Preprocess data with TensorFlow Transform

Copyright © 2020 Google Inc.

The Feature Engineering Component of TensorFlow Extended (TFX)

This example colab notebook provides a very simple example of how TensorFlow Transform (tf.Transform) can be used to preprocess data using exactly the same code for both training a model and serving inferences in production.

TensorFlow Transform is a library for preprocessing input data for TensorFlow, including creating features that require a full pass over the training dataset. For example, using TensorFlow Transform you could:

Normalize an input value by using the mean and standard deviation
Convert strings to integers by generating a vocabulary over all of the input values
Convert floats to integers by assigning them to buckets, based on the observed data distribution

TensorFlow has built-in support for manipulations on a single example or a batch of examples. tf.Transform extends these capabilities to support full passes over the entire training dataset.

The output of tf.Transform is exported as a TensorFlow graph which you can use for both training and serving. Using the same graph for both training and serving can prevent skew, since the same transformations are applied in both stages.

Upgrade Pip

To avoid upgrading Pip in a system when running locally, check to make sure that we're running in Colab. Local systems can of course be upgraded separately.

try:
  import colab
  !pip install --upgrade pip
except:
  pass

Install TensorFlow Transform

pip install -q -U tensorflow_transform==0.24.1

Did you restart the runtime?

If you are using Google Colab, the first time that you run the cell above, you must restart the runtime (Runtime > Restart runtime ...). This is because of the way that Colab loads packages.

Imports

import pprint
import tempfile

import tensorflow as tf
import tensorflow_transform as tft

import tensorflow_transform.beam as tft_beam
from tensorflow_transform.tf_metadata import dataset_metadata
from tensorflow_transform.tf_metadata import dataset_schema

Data: Create some dummy data

We'll create some simple dummy data for our simple example:

raw_data is the initial raw data that we're going to preprocess
raw_data_metadata contains the schema that tells us the types of each of the columns in raw_data. In this case, it's very simple.

raw_data = [
      {'x': 1, 'y': 1, 's': 'hello'},
      {'x': 2, 'y': 2, 's': 'world'},
      {'x': 3, 'y': 3, 's': 'hello'}
  ]

raw_data_metadata = dataset_metadata.DatasetMetadata(
    dataset_schema.from_feature_spec({
        'y': tf.io.FixedLenFeature([], tf.float32),
        'x': tf.io.FixedLenFeature([], tf.float32),
        's': tf.io.FixedLenFeature([], tf.string),
    }))

WARNING:tensorflow:From <ipython-input-1-e4eabb23bec3>:11: from_feature_spec (from tensorflow_transform.tf_metadata.dataset_schema) is deprecated and will be removed in a future version.
Instructions for updating:
from_feature_spec is a deprecated, use schema_utils.schema_from_feature_spec

Transform: Create a preprocessing function

The preprocessing function is the most important concept of tf.Transform. A preprocessing function is where the transformation of the dataset really happens. It accepts and returns a dictionary of tensors, where a tensor means a Tensor or SparseTensor. There are two main groups of API calls that typically form the heart of a preprocessing function:

TensorFlow Ops: Any function that accepts and returns tensors, which usually means TensorFlow ops. These add TensorFlow operations to the graph that transforms raw data into transformed data one feature vector at a time. These will run for every example, during both training and serving.
Tensorflow Transform Analyzers/Mappers: Any of the analyzers/mappers provided by tf.Transform. These also accept and return tensors, and typically contain a combination of Tensorflow ops and Beam computation, but unlike TensorFlow ops they only run in the Beam pipeline during analysis requiring a full pass over the entire training dataset. The Beam computation runs only once, during training, and typically make a full pass over the entire training dataset. They create tensor constants, which are added to your graph. For example, tft.min computes the minimum of a tensor over the training dataset while tft.scale_by_min_max first computes the min and max of a tensor over the training dataset and then scales the tensor to be within a user-specified range, [output_min, output_max]. tf.Transform provides a fixed set of such analyzers/mappers, but this will be extended in future versions.

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,
        's_integerized': s_integerized,
        'x_centered_times_y_normalized': x_centered_times_y_normalized,
    }

Putting it all together

Now we're ready to transform our data. We'll use Apache Beam with a direct runner, and supply three inputs:

raw_data - The raw input data that we created above
raw_data_metadata - The schema for the raw data
preprocessing_fn - The function that we created to do our transformation

Key Term: Apache Beam uses a special syntax to define and invoke transforms. For example, in this line: result = pass_this | 'name this step' >> to_this_call The method to_this_call is being invoked and passed the object called pass_this, and this operation will be referred to as name this step in a stack trace. The result of the call to to_this_call is returned in result. You will often see stages of a pipeline chained together like this: result = apache_beam.Pipeline() | 'first step' >> do_this_first() | 'second step' >> do_this_last() and since that started with a new pipeline, you can continue like this: next_result = result | 'doing more stuff' >> another_function()

def main():
  # Ignore the warnings
  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

  print('\nRaw data:\n{}\n'.format(pprint.pformat(raw_data)))
  print('Transformed data:\n{}'.format(pprint.pformat(transformed_data)))

if __name__ == '__main__':
  main()

WARNING:tensorflow:Tensorflow version (2.3.2) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended.

WARNING:apache_beam.runners.interactive.interactive_environment:Dependencies required for Interactive Beam PCollection visualization are not available, please use: `pip install apache-beam[interactive]` to install necessary dependencies to enable all data visualization features.

WARNING:tensorflow:Tensorflow version (2.3.2) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended.

WARNING:tensorflow:You are passing instance dicts and DatasetMetadata to TFT which will not provide optimal performance. Consider following the TFT guide to upgrade to the TFXIO format (Apache Arrow RecordBatch).

WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow_transform/tf_utils.py:218: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.

WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:201: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.

INFO:tensorflow:Assets added to graph.

INFO:tensorflow:No assets to write.

WARNING:tensorflow:Issue encountered when serializing tft_analyzer_use.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'Counter' object has no attribute 'name'

WARNING:tensorflow:Issue encountered when serializing tft_mapper_use.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'Counter' object has no attribute 'name'

INFO:tensorflow:SavedModel written to: /tmp/tmp70eqi_6g/tftransform_tmp/ff5f13c5719441dea300f05fd68e7426/saved_model.pb

INFO:tensorflow:Assets added to graph.

INFO:tensorflow:No assets to write.

INFO:tensorflow:SavedModel written to: /tmp/tmp70eqi_6g/tftransform_tmp/8c2cfdde815344f3aa317b99612cb837/saved_model.pb

WARNING:tensorflow:Tensorflow version (2.3.2) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended.

INFO:tensorflow:Saver not created because there are no variables in the graph to restore

INFO:tensorflow:Assets added to graph.

INFO:tensorflow:Assets written to: /tmp/tmp70eqi_6g/tftransform_tmp/45ca8d192fe740f288e9261e058cf9ff/assets

INFO:tensorflow:SavedModel written to: /tmp/tmp70eqi_6g/tftransform_tmp/45ca8d192fe740f288e9261e058cf9ff/saved_model.pb

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_3:0\022-vocab_compute_and_apply_vocabulary_vocabulary"

INFO:tensorflow:Saver not created because there are no variables in the graph to restore

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_3:0\022-vocab_compute_and_apply_vocabulary_vocabulary"

INFO:tensorflow:Saver not created because there are no variables in the graph to restore

Raw data:
[{'s': 'hello', 'x': 1, 'y': 1},
{'s': 'world', 'x': 2, 'y': 2},
{'s': 'hello', 'x': 3, 'y': 3}]

Transformed data:
[{'s_integerized': 0,
'x_centered': -1.0,
'x_centered_times_y_normalized': -0.0,
'y_normalized': 0.0},
{'s_integerized': 1,
'x_centered': 0.0,
'x_centered_times_y_normalized': 0.0,
'y_normalized': 0.5},
{'s_integerized': 0,
'x_centered': 1.0,
'x_centered_times_y_normalized': 1.0,
'y_normalized': 1.0}]

Is this the right answer?

Previously, we used tf.Transform to do this:

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)

x_centered

With input of [1, 2, 3] the mean of x is 2, and we subtract it from x to center our x values at 0. So our result of [-1.0, 0.0, 1.0] is correct.

y_normalized

We wanted to scale our y values between 0 and 1. Our input was [1, 2, 3] so our result of [0.0, 0.5, 1.0] is correct.

s_integerized

We wanted to map our strings to indexes in a vocabulary, and there were only 2 words in our vocabulary ("hello" and "world"). So with input of ["hello", "world", "hello"] our result of [0, 1, 0] is correct. Since "hello" occurs most frequently in this data, it will be the first entry in the vocabulary.

x_centered_times_y_normalized

We wanted to create a new feature by crossing x_centered and y_normalized using multiplication. Note that this multiplies the results, not the original values, and our new result of [-0.0, 0.0, 1.0] is correct.