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