Add suffix for vocab files in tft transforms (apache#29720)

* Add suffix for vocab files

* add test

* default params to None similar to TFT

* fix lint

* Update tft.py

sdks/python/apache_beam/ml/transforms/tft.py

@@ -172,8 +172,8 @@ def __init__(
       num_oov_buckets:  Any lookup of an out-of-vocabulary token will return a
         bucket ID based on its hash if `num_oov_buckets` is greater than zero.
         Otherwise it is assigned the `default_value`.
-      vocab_filename: The file name for the vocabulary file. If not provided,
-        the default name would be `compute_and_apply_vocab'
+      vocab_filename: The file name for the vocabulary file. The vocab file
+        will be suffixed with the column name.
         NOTE in order to make your pipelines resilient to implementation
         details please set `vocab_filename` when you are using
         the vocab_filename on a downstream component.
@@ -183,8 +183,7 @@ def __init__(
     self._top_k = top_k
     self._frequency_threshold = frequency_threshold
     self._num_oov_buckets = num_oov_buckets
-    self._vocab_filename = vocab_filename if vocab_filename else (
-        'compute_and_apply_vocab')
+    self._vocab_filename = vocab_filename
     self._name = name
     self.split_string_by_delimiter = split_string_by_delimiter
 
@@ -196,14 +195,17 @@ def apply_transform(
       data = self._split_string_with_delimiter(
           data, self.split_string_by_delimiter)
 
+    vocab_filename = self._vocab_filename
+    if vocab_filename:
+      vocab_filename = vocab_filename + f'_{output_column_name}'
     return {
         output_column_name: tft.compute_and_apply_vocabulary(
             x=data,
             default_value=self._default_value,
             top_k=self._top_k,
             frequency_threshold=self._frequency_threshold,
             num_oov_buckets=self._num_oov_buckets,
-            vocab_filename=self._vocab_filename,
+            vocab_filename=vocab_filename,
             name=self._name)
     }
 
@@ -535,7 +537,7 @@ def __init__(
       ngram_range: Tuple[int, int] = (1, 1),
       ngrams_separator: Optional[str] = None,
       compute_word_count: bool = False,
-      key_vocab_filename: str = 'key_vocab_mapping',
+      key_vocab_filename: Optional[str] = None,
       name: Optional[str] = None,
   ):
     """
@@ -558,7 +560,9 @@ def __init__(
       compute_word_count: A boolean that specifies whether to compute
         the unique word count over the entire dataset. Defaults to False.
       key_vocab_filename: The file name for the key vocabulary file when
-        compute_word_count is True.
+        compute_word_count is True. If empty, a file name 
+        will be chosen based on the current scope. If provided, the vocab
+        file will be suffixed with the column name.
       name: A name for the operation (optional).
 
     Note that original order of the input may not be preserved.
@@ -585,10 +589,14 @@ def apply_transform(self, data: tf.SparseTensor, output_col_name: str):
           data, self.split_string_by_delimiter)
     output = tft.bag_of_words(
         data, self.ngram_range, self.ngrams_separator, self.name)
-    # word counts are written to the key_vocab_filename
-    self.compute_word_count_fn(data, self.key_vocab_filename)
+    # word counts are written to the file only if compute_word_count is True
+    key_vocab_filename = self.key_vocab_filename
+    if key_vocab_filename:
+      key_vocab_filename = key_vocab_filename + f'_{output_col_name}'
+    self.compute_word_count_fn(data, key_vocab_filename)
     return {output_col_name: output}
 
 
-def count_unqiue_words(data: tf.SparseTensor, output_vocab_name: str) -> None:
+def count_unqiue_words(
+    data: tf.SparseTensor, output_vocab_name: Optional[str]) -> None:
   tft.count_per_key(data, key_vocabulary_filename=output_vocab_name)

sdks/python/apache_beam/ml/transforms/tft_test.py

@@ -357,6 +357,85 @@ def test_string_split_with_multiple_delimiters(self):
       ]
       assert_that(result, equal_to(expected_result, equals_fn=np.array_equal))
 
+  def test_multiple_columns_with_default_vocab_name(self):
+    data = [{
+        'x': ['I', 'like', 'pie'], 'y': ['Apach', 'Beam', 'is', 'awesome']
+    },
+            {
+                'x': ['yum', 'yum', 'pie'],
+                'y': ['Beam', 'is', 'a', 'unified', 'model']
+            }]
+    with beam.Pipeline() as p:
+      result = (
+          p
+          | "Create" >> beam.Create(data)
+          | "MLTransform" >> base.MLTransform(
+              write_artifact_location=self.artifact_location).with_transform(
+                  tft.ComputeAndApplyVocabulary(columns=['x', 'y'])))
+
+      expected_data_x = [np.array([3, 2, 1]), np.array([0, 0, 1])]
+
+      expected_data_y = [np.array([6, 1, 0, 4]), np.array([1, 0, 5, 2, 3])]
+
+      actual_data_x = (result | beam.Map(lambda x: x.x))
+      actual_data_y = (result | beam.Map(lambda x: x.y))
+
+      assert_that(
+          actual_data_x,
+          equal_to(expected_data_x, equals_fn=np.array_equal),
+          label='x')
+      assert_that(
+          actual_data_y,
+          equal_to(expected_data_y, equals_fn=np.array_equal),
+          label='y')
+    files = os.listdir(self.artifact_location)
+    files.remove(base._ATTRIBUTE_FILE_NAME)
+    assert len(files) == 1
+    tft_vocab_assets = os.listdir(
+        os.path.join(
+            self.artifact_location, files[0], 'transform_fn', 'assets'))
+    assert len(tft_vocab_assets) == 2
+
+  def test_multiple_columns_with_vocab_name(self):
+    data = [{
+        'x': ['I', 'like', 'pie'], 'y': ['Apach', 'Beam', 'is', 'awesome']
+    },
+            {
+                'x': ['yum', 'yum', 'pie'],
+                'y': ['Beam', 'is', 'a', 'unified', 'model']
+            }]
+    with beam.Pipeline() as p:
+      result = (
+          p
+          | "Create" >> beam.Create(data)
+          | "MLTransform" >> base.MLTransform(
+              write_artifact_location=self.artifact_location).with_transform(
+                  tft.ComputeAndApplyVocabulary(
+                      columns=['x', 'y'], vocab_filename='my_vocab')))
+
+      expected_data_x = [np.array([3, 2, 1]), np.array([0, 0, 1])]
+
+      expected_data_y = [np.array([6, 1, 0, 4]), np.array([1, 0, 5, 2, 3])]
+
+      actual_data_x = (result | beam.Map(lambda x: x.x))
+      actual_data_y = (result | beam.Map(lambda x: x.y))
+
+      assert_that(
+          actual_data_x,
+          equal_to(expected_data_x, equals_fn=np.array_equal),
+          label='x')
+      assert_that(
+          actual_data_y,
+          equal_to(expected_data_y, equals_fn=np.array_equal),
+          label='y')
+    files = os.listdir(self.artifact_location)
+    files.remove(base._ATTRIBUTE_FILE_NAME)
+    assert len(files) == 1
+    tft_vocab_assets = os.listdir(
+        os.path.join(
+            self.artifact_location, files[0], 'transform_fn', 'assets'))
+    assert len(tft_vocab_assets) == 2
+
 
 class TFIDIFTest(unittest.TestCase):
   def setUp(self) -> None:
@@ -717,7 +796,7 @@ def validate_count_per_key(key_vocab_filename):
           self.artifact_location,
           files[0],
           'transform_fn/assets',
-          key_vocab_filename)
+          key_vocab_filename + '_x')
       with open(key_vocab_location, 'r') as f:
         key_vocab_list = [line.strip() for line in f]
       return key_vocab_list