tft.scale_to_gaussian
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Returns an (approximately) normal column with mean to 0 and variance 1.
tft.scale_to_gaussian(
x: common_types.ConsistentTensorType,
elementwise: bool = False,
name: Optional[str] = None,
output_dtype: Optional[tf.DType] = None
) -> common_types.ConsistentTensorType
We transform the column to values that are approximately distributed
according to a standard normal distribution.
The transformation is obtained by applying the moments method to estimate
the parameters of a Tukey HH distribution and applying the inverse of the
estimated function to the column values.
The method is partially described in
Georg M. Georgm "The Lambert Way to Gaussianize Heavy-Tailed Data with the
Inverse of Tukey's h Transformation as a Special Case," The Scientific World
Journal, Vol. 2015, Hindawi Publishing Corporation.
We use the L-moments instead of conventional moments to be able to deal with
long-tailed distributions. The expressions of the L-moments for the Tukey HH
distribution is in
Todd C. Headrick, and Mohan D. Pant. "Characterizing Tukey H and
HH-Distributions through L-Moments and the L-Correlation," ISRN Applied
Mathematics, vol. 2012, 2012. doi:10.5402/2012/980153
Note that the transformation to Gaussian is applied only if the column has
long-tails. If this is not the case, for instance if values are uniformly
distributed, the values are only normalized using the z score. This applies
also to the cases where only one of the tails is long; the other tail is only
rescaled but not non linearly transformed.
Also, if the analysis set is empty, the transformation is set to to leave the
input vaules unchanged.
Args |
|---|
x
|
A numeric Tensor, SparseTensor, or RaggedTensor.
|
elementwise
|
If true, scales each element of the tensor independently;
otherwise uses the parameters of the whole tensor.
|
name
|
(Optional) A name for this operation.
|
output_dtype
|
(Optional) If not None, casts the output tensor to this type.
|
Returns |
|---|
A Tensor, SparseTensor, or RaggedTensor containing the input column
transformed to be approximately standard distributed (i.e. a Gaussian with
mean 0 and variance 1). If x is floating point, the mean will have the
same type as x. If x is integral, the output is cast to tf.float32.
Note that TFLearn generally permits only tf.int64 and tf.float32, so casting
this scaler's output may be necessary.
|
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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.scale_to_gaussian\n\n\u003cbr /\u003e\n\n|------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/transform/blob/v1.16.0/tensorflow_transform/mappers.py#L70-L126) |\n\nReturns an (approximately) normal column with mean to 0 and variance 1. \n\n tft.scale_to_gaussian(\n x: common_types.ConsistentTensorType,\n elementwise: bool = False,\n name: Optional[str] = None,\n output_dtype: Optional[tf.DType] = None\n ) -\u003e common_types.ConsistentTensorType\n\nWe transform the column to values that are approximately distributed\naccording to a standard normal distribution.\nThe transformation is obtained by applying the moments method to estimate\nthe parameters of a Tukey HH distribution and applying the inverse of the\nestimated function to the column values.\nThe method is partially described in\n\nGeorg M. Georgm \"The Lambert Way to Gaussianize Heavy-Tailed Data with the\nInverse of Tukey's h Transformation as a Special Case,\" The Scientific World\nJournal, Vol. 2015, Hindawi Publishing Corporation.\n\nWe use the L-moments instead of conventional moments to be able to deal with\nlong-tailed distributions. The expressions of the L-moments for the Tukey HH\ndistribution is in\n\nTodd C. Headrick, and Mohan D. Pant. \"Characterizing Tukey H and\nHH-Distributions through L-Moments and the L-Correlation,\" ISRN Applied\nMathematics, vol. 2012, 2012. doi:10.5402/2012/980153\n\nNote that the transformation to Gaussian is applied only if the column has\nlong-tails. If this is not the case, for instance if values are uniformly\ndistributed, the values are only normalized using the z score. This applies\nalso to the cases where only one of the tails is long; the other tail is only\nrescaled but not non linearly transformed.\nAlso, if the analysis set is empty, the transformation is set to to leave the\ninput vaules unchanged.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|----------------|--------------------------------------------------------------------------------------------------------------|\n| `x` | A numeric `Tensor`, `SparseTensor`, or `RaggedTensor`. |\n| `elementwise` | If true, scales each element of the tensor independently; otherwise uses the parameters of the whole tensor. |\n| `name` | (Optional) A name for this operation. |\n| `output_dtype` | (Optional) If not None, casts the output tensor to this type. |\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` containing the input column transformed to be approximately standard distributed (i.e. a Gaussian with mean 0 and variance 1). If `x` is floating point, the mean will have the same type as `x`. If `x` is integral, the output is cast to tf.float32. \u003cbr /\u003e Note that TFLearn generally permits only tf.int64 and tf.float32, so casting this scaler's output may be necessary. ||\n\n\u003cbr /\u003e"]]
View source on GitHub