model_id = configs['model']['model_id']
save_dir = configs['model']['save_dir']
dataloader = DataLoader()
x_scaler_filename = save_dir + "/" + model_id + "-x.scaler"
y_scaler_filename = save_dir + "/" + model_id + "-y.scaler"
dataloader.restore_scalers(x_scaler_filename, y_scaler_filename)
filename = os.path.join('data', configs['data']['filename'])
dataframe = pandas.read_csv(filename, sep=',', encoding='utf-8')
dataframe.index.name = 'fecha'
x_data = dataframe.get(configs['data']['x_cols'], ).values
in_seq_len = configs['data']['input_sequence_length']
x_data = x_data[:, :] # pick three sequences to make predictions
input_data = dataloader.prepare_input_data(x_data, in_seq_len)
print("Input vector shape: " + str(x_data.shape))
model_filename = sys.argv[2]
model = Model(configs['data']['output_mode'])
model.load_model(filepath=model_filename)
print("Plotting predictions point by point on validation set")
predictions = model.predict_point_by_point(input_data)
print(predictions.shape)
unscaled_predictions = dataloader.recompose_results(predictions[:, 0, :],
side="y")
plot_results(unscaled_predictions,
x_data[configs['data']['input_sequence_length']:, :])
def main():
"""
Keras Regression Metrics
•Mean Squared Error: mean_squared_error, MSE or mse
•Mean Absolute Error: mean_absolute_error, MAE, mae
•Mean Absolute Percentage Error: mean_absolute_percentage_error, MAPE, mape
•Cosine Proximity: cosine_proximity, cosine
Keras Classification Metrics
•Binary Accuracy: binary_accuracy, acc
•Categorical Accuracy: categorical_accuracy, acc
•Sparse Categorical Accuracy: sparse_categorical_accuracy
•Top k Categorical Accuracy: top_k_categorical_accuracy (requires you specify a k parameter)
•Sparse Top k Categorical Accuracy: sparse_top_k_categorical_accuracy (requires you specify a k parameter)
"""
configs = json.load(open(sys.argv[1], 'r'))
if not os.path.exists(configs['model']['save_dir']):
os.makedirs(configs['model']['save_dir'])
# check that the given configuration makes sense
assert configs['data']['output_mode'] in ["many_to_many", "many_to_one"]
assert configs['data']['input_mode'] in ["many_to_many", "one_to_many"]
if configs['data']['input_mode'] == "one_to_many": # 1-1 dense net
assert configs['data']['input_sequence_length'] == 0
assert configs['model']['validation_folds'] >= 1
else: # lstm mode
assert configs['data']['input_sequence_length'] > 0
assert configs['model']['validation_folds'] == 1
# load datasets
filename = os.path.join('data', configs['data']['filename'])
dataframe = pd.read_csv(filename, sep=',', encoding='utf-8')
if configs['data']['input_mode'] == "many_to_many":
dataframe.index.name = 'fecha'
dataloader = DataLoader()
dataloader.load_dataset(
dataframe,
configs['data']['train_test_split'],
configs['data']['x_cols'],
configs['data']['y_cols'],
configs['data']['cathegorical_cols'],
model_id=configs['model']['model_id'],
save_dir=configs['model']['save_dir'],
)
# start the k-folded cross-validation
scores = []
for i in range(configs['model']['validation_folds']):
print("Training with fold %d" % i)
model, history = train_network(configs, dataloader)
loss = history.history['val_loss'][-1]
scores.append((loss, model, history))
if configs['model']['validation_folds'] > 1:
print("Shuffling data for next fold validation!")
dataloader.shuffle_data()
sorted(scores, key=lambda x: x[0])
model = scores[0][1] # todo: is the last loss metric the best one to sort
model.save(save_dir=configs['model']['save_dir'],
model_id=configs['model']['model_id'])
print("Best model has %f loss rate!" % scores[0][0])
plot_historials([x[2] for x in scores])
# test the thing!
out_seq_len = configs['data']['input_sequence_length'] if configs['data'][
'output_mode'] == "many_to_many" else 1
x_test, y_test = dataloader.get_test_data(
in_seq_len=configs['data']['input_sequence_length'],
out_seq_len=out_seq_len)
unscaled_y_test = dataloader.recompose_results(y_test[:, 0, :],
side="y").values
# predict point by point
print("Plotting predictions point by point on validation set")
predictions = model.predict_point_by_point(x_test)
unscaled_predictions = dataloader.recompose_results(predictions[:, 0, :],
side="y").values
plot_results(unscaled_predictions, unscaled_y_test)
if model.predictions_are_refeedeable(): # only for lstm mode
print("Plotting predictions as refeeding window on validation set")
predictions = model.predict_sequence_full(x_test[1, :, :],
len(unscaled_y_test))
unscaled_predictions = dataloader.recompose_results(predictions[:,
0, :],
side="y").values
plot_results(unscaled_predictions, unscaled_y_test)
