# Copyright 2017 Google Inc. All Rights Reserved.

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

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#

# http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

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"""Simple Example of tf.Transform usage."""

import pprint

import tempfile

import tensorflow as tf

import tensorflow_transform as tft

import tensorflow_transform.beam as tft_beam

_RAW_DATA_METADATA = tft.DatasetMetadata.from_feature_spec(

{

"s": tf.io.FixedLenFeature([], tf.string),

"y": tf.io.FixedLenFeature([], tf.float32),

"x": tf.io.FixedLenFeature([], tf.float32),

}

)

_RAW_DATA = [

{"x": 1, "y": 1, "s": "hello"},

{"x": 2, "y": 2, "s": "world"},

{"x": 3, "y": 3, "s": "hello"},

]

def _preprocessing_fn(inputs):

"""Preprocess input columns into transformed columns."""

x = inputs["x"]

y = inputs["y"]

s = inputs["s"]

x_centered = x - tft.mean(x)

y_normalized = tft.scale_to_0_1(y)

s_integerized = tft.compute_and_apply_vocabulary(s)

x_centered_times_y_normalized = x_centered * y_normalized

return {

"x_centered": x_centered,

"y_normalized": y_normalized,

"x_centered_times_y_normalized": x_centered_times_y_normalized,

"s_integerized": s_integerized,

}

def main():

with tft_beam.Context(temp_dir=tempfile.mkdtemp()):

transformed_dataset, transform_fn = ( # pylint: disable=unused-variable

(_RAW_DATA, _RAW_DATA_METADATA)

| tft_beam.AnalyzeAndTransformDataset(_preprocessing_fn)

)

transformed_data, transformed_metadata = transformed_dataset # pylint: disable=unused-variable

pprint.pprint(transformed_data)

if __name__ == "__main__":

main()