tft.scale_to_0_1_per_key

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

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}]

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.