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Posted to github@beam.apache.org by "AnandInguva (via GitHub)" <gi...@apache.org> on 2023/07/13 20:28:45 UTC
[GitHub] [beam] AnandInguva opened a new pull request, #27493: Add ScaleByMinMax and NGrams data processing transformations
AnandInguva opened a new pull request, #27493:
URL: https://github.com/apache/beam/pull/27493
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[GitHub] [beam] codecov[bot] commented on pull request #27493: Add ScaleByMinMax and NGrams data processing transformations
Posted by "codecov[bot] (via GitHub)" <gi...@apache.org>.
codecov[bot] commented on PR #27493:
URL: https://github.com/apache/beam/pull/27493#issuecomment-1636090299
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> Merging [#27493](https://app.codecov.io/gh/apache/beam/pull/27493?src=pr&el=desc&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=apache) (824a32f) into [master](https://app.codecov.io/gh/apache/beam/commit/85a78c67f011b593e11fe0f314440d86ed6e065c?el=desc&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=apache) (85a78c6) will **decrease** coverage by `0.01%`.
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```diff
@@ Coverage Diff @@
## master #27493 +/- ##
==========================================
- Coverage 71.16% 71.16% -0.01%
==========================================
Files 861 861
Lines 104519 104540 +21
==========================================
+ Hits 74386 74391 +5
- Misses 28584 28600 +16
Partials 1549 1549
```
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[GitHub] [beam] AnandInguva commented on a diff in pull request #27493: Add ScaleByMinMax and NGrams data processing transformations
Posted by "AnandInguva (via GitHub)" <gi...@apache.org>.
AnandInguva commented on code in PR #27493:
URL: https://github.com/apache/beam/pull/27493#discussion_r1263881924
##########
sdks/python/apache_beam/ml/transforms/tft.py:
##########
@@ -434,3 +437,71 @@ def apply_transform(
output_column_name + '_tfidf_weight': tfidf_weight
}
return output
+
+
+@register_input_dtype(float)
+class ScaleByMinMax(TFTOperation):
+ def __init__(
+ self,
+ columns: List[str],
+ min_value: float = 0.0,
+ max_value: float = 1.0,
+ name: Optional[str] = None):
+ """
+ This function applies a scaling transformation on the given columns
+ of incoming data. The transformation scales the input values to the
+ range [min_value, max_value].
+
+ Args:
+ columns: A list of column names to apply the transformation on.
+ min_value: The minimum value of the output range.
+ max_value: The maximum value of the output range.
+ name: A name for the operation (optional).
+ """
+ super().__init__(columns)
+ self.min_value = min_value
+ self.max_value = max_value
+ self.name = name
+
+ if self.max_value <= self.min_value:
+ raise ValueError('max_value must be greater than min_value')
+
+ def apply_transform(
+ self, data: tf.Tensor, output_column_name: str) -> tf.Tensor:
+
+ output = tft.scale_by_min_max(
+ x=data, output_min=self.min_value, output_max=self.max_value)
+ return {output_column_name: output}
+
+
+@register_input_dtype(str)
+class NGrams(TFTOperation):
+ def __init__(
+ self,
+ columns: List[str],
+ ngram_range: Tuple[int, int],
+ separator: str,
+ name: Optional[str] = None):
+ """
+ An n-gram is a contiguous sequence of n items from a given sample of text
+ or speech. This operation applies an n-gram transformation to
+ specified columns of incoming data, splitting the input data into a
+ set of consecutive n-grams.
+
+ Args:
+ columns: A list of column names to apply the transformation on.
+ ngram_range: A tuple of integers(inclusive) specifying the range of
+ n-gram sizes.
+ separator: A string that specifies the separator between tokens.
+ name: A name for the operation (optional).
+ """
+ super().__init__(columns)
+ self.ngram_range = ngram_range
+ self.separator = separator
+ self.name = name
+
+ def apply_transform(self, data: tf.SparseTensor,
+ output_column_name: str) -> Dict[str, tf.SparseTensor]:
+ # TODO: Perform splitting using separator when the input is a string.
Review Comment:
the words were given in a list as input to the MLTransform. Instead of list, if a string were given as input, we might need to separate a string into list of words using the separator.
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[GitHub] [beam] damccorm commented on a diff in pull request #27493: Add ScaleByMinMax and NGrams data processing transformations
Posted by "damccorm (via GitHub)" <gi...@apache.org>.
damccorm commented on code in PR #27493:
URL: https://github.com/apache/beam/pull/27493#discussion_r1263874412
##########
sdks/python/apache_beam/ml/transforms/tft_test.py:
##########
@@ -391,5 +392,81 @@ def equals_fn(a, b):
assert_that(actual_output, equal_to(expected_output, equals_fn=equals_fn))
+class ScaleToMinMaxTest(unittest.TestCase):
+ def setUp(self) -> None:
+ self.artifact_location = tempfile.mkdtemp()
+
+ def tearDown(self):
+ shutil.rmtree(self.artifact_location)
+
+ def test_scale_to_min_max(self):
+ data = [{
+ 'x': 4,
+ }, {
+ 'x': 4,
+ }, {
+ 'x': 4,
+ }]
Review Comment:
Nit: could we add a test where they're not all the same value?
##########
sdks/python/apache_beam/ml/transforms/tft.py:
##########
@@ -434,3 +437,71 @@ def apply_transform(
output_column_name + '_tfidf_weight': tfidf_weight
}
return output
+
+
+@register_input_dtype(float)
+class ScaleByMinMax(TFTOperation):
+ def __init__(
+ self,
+ columns: List[str],
+ min_value: float = 0.0,
+ max_value: float = 1.0,
+ name: Optional[str] = None):
+ """
+ This function applies a scaling transformation on the given columns
+ of incoming data. The transformation scales the input values to the
+ range [min_value, max_value].
+
+ Args:
+ columns: A list of column names to apply the transformation on.
+ min_value: The minimum value of the output range.
+ max_value: The maximum value of the output range.
+ name: A name for the operation (optional).
+ """
+ super().__init__(columns)
+ self.min_value = min_value
+ self.max_value = max_value
+ self.name = name
+
+ if self.max_value <= self.min_value:
+ raise ValueError('max_value must be greater than min_value')
+
+ def apply_transform(
+ self, data: tf.Tensor, output_column_name: str) -> tf.Tensor:
+
+ output = tft.scale_by_min_max(
+ x=data, output_min=self.min_value, output_max=self.max_value)
+ return {output_column_name: output}
+
+
+@register_input_dtype(str)
+class NGrams(TFTOperation):
+ def __init__(
+ self,
+ columns: List[str],
+ ngram_range: Tuple[int, int],
+ separator: str,
+ name: Optional[str] = None):
+ """
+ An n-gram is a contiguous sequence of n items from a given sample of text
+ or speech. This operation applies an n-gram transformation to
+ specified columns of incoming data, splitting the input data into a
+ set of consecutive n-grams.
+
+ Args:
+ columns: A list of column names to apply the transformation on.
+ ngram_range: A tuple of integers(inclusive) specifying the range of
+ n-gram sizes.
+ separator: A string that specifies the separator between tokens.
+ name: A name for the operation (optional).
+ """
+ super().__init__(columns)
+ self.ngram_range = ngram_range
+ self.separator = separator
+ self.name = name
+
+ def apply_transform(self, data: tf.SparseTensor,
+ output_column_name: str) -> Dict[str, tf.SparseTensor]:
+ # TODO: Perform splitting using separator when the input is a string.
Review Comment:
Not sure what this TODO means - aren't we already doing this?
Also, should it have a linked issue?
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[GitHub] [beam] damccorm commented on a diff in pull request #27493: Add ScaleByMinMax and NGrams data processing transformations
Posted by "damccorm (via GitHub)" <gi...@apache.org>.
damccorm commented on code in PR #27493:
URL: https://github.com/apache/beam/pull/27493#discussion_r1263885508
##########
sdks/python/apache_beam/ml/transforms/tft.py:
##########
@@ -434,3 +437,71 @@ def apply_transform(
output_column_name + '_tfidf_weight': tfidf_weight
}
return output
+
+
+@register_input_dtype(float)
+class ScaleByMinMax(TFTOperation):
+ def __init__(
+ self,
+ columns: List[str],
+ min_value: float = 0.0,
+ max_value: float = 1.0,
+ name: Optional[str] = None):
+ """
+ This function applies a scaling transformation on the given columns
+ of incoming data. The transformation scales the input values to the
+ range [min_value, max_value].
+
+ Args:
+ columns: A list of column names to apply the transformation on.
+ min_value: The minimum value of the output range.
+ max_value: The maximum value of the output range.
+ name: A name for the operation (optional).
+ """
+ super().__init__(columns)
+ self.min_value = min_value
+ self.max_value = max_value
+ self.name = name
+
+ if self.max_value <= self.min_value:
+ raise ValueError('max_value must be greater than min_value')
+
+ def apply_transform(
+ self, data: tf.Tensor, output_column_name: str) -> tf.Tensor:
+
+ output = tft.scale_by_min_max(
+ x=data, output_min=self.min_value, output_max=self.max_value)
+ return {output_column_name: output}
+
+
+@register_input_dtype(str)
+class NGrams(TFTOperation):
+ def __init__(
+ self,
+ columns: List[str],
+ ngram_range: Tuple[int, int],
+ separator: str,
+ name: Optional[str] = None):
+ """
+ An n-gram is a contiguous sequence of n items from a given sample of text
+ or speech. This operation applies an n-gram transformation to
+ specified columns of incoming data, splitting the input data into a
+ set of consecutive n-grams.
+
+ Args:
+ columns: A list of column names to apply the transformation on.
+ ngram_range: A tuple of integers(inclusive) specifying the range of
+ n-gram sizes.
+ separator: A string that specifies the separator between tokens.
+ name: A name for the operation (optional).
+ """
+ super().__init__(columns)
+ self.ngram_range = ngram_range
+ self.separator = separator
+ self.name = name
+
+ def apply_transform(self, data: tf.SparseTensor,
+ output_column_name: str) -> Dict[str, tf.SparseTensor]:
+ # TODO: Perform splitting using separator when the input is a string.
Review Comment:
Cool, could you please add that detail (and the tracking issue)
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[GitHub] [beam] AnandInguva commented on a diff in pull request #27493: Add ScaleByMinMax and NGrams data processing transformations
Posted by "AnandInguva (via GitHub)" <gi...@apache.org>.
AnandInguva commented on code in PR #27493:
URL: https://github.com/apache/beam/pull/27493#discussion_r1263026582
##########
sdks/python/apache_beam/ml/transforms/tft.py:
##########
@@ -56,6 +57,7 @@
'Bucketize',
'TFIDF',
'TFTOperation',
+ 'ScaleByMinMax'
Review Comment:
```suggestion
'ScaleByMinMax',
'NGrams',
```
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[GitHub] [beam] AnandInguva merged pull request #27493: Add ScaleByMinMax and NGrams data processing transformations
Posted by "AnandInguva (via GitHub)" <gi...@apache.org>.
AnandInguva merged PR #27493:
URL: https://github.com/apache/beam/pull/27493
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[GitHub] [beam] AnandInguva commented on a diff in pull request #27493: Add ScaleByMinMax and NGrams data processing transformations
Posted by "AnandInguva (via GitHub)" <gi...@apache.org>.
AnandInguva commented on code in PR #27493:
URL: https://github.com/apache/beam/pull/27493#discussion_r1263882151
##########
sdks/python/apache_beam/ml/transforms/tft_test.py:
##########
@@ -391,5 +392,81 @@ def equals_fn(a, b):
assert_that(actual_output, equal_to(expected_output, equals_fn=equals_fn))
+class ScaleToMinMaxTest(unittest.TestCase):
+ def setUp(self) -> None:
+ self.artifact_location = tempfile.mkdtemp()
+
+ def tearDown(self):
+ shutil.rmtree(self.artifact_location)
+
+ def test_scale_to_min_max(self):
+ data = [{
+ 'x': 4,
+ }, {
+ 'x': 4,
+ }, {
+ 'x': 4,
+ }]
Review Comment:
Yes, I added this test case, which was used in TFT tests.
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[GitHub] [beam] github-actions[bot] commented on pull request #27493: Add ScaleByMinMax and NGrams data processing transformations
Posted by "github-actions[bot] (via GitHub)" <gi...@apache.org>.
github-actions[bot] commented on PR #27493:
URL: https://github.com/apache/beam/pull/27493#issuecomment-1634979290
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[GitHub] [beam] AnandInguva commented on a diff in pull request #27493: Add ScaleByMinMax and NGrams data processing transformations
Posted by "AnandInguva (via GitHub)" <gi...@apache.org>.
AnandInguva commented on code in PR #27493:
URL: https://github.com/apache/beam/pull/27493#discussion_r1263910101
##########
sdks/python/apache_beam/ml/transforms/tft.py:
##########
@@ -434,3 +437,71 @@ def apply_transform(
output_column_name + '_tfidf_weight': tfidf_weight
}
return output
+
+
+@register_input_dtype(float)
+class ScaleByMinMax(TFTOperation):
+ def __init__(
+ self,
+ columns: List[str],
+ min_value: float = 0.0,
+ max_value: float = 1.0,
+ name: Optional[str] = None):
+ """
+ This function applies a scaling transformation on the given columns
+ of incoming data. The transformation scales the input values to the
+ range [min_value, max_value].
+
+ Args:
+ columns: A list of column names to apply the transformation on.
+ min_value: The minimum value of the output range.
+ max_value: The maximum value of the output range.
+ name: A name for the operation (optional).
+ """
+ super().__init__(columns)
+ self.min_value = min_value
+ self.max_value = max_value
+ self.name = name
+
+ if self.max_value <= self.min_value:
+ raise ValueError('max_value must be greater than min_value')
+
+ def apply_transform(
+ self, data: tf.Tensor, output_column_name: str) -> tf.Tensor:
+
+ output = tft.scale_by_min_max(
+ x=data, output_min=self.min_value, output_max=self.max_value)
+ return {output_column_name: output}
+
+
+@register_input_dtype(str)
+class NGrams(TFTOperation):
+ def __init__(
+ self,
+ columns: List[str],
+ ngram_range: Tuple[int, int],
+ separator: str,
+ name: Optional[str] = None):
+ """
+ An n-gram is a contiguous sequence of n items from a given sample of text
+ or speech. This operation applies an n-gram transformation to
+ specified columns of incoming data, splitting the input data into a
+ set of consecutive n-grams.
+
+ Args:
+ columns: A list of column names to apply the transformation on.
+ ngram_range: A tuple of integers(inclusive) specifying the range of
+ n-gram sizes.
+ separator: A string that specifies the separator between tokens.
+ name: A name for the operation (optional).
+ """
+ super().__init__(columns)
+ self.ngram_range = ngram_range
+ self.separator = separator
+ self.name = name
+
+ def apply_transform(self, data: tf.SparseTensor,
+ output_column_name: str) -> Dict[str, tf.SparseTensor]:
+ # TODO: Perform splitting using separator when the input is a string.
Review Comment:
Done
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