tft.apply_buckets
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Returns a bucketized column, with a bucket index assigned to each input.
tft.apply_buckets(
x: common_types.ConsistentTensorType,
bucket_boundaries: common_types.BucketBoundariesType,
name: Optional[str] = None
) -> common_types.ConsistentTensorType
Each element e in x is mapped to a positive index i for which
bucket_boundaries[i-1] <= e < bucket_boundaries[i], if it exists.
If e < bucket_boundaries[0], then e is mapped to 0. If
e >= bucket_boundaries[-1], then e is mapped to len(bucket_boundaries).
NaNs are mapped to len(bucket_boundaries).
Example:
x = tf.constant([[4.0, float('nan'), 1.0], [float('-inf'), 7.5, 10.0]])
bucket_boundaries = tf.constant([[2.0, 5.0, 10.0]])
tft.apply_buckets(x, bucket_boundaries)
<tf.Tensor: shape=(2, 3), dtype=int64, numpy=
array([[1, 3, 0],
[0, 2, 3]])>
Args |
|---|
x
|
A numeric input Tensor, SparseTensor, or RaggedTensor whose values
should be mapped to buckets. For CompositeTensors, the non-missing
values will be mapped to buckets and missing value left missing.
|
bucket_boundaries
|
A rank 2 Tensor or list representing the bucket
boundaries sorted in ascending order.
|
name
|
(Optional) A name for this operation.
|
Returns |
|---|
A Tensor, SparseTensor, or RaggedTensor of the same shape as x, with
each element in the returned tensor representing the bucketized value.
Bucketized value is in the range [0, len(bucket_boundaries)].
|
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Last updated 2024-11-01 UTC.
[null,null,["Last updated 2024-11-01 UTC."],[],[],null,["# tft.apply_buckets\n\n\u003cbr /\u003e\n\n|---------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/transform/blob/v1.16.0/tensorflow_transform/mappers.py#L2101-L2151) |\n\nReturns a bucketized column, with a bucket index assigned to each input. \n\n tft.apply_buckets(\n x: common_types.ConsistentTensorType,\n bucket_boundaries: common_types.BucketBoundariesType,\n name: Optional[str] = None\n ) -\u003e common_types.ConsistentTensorType\n\nEach element `e` in `x` is mapped to a positive index `i` for which\n`bucket_boundaries[i-1] \u003c= e \u003c bucket_boundaries[i]`, if it exists.\nIf `e \u003c bucket_boundaries[0]`, then `e` is mapped to `0`. If\n`e \u003e= bucket_boundaries[-1]`, then `e` is mapped to `len(bucket_boundaries)`.\nNaNs are mapped to `len(bucket_boundaries)`.\n\n#### Example:\n\n x = tf.constant([[4.0, float('nan'), 1.0], [float('-inf'), 7.5, 10.0]])\n bucket_boundaries = tf.constant([[2.0, 5.0, 10.0]])\n tft.apply_buckets(x, bucket_boundaries)\n \u003ctf.Tensor: shape=(2, 3), dtype=int64, numpy=\n array([[1, 3, 0],\n [0, 2, 3]])\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|---------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `x` | A numeric input `Tensor`, `SparseTensor`, or `RaggedTensor` whose values should be mapped to buckets. For `CompositeTensor`s, the non-missing values will be mapped to buckets and missing value left missing. |\n| `bucket_boundaries` | A rank 2 `Tensor` or list representing the bucket boundaries sorted in ascending order. |\n| `name` | (Optional) A name for this operation. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A `Tensor`, `SparseTensor`, or `RaggedTensor` of the same shape as `x`, with each element in the returned tensor representing the bucketized value. Bucketized value is in the range \\[0, len(bucket_boundaries)\\]. ||\n\n\u003cbr /\u003e"]]
View source on GitHub