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)