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Change metric logging method in custom script training #116

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Change metric logging method in custom script training #116

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17 changes: 9 additions & 8 deletions Custom_Script/scripts/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -82,20 +82,21 @@ def run(input_data):
mape = np.mean(np.abs((actuals - preds) / actuals) * 100)

# 6.0 Log metrics
current_run.log(model_name + '_mse', mse)
current_run.log(model_name + '_rmse', rmse)
current_run.log(model_name + '_mae', mae)
current_run.log(model_name + '_mape', mape)
current_run.log_row('mape', **ts_id_dict, value=mape, model_name=model_name)
current_run.log_row('rmse', **ts_id_dict, value=rmse, model_name=model_name)
current_run.log_row('mae', **ts_id_dict, value=mae, model_name=model_name)
current_run.log_row('mse', **ts_id_dict, value=mse, model_name=model_name)

# 7.0 Train model with full dataset
forecaster.fit(data)

# 8.0 Save the forecasting pipeline
joblib.dump(forecaster, filename=os.path.join('./outputs/', model_name))
# 8.0 Save the forecasting pipeline and upload to workspace
model_path = os.path.join('./outputs/', model_name)
joblib.dump(forecaster, filename=model_path)
current_run.upload_file(model_name, model_path)

# 9.0 Register the model to the workspace
# Uses the values in the timeseries id columns from the first row of data to form tags for the model
current_run.upload_file(model_name, os.path.join('./outputs/', model_name))
ts_id_dict = {id_col: str(data[id_col].iloc[0]) for id_col in args.timeseries_id_columns}
tags_dict = {**ts_id_dict, 'ModelType': args.model_type}
current_run.register_model(model_path=model_name, model_name=model_name,
Expand All @@ -109,7 +110,7 @@ def run(input_data):
result['model_name'] = model_name
result['start_date'] = str(start_datetime)
result['end_date'] = str(end_datetime)
result['duration'] = str(end_datetime-start_datetime)
result['duration'] = str(end_datetime - start_datetime)
result['mse'] = mse
result['rmse'] = rmse
result['mae'] = mae
Expand Down