def main():
storage_name = '104'
do_preprocessing = True
train_new_model = True
do_prediction = True
plot_results = True
plot_baseline = False
use_temporal_features = True
passenger_amount = True
test_set_length = 24 * 28
if do_preprocessing:
sequence_length = 24 * 7
path_to_data_folder = 'raw_data/'
difference_length = 24 * 7
use_difference = True
bus_nr = '150'
bus_direction = True
pp = preprocess(storage_name, sequence_length, test_set_length,
path_to_data_folder, difference_length, use_difference,
bus_nr, bus_direction, passenger_amount,
use_temporal_features)
# get and print some preprocessorially defined hyperparameters
utils = Utils(storage_name)
#print(utils.partition['test'])
for key, value in utils.hyper_params.items():
print(f'{key}:\t{value}')
# Parameters
train_params = {'batch_size': 64, 'shuffle': True, 'num_workers': 0}
max_epochs = 4
test_params = {'batch_size': 2, 'shuffle': False, 'num_workers': 0}
n_samples = 32 # number of samples when sampling the predictions
n_std = 2 # number of standard deviations away from the mean to set confidence interval
if use_temporal_features:
input_dimension = 41 # number of features
else:
input_dimension = 10
hidden_dimension = 128 # dimension of hidden lstm layer
linear_dimension = 64 # dimension of output of first linear layer
output_dimension = 10 # output dimension of network
sequence_length = utils.hyper_params['sequence_length']
difference_length = utils.hyper_params['difference_length']
elbo_sample_nbr = 3 # The number of times of the weight-sampling and predictions done to gather the loss
net = LSTM_Net(input_dim=input_dimension,
hidden_dim=hidden_dimension,
linear_dim=linear_dimension,
sequence_length=sequence_length,
output_dim=output_dimension)
#print(net)
stOps = StorageOps(storage_name)
if train_new_model:
trained_model, train_losses, test_losses = train_model(
model=net,
max_epochs=max_epochs,
params=train_params,
partition=utils.partition,
elbo_sample_nbr=elbo_sample_nbr,
)
stOps.store_model(trained_model, train_losses, test_losses,
input_dimension, hidden_dimension, linear_dimension,
output_dimension)
else:
trained_model, train_losses, test_losses, input_dimension, hidden_dimension, linear_dimension, output_dimension = stOps.load_model(
)
print(input_dimension, hidden_dimension, linear_dimension,
output_dimension)
predicting = Predict(name=storage_name,
trained_model=trained_model,
difference_length=difference_length,
test_set_IDs=utils.partition['test'],
params=test_params,
n_samples=n_samples,
n_std=n_std,
passenger_amount=passenger_amount)
if do_prediction:
predicting.predict()
stOps.save_targets_means_stds(predicting.targets, predicting.means,
predicting.stds)
stOps.save_mse_and_accuracy(
predicting.get_MSE(predicting.targets, predicting.means),
predicting.get_accuracy(predicting.targets, predicting.means,
predicting.stds))
else:
targets, means, stds = stOps.load_targets_means_stds()
stOps.save_mse_and_accuracy(
predicting.get_MSE(targets, means),
predicting.get_accuracy(targets, means, stds))
if plot_results:
res = Results(storage_name, passenger_amount)
res.plot_trend_given_time(7)
res.plot_trend_given_stop(0)
res.plot_predictions_all_stops(7)
res.plot_predictions_given_stop(0)
'''for i in range(24):
res.plot_trend_given_time(i)
for i in range(10):
res.plot_trend_given_stop(i)
for i in range(test_set_length):
res.plot_predictions_all_stops(i)
for i in range(10):
res.plot_predictions_given_stop(i)
res.plot_predictions_given_stop_48h(i)
res.plot_training()'''
res.print_performance_measures()
if plot_baseline:
baseline = Baseline(storage_name)
baseline.get_predictions_lower_upper()
baseline.get_mse_and_acc()
for hour in range(24):
baseline.plot_predictions_all_stops(hour)
for stop in range(10):
baseline.plot_predictions_given_stop(stop)
baseline.plot_predictions_given_stop_48h(stop)
print(baseline.mse)
print(baseline.acc)
