def load_model_and_predict(data=None): transaction_classifier = TransactionClassifier() transaction_classifier.load_model() if data is None: data = LoadData(x_input_features=[5, 6, 7]) data.load_processed_data() val_data = data.load_validation_data() test_data = data.load_test_data() print(transaction_classifier.get_confusion_matrix(val_data)) print(transaction_classifier.get_confusion_matrix(test_data))
def train_and_get_metrics(): print('LOADING DATA') # Load Data data = LoadData(x_input_features=[5, 6, 7]) data.load_processed_data() train_data = data.load_train_data() validation_data = data.load_validation_data() test_data = data.load_test_data() print('TRAINING CLASSIFIER') # Run Model transaction_classifier = TransactionClassifier() transaction_classifier.train(train_data) print('TESTING CLASSIFIER') train_results = transaction_classifier.test(train_data) val_results = transaction_classifier.test(validation_data) test_results = transaction_classifier.test(test_data) print('STATS:') print('------------ TRAIN SET -------------') print('LENGTH =', len(train_data[0])) print('Metrics:\n', train_results) print('------------ VALIDATION SET -------------') print('LENGTH =', len(validation_data[0])) print('Metrics:\n', val_results) print('------------ TEST SET -------------') print('LENGTH =', len(test_data[0])) print('Metrics:\n', test_results) print('++++++++++++++++++++++++++++++++++++++++') print('SAVING CLASSIFIER') transaction_classifier.save_model()