[GitHub] [beam] AnandInguva commented on a diff in pull request #27430: Refactor MLTransform basic example

["AnandInguva (via GitHub)" <gi...@apache.org>]

AnandInguva commented on code in PR #27430:
URL: https://github.com/apache/beam/pull/27430#discussion_r1258904482


##########
sdks/python/apache_beam/examples/ml_transform/ml_transform_basic.py:
##########
@@ -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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