def _baseline_model(self, input_dim):
# create model
model = Sequential()
model.add(
Dense(12,
input_dim=input_dim,
kernel_initializer='normal',
activation='relu'))
model.add(Dense(10, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(6, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
# Compile model
model.compile(loss=losses.MeanSquaredError(),
optimizer='adam',
metrics=[
losses.MeanSquaredError(),
losses.MeanAbsoluteError(),
losses.MeanAbsolutePercentageError()
])
tensorboard = TensorBoard(
log_dir=
f"./logs/target_prediction_on_high_pca_percentage_loss/{str(round(time()))}",
histogram_freq=5)
keras_callbacks = [tensorboard]
return model, keras_callbacks
def train_step(inputs, target):
with tf.GradientTape() as tape:
predictions = model(inputs, training=True)
loss = loss_fn(target, predictions)
mae=losses.MeanAbsoluteError()(target, predictions)
mape=losses.MeanAbsolutePercentageError()(target, predictions)
loss += sum(model.losses)
gradients = tape.gradient(loss, model.trainable_variables)
opt.apply_gradients(zip(gradients, model.trainable_variables))
return loss,mae,mape
def forecast(self, local_mse, local_normalized_scaled_unit_sales,
local_mean_unit_complete_time_serie, local_raw_unit_sales,
local_settings):
try:
print(
'starting high loss (mse in aggregated LSTM) specific time_serie forecast submodule'
)
# set training parameters
with open(''.join([local_settings['hyperparameters_path'],
'individual_time_serie_based_model_hyperparameters.json'])) \
as local_r_json_file:
model_hyperparameters = json.loads(local_r_json_file.read())
local_r_json_file.close()
time_steps_days = int(local_settings['time_steps_days'])
epochs = int(model_hyperparameters['epochs'])
batch_size = int(model_hyperparameters['batch_size'])
workers = int(model_hyperparameters['workers'])
optimizer_function = model_hyperparameters['optimizer']
optimizer_learning_rate = model_hyperparameters['learning_rate']
if optimizer_function == 'adam':
optimizer_function = optimizers.Adam(optimizer_learning_rate)
elif optimizer_function == 'ftrl':
optimizer_function = optimizers.Ftrl(optimizer_learning_rate)
losses_list = []
loss_1 = model_hyperparameters['loss_1']
loss_2 = model_hyperparameters['loss_2']
loss_3 = model_hyperparameters['loss_3']
union_settings_losses = [loss_1, loss_2, loss_3]
if 'mape' in union_settings_losses:
losses_list.append(losses.MeanAbsolutePercentageError())
if 'mse' in union_settings_losses:
losses_list.append(losses.MeanSquaredError())
if 'mae' in union_settings_losses:
losses_list.append(losses.MeanAbsoluteError())
if 'm_mape' in union_settings_losses:
losses_list.append(modified_mape())
if 'customized_loss_function' in union_settings_losses:
losses_list.append(customized_loss())
metrics_list = []
metric1 = model_hyperparameters['metrics1']
metric2 = model_hyperparameters['metrics2']
union_settings_metrics = [metric1, metric2]
if 'rmse' in union_settings_metrics:
metrics_list.append(metrics.RootMeanSquaredError())
if 'mse' in union_settings_metrics:
metrics_list.append(metrics.MeanSquaredError())
if 'mae' in union_settings_metrics:
metrics_list.append(metrics.MeanAbsoluteError())
if 'mape' in union_settings_metrics:
metrics_list.append(metrics.MeanAbsolutePercentageError())
l1 = model_hyperparameters['l1']
l2 = model_hyperparameters['l2']
if model_hyperparameters['regularizers_l1_l2'] == 'True':
activation_regularizer = regularizers.l1_l2(l1=l1, l2=l2)
else:
activation_regularizer = None
nof_features_by_training = 1
forecaster = tf.keras.Sequential()
print(
'current model for specific high loss time_series: Mix_Bid_PeepHole_LSTM_Dense_ANN'
)
# first layer (DENSE)
if model_hyperparameters['units_layer_1'] > 0:
forecaster.add(
layers.Dense(
units=model_hyperparameters['units_layer_1'],
activation=model_hyperparameters['activation_1'],
activity_regularizer=activation_regularizer))
forecaster.add(
layers.Dropout(
rate=float(model_hyperparameters['dropout_layer_1'])))
# second LSTM layer
if model_hyperparameters['units_layer_2'] > 0:
forecaster.add(
layers.Bidirectional(
layers.RNN(PeepholeLSTMCell(
units=model_hyperparameters['units_layer_2'],
activation=model_hyperparameters['activation_2'],
activity_regularizer=activation_regularizer,
dropout=float(
model_hyperparameters['dropout_layer_2'])),
return_sequences=False)))
forecaster.add(
RepeatVector(model_hyperparameters['repeat_vector']))
# third LSTM layer
if model_hyperparameters['units_layer_3'] > 0:
forecaster.add(
layers.Bidirectional(
layers.RNN(PeepholeLSTMCell(
units=model_hyperparameters['units_layer_3'],
activation=model_hyperparameters['activation_3'],
activity_regularizer=activation_regularizer,
dropout=float(
model_hyperparameters['dropout_layer_3'])),
return_sequences=False)))
forecaster.add(
RepeatVector(model_hyperparameters['repeat_vector']))
# fourth layer (DENSE)
if model_hyperparameters['units_layer_4'] > 0:
forecaster.add(
layers.Dense(
units=model_hyperparameters['units_layer_4'],
activation=model_hyperparameters['activation_4'],
activity_regularizer=activation_regularizer))
forecaster.add(
layers.Dropout(
rate=float(model_hyperparameters['dropout_layer_4'])))
# final layer
forecaster.add(layers.Dense(units=nof_features_by_training))
forecaster.compile(optimizer=optimizer_function,
loss=losses_list,
metrics=metrics_list)
# forecaster.saves(''.join([local_settings['models_path'], '_model_structure_']),
# save_format='tf')
forecaster.build(
input_shape=(1, local_settings['forecast_horizon_days'], 1))
forecaster_yaml = forecaster.to_yaml()
with open(
''.join([local_settings['models_path'],
'forecaster.yaml']), 'w') as yaml_file:
yaml_file.write(forecaster_yaml)
forecaster_untrained = forecaster
print('specific time_serie model initialized and compiled')
poor_results_mse_threshold = local_settings[
'poor_results_mse_threshold']
nof_selling_days = local_normalized_scaled_unit_sales.shape[1]
last_learning_day_in_year = np.mod(nof_selling_days, 365)
max_selling_time = local_settings['max_selling_time']
days_in_focus_frame = model_hyperparameters['days_in_focus_frame']
window_input_length = local_settings['moving_window_input_length']
window_output_length = local_settings[
'moving_window_output_length']
moving_window_length = window_input_length + window_output_length
nof_years = local_settings['number_of_years_ceil']
time_series_individually_treated = []
time_series_not_improved = []
dirname = os.path.dirname(__file__)
for result in local_mse:
time_serie = int(result[0])
file_path = os.path.join(
dirname, ''.join([
'.', local_settings['models_path'],
'specific_time_serie_',
str(time_serie), 'model_forecast_.h5'
]))
if os.path.isfile(
file_path) or result[1] <= poor_results_mse_threshold:
continue
# training
print('\ntime_serie: ', time_serie)
time_serie_data = local_normalized_scaled_unit_sales[
time_serie, :]
time_serie_data = time_serie_data.reshape(
time_serie_data.shape[0])
nof_selling_days = time_serie_data.shape[0]
# nof_moving_windows = np.int32(nof_selling_days / moving_window_length)
remainder_days = np.mod(nof_selling_days, moving_window_length)
window_first_days = [
first_day for first_day in range(0, nof_selling_days,
moving_window_length)
]
length_window_walk = len(window_first_days)
# last_window_start = window_first_days[length_window_walk - 1]
if remainder_days != 0:
window_first_days[
length_window_walk -
1] = nof_selling_days - moving_window_length
day_in_year = []
[
day_in_year.append(last_learning_day_in_year + year * 365)
for year in range(nof_years)
]
stride_window_walk = model_hyperparameters[
'stride_window_walk']
print('defining x_train')
x_train = []
if local_settings['train_model_input_data_approach'] == "all":
[
x_train.append(
time_serie_data[day - time_steps_days:day -
window_output_length])
for day in range(time_steps_days, max_selling_time,
stride_window_walk)
]
elif local_settings[
'train_model_input_data_approach'] == "focused":
[
x_train.append(time_serie_data[day:day +
window_input_length])
for last_day in day_in_year[:-1] for day in range(
last_day + window_output_length, last_day +
window_output_length -
days_in_focus_frame, -stride_window_walk)
]
# border condition, take care with last year, working with last data available
[
x_train.append(
time_serie_data[day - window_input_length:day])
for last_day in day_in_year[-1:] for day in range(
last_day, last_day -
days_in_focus_frame, -stride_window_walk)
]
x_train = np.array(x_train)
print('x_train_shape: ', x_train.shape)
else:
logging.info(
"\ntrain_model_input_data_approach is not defined")
print('-a problem occurs with the data_approach settings')
return False, None
print('defining y_train')
y_train = []
if local_settings['train_model_input_data_approach'] == "all":
[
y_train.append(
time_serie_data[day - window_output_length:day])
for day in range(time_steps_days, max_selling_time,
stride_window_walk)
]
elif local_settings[
'train_model_input_data_approach'] == "focused":
[
y_train.append(time_serie_data[day:day +
window_output_length])
for last_day in day_in_year[:-1] for day in range(
last_day + window_output_length, last_day +
window_output_length -
days_in_focus_frame, -stride_window_walk)
]
# border condition, take care with last year, working with last data available
[
y_train.append(
time_serie_data[day - window_output_length:day])
for last_day in day_in_year[-1:] for day in range(
last_day, last_day -
days_in_focus_frame, -stride_window_walk)
]
y_train = np.array(y_train)
factor = local_settings['amplification_factor']
max_time_serie = np.amax(x_train)
x_train[x_train > 0] = max_time_serie * factor
max_time_serie = np.amax(y_train)
y_train[y_train > 0] = max_time_serie * factor
print('x_train and y_train built done')
# define callbacks, checkpoints namepaths
model_weights = ''.join([
local_settings['checkpoints_path'],
'model_for_specific_time_serie_',
str(time_serie),
model_hyperparameters['current_model_name'],
"_loss_-{loss:.4f}-.hdf5"
])
callback1 = cb.EarlyStopping(
monitor='loss',
patience=model_hyperparameters['early_stopping_patience'])
callback2 = cb.ModelCheckpoint(model_weights,
monitor='loss',
verbose=1,
save_best_only=True,
mode='min')
callbacks = [callback1, callback2]
x_train = x_train.reshape(
(np.shape(x_train)[0], np.shape(x_train)[1], 1))
y_train = y_train.reshape(
(np.shape(y_train)[0], np.shape(y_train)[1], 1))
print('input_shape: ', np.shape(x_train))
# train for each time_serie
# check settings for repeat or not the training
need_store_time_serie = True
# load model
time_series_individually_treated = np.load(''.join([
local_settings['models_evaluation_path'],
'improved_time_series_forecast.npy'
]))
time_series_individually_treated = time_series_individually_treated.tolist(
)
model_name = ''.join([
'specific_time_serie_',
str(time_serie), 'model_forecast_.h5'
])
model_path = ''.join(
[local_settings['models_path'], model_name])
if os.path.isfile(model_path) and model_hyperparameters[
'repeat_one_by_one_training'] == "False":
forecaster = models.load_model(model_path,
custom_objects={
'modified_mape':
modified_mape,
'customized_loss':
customized_loss
})
need_store_time_serie = False
elif model_hyperparameters['one_by_one_feature_training_done'] == "False"\
or model_hyperparameters['repeat_one_by_one_training'] == "True":
forecaster = forecaster_untrained
forecaster.fit(x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
workers=workers,
callbacks=callbacks,
shuffle=False)
# print summary (informative; but if says "shape = multiple", probably useless)
forecaster.summary()
# compile model and make forecast
forecaster.compile(optimizer='adam', loss='mse')
# evaluating model and comparing with aggregated (in-block) LSTM
print('evaluating the model trained..')
forecast_horizon_days = local_settings['forecast_horizon_days']
time_serie_data = time_serie_data.reshape(
(1, time_serie_data.shape[0], 1))
x_input = time_serie_data[:, -forecast_horizon_days:, :]
y_pred_normalized = forecaster.predict(x_input)
print('output shape: ', y_pred_normalized.shape)
y_truth = local_raw_unit_sales[time_serie,
-forecast_horizon_days:]
y_truth = y_truth.reshape(1, np.shape(y_truth)[0])
print('y_truth shape:', y_truth.shape)
# reversing preprocess: rescale, denormalize, reshape
# inverse reshape
y_pred_reshaped = y_pred_normalized.reshape(
(y_pred_normalized.shape[2], y_pred_normalized.shape[1]))
print('y_pred_reshaped shape:', y_pred_reshaped.shape)
# inverse transform (first moving_windows denormalizing and then general rescaling)
time_serie_data = time_serie_data.reshape(
np.shape(time_serie_data)[1], 1)
print('time_serie data shape: ', np.shape(time_serie_data))
time_serie_normalized_window_mean = np.mean(
time_serie_data[-moving_window_length:])
print('mean of this time serie (normalized values): ',
time_serie_normalized_window_mean)
local_denormalized_array = window_based_denormalizer(
y_pred_reshaped, time_serie_normalized_window_mean,
forecast_horizon_days)
local_point_forecast = general_mean_rescaler(
local_denormalized_array,
local_mean_unit_complete_time_serie[time_serie],
forecast_horizon_days)
print('rescaled denormalized forecasts array shape: ',
local_point_forecast.shape)
# calculating MSE
local_error_metric_mse = mean_squared_error(
y_truth, local_point_forecast)
print('time_serie: ', time_serie, '\tMean_Squared_Error: ',
local_error_metric_mse)
if local_error_metric_mse < result[1]:
print(
'better results with time_serie specific model training'
)
print('MSE improved from ', result[1], 'to ',
local_error_metric_mse)
# save models for this time serie
forecaster.save(''.join([
local_settings['models_path'], 'specific_time_serie_',
str(time_serie), 'model_forecast_.h5'
]))
print('model for time_serie ', str(time_serie), " saved")
if need_store_time_serie:
time_series_individually_treated.append(
int(time_serie))
else:
print(
'no better results with time serie specific model training'
)
time_series_not_improved.append(int(time_serie))
time_series_individually_treated = np.array(
time_series_individually_treated)
time_series_not_improved = np.array(time_series_not_improved)
# store data of (individual-approach) time_series forecast successfully improved and those that not
np.save(
''.join([
local_settings['models_evaluation_path'],
'improved_time_series_forecast'
]), time_series_individually_treated)
np.save(
''.join([
local_settings['models_evaluation_path'],
'time_series_not_improved'
]), time_series_not_improved)
print(
'forecast improvement done. (specific time_serie focused) submodule has finished'
)
except Exception as submodule_error:
print('time_series individual forecast submodule_error: ',
submodule_error)
logger.info(
'error in forecast of individual (high_loss_identified_ts_forecast submodule)'
)
logger.error(str(submodule_error), exc_info=True)
return False
return True
def pipeline():
featurearr, simarr, labelarr=load_data()
xarr, yarr, aarr, edge_attrarr=graphdatageneration(featurearr, simarr, labelarr)
dataset = MyDataset(xarr,yarr,aarr,edge_attrarr)
np.random.seed(10)
# Train/test split
idxs = np.random.permutation(len(dataset))
split = int(0.8 * len(dataset))
idx_tr, idx_te = np.split(idxs, [split])
dataset_tr, dataset_te = dataset[idx_tr], dataset[idx_te]
loader_tr = DisjointLoader(dataset_tr, batch_size=32, epochs=30,shuffle=True)
loader_te = DisjointLoader(dataset_te, batch_size=32, epochs=1,shuffle=True)
model=buildmodel(dataset)
opt = optimizers.Adam(lr=learning_rate)
loss_fn = losses.MeanSquaredError()
@tf.function(input_signature=loader_tr.tf_signature(), experimental_relax_shapes=True)
def train_step(inputs, target):
with tf.GradientTape() as tape:
predictions = model(inputs, training=True)
loss = loss_fn(target, predictions)
mae=losses.MeanAbsoluteError()(target, predictions)
mape=losses.MeanAbsolutePercentageError()(target, predictions)
loss += sum(model.losses)
gradients = tape.gradient(loss, model.trainable_variables)
opt.apply_gradients(zip(gradients, model.trainable_variables))
return loss,mae,mape
print("training")
current_batch = 0
model_loss = 0
total_mape=0
total_mae=0
for batch in loader_tr:
outs,mae,mape= train_step(*batch)
model_loss += outs
total_mae+=mae
total_mape+=mape
current_batch += 1
if current_batch == loader_tr.steps_per_epoch:
print("MSE: {}".format(model_loss / loader_tr.steps_per_epoch),
"MAE: {}".format(total_mae/ loader_tr.steps_per_epoch),
"MAPE: {}".format(total_mape/ loader_tr.steps_per_epoch))
model_loss = 0
total_mae = 0
total_mape = 0
current_batch = 0
print("testing")
model_loss = 0
model_mae=0
model_mape = 0
for batch in loader_te:
inputs, target = batch
predictions = model(inputs, training=False)
model_loss += loss_fn(target, predictions)
model_mae += losses.MeanAbsoluteError()(target, predictions)
model_mape+= losses.MeanAbsolutePercentageError()(target, predictions)
model_loss /= loader_te.steps_per_epoch
model_mae /= loader_te.steps_per_epoch
model_mape /= loader_te.steps_per_epoch
print("Done. Test MSE: {}".format(model_loss),
"Test MAE: {}".format(model_mae),
"Test MAPE: {}".format(model_mape))
model.save('/home/som/lab/seed-yzj/newpaper4/laboratory/model/fusion.hdf5')
def forecast(self, local_mse, local_normalized_scaled_unit_sales,
local_mean_unit_complete_time_serie, local_raw_unit_sales,
local_settings):
try:
print(
'starting high loss (mse in previous LSTM) time_series in-block forecast submodule'
)
# set training parameters
with open(''.join([local_settings['hyperparameters_path'],
'in_block_time_serie_based_model_hyperparameters.json'])) \
as local_r_json_file:
model_hyperparameters = json.loads(local_r_json_file.read())
local_r_json_file.close()
local_time_series_group = np.load(''.join(
[local_settings['train_data_path'], 'time_serie_group.npy']),
allow_pickle=True)
time_steps_days = int(local_settings['time_steps_days'])
epochs = int(model_hyperparameters['epochs'])
batch_size = int(model_hyperparameters['batch_size'])
workers = int(model_hyperparameters['workers'])
optimizer_function = model_hyperparameters['optimizer']
optimizer_learning_rate = model_hyperparameters['learning_rate']
if optimizer_function == 'adam':
optimizer_function = optimizers.Adam(optimizer_learning_rate)
elif optimizer_function == 'ftrl':
optimizer_function = optimizers.Ftrl(optimizer_learning_rate)
losses_list = []
loss_1 = model_hyperparameters['loss_1']
loss_2 = model_hyperparameters['loss_2']
loss_3 = model_hyperparameters['loss_3']
union_settings_losses = [loss_1, loss_2, loss_3]
if 'mape' in union_settings_losses:
losses_list.append(losses.MeanAbsolutePercentageError())
if 'mse' in union_settings_losses:
losses_list.append(losses.MeanSquaredError())
if 'mae' in union_settings_losses:
losses_list.append(losses.MeanAbsoluteError())
if 'm_mape' in union_settings_losses:
losses_list.append(modified_mape())
if 'customized_loss_function' in union_settings_losses:
losses_list.append(customized_loss())
metrics_list = []
metric1 = model_hyperparameters['metrics1']
metric2 = model_hyperparameters['metrics2']
union_settings_metrics = [metric1, metric2]
if 'rmse' in union_settings_metrics:
metrics_list.append(metrics.RootMeanSquaredError())
if 'mse' in union_settings_metrics:
metrics_list.append(metrics.MeanSquaredError())
if 'mae' in union_settings_metrics:
metrics_list.append(metrics.MeanAbsoluteError())
if 'mape' in union_settings_metrics:
metrics_list.append(metrics.MeanAbsolutePercentageError())
l1 = model_hyperparameters['l1']
l2 = model_hyperparameters['l2']
if model_hyperparameters['regularizers_l1_l2'] == 'True':
activation_regularizer = regularizers.l1_l2(l1=l1, l2=l2)
else:
activation_regularizer = None
# searching for time_series with high loss forecast
time_series_treated = []
poor_results_mse_threshold = local_settings[
'poor_results_mse_threshold']
poor_result_time_serie_list = []
nof_features_for_training = 0
for result in local_mse:
if result[1] > poor_results_mse_threshold:
nof_features_for_training += 1
poor_result_time_serie_list.append(int(result[0]))
# nof_features_for_training = local_normalized_scaled_unit_sales.shape[0]
nof_features_for_training = len(poor_result_time_serie_list)
# creating model
forecaster_in_block = tf.keras.Sequential()
print(
'current model for specific high loss time_series: Mix_Bid_PeepHole_LSTM_Dense_ANN'
)
# first layer (DENSE)
if model_hyperparameters['units_layer_1'] > 0:
forecaster_in_block.add(
layers.Dense(
units=model_hyperparameters['units_layer_1'],
activation=model_hyperparameters['activation_1'],
input_shape=(model_hyperparameters['time_steps_days'],
nof_features_for_training),
activity_regularizer=activation_regularizer))
forecaster_in_block.add(
layers.Dropout(
rate=float(model_hyperparameters['dropout_layer_1'])))
# second LSTM layer
if model_hyperparameters['units_layer_2'] > 0:
forecaster_in_block.add(
layers.Bidirectional(
layers.RNN(PeepholeLSTMCell(
units=model_hyperparameters['units_layer_2'],
activation=model_hyperparameters['activation_2'],
activity_regularizer=activation_regularizer,
dropout=float(
model_hyperparameters['dropout_layer_2'])),
return_sequences=False)))
forecaster_in_block.add(
RepeatVector(model_hyperparameters['repeat_vector']))
# third LSTM layer
if model_hyperparameters['units_layer_3'] > 0:
forecaster_in_block.add(
layers.Bidirectional(
layers.RNN(PeepholeLSTMCell(
units=model_hyperparameters['units_layer_3'],
activation=model_hyperparameters['activation_3'],
activity_regularizer=activation_regularizer,
dropout=float(
model_hyperparameters['dropout_layer_3'])),
return_sequences=False)))
forecaster_in_block.add(
RepeatVector(model_hyperparameters['repeat_vector']))
# fourth layer (DENSE)
if model_hyperparameters['units_layer_4'] > 0:
forecaster_in_block.add(
layers.Dense(
units=model_hyperparameters['units_layer_4'],
activation=model_hyperparameters['activation_4'],
activity_regularizer=activation_regularizer))
forecaster_in_block.add(
layers.Dropout(
rate=float(model_hyperparameters['dropout_layer_4'])))
# final layer
forecaster_in_block.add(
TimeDistributed(layers.Dense(units=nof_features_for_training)))
# forecaster_in_block.saves(''.join([local_settings['models_path'], '_model_structure_']),
# save_format='tf')
forecast_horizon_days = local_settings['forecast_horizon_days']
forecaster_in_block.build(input_shape=(1, forecast_horizon_days,
nof_features_for_training))
forecaster_in_block.compile(optimizer=optimizer_function,
loss=losses_list,
metrics=metrics_list)
forecaster_in_block_json = forecaster_in_block.to_json()
with open(
''.join([
local_settings['models_path'],
'forecaster_in_block.json'
]), 'w') as json_file:
json_file.write(forecaster_in_block_json)
json_file.close()
forecaster_in_block_untrained = forecaster_in_block
print('specific time_serie model initialized and compiled')
nof_selling_days = local_normalized_scaled_unit_sales.shape[1]
last_learning_day_in_year = np.mod(nof_selling_days, 365)
max_selling_time = local_settings['max_selling_time']
days_in_focus_frame = model_hyperparameters['days_in_focus_frame']
window_input_length = local_settings['moving_window_input_length']
window_output_length = local_settings[
'moving_window_output_length']
moving_window_length = window_input_length + window_output_length
nof_years = local_settings['number_of_years_ceil']
# training
# time_serie_data = local_normalized_scaled_unit_sales
nof_poor_result_time_series = len(poor_result_time_serie_list)
time_serie_data = np.zeros(shape=(nof_poor_result_time_series,
max_selling_time))
time_serie_iterator = 0
for time_serie in poor_result_time_serie_list:
time_serie_data[
time_serie_iterator, :] = local_normalized_scaled_unit_sales[
time_serie, :]
time_serie_iterator += 1
if local_settings['repeat_training_in_block'] == "True":
print(
'starting in-block training of model for high_loss time_series in previous model'
)
nof_selling_days = time_serie_data.shape[1]
# nof_moving_windows = np.int32(nof_selling_days / moving_window_length)
remainder_days = np.mod(nof_selling_days, moving_window_length)
window_first_days = [
first_day for first_day in range(0, nof_selling_days,
moving_window_length)
]
length_window_walk = len(window_first_days)
# last_window_start = window_first_days[length_window_walk - 1]
if remainder_days != 0:
window_first_days[
length_window_walk -
1] = nof_selling_days - moving_window_length
day_in_year = []
[
day_in_year.append(last_learning_day_in_year + year * 365)
for year in range(nof_years)
]
stride_window_walk = model_hyperparameters[
'stride_window_walk']
print('defining x_train')
x_train = []
if local_settings['train_model_input_data_approach'] == "all":
[
x_train.append(
time_serie_data[:, day - time_steps_days:day -
window_output_length])
for day in range(time_steps_days, max_selling_time,
stride_window_walk)
]
elif local_settings[
'train_model_input_data_approach'] == "focused":
[
x_train.append(time_serie_data[:, day:day +
time_steps_days])
for last_day in day_in_year[:-1] for day in range(
last_day + window_output_length, last_day +
window_output_length -
days_in_focus_frame, -stride_window_walk)
]
# border condition, take care with last year, working with last data available, yeah really!!
[
x_train.append(
np.concatenate(
(time_serie_data[:, day -
window_output_length:day],
np.zeros(shape=(nof_poor_result_time_series,
time_steps_days -
window_output_length))),
axis=1))
for last_day in day_in_year[-1:] for day in range(
last_day, last_day -
days_in_focus_frame, -stride_window_walk)
]
else:
logging.info(
"\ntrain_model_input_data_approach is not defined")
print('-a problem occurs with the data_approach settings')
return False, None
print('defining y_train')
y_train = []
if local_settings['train_model_input_data_approach'] == "all":
[
y_train.append(time_serie_data[:, day -
time_steps_days:day])
for day in range(time_steps_days, max_selling_time,
stride_window_walk)
]
elif local_settings[
'train_model_input_data_approach'] == "focused":
[
y_train.append(time_serie_data[:, day:day +
time_steps_days])
for last_day in day_in_year[:-1] for day in range(
last_day + window_output_length, last_day +
window_output_length -
days_in_focus_frame, -stride_window_walk)
]
# border condition, take care with last year, working with last data available, yeah really!!
[
y_train.append(
np.concatenate(
(time_serie_data[:, day -
window_output_length:day],
np.zeros(shape=(nof_poor_result_time_series,
time_steps_days -
window_output_length))),
axis=1))
for last_day in day_in_year[-1:] for day in range(
last_day, last_day -
days_in_focus_frame, -stride_window_walk)
]
# if time_enhance is active, assigns more weight to the last time_steps according to enhance_last_stride
if local_settings['time_enhance'] == 'True':
enhance_last_stride = local_settings['enhance_last_stride']
last_elements = []
length_x_y_train = len(x_train)
x_train_enhanced, y_train_enhanced = [], []
enhance_iterator = 1
for position in range(
length_x_y_train - enhance_last_stride,
length_x_y_train, -1):
[
x_train_enhanced.append(x_train[position])
for enhance in range(1, 3 * (enhance_iterator + 1))
]
[
y_train_enhanced.append(y_train[position])
for enhance in range(1, 3 * (enhance_iterator + 1))
]
enhance_iterator += 1
x_train = x_train[:-enhance_last_stride]
[
x_train.append(time_step)
for time_step in x_train_enhanced
]
y_train = y_train[:-enhance_last_stride]
[
y_train.append(time_step)
for time_step in y_train_enhanced
]
# broadcasts lists to np arrays and applies the last pre-training preprocessing (amplification)
x_train = np.array(x_train)
y_train = np.array(y_train)
print('x_train_shape: ', x_train.shape)
if local_settings['amplification'] == 'True':
factor = local_settings[
'amplification_factor'] # factor tuning was done previously
for time_serie_iterator in range(np.shape(x_train)[1]):
max_time_serie = np.amax(
x_train[:, time_serie_iterator, :])
x_train[:, time_serie_iterator, :][x_train[:, time_serie_iterator, :] > 0] = \
max_time_serie * factor
max_time_serie = np.amax(
y_train[:, time_serie_iterator, :])
y_train[:, time_serie_iterator, :][y_train[:, time_serie_iterator, :] > 0] = \
max_time_serie * factor
print('x_train and y_train built done')
# define callbacks, checkpoints namepaths
model_weights = ''.join([
local_settings['checkpoints_path'],
'check_point_model_for_high_loss_time_serie_',
model_hyperparameters['current_model_name'],
"_loss_-{loss:.4f}-.hdf5"
])
callback1 = cb.EarlyStopping(
monitor='loss',
patience=model_hyperparameters['early_stopping_patience'])
callback2 = cb.ModelCheckpoint(model_weights,
monitor='loss',
verbose=1,
save_best_only=True,
mode='min')
callbacks = [callback1, callback2]
x_train = x_train.reshape(
(np.shape(x_train)[0], np.shape(x_train)[2],
np.shape(x_train)[1]))
y_train = y_train.reshape(
(np.shape(y_train)[0], np.shape(y_train)[2],
np.shape(y_train)[1]))
print('input_shape: ', np.shape(x_train))
# train for each time_serie
# check settings for repeat or not the training
forecaster_in_block.fit(x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
workers=workers,
callbacks=callbacks,
shuffle=False)
# print summary (informative; but if says "shape = multiple", probably useless)
forecaster_in_block.summary()
forecaster_in_block.save(''.join([
local_settings['models_path'],
'_high_loss_time_serie_model_forecaster_in_block_.h5'
]))
forecaster_in_block.save_weights(''.join([
local_settings['models_path'],
'_weights_high_loss_ts_model_forecaster_in_block_.h5'
]))
print(
'high loss time_series model trained and saved in hdf5 format .h5'
)
else:
forecaster_in_block.load_weights(''.join([
local_settings['models_path'],
'_weights_high_loss_ts_model_forecaster_in_block_.h5'
]))
# forecaster_in_block = models.load_model(''.join([local_settings['models_path'],
# '_high_loss_time_serie_model_forecaster_.h5']))
print('weights of previously trained model loaded')
# compile model and make forecast (not necessary)
# forecaster_in_block.compile(optimizer='adam', loss='mse')
# evaluating model and comparing with aggregated (in-block) LSTM
print('evaluating the model trained..')
time_serie_data = time_serie_data.reshape(
(1, time_serie_data.shape[1], time_serie_data.shape[0]))
x_input = time_serie_data[:, -forecast_horizon_days:, :]
y_pred_normalized = forecaster_in_block.predict(x_input)
# print('output shape: ', y_pred_normalized.shape)
time_serie_data = time_serie_data.reshape(
(time_serie_data.shape[2], time_serie_data.shape[1]))
# print('time_serie data shape: ', np.shape(time_serie_data))
time_serie_iterator = 0
improved_time_series_forecast = []
time_series_not_improved = []
improved_mse = []
for time_serie in poor_result_time_serie_list:
# for time_serie in range(local_normalized_scaled_unit_sales.shape[0]):
y_truth = local_raw_unit_sales[time_serie:time_serie + 1,
-forecast_horizon_days:]
# print('y_truth shape:', y_truth.shape)
# reversing preprocess: rescale, denormalize, reshape
# inverse reshape
y_pred_reshaped = y_pred_normalized.reshape(
(y_pred_normalized.shape[2], y_pred_normalized.shape[1]))
y_pred_reshaped = y_pred_reshaped[
time_serie_iterator:time_serie_iterator + 1, :]
# print('y_pred_reshaped shape:', y_pred_reshaped.shape)
# inverse transform (first moving_windows denormalizing and then general rescaling)
time_serie_normalized_window_mean = np.mean(
time_serie_data[time_serie_iterator,
-moving_window_length:])
# print('mean of this time serie (normalized values): ', time_serie_normalized_window_mean)
local_denormalized_array = window_based_denormalizer(
y_pred_reshaped, time_serie_normalized_window_mean,
forecast_horizon_days)
local_point_forecast = general_mean_rescaler(
local_denormalized_array,
local_mean_unit_complete_time_serie[time_serie],
forecast_horizon_days)
# print('rescaled denormalized forecasts array shape: ', local_point_forecast.shape)
# calculating MSE
# print(y_truth.shape)
# print(local_point_forecast.shape)
local_error_metric_mse = mean_squared_error(
y_truth, local_point_forecast)
# print('time_serie: ', time_serie, '\tMean_Squared_Error: ', local_error_metric_mse)
previous_result = local_mse[:, 1][local_mse[:, 0] ==
time_serie].item()
time_series_treated.append(
[int(time_serie), previous_result, local_error_metric_mse])
if local_error_metric_mse < previous_result:
# print('better results with time_serie specific model training')
print(time_serie, 'MSE improved from ', previous_result,
'to ', local_error_metric_mse)
improved_time_series_forecast.append(int(time_serie))
improved_mse.append(local_error_metric_mse)
else:
# print('no better results with time serie specific model training')
# print('MSE not improved from: ', previous_result, '\t current mse: ', local_error_metric_mse)
time_series_not_improved.append(int(time_serie))
time_serie_iterator += 1
time_series_treated = np.array(time_series_treated)
improved_mse = np.array(improved_mse)
average_mse_in_block_forecast = np.mean(time_series_treated[:, 2])
average_mse_improved_ts = np.mean(improved_mse)
print('poor result time serie list len:',
len(poor_result_time_serie_list))
print('mean_mse for in-block forecast:',
average_mse_in_block_forecast)
print(
'number of time series with better results with this forecast: ',
len(improved_time_series_forecast))
print(
'mean_mse of time series with better results with this forecast: ',
average_mse_improved_ts)
print('not improved time series =', len(time_series_not_improved))
time_series_treated = np.array(time_series_treated)
improved_time_series_forecast = np.array(
improved_time_series_forecast)
time_series_not_improved = np.array(time_series_not_improved)
poor_result_time_serie_array = np.array(
poor_result_time_serie_list)
# store data of (individual-approach) time_series forecast successfully improved and those that not
np.save(
''.join([
local_settings['models_evaluation_path'],
'poor_result_time_serie_array'
]), poor_result_time_serie_array)
np.save(
''.join([
local_settings['models_evaluation_path'],
'time_series_forecast_results'
]), time_series_treated)
np.save(
''.join([
local_settings['models_evaluation_path'],
'improved_time_series_forecast'
]), improved_time_series_forecast)
np.save(
''.join([
local_settings['models_evaluation_path'],
'time_series_not_improved'
]), time_series_not_improved)
np.savetxt(''.join([
local_settings['models_evaluation_path'],
'time_series_forecast_results.csv'
]),
time_series_treated,
fmt='%10.15f',
delimiter=',',
newline='\n')
forecaster_in_block_json = forecaster_in_block.to_json()
with open(''.join([local_settings['models_path'], 'high_loss_time_serie_model_forecaster_in_block.json']), 'w') \
as json_file:
json_file.write(forecaster_in_block_json)
json_file.close()
print('trained model weights and architecture saved')
print('metadata (results, time_serie with high loss) saved')
print(
'forecast improvement done. (high loss time_serie focused) submodule has finished'
)
except Exception as submodule_error:
print('time_series in-block forecast submodule_error: ',
submodule_error)
logger.info(
'error in forecast of in-block time_series (high_loss_identified_ts_forecast submodule)'
)
logger.error(str(submodule_error), exc_info=True)
return False
return True
def train_model(self, local_settings, local_raw_unit_sales,
local_model_hyperparameters):
try:
# loading hyperparameters
local_days_in_focus = local_model_hyperparameters[
'days_in_focus_frame']
local_raw_unit_sales_data = local_raw_unit_sales[:,
-local_days_in_focus:]
local_nof_ts = local_raw_unit_sales.shape[0]
local_forecast_horizon_days = local_settings[
'forecast_horizon_days']
local_features_for_each_training = 1
print(
'starting neural network - individual time_serie training unit_sale_approach'
)
# building architecture and compiling model_template
# set training parameters
local_time_steps_days = int(local_settings['time_steps_days'])
local_epochs = int(local_model_hyperparameters['epochs'])
local_batch_size = int(local_model_hyperparameters['batch_size'])
local_workers = int(local_model_hyperparameters['workers'])
local_optimizer_function = local_model_hyperparameters['optimizer']
local_optimizer_learning_rate = local_model_hyperparameters[
'learning_rate']
local_validation_split = local_model_hyperparameters[
'validation_split']
if local_optimizer_function == 'adam':
local_optimizer_function = optimizers.Adam(
local_optimizer_learning_rate)
elif local_optimizer_function == 'ftrl':
local_optimizer_function = optimizers.Ftrl(
local_optimizer_learning_rate)
local_losses_list = []
local_loss_1 = local_model_hyperparameters['loss_1']
local_loss_2 = local_model_hyperparameters['loss_2']
local_loss_3 = local_model_hyperparameters['loss_3']
local_union_settings_losses = [
local_loss_1, local_loss_2, local_loss_3
]
if 'mape' in local_union_settings_losses:
local_losses_list.append(losses.MeanAbsolutePercentageError())
if 'mse' in local_union_settings_losses:
local_losses_list.append(losses.MeanSquaredError())
if 'mae' in local_union_settings_losses:
local_losses_list.append(losses.MeanAbsoluteError())
if 'm_mape' in local_union_settings_losses:
local_losses_list.append(modified_mape())
if 'customized_loss_function' in local_union_settings_losses:
local_losses_list.append(customized_loss())
if 'pinball_loss_function' in local_union_settings_losses:
local_losses_list.append(pinball_function_loss())
local_metrics_list = []
local_metric1 = local_model_hyperparameters['metrics1']
local_metric2 = local_model_hyperparameters['metrics2']
local_union_settings_metrics = [local_metric1, local_metric2]
if 'rmse' in local_union_settings_metrics:
local_metrics_list.append(metrics.RootMeanSquaredError())
if 'mse' in local_union_settings_metrics:
local_metrics_list.append(metrics.MeanSquaredError())
if 'mae' in local_union_settings_metrics:
local_metrics_list.append(metrics.MeanAbsoluteError())
if 'mape' in local_union_settings_metrics:
local_metrics_list.append(
metrics.MeanAbsolutePercentageError())
local_l1 = local_model_hyperparameters['l1']
local_l2 = local_model_hyperparameters['l2']
if local_model_hyperparameters['regularizers_l1_l2'] == 'True':
local_activation_regularizer = regularizers.l1_l2(l1=local_l1,
l2=local_l2)
else:
local_activation_regularizer = None
# define callbacks, checkpoints namepaths
local_callback1 = cb.EarlyStopping(
monitor='loss',
patience=local_model_hyperparameters['early_stopping_patience']
)
local_callbacks = [local_callback1]
print(
'building current model: individual_time_serie_acc_freq_LSTM_Dense_ANN'
)
local_base_model = tf.keras.Sequential()
# first layer (LSTM)
if local_model_hyperparameters['units_layer_1'] > 0:
local_base_model.add(
layers.LSTM(
units=local_model_hyperparameters['units_layer_1'],
activation=local_model_hyperparameters['activation_1'],
input_shape=(
local_model_hyperparameters['time_steps_days'],
local_features_for_each_training),
dropout=float(
local_model_hyperparameters['dropout_layer_1']),
activity_regularizer=local_activation_regularizer,
return_sequences=True))
# second LSTM layer
if local_model_hyperparameters['units_layer_2'] > 0:
local_base_model.add(
layers.Bidirectional(
layers.LSTM(
units=local_model_hyperparameters['units_layer_2'],
activation=local_model_hyperparameters[
'activation_2'],
activity_regularizer=local_activation_regularizer,
dropout=float(
local_model_hyperparameters['dropout_layer_2']
),
return_sequences=False)))
local_base_model.add(
RepeatVector(local_model_hyperparameters['repeat_vector']))
# third LSTM layer
if local_model_hyperparameters['units_layer_3'] > 0:
local_base_model.add(
layers.Bidirectional(
layers.
RNN(PeepholeLSTMCell(
units=local_model_hyperparameters['units_layer_3'],
dropout=float(
local_model_hyperparameters['dropout_layer_3'])
),
activity_regularizer=local_activation_regularizer,
return_sequences=False)))
local_base_model.add(
RepeatVector(local_model_hyperparameters['repeat_vector']))
# fourth layer (DENSE)
if local_model_hyperparameters['units_layer_4'] > 0:
local_base_model.add(
layers.Dense(
units=local_model_hyperparameters['units_layer_4'],
activation=local_model_hyperparameters['activation_4'],
activity_regularizer=local_activation_regularizer))
local_base_model.add(
layers.Dropout(rate=float(
local_model_hyperparameters['dropout_layer_4'])))
# final layer
local_base_model.add(
layers.Dense(
units=local_model_hyperparameters['units_final_layer']))
# build and compile model
local_base_model.build(
input_shape=(1, local_time_steps_days,
local_features_for_each_training))
local_base_model.compile(optimizer=local_optimizer_function,
loss=local_losses_list,
metrics=local_metrics_list)
# save model architecture (template for specific models)
local_base_model.save(''.join([
local_settings['models_path'],
'_unit_sales_forecaster_template_individual_ts.h5'
]))
local_base_model_json = local_base_model.to_json()
with open(''.join([local_settings['models_path'],
'_unit_sales_forecaster_forecaster_template_individual_ts.json']), 'w') \
as json_file:
json_file.write(local_base_model_json)
json_file.close()
local_base_model.summary()
# training model
local_moving_window_length = local_settings['moving_window_input_length'] + \
local_settings['moving_window_output_length']
# loading x_train and y_train, previously done for third and fourth models trainings
local_builder = local_bxy_x_y_builder()
local_x_train, local_y_train = local_builder.build_x_y_train_arrays(
local_raw_unit_sales, local_settings,
local_model_hyperparameters)
local_x_train = local_x_train.reshape(local_x_train.shape[0],
local_x_train.shape[2],
local_x_train.shape[1])
local_y_train = local_x_train.reshape(local_y_train.shape[0],
local_y_train.shape[2],
local_y_train.shape[1])
# star training time_serie by time_serie
local_y_pred_array = np.zeros(shape=(local_raw_unit_sales.shape[0],
local_forecast_horizon_days),
dtype=np.dtype('float32'))
for time_serie in range(local_nof_ts):
print('training time_serie:', time_serie)
local_x, local_y = local_x_train[:, :, time_serie: time_serie + 1], \
local_y_train[:, :, time_serie: time_serie + 1]
# training, saving model and storing forecasts
local_base_model.fit(local_x,
local_y,
batch_size=local_batch_size,
epochs=local_epochs,
workers=local_workers,
callbacks=local_callbacks,
shuffle=False,
validation_split=local_validation_split)
local_base_model.save_weights(''.join([
local_settings['models_path'],
'/_weights_unit_sales_NN_35_days/_individual_ts_',
str(time_serie), '_model_weights_.h5'
]))
local_x_input = local_raw_unit_sales[
time_serie:time_serie + 1, -local_forecast_horizon_days:]
local_x_input = local_x_input.reshape(1,
local_x_input.shape[1],
1)
# print('x_input shape:', local_x_input.shape)
local_y_pred = local_base_model.predict(local_x_input)
# print('x_input:\n', local_x_input)
# print('y_pred shape:', local_y_pred.shape)
local_y_pred = local_y_pred.reshape(local_y_pred.shape[1])
# print('ts:', time_serie)
# print(local_y_pred)
local_y_pred_array[time_serie:time_serie + 1, :] = local_y_pred
local_point_forecast_normalized = local_y_pred_array.reshape(
(local_y_pred_array.shape[0], local_y_pred_array.shape[1]))
local_point_forecast = local_point_forecast_normalized.clip(0)
# save points forecast
np.save(
''.join([
local_settings['train_data_path'],
'point_forecast_NN_from_unit_sales_training'
]), local_point_forecast)
np.save(
''.join([
local_settings['train_data_path'],
'eleventh_model_NN_unit_sales_forecast_data'
]), local_point_forecast)
np.savetxt(''.join([
local_settings['others_outputs_path'],
'point_forecast_NN_from_unit_sales_training.csv'
]),
local_point_forecast,
fmt='%10.15f',
delimiter=',',
newline='\n')
print('point forecasts saved to file')
print(
'submodule for build, train and forecast time_serie unit_sales individually finished successfully'
)
return True, local_point_forecast
except Exception as submodule_error:
print(
'train model and forecast individual time_series units_sales_ submodule_error: ',
submodule_error)
logger.info(
'error in training and forecast-individual time_serie unit_sales_ schema'
)
logger.error(str(submodule_error), exc_info=True)
return False, []
def build_model(local_bm_hyperparameters, local_bm_settings):
model_built = 0
time_steps_days = int(local_bm_hyperparameters['time_steps_days'])
epochs = int(local_bm_hyperparameters['epochs'])
batch_size = int(local_bm_hyperparameters['batch_size'])
workers = int(local_bm_hyperparameters['workers'])
optimizer_function = local_bm_hyperparameters['optimizer']
optimizer_learning_rate = local_bm_hyperparameters['learning_rate']
if optimizer_function == 'adam':
optimizer_function = optimizers.Adam(optimizer_learning_rate)
elif optimizer_function == 'ftrl':
optimizer_function = optimizers.Ftrl(optimizer_learning_rate)
losses_list = []
loss_1 = local_bm_hyperparameters['loss_1']
loss_2 = local_bm_hyperparameters['loss_2']
loss_3 = local_bm_hyperparameters['loss_3']
union_settings_losses = [loss_1, loss_2, loss_3]
if 'mape' in union_settings_losses:
losses_list.append(losses.MeanAbsolutePercentageError())
if 'mse' in union_settings_losses:
losses_list.append(losses.MeanSquaredError())
if 'mae' in union_settings_losses:
losses_list.append(losses.MeanAbsoluteError())
if 'm_mape' in union_settings_losses:
losses_list.append(modified_mape())
if 'customized_loss_function' in union_settings_losses:
losses_list.append(customized_loss())
metrics_list = []
metric1 = local_bm_hyperparameters['metrics1']
metric2 = local_bm_hyperparameters['metrics2']
union_settings_metrics = [metric1, metric2]
if 'rmse' in union_settings_metrics:
metrics_list.append(metrics.RootMeanSquaredError())
if 'mse' in union_settings_metrics:
metrics_list.append(metrics.MeanSquaredError())
if 'mae' in union_settings_metrics:
metrics_list.append(metrics.MeanAbsoluteError())
if 'mape' in union_settings_metrics:
metrics_list.append(metrics.MeanAbsolutePercentageError())
l1 = local_bm_hyperparameters['l1']
l2 = local_bm_hyperparameters['l2']
if local_bm_hyperparameters['regularizers_l1_l2'] == 'True':
activation_regularizer = regularizers.l1_l2(l1=l1, l2=l2)
else:
activation_regularizer = None
nof_features_for_training = local_bm_hyperparameters[
'nof_features_for_training']
# creating model
forecaster_in_block = tf.keras.Sequential()
print('creating the ANN model...')
# first layer (DENSE)
if local_bm_hyperparameters['units_layer_1'] > 0:
forecaster_in_block.add(
layers.Dense(
units=local_bm_hyperparameters['units_layer_1'],
activation=local_bm_hyperparameters['activation_1'],
input_shape=(local_bm_hyperparameters['time_steps_days'],
nof_features_for_training),
activity_regularizer=activation_regularizer))
forecaster_in_block.add(
layers.Dropout(
rate=float(local_bm_hyperparameters['dropout_layer_1'])))
# second LSTM layer
if local_bm_hyperparameters[
'units_layer_2'] > 0 and local_bm_hyperparameters[
'units_layer_1'] > 0:
forecaster_in_block.add(
layers.Bidirectional(
layers.LSTM(
units=local_bm_hyperparameters['units_layer_2'],
activation=local_bm_hyperparameters['activation_2'],
activity_regularizer=activation_regularizer,
dropout=float(local_bm_hyperparameters['dropout_layer_2']),
return_sequences=False)))
forecaster_in_block.add(
RepeatVector(local_bm_hyperparameters['repeat_vector']))
# third LSTM layer
if local_bm_hyperparameters['units_layer_3'] > 0:
forecaster_in_block.add(
layers.Bidirectional(
layers.LSTM(
units=local_bm_hyperparameters['units_layer_3'],
activation=local_bm_hyperparameters['activation_3'],
activity_regularizer=activation_regularizer,
dropout=float(local_bm_hyperparameters['dropout_layer_3']),
return_sequences=True)))
if local_bm_hyperparameters['units_layer_4'] == 0:
forecaster_in_block.add(
RepeatVector(local_bm_hyperparameters['repeat_vector']))
# fourth layer (DENSE)
if local_bm_hyperparameters['units_layer_4'] > 0:
forecaster_in_block.add(
layers.Dense(units=local_bm_hyperparameters['units_layer_4'],
activation=local_bm_hyperparameters['activation_4'],
activity_regularizer=activation_regularizer))
forecaster_in_block.add(
layers.Dropout(
rate=float(local_bm_hyperparameters['dropout_layer_4'])))
# final layer
forecaster_in_block.add(
TimeDistributed(layers.Dense(units=nof_features_for_training)))
forecaster_in_block.save(''.join(
[local_bm_settings['models_path'], 'in_block_NN_model_structure_']),
save_format='tf')
forecast_horizon_days = local_bm_settings['forecast_horizon_days']
forecaster_in_block.build(input_shape=(1, forecast_horizon_days + 1,
nof_features_for_training))
forecaster_in_block.compile(optimizer=optimizer_function,
loss=losses_list,
metrics=metrics_list)
forecaster_in_block_json = forecaster_in_block.to_json()
with open(
''.join([
local_bm_settings['models_path'],
'freq_acc_forecaster_in_block.json'
]), 'w') as json_file:
json_file.write(forecaster_in_block_json)
json_file.close()
print(
'build_model function finish (model structure saved in json and ts formats)'
)
return True, model_built
def train(self, local_settings, local_raw_unit_sales, local_model_hyperparameters, local_time_series_not_improved,
raw_unit_sales_ground_truth):
try:
# data normalization
local_forecast_horizon_days = local_settings['forecast_horizon_days']
local_x_train, local_y_train = build_x_y_train_arrays(local_raw_unit_sales, local_settings,
local_model_hyperparameters,
local_time_series_not_improved)
local_forecast_horizon_days = local_settings['forecast_horizon_days']
local_features_for_each_training = 1
print('starting neural network - individual time_serie training')
# building architecture and compiling model_template
# set training parameters
local_time_steps_days = int(local_settings['time_steps_days'])
local_epochs = int(local_model_hyperparameters['epochs'])
local_batch_size = int(local_model_hyperparameters['batch_size'])
local_workers = int(local_model_hyperparameters['workers'])
local_optimizer_function = local_model_hyperparameters['optimizer']
local_optimizer_learning_rate = local_model_hyperparameters['learning_rate']
if local_optimizer_function == 'adam':
local_optimizer_function = optimizers.Adam(local_optimizer_learning_rate)
elif local_optimizer_function == 'ftrl':
local_optimizer_function = optimizers.Ftrl(local_optimizer_learning_rate)
local_losses_list = []
local_loss_1 = local_model_hyperparameters['loss_1']
local_loss_2 = local_model_hyperparameters['loss_2']
local_loss_3 = local_model_hyperparameters['loss_3']
local_union_settings_losses = [local_loss_1, local_loss_2, local_loss_3]
if 'mape' in local_union_settings_losses:
local_losses_list.append(losses.MeanAbsolutePercentageError())
if 'mse' in local_union_settings_losses:
local_losses_list.append(losses.MeanSquaredError())
if 'mae' in local_union_settings_losses:
local_losses_list.append(losses.MeanAbsoluteError())
if 'm_mape' in local_union_settings_losses:
local_losses_list.append(modified_mape())
if 'customized_loss_function' in local_union_settings_losses:
local_losses_list.append(customized_loss())
if 'pinball_loss_function' in local_union_settings_losses:
local_losses_list.append(pinball_function_loss())
local_metrics_list = []
local_metric1 = local_model_hyperparameters['metrics1']
local_metric2 = local_model_hyperparameters['metrics2']
local_union_settings_metrics = [local_metric1, local_metric2]
if 'rmse' in local_union_settings_metrics:
local_metrics_list.append(metrics.RootMeanSquaredError())
if 'mse' in local_union_settings_metrics:
local_metrics_list.append(metrics.MeanSquaredError())
if 'mae' in local_union_settings_metrics:
local_metrics_list.append(metrics.MeanAbsoluteError())
if 'mape' in local_union_settings_metrics:
local_metrics_list.append(metrics.MeanAbsolutePercentageError())
local_l1 = local_model_hyperparameters['l1']
local_l2 = local_model_hyperparameters['l2']
if local_model_hyperparameters['regularizers_l1_l2'] == 'True':
local_activation_regularizer = regularizers.l1_l2(l1=local_l1, l2=local_l2)
else:
local_activation_regularizer = None
# define callbacks, checkpoints namepaths
local_callback1 = cb.EarlyStopping(monitor='loss',
patience=local_model_hyperparameters['early_stopping_patience'])
local_callbacks = [local_callback1]
print('building current model: Mix_Bid_PeepHole_LSTM_Dense_ANN')
local_base_model = tf.keras.Sequential()
# first layer (DENSE)
if local_model_hyperparameters['units_layer_1'] > 0:
# strictly dim 1 of input_shape is ['time_steps_days'] (dim 0 is number of batches: None)
local_base_model.add(layers.Dense(units=local_model_hyperparameters['units_layer_1'],
activation=local_model_hyperparameters['activation_1'],
input_shape=(local_time_steps_days,
local_features_for_each_training),
activity_regularizer=local_activation_regularizer))
local_base_model.add(layers.Dropout(rate=float(local_model_hyperparameters['dropout_layer_1'])))
# second layer
if local_model_hyperparameters['units_layer_2']:
if local_model_hyperparameters['units_layer_1'] == 0:
local_base_model.add(layers.RNN(
PeepholeLSTMCell(units=local_model_hyperparameters['units_layer_2'],
activation=local_model_hyperparameters['activation_2'],
input_shape=(local_time_steps_days,
local_features_for_each_training),
dropout=float(local_model_hyperparameters['dropout_layer_2']))))
else:
local_base_model.add(layers.RNN(
PeepholeLSTMCell(units=local_model_hyperparameters['units_layer_2'],
activation=local_model_hyperparameters['activation_2'],
dropout=float(local_model_hyperparameters['dropout_layer_2']))))
# local_base_model.add(RepeatVector(local_model_hyperparameters['repeat_vector']))
# third layer
if local_model_hyperparameters['units_layer_3'] > 0:
local_base_model.add(layers.Dense(units=local_model_hyperparameters['units_layer_3'],
activation=local_model_hyperparameters['activation_3'],
activity_regularizer=local_activation_regularizer))
local_base_model.add(layers.Dropout(rate=float(local_model_hyperparameters['dropout_layer_3'])))
# fourth layer
if local_model_hyperparameters['units_layer_4'] > 0:
local_base_model.add(layers.RNN(
PeepholeLSTMCell(units=local_model_hyperparameters['units_layer_4'],
activation=local_model_hyperparameters['activation_4'],
dropout=float(local_model_hyperparameters['dropout_layer_4']))))
local_base_model.add(layers.Dense(units=local_forecast_horizon_days))
# build and compile model
local_base_model.build(input_shape=(1, local_time_steps_days, local_features_for_each_training))
local_base_model.compile(optimizer=local_optimizer_function,
loss=local_losses_list,
metrics=local_metrics_list)
# save model architecture (template for specific models)
local_base_model.save(''.join([local_settings['models_path'],
'generic_forecaster_template_individual_ts.h5']))
local_base_model_json = local_base_model.to_json()
with open(''.join([local_settings['models_path'],
'generic_forecaster_template_individual_ts.json']), 'w') as json_file:
json_file.write(local_base_model_json)
json_file.close()
local_base_model.summary()
# training model
local_moving_window_length = local_settings['moving_window_input_length'] + \
local_settings['moving_window_output_length']
# all input data in the correct type
local_x_train = np.array(local_x_train, dtype=np.dtype('float32'))
local_y_train = np.array(local_y_train, dtype=np.dtype('float32'))
local_raw_unit_sales = np.array(local_raw_unit_sales, dtype=np.dtype('float32'))
# specific time_serie models training loop
local_y_pred_list = []
local_nof_time_series = local_settings['number_of_time_series']
remainder = np.array([time_serie for time_serie in range(local_nof_time_series)
if time_serie not in local_time_series_not_improved])
for time_serie in remainder:
# ----------------------key_point---------------------------------------------------------------------
# take note that each loop the weights and internal last states of previous training are conserved
# that's probably save times and (in aggregated or ordered) connected time series will improve results
# ----------------------key_point---------------------------------------------------------------------
print('training time_serie:', time_serie)
local_x, local_y = local_x_train[:, time_serie: time_serie + 1, :], \
local_y_train[:, time_serie: time_serie + 1, :]
local_x = local_x.reshape(local_x.shape[0], local_x.shape[2], 1)
local_y = local_y.reshape(local_y.shape[0], local_y.shape[2], 1)
# training, saving model and storing forecasts
local_base_model.fit(local_x, local_y, batch_size=local_batch_size, epochs=local_epochs,
workers=local_workers, callbacks=local_callbacks, shuffle=False)
local_base_model.save_weights(''.join([local_settings['models_path'],
'/weights_last_year/_individual_ts_',
str(time_serie), '_model_weights_.h5']))
local_x_input = local_raw_unit_sales[time_serie: time_serie + 1, -local_forecast_horizon_days:]
local_x_input = cof_zeros(local_x_input, local_settings)
local_x_input = local_x_input.reshape(1, local_x_input.shape[1], 1)
print('x_input shape:', local_x_input.shape)
local_y_pred = local_base_model.predict(local_x_input)
print('x_input:\n', local_x_input)
print('y_pred shape:', local_y_pred.shape)
local_y_pred = local_y_pred.reshape(local_y_pred.shape[1])
local_y_pred = cof_zeros(local_y_pred, local_settings)
if local_settings['mini_ts_evaluator'] == "True" and \
local_settings['competition_stage'] != 'submitting_after_June_1th_using_1941days':
mini_evaluator = mini_evaluator_submodule()
evaluation = mini_evaluator.evaluate_ts_forecast(
raw_unit_sales_ground_truth[time_serie, -local_forecast_horizon_days:], local_y_pred)
print('ts:', time_serie, 'with cof_zeros ts mse:', evaluation)
else:
print('ts:', time_serie)
print(local_y_pred)
local_y_pred_list.append(local_y_pred)
local_point_forecast_array = np.array(local_y_pred_list)
local_point_forecast_normalized = local_point_forecast_array.reshape(
(local_point_forecast_array.shape[0], local_point_forecast_array.shape[1]))
local_point_forecast = local_point_forecast_normalized
# save points forecast
np.savetxt(''.join([local_settings['others_outputs_path'], 'point_forecast_NN_LSTM_simulation.csv']),
local_point_forecast, fmt='%10.15f', delimiter=',', newline='\n')
print('point forecasts saved to file')
print('submodule for build, train and forecast time_serie individually finished successfully')
return True
except Exception as submodule_error:
print('train model and forecast individual time_series submodule_error: ', submodule_error)
logger.info('error in training and forecast-individual time_serie schema')
logger.error(str(submodule_error), exc_info=True)
return False