tft.scale_to_0_1_per_key | TFX | TensorFlow (original) (raw)
tft.scale_to_0_1_per_key
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Returns a column which is the input column scaled to have range [0,1].
tft.scale_to_0_1_per_key(
x: common_types.ConsistentTensorType,
key: common_types.TensorType,
elementwise: bool = False,
key_vocabulary_filename: Optional[str] = None,
name: Optional[str] = None
) -> common_types.ConsistentTensorType
| Args | |
|---|---|
| x | A numeric Tensor, SparseTensor, or RaggedTensor. |
| key | A Tensor, SparseTensor, or RaggedTensor of type string. |
| elementwise | If true, scale each element of the tensor independently. |
| key_vocabulary_filename | (Optional) The file name for the per-key file. If None, this combiner will assume the keys fit in memory and will not store the analyzer result in a file. If '', a file name will be chosen based on the current TensorFlow scope. If not '', it should be unique within a given preprocessing function. |
| name | (Optional) A name for this operation. |
Example:
def preprocessing_fn(inputs):
return {
'scaled': tft.scale_to_0_1_per_key(inputs['x'], inputs['s'])
}
raw_data = [dict(x=1, s='a'), dict(x=0, s='b'), dict(x=3, s='a')]
feature_spec = dict(
x=tf.io.FixedLenFeature([], tf.float32),
s=tf.io.FixedLenFeature([], tf.string))
raw_data_metadata = tft.DatasetMetadata.from_feature_spec(feature_spec)
with tft_beam.Context(temp_dir=tempfile.mkdtemp()):
transformed_dataset, transform_fn = (
(raw_data, raw_data_metadata)
| tft_beam.AnalyzeAndTransformDataset(preprocessing_fn))
transformed_data, transformed_metadata = transformed_dataset
transformed_data
[{'scaled': 0.0}, {'scaled': 0.5}, {'scaled': 1.0}]
| Returns |
|---|
| A Tensor, SparseTensor, or RaggedTensor containing the input column scaled to [0, 1], per key. If the analysis dataset is empty, contains a single distinct value or the computed key vocabulary doesn't have an entry for key, then x is scaled using a sigmoid function. |
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Last updated 2024-11-01 UTC.