tft.bucketize
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Returns a bucketized column, with a bucket index assigned to each input.
tft.bucketize( x: common_types.ConsistentTensorType, num_buckets: int, epsilon: Optional[float] = None, weights: Optional[tf.Tensor] = None, elementwise: bool = False, name: Optional[str] = None ) -> common_types.ConsistentTensorType
Used in the notebooks
Args |
|---|
x | A numeric input Tensor, SparseTensor, or RaggedTensor whose values should be mapped to buckets. For a CompositeTensor only non-missing values will be included in the quantiles computation, and the result of bucketize will be a CompositeTensor with non-missing values mapped to buckets. If elementwise=True then x must be dense. |
num_buckets | Values in the input x are divided into approximately equal-sized buckets, where the number of buckets is num_buckets. |
epsilon | (Optional) Error tolerance, typically a small fraction close to zero. If a value is not specified by the caller, a suitable value is computed based on experimental results. For num_buckets less than 100, the value of 0.01 is chosen to handle a dataset of up to ~1 trillion input data values. If num_buckets is larger, then epsilon is set to (1/num_buckets) to enforce a stricter error tolerance, because more buckets will result in smaller range for each bucket, and so we want the boundaries to be less fuzzy. See analyzers.quantiles() for details. |
weights | (Optional) Weights tensor for the quantiles. Tensor must have the same shape as x. |
elementwise | (Optional) If true, bucketize each element of the tensor independently. |
name | (Optional) A name for this operation. |
Returns |
|---|
A Tensor of the same shape as x, with each element in the returned tensor representing the bucketized value. Bucketized value is in the range [0, actual_num_buckets). Sometimes the actual number of buckets can be different than num_buckets hint, for example in case the number of distinct values is smaller than num_buckets, or in cases where the input values are not uniformly distributed. NaN values are mapped to the last bucket. Values with NaN weights are ignored in bucket boundaries calculation. |
Raises |
|---|
TypeError | If num_buckets is not an int. |
ValueError | If value of num_buckets is not > 1. |
ValueError | If elementwise=True and x is a CompositeTensor. |
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Last updated 2024-11-01 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-11-01 UTC."],[],[],null,["# tft.bucketize\n\n\u003cbr /\u003e\n\n|---------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/transform/blob/v1.16.0/tensorflow_transform/mappers.py#L1746-L1826) |\n\nReturns a bucketized column, with a bucket index assigned to each input. \n\n tft.bucketize(\n x: common_types.ConsistentTensorType,\n num_buckets: int,\n epsilon: Optional[float] = None,\n weights: Optional[tf.Tensor] = None,\n elementwise: bool = False,\n name: Optional[str] = None\n ) -\u003e common_types.ConsistentTensorType\n\n### Used in the notebooks\n\n| Used in the tutorials |\n|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| - [TFX Estimator Component Tutorial](https://www.tensorflow.org/tfx/tutorials/tfx/components) - [TFX Keras Component Tutorial](https://www.tensorflow.org/tfx/tutorials/tfx/components_keras) |\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 a `CompositeTensor` only non-missing values will be included in the quantiles computation, and the result of `bucketize` will be a `CompositeTensor` with non-missing values mapped to buckets. If elementwise=True then `x` must be dense. |\n| `num_buckets` | Values in the input `x` are divided into approximately equal-sized buckets, where the number of buckets is `num_buckets`. |\n| `epsilon` | (Optional) Error tolerance, typically a small fraction close to zero. If a value is not specified by the caller, a suitable value is computed based on experimental results. For `num_buckets` less than 100, the value of 0.01 is chosen to handle a dataset of up to \\~1 trillion input data values. If `num_buckets` is larger, then epsilon is set to (1/`num_buckets`) to enforce a stricter error tolerance, because more buckets will result in smaller range for each bucket, and so we want the boundaries to be less fuzzy. See analyzers.quantiles() for details. |\n| `weights` | (Optional) Weights tensor for the quantiles. Tensor must have the same shape as x. |\n| `elementwise` | (Optional) If true, bucketize each element of the tensor independently. |\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` of the same shape as `x`, with each element in the returned tensor representing the bucketized value. Bucketized value is in the range \\[0, actual_num_buckets). Sometimes the actual number of buckets can be different than num_buckets hint, for example in case the number of distinct values is smaller than num_buckets, or in cases where the input values are not uniformly distributed. NaN values are mapped to the last bucket. Values with NaN weights are ignored in bucket boundaries calculation. ||\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Raises ------ ||\n|--------------|---------------------------------------------------|\n| `TypeError` | If num_buckets is not an int. |\n| `ValueError` | If value of num_buckets is not \\\u003e 1. |\n| `ValueError` | If elementwise=True and x is a `CompositeTensor`. |\n\n\u003cbr /\u003e"]]
View source on GitHub