FD_predictions = LSTMIMO.predict(concat_input)
FD_eval_df = create_evaluation_df(FD_predictions, test_inputs, HORIZON,
y_scaler)
preds, actuals = format_output(FD_eval_df)
preds = preds[np.where(preds.index.hour == 0)[0][0]:][::24]
actuals = actuals[np.where(actuals.index.hour == 0)[0][0]:][::24]
full = actuals.merge(preds,
how='inner',
left_index=True,
right_index=True,
suffixes=('_actuals', '_preds'))
full.to_csv('./results/' + dset + '/' + wandb.run.name + '_' + str(i) +
'.csv')
preds = flatten(preds.values.tolist())
actuals = flatten(actuals.values.tolist())
mae = validation(preds, actuals, 'MAE')
mape = validation(preds, actuals, 'MAPE')
rmse = validation(preds, actuals, 'RMSE')
#print('rmse {}'.format(rmse))
metrics.loc[i] = pd.Series({
'mae': mae,
'mape': mape,
'rmse': rmse,
'B': names[i]
})
wandb.log({"mape": metrics.mape.mean()})
wandb.log({"rmse": metrics.rmse.mean()})
wandb.log({"mae": metrics.mae.mean()})
model_path = './models/' + dset + '_models/local/' + names[
i] + '_' + wandb.run.name
save_model(LSTMIMO, model_path)
batch_size=BATCHSIZE,
epochs=MAX_EPOCHS,
validation_split=0.15,
callbacks=[early_stopping,
WandbCallback()],
verbose=1)
concat_input = tf.concat([test_inputs['X'], test_inputs['X2']], axis=2)
FD_predictions = LSTMIMO.predict(concat_input)
FD_eval_df = create_evaluation_df(FD_predictions, test_inputs, HORIZON,
y_scaler)
FD_eval_df.index = pd.to_datetime(FD_eval_df['timestamp'])
FD_eval_df = FD_eval_df[np.where(
FD_eval_df.index.hour == 0)[0][0]:][::24]
FD_eval_df.to_csv('./results/' + dset + '/local/' + wandb.run.name +
'_' + str(i) + '.csv')
mae = validation(FD_eval_df['prediction'], FD_eval_df['actual'], 'MAE')
mape = validation(FD_eval_df['prediction'], FD_eval_df['actual'],
'MAPE')
rmse = validation(FD_eval_df['prediction'], FD_eval_df['actual'],
'RMSE')
#print('rmse {}'.format(rmse))
metrics.loc[i] = pd.Series({
'mae': mae,
'mape': mape,
'rmse': rmse,
'B': names[i]
})
wandb.log({"mape": metrics.mape.mean()})
wandb.log({"rmse": metrics.rmse.mean()})
wandb.log({"mae": metrics.mae.mean()})
model_path = './models/' + dset + '_models/local/' + names[
