def test_can_read_from_file_and_run_ann_and_plot_and_save_results(self):
# load in data settings about that data set from a ini file
data_set1_settings = DataSetSettings(data_set_settings_ini)
# create meter groups from csv data files
east_allers = MeterGroup('east_allers', 'flow', allers_flow_csv)
roodloft_turbidity = MeterGroup('roodloft_turbidity', 'turbidity', roodloft_turbidity_csv)
# set meter group settings
east_allers.set_meter_group_settings(data_set1_settings.meter_groups())
roodloft_turbidity.set_meter_group_settings(data_set1_settings.meter_groups())
# create data packer and choose meter groups for input and target
data_packer1 = DataPacker()
data_packer1.add_target_group(roodloft_turbidity)
data_packer1.add_training_group(east_allers)
data_packer1.add_training_group(roodloft_turbidity)
# package data for machine learning
data_package1 = data_packer1.package(data_set1_settings.packer_input(), data_set1_settings.packer_target())
# create machine learning obj to run ANN
ann1 = MachineLearning(data_package1.training_input, data_package1.training_target,
data_package1.validation_input, data_package1.validation_target,
data_package1.get_min_max_list(), data_set1_settings.ann())
# setup: ml_type, package_name, network_layout, layer_func
ann1.setup()
# run: max_epochs, stop_function, training_goal, verbose_settings
ann1.run(max_epochs=50, trains_per_validation_run=10)
data_collector1 = DataCollector(ann1, data_package1, data_set1_settings)
data_collector1.plot_training_and_validation_errors(ann1.get_error_list())
#data_collector1.plot(data_set1_settings.plot1)
#data_collector1.store(folder_location, data_set1_settings.store1)
print()
def run_main():
# load in data settings about a data set from a ini file, ini file must be in same directory as this file
data_settings = DataSetSettings(os.path.join(os.path.dirname(os.getcwd()), ini_file))
data_set_files = data_settings.get_data_set_files()
meter_groups = []
for meter_file in data_set_files:
# create meter groups from csv data
meter_groups.append(MeterGroup(meter_file[0], meter_file[1], meter_file[2]))
# set meter group settings
meter_groups[-1].set_meter_group_settings(data_settings.meter_groups())
# create data packer and add meter groups
data_packer1 = DataPacker(meter_groups)
for time_horizon in range(1, 2):
# package data for machine learning
data_package1 = data_packer1.package_data(data_settings.packer_input(), data_settings.packer_target(),
forecast_horizon_override=time_horizon)
print('Data Packaged')
if False:
data = data_package1.training_input.T
# data = data_package1.validation_input.T
meta = data_package1.training_input_meta[0]
embedding_search_fnn(data, meta, go_to=15, threshold=50, ratio=0.001)
if False:
for ann_count in range(10):
with open(os.path.join(os.path.dirname(os.getcwd()), log_file), 'a') as f:
f.write('Forecast Horizon: {} ANN Count: {}\n'.format(time_horizon, ann_count))
while True:
# create machine learning obj to run ANN
ann1 = MachineLearning('ANN', data_package1, data_settings.ann())
print('Machine Learning Obj Initialized')
# run: ml_type, package_name, network_layout, layer_func, max_epochs, trains_per_validation_run,
# max_worse_validations stop_function, training_goal, verbose_settings
#code, msg = ann1.run(network_layout='{}, 1'.format(time_horizon))
code, msg = ann1.run()
if code == 1: # Failed to learn
print(msg)
with open(os.path.join(os.path.dirname(os.getcwd()), log_file), 'a') as f:
f.write(msg + '\n')
else:
print(msg)
with open(os.path.join(os.path.dirname(os.getcwd()), log_file), 'a') as f:
f.write(msg + '\n')
print('Machine Learning Obj Finished Run')
data_collector1 = DataCollector(ann1, data_package1, data_settings)
pickle_file = 'data\\Hillersdon\\hill-exp{}_p{}-h50-t{}-r{}.pickle'.format(
experiment_id, process_id, time_horizon, ann_count)
with open(pickle_file, 'wb') as pf:
pickle.dump(data_collector1, pf, pickle.HIGHEST_PROTOCOL)
print('Data Collector Pickled')
with open(os.path.join(os.path.dirname(os.getcwd()), log_file), 'a') as f:
f.write('Run Count: {}, Pickled: {}\n'.format(str(len(data_collector1.ml_obj.inbuilt_errors)), pickle_file))
f.write('Training Errors: {}\n'.format(get_last_errors(ann1.training_errors)))
f.write('Validation Errors: {}\n'.format(get_last_errors(ann1.validation_errors)))
predictions = data_collector1.ml_obj.best_network[0].sim(data_collector1.ml_obj.training_input)
observations = data_collector1.ml_obj.training_target
train_errors_str = get_all_error_metrics(observations, predictions, data_collector1.data_package)
predictions = data_collector1.ml_obj.best_network[0].sim(data_collector1.ml_obj.validation_input)
observations = data_collector1.ml_obj.validation_target
val_errors_str = get_all_error_metrics(observations, predictions, data_collector1.data_package)
with open(os.path.join(os.path.dirname(os.getcwd()), results_file), 'a') as f:
f.write('Run Count: {}, Pickled: {}\n'.format(str(len(data_collector1.ml_obj.inbuilt_errors)), pickle_file))
f.write('Training Errors, {}\n'.format(train_errors_str))
f.write('Validation Errors, {}\n'.format(val_errors_str))
#data_collector1.plot_all()
break
print('Done!')