from data.access import DataPipeLine
import os
# [1, 12, 55, 80, 17, 77, 71, 6, 28]
detail_wgs = [4891, 2426, 2363, 2456, 2447, 2453, 2451, 2448, 2455, 2454, 2764, 2452, 2444, 2443, 2427,
2428, 2449, 2399, 2457, 2437, 2419, 2436, 2450]
dic = dict.fromkeys(detail_wgs)
dic2 = dict.fromkeys(detail_wgs)
pipeline = DataPipeLine(ZielWarengruppen=[55])
simulation_data = pipeline.get_regression_data()
print([df.shape for df in simulation_data])
for detail_wg in detail_wgs:
print('Starte mit Warengruppe', detail_wg)
simulation_params = {
'ZielWarengruppen': [55],
'DetailWarengruppe': [detail_wg]
}
try:
pipeline = DataPipeLine(**simulation_params)
simulation_data = pipeline.get_regression_data()
dic[detail_wg] = [df.shape for df in simulation_data]
except ValueError: # Nach Filtern keine Daten mehr übrig. Erzeugt ValueError bei max(Absatzjahre)
print('\t\tDas hat nicht geklappt')
'epochs': 50,
'batch_size': 512
}
regression_params = {
'InputDirectory': os.path.join('files', 'raw'),
'OutputDirectory': os.path.join('files', 'prepared'),
'ZielWarengruppen': [warengruppe],
'StatStateCategoricals': {
'MHDgroup': 7,
'Detailwarengruppe': None,
'Einheit': None,
'Markt': 6
},
}
pipeline = DataPipeLine(**regression_params)
lab, dyn, stat, split_helper = pipeline.get_regression_data()
train_data, test_data = split_np_arrays(lab, dyn, stat, split_helper)
params.update({
'steps_per_epoch':
int(train_data[1].shape[0] / params['batch_size']),
'val_steps_per_epoch':
int(test_data[1].shape[0] / params['batch_size']),
'dynamic_state_shape':
dyn.shape[2],
'static_state_shape':
stat.shape[1],
'Name':
'02RegWG' + str(warengruppe)
})
dataset = create_dataset(*train_data[:3], params)
val_dataset = create_dataset(*test_data[:3], params)