from sklearn import linear_model from sklearn.metrics import confusion_matrix from sklearn.metrics import precision_score from sklearn.metrics import recall_score from sklearn.metrics import f1_score import FileOperations as fo training_data, training_data_class_labels = fo.load_twitter_data_from_file(fo.TRAINING_DATA_FILE_NAME) testing_data, testing_data_class_labels = fo.load_twitter_data_from_file(fo.TESTING_DATA_FILE_NAME) classifier = linear_model.LogisticRegression(C=1.0, penalty='l1', tol=1e-6) classifier.fit(training_data, training_data_class_labels) predicted_labels = classifier.predict(testing_data) print("Confusion Matrix") print(confusion_matrix(testing_data_class_labels, predicted_labels)) print("Precision") print(precision_score(testing_data_class_labels, predicted_labels, average=None)) print("Recall") print(recall_score(testing_data_class_labels, predicted_labels, average=None)) print("F1 score") print(f1_score(testing_data_class_labels, predicted_labels, average=None))