def train(pipeline,
output_dir,
train_args_dict,
train_features=None,
eval_features=None,
metadata=None):
if not train_features:
train_features = (pipeline
| 'ReadTrain' >> io.LoadFeatures(
os.path.join(output_dir, 'features_train*')))
if not eval_features:
eval_features = (pipeline
| 'ReadEval' >> io.LoadFeatures(
os.path.join(output_dir, 'features_eval*')))
if not metadata:
metadata = (pipeline
| 'ReadMetadata' >> io.LoadMetadata(
os.path.join(output_dir, METADATA_FILE_NAME)))
trained_model, results = ((train_features, eval_features)
| 'Train' >> ml.Train(**train_args_dict))
trained_model | 'SaveModel' >> io.SaveModel(
os.path.join(output_dir, 'saved_model'))
results | io.SaveTrainingJobResult(
os.path.join(output_dir, 'train_results'))
return trained_model, results
def train(pipeline, train_features=None, eval_features=None, metadata=None):
if not train_features:
train_features = (
pipeline
| 'ReadTrain'
>> io.LoadFeatures(os.path.join(args.output_dir, 'features_train*')))
if not eval_features:
eval_features = (
pipeline
| 'ReadEval'
>> io.LoadFeatures(os.path.join(args.output_dir, 'features_eval*')))
trained_model, results = ((train_features, eval_features)
| ml.Train(**get_train_parameters(metadata)))
trained_model | 'SaveModel' >> io.SaveModel(os.path.join(args.output_dir,
'saved_model'))
results | io.SaveTrainingJobResult(os.path.join(args.output_dir,
'train_results'))
return trained_model, results
