def test_fhmm_correctness(self): elec = self.dataset.buildings[1].elec fhmm = FHMM() fhmm.train(elec) mains = elec.mains() output = HDFDataStore('output.h5', 'w') fhmm.disaggregate(mains, output, sample_period=1) for meter in range(2, 4): df1 = output.store.get('/building1/elec/meter{}'.format(meter)) df2 = self.dataset.store.store.get( '/building1/elec/meter{}'.format(meter)) self.assertEqual((df1 == df2).sum().values[0], len(df1.index)) self.assertEqual(len(df1.index), len(df2.index)) output.close() remove("output.h5")
def fhmm(dataset_path, train_building, train_start, train_end, val_building, val_start, val_end, test_building, test_start, test_end, meter_key, sample_period): # Start tracking time start = time.time() # Prepare dataset and options # print("========== OPEN DATASETS ============") dataset_path = dataset_path train = DataSet(dataset_path) train.set_window(start=train_start, end=train_end) val = DataSet(dataset_path) val.set_window(start=val_start, end=val_end) test = DataSet(dataset_path) test.set_window(start=test_start, end=test_end) train_building = train_building test_building = test_building meter_key = meter_key sample_period = sample_period train_elec = train.buildings[train_building].elec val_elec = val.buildings[val_building].elec test_elec = test.buildings[test_building].elec appliances = [meter_key] selected_meters = [train_elec[app] for app in appliances] selected_meters.append(train_elec.mains()) selected = MeterGroup(selected_meters) fhmm = FHMM() # print("========== TRAIN ============") fhmm.train(selected, sample_period=sample_period) # print("========== DISAGGREGATE ============") # Validation val_disag_filename = 'disag-out-val.h5' output = HDFDataStore(val_disag_filename, 'w') fhmm.disaggregate(val_elec.mains(), output_datastore=output) output.close() # Test test_disag_filename = 'disag-out-test.h5' output = HDFDataStore(test_disag_filename, 'w') fhmm.disaggregate(test_elec.mains(), output_datastore=output) output.close() # print("========== RESULTS ============") # Validation result_val = DataSet(val_disag_filename) res_elec_val = result_val.buildings[val_building].elec rpaf_val = metrics.recall_precision_accuracy_f1(res_elec_val[meter_key], val_elec[meter_key]) val_metrics_results_dict = { 'recall_score': rpaf_val[0], 'precision_score': rpaf_val[1], 'accuracy_score': rpaf_val[2], 'f1_score': rpaf_val[3], 'mean_absolute_error': metrics.mean_absolute_error(res_elec_val[meter_key], val_elec[meter_key]), 'mean_squared_error': metrics.mean_square_error(res_elec_val[meter_key], val_elec[meter_key]), 'relative_error_in_total_energy': metrics.relative_error_total_energy(res_elec_val[meter_key], val_elec[meter_key]), 'nad': metrics.nad(res_elec_val[meter_key], val_elec[meter_key]), 'disaggregation_accuracy': metrics.disaggregation_accuracy(res_elec_val[meter_key], val_elec[meter_key]) } # Test result = DataSet(test_disag_filename) res_elec = result.buildings[test_building].elec rpaf = metrics.recall_precision_accuracy_f1(res_elec[meter_key], test_elec[meter_key]) test_metrics_results_dict = { 'recall_score': rpaf[0], 'precision_score': rpaf[1], 'accuracy_score': rpaf[2], 'f1_score': rpaf[3], 'mean_absolute_error': metrics.mean_absolute_error(res_elec[meter_key], test_elec[meter_key]), 'mean_squared_error': metrics.mean_square_error(res_elec[meter_key], test_elec[meter_key]), 'relative_error_in_total_energy': metrics.relative_error_total_energy(res_elec[meter_key], test_elec[meter_key]), 'nad': metrics.nad(res_elec[meter_key], test_elec[meter_key]), 'disaggregation_accuracy': metrics.disaggregation_accuracy(res_elec[meter_key], test_elec[meter_key]) } # end tracking time end = time.time() time_taken = end - start # in seconds # model_result_data = { # 'algorithm_name': 'FHMM', # 'datapath': dataset_path, # 'train_building': train_building, # 'train_start': str(train_start.date()) if train_start != None else None , # 'train_end': str(train_end.date()) if train_end != None else None , # 'test_building': test_building, # 'test_start': str(test_start.date()) if test_start != None else None , # 'test_end': str(test_end.date()) if test_end != None else None , # 'appliance': meter_key, # 'sampling_rate': sample_period, # # 'algorithm_info': { # 'options': { # 'epochs': None # }, # 'hyperparameters': { # 'sequence_length': None, # 'min_sample_split': None, # 'num_layers': None # }, # 'profile': { # 'parameters': None # } # }, # # 'metrics': metrics_results_dict, # # 'time_taken': format(time_taken, '.2f'), # } model_result_data = { 'val_metrics': val_metrics_results_dict, 'test_metrics': test_metrics_results_dict, 'time_taken': format(time_taken, '.2f'), 'epochs': None, } # Close digag_filename result.store.close() result_val.store.close() # Close Dataset files train.store.close() val.store.close() test.store.close() return model_result_data