def test_build_samples_from_2d(self):
data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
expected_samples_2 = [[1, 2], [4, 5], [7, 8]]
expected_samples_3 = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
actual_data_2 = build_samples(data, 2).tolist()
actual_data_3 = build_samples(data, 3).tolist()
self.assertListEqual(actual_data_2, expected_samples_2)
self.assertListEqual(actual_data_3, expected_samples_3)
def pre_processing(data_frame):
if run_config['scaling'] == 'min_max':
samples = Scaler(MinMaxScaler, fit_mode=run_config['fit_mode']).fit_transform(data_frame.to_numpy())
else:
samples = data_frame.to_numpy()
return build_samples(samples, target_sample_length=config['input_size'], target_dimensions=3)
def pre_processing_y(data_frame):
numpy_data = data_frame.to_numpy()
numpy_data = numpy_data[config['prediction_shift']:, :]
samples = build_samples(numpy_data.flatten(), config['output_size'], target_dimensions=3)
if run_config['scaling'] is 'min_max':
samples = Scaler(MinMaxScaler, fit_mode=run_config['fit_mode']).fit_transform(numpy_data)
return samples
def pre_processing(data_frame: DataFrame):
samples = build_samples(data_frame.to_numpy().flatten(),
config['input_size'])
if run_config['fit_mode'] == 'train':
return train_scaler.transform(samples)
else:
return Scaler(
MinMaxScaler,
fit_mode=run_config['fit_mode']).fit_transform(samples)
def pre_processing(data_frame):
samples = build_samples(data_frame.to_numpy().flatten(),
config['input_size'],
target_dimensions=3)
if run_config['scaling'] == 'min_max':
samples = Scaler(MinMaxScaler,
fit_mode=run_config['fit_mode']).fit_transform(
data_frame.to_numpy())
return samples
def run_callable(run_config: dict):
def pre_processing(data_frame: DataFrame):
samples = build_samples(data_frame.to_numpy().flatten(),
config['input_size'])
if run_config['fit_mode'] == 'train':
return train_scaler.transform(samples)
else:
return Scaler(
MinMaxScaler,
fit_mode=run_config['fit_mode']).fit_transform(samples)
experiment.print('Loading data')
bearing_dataset = load_data(run_config['data_set'])
data_frames = bearing_dataset.as_dict(column=run_config['data_column'])
train_samples = build_samples(data_frames['train'].to_numpy().flatten(),
config['input_size'])
fit_mode = 'per_feature' if run_config[
'fit_mode'] == 'train' else run_config['fit_mode']
train_scaler = Scaler(MinMaxScaler, fit_mode=fit_mode)
train_samples = train_scaler.fit_transform(train_samples)
experiment.print('Fitting model')
history = model.fit(
train_samples,
train_samples,
verbose=0,
epochs=config['epochs'],
callbacks=experiment.keras_callbacks(),
validation_split=config['validation_split'],
shuffle=config['shuffle'],
)
experiment.log_history(history)
experiment.log_keras_model(model)
experiment.log_keras_predictions(
model=model,
data_frames=data_frames,
pre_processing=pre_processing,
)
