Upload classifier.py

classifier.py

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+from sklearn.naive_bayes import MultinomialNB
+from sklearn.linear_model import LogisticRegression
+from sklearn.neighbors import KNeighborsClassifier
+# from sklearn.linear_model import SGDClassifier
+from sklearn.svm import LinearSVC
+from sklearn.metrics import accuracy_score
+from sklearn.metrics import recall_score
+from sklearn.metrics import f1_score
+from sklearn.metrics import precision_score
+
+
+class Classifier():
+    def __init__(self, classifier_type):
+        if classifier_type == "Logistic Regression":
+            self.classifier = LogisticRegression(max_iter=1000)
+        elif classifier_type == "KNN":
+            self.classifier = KNeighborsClassifier(
+                algorithm='brute', n_jobs=-1)
+        elif classifier_type == "Naive Bayes":
+            self.classifier = MultinomialNB()
+        elif classifier_type == "SVM":
+            self.classifier = LinearSVC(C=0.0001)
+
+    def classify(self, X):
+        prediction = self.classifier.predict(X)
+        return prediction
+
+    def train(self, X_train, Y_train):
+        self.classifier.fit(X_train, Y_train)
+
+    def validate(self, X_validate, y_validate):
+        prediction = self.classify(X_validate)
+        accuracy = accuracy_score(y_validate, prediction)*100
+        precision = precision_score(y_validate, prediction, average="weighted")
+        recall = recall_score(y_validate, prediction, average="weighted")
+        f1_scoree = f1_score(y_validate, prediction, average="weighted")
+
+        return precision, recall, f1_scoree, accuracy
+
+    def score(self, data, labels):
+        return self.classifier.score(data, labels)