)