AnandInguva commented on code in PR #27430:
URL: https://github.com/apache/beam/pull/27430#discussion_r1258904482
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sdks/python/apache_beam/examples/ml_transform/ml_transform_basic.py:
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@@ -61,49 +61,75 @@ def parse_args():
return parser.parse_known_args()
-def run(args):
- data = [
- dict(x=["Let's", "go", "to", "the", "park"]),
- dict(x=["I", "enjoy", "going", "to", "the", "park"]),
- dict(x=["I", "enjoy", "reading", "books"]),
- dict(x=["Beam", "can", "be", "fun"]),
- dict(x=["The", "weather", "is", "really", "nice", "today"]),
- dict(x=["I", "love", "to", "go", "to", "the", "park"]),
- dict(x=["I", "love", "to", "read", "books"]),
- dict(x=["I", "love", "to", "program"]),
- ]
-
+def preprocess_data_for_ml_training(train_data, artifact_mode, args):
with beam.Pipeline() as p:
- input_data = p | beam.Create(data)
-
- # arfifacts produce mode.
- input_data |= (
- 'MLTransform' >> MLTransform(
+ input_data = (p | "CreateData" >> beam.Create(train_data))
+ transformed_data_pcoll = (
+ input_data
+ | 'MLTransform' >> MLTransform(
artifact_location=args.artifact_location,
- artifact_mode=ArtifactMode.PRODUCE,
+ artifact_mode=artifact_mode,
).with_transform(ComputeAndApplyVocabulary(
columns=['x'])).with_transform(TFIDF(columns=['x'])))
- # _ = input_data | beam.Map(logging.info)
+ _ = transformed_data_pcoll | beam.Map(logging.info)
+ return transformed_data_pcoll
Review Comment:
Yes, basically return here is a moot. Thought it would look good on the
function
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