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()
