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Deeptim/customscript #119

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082a606
update many model script to create child runs
deeptim123 Feb 9, 2021
c48f5dd
changes to customscript
deeptim123 Feb 9, 2021
f27360a
changes wrt scale
deeptim123 Feb 9, 2021
5dc8f67
child_run
deeptim123 Feb 11, 2021
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2 changes: 1 addition & 1 deletion Custom_Script/02_CustomScript_Training_Pipeline.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -243,7 +243,7 @@
" entry_script='train.py',\n",
" mini_batch_size=\"1\",\n",
" run_invocation_timeout=timeout,\n",
" error_threshold=10,\n",
" error_threshold=-1,\n",
" output_action=\"append_row\",\n",
" environment=train_env,\n",
" process_count_per_node=processes_per_node,\n",
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158 changes: 97 additions & 61 deletions Custom_Script/scripts/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -59,67 +59,103 @@ def run(input_data):
train = data[:-args.test_size]
test = data[-args.test_size:]

# 3.0 Create and fit the forecasting pipeline
# The pipeline will drop unhelpful features, make a calendar feature, and make lag features
lagger = SimpleLagger(args.target_column, lag_orders=[1, 2, 3, 4])
transform_steps = [('column_dropper', ColumnDropper(args.drop_columns)),
('calendar_featurizer', SimpleCalendarFeaturizer()), ('lagger', lagger)]
forecaster = SimpleForecaster(transform_steps, LinearRegression(), args.target_column, args.timestamp_column)
forecaster.fit(train)
print('Featurized data example:')
print(forecaster.transform(train).head())

# 4.0 Get predictions on test set
forecasts = forecaster.forecast(test)
compare_data = test.assign(forecasts=forecasts).dropna()

# 5.0 Calculate accuracy metrics for the fit
mse = mean_squared_error(compare_data[args.target_column], compare_data['forecasts'])
rmse = np.sqrt(mse)
mae = mean_absolute_error(compare_data[args.target_column], compare_data['forecasts'])
actuals = compare_data[args.target_column].values
preds = compare_data['forecasts'].values
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)

# 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))

# 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,
model_framework=args.model_type, tags=tags_dict)

# 10.0 Add data to output
end_datetime = datetime.datetime.now()
result.update(ts_id_dict)
result['model_type'] = args.model_type
result['file_name'] = file_name
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['mse'] = mse
result['rmse'] = rmse
result['mae'] = mae
result['mape'] = mape
result['index'] = idx
result['num_models'] = len(input_data)
result['status'] = current_run.get_status()

print('ending (' + csv_file_path + ') ' + str(end_datetime))
result_list.append(result)
child_run = None
try:
child_run = current_run.child_run(name=model_name)

# 3.0 Create and fit the forecasting pipeline
# The pipeline will drop unhelpful features, make a calendar feature, and make lag features
lagger = SimpleLagger(args.target_column, lag_orders=[1, 2, 3, 4])
transform_steps = [('column_dropper', ColumnDropper(args.drop_columns)),
('calendar_featurizer', SimpleCalendarFeaturizer()), ('lagger', lagger)]
forecaster = SimpleForecaster(transform_steps, LinearRegression(), args.target_column,
args.timestamp_column)
forecaster.fit(train)
print('Featurized data example:')
print(forecaster.transform(train).head())

# 4.0 Get predictions on test set
forecasts = forecaster.forecast(test)
compare_data = test.assign(forecasts=forecasts).dropna()

# 5.0 Calculate accuracy metrics for the fit
mse = mean_squared_error(compare_data[args.target_column], compare_data['forecasts'])
rmse = np.sqrt(mse)
mae = mean_absolute_error(compare_data[args.target_column], compare_data['forecasts'])
actuals = compare_data[args.target_column].values
preds = compare_data['forecasts'].values
mape = np.mean(np.abs((actuals - preds) / actuals) * 100)

# 6.0 Log metrics
child_run.log(model_name + '_mse', mse)
child_run.log(model_name + '_rmse', rmse)
child_run.log(model_name + '_mae', mae)
child_run.log(model_name + '_mape', mape)

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

# Simulating the 3 minutes run to test concurrency
import time
time.sleep(180)

# 8.0 Save the forecasting pipeline
joblib.dump(forecaster, filename=os.path.join('./outputs/', model_name))

# 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
child_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}
tags_dict.update({'InputData': os.path.basename(csv_file_path)})
tags_dict.update({'StepRunId': current_run.id})
tags_dict.update({'RunId': current_run.parent.id})
child_run.register_model(model_path=model_name, model_name=model_name,
model_framework=args.model_type, tags=tags_dict)

child_run.complete()
# 10.0 Add data to output
end_datetime = datetime.datetime.now()
result.update(ts_id_dict)
result['model_type'] = args.model_type
result['file_name'] = file_name
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['mse'] = mse
result['rmse'] = rmse
result['mae'] = mae
result['mape'] = mape
result['index'] = idx
result['num_models'] = len(input_data)
result['status'] = child_run.get_status()
result['run_id'] = str(child_run.id)

print('ending (' + csv_file_path + ') ' + str(end_datetime))
result_list.append(result)
except Exception:
if child_run and child_run.get_status() != 'Completed':
child_run.fail()
result['model_type'] = args.model_type
end_datetime = datetime.datetime.now()
result['file_name'] = file_name
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['mse'] = str(None)
result['rmse'] = str(None)
result['mae'] = str(None)
result['mape'] = str(None)
result['index'] = idx
result['num_models'] = len(input_data)
if child_run:
result['status'] = child_run.get_status()
result['run_id'] = str(child_run.id)
else:
result['status'] = 'Failed'
result['run_id'] = str(None)

# Data returned by this function will be available in parallel_run_step.txt
return pd.DataFrame(result_list)