jrmccluskey commented on code in PR #31291:
URL: https://github.com/apache/beam/pull/31291#discussion_r1619089783
##########
sdks/python/apache_beam/ml/transforms/tft.py:
##########
@@ -363,6 +363,42 @@ def apply_transform(
return output
+@register_input_dtype(float)
+class ApplyBucketsWithInterpolation(TFTOperation):
+ def __init__(
+ self,
+ columns: List[str],
+ bucket_boundaries: Iterable[Union[int, float]],
+ name: Optional[str] = None):
+ """ Interpolates values within the provided buckest and then normalizes to
+ [0, 1].
+
+ Input values are bucketized based on the provided boundaries such that the
+ input is mapped to a positive index i for which bucket_boundaries[i-1] <=
+ element < bucket_boundaries[i], if it exists. The values are then
+ normalized to the range [0,1] within the bucket, with NaN values being
+ mapped to 0.5.
+
+ Args:
+ columns: A list of column names to apply the transformation on.
+ bucket_boundaries: A rank 2 Tensor or list representing the bucket
Review Comment:
it's consistent with what we're accepting as valid input in ApplyBuckets,
both co-opt the language from TFT. Updating those to be more accurate to our
function signature seems reasonable, changed for both functions
##########
sdks/python/apache_beam/ml/transforms/tft.py:
##########
@@ -363,6 +363,42 @@ def apply_transform(
return output
+@register_input_dtype(float)
+class ApplyBucketsWithInterpolation(TFTOperation):
+ def __init__(
+ self,
+ columns: List[str],
+ bucket_boundaries: Iterable[Union[int, float]],
+ name: Optional[str] = None):
+ """ Interpolates values within the provided buckest and then normalizes to
+ [0, 1].
+
+ Input values are bucketized based on the provided boundaries such that the
+ input is mapped to a positive index i for which bucket_boundaries[i-1] <=
+ element < bucket_boundaries[i], if it exists. The values are then
+ normalized to the range [0,1] within the bucket, with NaN values being
+ mapped to 0.5.
Review Comment:
done
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