This guide introduces the basic concepts of `tf.Transform` and how to use them.
It will:
- Define a *preprocessing function*, a logical description of the pipeline that transforms the raw data into the data used to train a machine learning model.
- Show the [Apache Beam](https://beam.apache.org/) implementation used to transform data by converting the *preprocessing function* into a *Beam
pipeline*.
- Show additional usage examples.
## Setup
pip install -U tensorflow_transform
pip install pyarrow
import pkg_resources
import importlib
importlib.reload(pkg_resources)
```
```
import os
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 schema_utils
from tfx_bsl.public import tfxio
```
2023-04-13 09:15:54.685940: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
2023-04-13 09:15:54.686060: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
2023-04-13 09:15:54.686073: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
```
## Define a preprocessing function
The *preprocessing function* is the most important concept of `tf.Transform`.
The preprocessing function is a logical description of a transformation of the
dataset. The preprocessing function accepts and returns a dictionary of tensors,
where a *tensor* means `Tensor` or `SparseTensor`. There are two kinds of
functions used to define the preprocessing function:
1. Any function that accepts and returns tensors. These add TensorFlow operations to the graph that transform raw data into transformed data.
2. Any of the *analyzers* provided by `tf.Transform`. Analyzers also accept and return tensors, but unlike TensorFlow functions, they *do not* add operations to the graph. Instead, analyzers cause `tf.Transform` to compute a full-pass operation outside of TensorFlow. They use the input tensor values over the entire dataset to generate a constant tensor that is returned as the output. For example, [`tft.min`](https://www.tensorflow.org/tfx/transform/api_docs/python/tft/min) computes the minimum of a tensor over the dataset. `tf.Transform` provides a fixed set of analyzers, but this will be extended in future versions.
### Preprocessing function example
By combining analyzers and regular TensorFlow functions, users can create
flexible pipelines for transforming data. The following preprocessing function
transforms each of the three features in different ways, and combines two of the
features:
def preprocessing_fn(inputs):
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
}
Here, `x`, `y` and `s` are `Tensor`s that represent input features. The first
new tensor that is created, `x_centered`, is built by applying [`tft.mean`](https://www.tensorflow.org/tfx/transform/api_docs/python/tft/mean) to `x`
and subtracting this from `x`. [`tft.mean(x)`](https://www.tensorflow.org/tfx/transform/api_docs/python/tft/mean) returns a tensor representing the
mean of the tensor `x`. `x_centered` is the tensor `x` with the mean subtracted.
The second new tensor, `y_normalized`, is created in a similar manner but using
the convenience method [`tft.scale_to_0_1`](https://www.tensorflow.org/tfx/transform/api_docs/python/tft/scale_to_0_1). This method does something similar to
computing `x_centered`, namely computing a maximum and minimum and using these
to scale `y`.
The tensor `s_integerized` shows an example of string manipulation. In this
case, we take a string and map it to an integer. This uses the convenience
function [`tft.compute_and_apply_vocabulary`](https://www.tensorflow.org/tfx/transform/api_docs/python/tft/compute_and_apply_vocabulary). This function uses an analyzer to
compute the unique values taken by the input strings, and then uses TensorFlow
operations to convert the input strings to indices in the table of unique
values.
The final column shows that it is possible to use TensorFlow operations to
create new features by combining tensors.
The preprocessing function defines a pipeline of operations on a dataset. In
order to apply the pipeline, we rely on a concrete implementation of the
`tf.Transform` API. The Apache Beam implementation provides `PTransform` which
applies a user's preprocessing function to data. The typical workflow of a
`tf.Transform` user will construct a preprocessing function, then incorporate
this into a larger Beam pipeline, creating the data for training.
### Batching
Batching is an important part of TensorFlow. Since one of the goals of
`tf.Transform` is to provide a TensorFlow graph for preprocessing that can be
incorporated into the serving graph (and, optionally, the training graph),
batching is also an important concept in `tf.Transform`.
While not obvious in the example above, the user defined preprocessing function
is passed tensors representing *batches* and not individual instances, as
happens during training and serving with TensorFlow. On the other hand,
analyzers perform a computation over the entire dataset that returns a single
value and not a batch of values. `x` is a `Tensor` with a shape of
`(batch_size,)`, while [`tft.mean(x)`](https://www.tensorflow.org/tfx/transform/api_docs/python/tft/mean) is a `Tensor` with a shape of `()`. The
subtraction `x - tft.mean(x)` broadcasts where the value of `tft.mean(x)` is
subtracted from every element of the batch represented by `x`.
## Apache Beam Implementation
While the *preprocessing function* is intended as a logical description of a
*preprocessing pipeline* implemented on multiple data processing frameworks,
`tf.Transform` provides a canonical implementation used on Apache Beam. This
implementation demonstrates the functionality required from an implementation.
There is no formal API for this functionality, so each implementation can use an
API that is idiomatic for its particular data processing framework.
The Apache Beam implementation provides two `PTransform`s used to process data
for a preprocessing function. The following shows the usage for the composite
`PTransform` - [`tft_beam.AnalyzeAndTransformDataset`](https://www.tensorflow.org/tfx/transform/api_docs/python/tft_beam/AnalyzeAndTransformDataset):
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(
schema_utils.schema_from_feature_spec({
'y': tf.io.FixedLenFeature([], tf.float32),
'x': tf.io.FixedLenFeature([], tf.float32),
's': tf.io.FixedLenFeature([], tf.string),
}))
with tft_beam.Context(temp_dir=tempfile.mkdtemp()):
transformed_dataset, transform_fn = (
(raw_data, raw_data_metadata) |
tft_beam.AnalyzeAndTransformDataset(preprocessing_fn))
```
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: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: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/python3.9/site-packages/tensorflow_transform/tf_utils.py:324: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.
2023-04-13 09:15:56.867283: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:267] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_transform/tf_utils.py:324: 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: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: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:apache_beam.options.pipeline_options:Discarding unparseable args: ['/tmpfs/src/tf_docs_env/lib/python3.9/site-packages/ipykernel_launcher.py', '-f', '/tmpfs/tmp/tmpzu0d2pwa.json', '--HistoryManager.hist_file=:memory:']
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpdhm3m_yu/tftransform_tmp/88750e1500194862a87b2f23e04367bc/assets
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpdhm3m_yu/tftransform_tmp/88750e1500194862a87b2f23e04367bc/assets
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpdhm3m_yu/tftransform_tmp/8fad0af5a26242cc9733a752a7652277/assets
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpdhm3m_yu/tftransform_tmp/8fad0af5a26242cc9733a752a7652277/assets
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_text is not available.
```
transformed_data, transformed_metadata = transformed_dataset
The `transformed_data` content is shown below and contains the transformed
columns in the same format as the raw data. In particular, the values of
`s_integerized` are `[0, 1, 0]`---these values depend on how the words `hello` and
`world` were mapped to integers, which is deterministic. For the column
`x_centered`, we subtracted the mean so the values of the column `x`, which were
`[1.0, 2.0, 3.0]`, became `[-1.0, 0.0, 1.0]`. Similarly, the rest of the columns
match their expected values.
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}]
```
Both `raw_data` and `transformed_data` are datasets. The next two sections show
how the Beam implementation represents datasets and how to read and write data
to disk. The other return value, `transform_fn`, represents the transformation
applied to the data, covered in detail below.
The [`tft_beam.AnalyzeAndTransformDataset`](https://www.tensorflow.org/tfx/transform/api_docs/python/tft_beam/AnalyzeAndTransformDataset) class is the composition of the two
fundamental transforms provided by the implementation
[`tft_beam.AnalyzeDataset`](https://www.tensorflow.org/tfx/transform/api_docs/python/tft_beam/AnalyzeDataset) and [`tft_beam.TransformDataset`](https://www.tensorflow.org/tfx/transform/api_docs/python/tft_beam/TransformDataset). So the following
two code snippets are equivalent:
my_data = (raw_data, raw_data_metadata)
with tft_beam.Context(temp_dir=tempfile.mkdtemp()):
transformed_data, transform_fn = (
my_data | tft_beam.AnalyzeAndTransformDataset(preprocessing_fn))
```
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: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: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: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:apache_beam.options.pipeline_options:Discarding unparseable args: ['/tmpfs/src/tf_docs_env/lib/python3.9/site-packages/ipykernel_launcher.py', '-f', '/tmpfs/tmp/tmpzu0d2pwa.json', '--HistoryManager.hist_file=:memory:']
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmp8afa0l99/tftransform_tmp/8dc250e431e848a386d53f050ae886df/assets
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmp8afa0l99/tftransform_tmp/8dc250e431e848a386d53f050ae886df/assets
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmp8afa0l99/tftransform_tmp/46d2e23e8b9745219e9812f9b7f5aee1/assets
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmp8afa0l99/tftransform_tmp/46d2e23e8b9745219e9812f9b7f5aee1/assets
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_text is not available.
```
with tft_beam.Context(temp_dir=tempfile.mkdtemp()):
transform_fn = my_data | tft_beam.AnalyzeDataset(preprocessing_fn)
transformed_data = (my_data, transform_fn) | tft_beam.TransformDataset()
```
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: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:apache_beam.options.pipeline_options:Discarding unparseable args: ['/tmpfs/src/tf_docs_env/lib/python3.9/site-packages/ipykernel_launcher.py', '-f', '/tmpfs/tmp/tmpzu0d2pwa.json', '--HistoryManager.hist_file=:memory:']
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpoezjiky4/tftransform_tmp/2f6feb69b15d4a429fa4f56dd7fb02a3/assets
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpoezjiky4/tftransform_tmp/2f6feb69b15d4a429fa4f56dd7fb02a3/assets
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpoezjiky4/tftransform_tmp/26cbcc6000e947c798b5af9ad57c0b42/assets
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpoezjiky4/tftransform_tmp/26cbcc6000e947c798b5af9ad57c0b42/assets
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: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:apache_beam.options.pipeline_options:Discarding unparseable args: ['/tmpfs/src/tf_docs_env/lib/python3.9/site-packages/ipykernel_launcher.py', '-f', '/tmpfs/tmp/tmpzu0d2pwa.json', '--HistoryManager.hist_file=:memory:']
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_text is not available.
```
`transform_fn` is a pure function that represents an operation that is applied
to each row of the dataset. In particular, the analyzer values are already
computed and treated as constants. In the example, the `transform_fn` contains
as constants the mean of column `x`, the min and max of column `y`, and the
vocabulary used to map the strings to integers.
An important feature of `tf.Transform` is that `transform_fn` represents a map
*over rows* ---it is a pure function applied to each row separately. All of the
computation for aggregating rows is done in `AnalyzeDataset`. Furthermore, the
`transform_fn` is represented as a TensorFlow `Graph` which can be embedded into
the serving graph.
`AnalyzeAndTransformDataset` is provided for optimizations in this special case.
This is the same pattern used in
[scikit-learn](http://scikit-learn.org/stable/index.html), providing the `fit`,
`transform`, and `fit_transform` methods.
## Data Formats and Schema
TFT Beam implementation accepts two different input data formats. The
"instance dict" format (as seen in the example above and [simple.ipynb](https://www.tensorflow.org/tfx/tutorials/transform/simple) \& [simple_example.py](https://github.com/tensorflow/transform/blob/master/examples/simple_example.py))
is an intuitive format and is suitable for small datasets while the TFXIO
([Apache Arrow](https://arrow.apache.org)) format provides improved performance
and is suitble for large datasets.
The "metadata" accompanying the `PCollection` tells the Beam implementation the format of the `PCollection`.
(raw_data, raw_data_metadata) | tft.AnalyzeDataset(...)
- If `raw_data_metadata` is a `dataset_metadata.DatasetMetadata` (see below, "The 'instance dict' format" section), then `raw_data` is expected to be in the "instance dict" format.
- If `raw_data_metadata` is a [`tfxio.TensorAdapterConfig`](https://www.tensorflow.org/tfx/tfx_bsl/api_docs/python/tfx_bsl/public/tfxio/TensorAdapterConfig) (see below, "The TFXIO format" section), then `raw_data` is expected to be in the TFXIO format.
### The "instance dict" format
The previous code examples used this format. The metadata contains the schema that defines the layout of the data and how it is read from and written to various formats. Even this in-memory format is not self-describing and requires the schema in order to be interpreted as tensors.
Again, here is the definition of the schema for the example data:
import tensorflow_transform as tft
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),
})
The `Schema` proto contains the information needed to parse the
data from its on-disk or in-memory format, into tensors. It is typically
constructed by calling `schema_utils.schema_from_feature_spec` with a dict
mapping feature keys to [`tf.io.FixedLenFeature`](https://www.tensorflow.org/api_docs/python/tf/io/FixedLenFeature), [`tf.io.VarLenFeature`](https://www.tensorflow.org/api_docs/python/tf/io/VarLenFeature), and
[`tf.io.SparseFeature`](https://www.tensorflow.org/api_docs/python/tf/io/SparseFeature) values. See the documentation for
[`tf.parse_example`](https://www.tensorflow.org/api_docs/python/tf/parse_example)
for more details.
Above we use [`tf.io.FixedLenFeature`](https://www.tensorflow.org/api_docs/python/tf/io/FixedLenFeature) to indicate that each feature contains a
fixed number of values, in this case a single scalar value. Because
`tf.Transform` batches instances, the actual `Tensor` representing the feature
will have shape `(None,)` where the unknown dimension is the batch dimension.
### The TFXIO format
With this format, the data is expected to be contained in a
[`pyarrow.RecordBatch`](https://arrow.apache.org/docs/python/generated/pyarrow.RecordBatch.html).
For tabular data, our Apache Beam implementation
accepts Arrow `RecordBatch`es that consist of columns of the following types:
- `pa.list_()`, where `` is `pa.int64()`, `pa.float32()`
`pa.binary()` or `pa.large_binary()`.
- `pa.large_list()`
The toy input dataset we used above, when represented as a `RecordBatch`, looks
like the following:
import pyarrow as pa
raw_data = [
pa.record_batch(
data=[
pa.array([[1], [2], [3]], pa.list_(pa.float32())),
pa.array([[1], [2], [3]], pa.list_(pa.float32())),
pa.array([['hello'], ['world'], ['hello']], pa.list_(pa.binary())),
],
names=['x', 'y', 's'])
]
Similar to the `dataset_metadata.DatasetMetadata` instance that accompanies the "instance dict" format, a [`tfxio.TensorAdapterConfig`](https://www.tensorflow.org/tfx/tfx_bsl/api_docs/python/tfx_bsl/public/tfxio/TensorAdapterConfig)
is must accompany the `RecordBatch`es. It consists of the Arrow schema of
the `RecordBatch`es, and
[`tfxio.TensorRepresentations`](https://www.tensorflow.org/tfx/tfx_bsl/api_docs/python/tfx_bsl/public/tfxio/TensorRepresentations) to uniquely determine how columns in `RecordBatch`es can be interpreted as TensorFlow Tensors (including but not limited to [`tf.Tensor`](https://www.tensorflow.org/api_docs/python/tf/Tensor), [`tf.SparseTensor`](https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor)).
[`tfxio.TensorRepresentations`](https://www.tensorflow.org/tfx/tfx_bsl/api_docs/python/tfx_bsl/public/tfxio/TensorRepresentations) is type alias for a `Dict[str, tensorflow_metadata.proto.v0.schema_pb2.TensorRepresentation]` which
establishes the relationship between a Tensor that a `preprocessing_fn` accepts
and columns in the `RecordBatch`es. For example:
from google.protobuf import text_format
from tensorflow_metadata.proto.v0 import schema_pb2
tensor_representation = {
'x': text_format.Parse(
"""dense_tensor { column_name: "col1" shape { dim { size: 2 } } }""",
schema_pb2.TensorRepresentation())
}
Means that `inputs['x']` in `preprocessing_fn` should be a dense `tf.Tensor`,
whose values come from a column of name `'col1'` in the input `RecordBatch`es,
and its (batched) shape should be `[batch_size, 2]`.
A [`schema_pb2.TensorRepresentation`](https://www.tensorflow.org/tfx/tf_metadata/api_docs/python/tfmd/proto/schema_pb2/TensorRepresentation) is a Protobuf defined in
[TensorFlow Metadata](https://github.com/tensorflow/metadata/blob/v0.22.2/tensorflow_metadata/proto/v0/schema.proto#L592).
## Compatibility with TensorFlow
`tf.Transform` provides support for exporting the `transform_fn` as
a SavedModel, see the [simple tutorial](https://www.tensorflow.org/tfx/tutorials/transform/simple) for an example. The default behavior before the `0.30` release
exported a TF 1.x SavedModel. Starting with the `0.30` release, the default
behavior is to export a TF 2.x SavedModel unless TF 2.x behaviors are explicitly
disabled (by calling [`tf.compat.v1.disable_v2_behavior()`](https://www.tensorflow.org/api_docs/python/tf/compat/v1/disable_v2_behavior)).
If using TF 1.x concepts such as `tf.estimator` and `tf.Sessions`, you can retain the previous behavior by passing `force_tf_compat_v1=True` to
[`tft_beam.Context`](https://www.tensorflow.org/tfx/transform/api_docs/python/tft_beam/Context)
if using `tf.Transform` as a standalone library or to the
[Transform](https://www.tensorflow.org/tfx/api_docs/python/tfx/components/Transform)
component in TFX.
When exporting the `transform_fn` as a TF 2.x SavedModel, the `preprocessing_fn`
is expected to be traceable using [`tf.function`](https://www.tensorflow.org/api_docs/python/tf/function). Additionally, if running your
pipeline remotely (for example with the `DataflowRunner`), ensure that the
`preprocessing_fn` and any dependencies are packaged properly as described
[here](https://beam.apache.org/documentation/sdks/python-pipeline-dependencies/#multiple-file-dependencies).
Known issues with using `tf.Transform` to export a TF 2.x SavedModel are
documented [here](https://www.tensorflow.org/tfx/transform/tf2_support).
## Input and output with Apache Beam
So far, we've seen input and output data in python lists (of `RecordBatch`es or
instance dictionaries). This is a simplification that relies on Apache Beam's
ability to work with lists as well as its main representation of data, the
`PCollection`.
A `PCollection` is a data representation that forms a part of a Beam pipeline.
A Beam pipeline is formed by applying various `PTransform`s, including
`AnalyzeDataset` and `TransformDataset`, and running the pipeline. A
`PCollection` is not created in the memory of the main binary, but instead is
distributed among the workers (although this section uses the in-memory
execution mode).
### Pre-canned `PCollection` Sources (`TFXIO`)
The `RecordBatch` format that our implementation accepts is a common format that
other TFX libraries accept. Therefore TFX offers convenient "sources" (a.k.a
`TFXIO`) that read files of various formats on disk and produce `RecordBatch`es
and can also give [`tfxio.TensorAdapterConfig`](https://www.tensorflow.org/tfx/tfx_bsl/api_docs/python/tfx_bsl/public/tfxio/TensorAdapterConfig), including inferred
[`tfxio.TensorRepresentations`](https://www.tensorflow.org/tfx/tfx_bsl/api_docs/python/tfx_bsl/public/tfxio/TensorRepresentations).
Those `TFXIO`s can be found in package `tfx_bsl` ([`tfx_bsl.public.tfxio`](https://www.tensorflow.org/tfx/tfx_bsl/api_docs/python/tfx_bsl/public/tfxio)).
## Example: "Census Income" dataset
The following example requires both reading and writing data on disk and
representing data as a `PCollection` (not a list), see:
[`census_example.py`](https://github.com/tensorflow/transform/tree/master/examples/census_example.py).
Below we show how to download the data and run this example. The "Census Income"
dataset is provided by the
[UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Census+Income).
This dataset contains both categorical and numeric data.
Here is some code to download and preview this data:
wget https://storage.googleapis.com/artifacts.tfx-oss-public.appspot.com/datasets/census/adult.data
```
--2023-04-13 09:16:10-- https://storage.googleapis.com/artifacts.tfx-oss-public.appspot.com/datasets/census/adult.data
Resolving storage.googleapis.com (storage.googleapis.com)... 172.217.203.128, 74.125.141.128, 142.250.98.128, ...
Connecting to storage.googleapis.com (storage.googleapis.com)|172.217.203.128|:443... connected.
HTTP request sent, awaiting response...
200 OK
Length: 3974305 (3.8M) [application/octet-stream]
Saving to: ‘adult.data’
adult.data 0%[ ] 0 --.-KB/s
adult.data 100%[===================>] 3.79M --.-KB/s in 0.02s
2023-04-13 09:16:10 (153 MB/s) - ‘adult.data’ saved [3974305/3974305]
```
import pandas as pd
train_data_file = "adult.data"
There's some configuration code hidden in the cell below.
pd.read_csv(train_data_file, names = ORDERED_CSV_COLUMNS).head()
The columns of the dataset are either categorical or numeric. This dataset
describes a classification problem: predicting the last column where the
individual earns more or less than 50K per year. However, from the perspective
of `tf.Transform`, this label is just another categorical column.
We use a Pre-canned [`tfxio.BeamRecordCsvTFXIO`](https://www.tensorflow.org/tfx/tfx_bsl/api_docs/python/tfx_bsl/public/tfxio/BeamRecordCsvTFXIO) to translate the CSV lines
into `RecordBatches`. `TFXIO` requires two important piece of information:
- a TensorFlow Metadata Schema,`tfmd.proto.v0.shema_pb2`, that contains type and shape information about each CSV column. [`schema_pb2.TensorRepresentation`](https://www.tensorflow.org/tfx/tf_metadata/api_docs/python/tfmd/proto/schema_pb2/TensorRepresentation)s are an optional part of the Schema; if not provided (which is the case in this example), they will be inferred from the type and shape information. One can get the Schema either by using a helper function we provide to translate from TF parsing specs (shown in this example), or by running [TensorFlow Data Validation](https://www.tensorflow.org/tfx/tutorials/data_validation/tfdv_basic).
- a list of column names, in the order they appear in the CSV file. Note that those names must match the feature names in the Schema.
pip install -U -q tfx_bsl
from tfx_bsl.public import tfxio
from tfx_bsl.coders.example_coder import RecordBatchToExamples
import apache_beam as beam
pipeline = beam.Pipeline()
csv_tfxio = tfxio.BeamRecordCsvTFXIO(
physical_format='text', column_names=ORDERED_CSV_COLUMNS, schema=SCHEMA)
raw_data = (
pipeline
| 'ReadTrainData' >> beam.io.ReadFromText(
train_data_file, coder=beam.coders.BytesCoder())
| 'FixCommasTrainData' >> beam.Map(
lambda line: line.replace(b', ', b','))
| 'DecodeTrainData' >> csv_tfxio.BeamSource())
raw_data
```
```
Note that we had to do some additional fix-ups after the CSV lines are read
in. Otherwise, we could rely on the [`tfxio.CsvTFXIO`](https://www.tensorflow.org/tfx/tfx_bsl/api_docs/python/tfx_bsl/public/tfxio/CsvTFXIO) to handle both reading the files
and translating to `RecordBatch`es:
csv_tfxio = tfxio.CsvTFXIO(train_data_file,
telemetry_descriptors=[], #???
column_names=ORDERED_CSV_COLUMNS,
schema=SCHEMA)
p2 = beam.Pipeline()
raw_data_2 = p2 | 'TFXIORead' >> csv_tfxio.BeamSource()
Preprocessing for this dataset is similar to the previous example,
except the preprocessing function is programmatically generated instead of manually specifying each column. In the preprocessing function below, `NUMERICAL_COLUMNS` and `CATEGORICAL_COLUMNS` are lists that contain the names of the numeric and categorical columns:
NUM_OOV_BUCKETS = 1
def preprocessing_fn(inputs):
"""Preprocess input columns into transformed columns."""
# Since we are modifying some features and leaving others unchanged, we
# start by setting `outputs` to a copy of `inputs.
outputs = inputs.copy()
# Scale numeric columns to have range [0, 1].
for key in NUMERIC_FEATURE_KEYS:
outputs[key] = tft.scale_to_0_1(outputs[key])
# For all categorical columns except the label column, we generate a
# vocabulary but do not modify the feature. This vocabulary is instead
# used in the trainer, by means of a feature column, to convert the feature
# from a string to an integer id.
for key in CATEGORICAL_FEATURE_KEYS:
outputs[key] = tft.compute_and_apply_vocabulary(
tf.strings.strip(inputs[key]),
num_oov_buckets=NUM_OOV_BUCKETS,
vocab_filename=key)
# For the label column we provide the mapping from string to index.
with tf.init_scope():
# `init_scope` - Only initialize the table once.
initializer = tf.lookup.KeyValueTensorInitializer(
keys=['>50K', '<=50K'],
values=tf.cast(tf.range(2), tf.int64),
key_dtype=tf.string,
value_dtype=tf.int64)
table = tf.lookup.StaticHashTable(initializer, default_value=-1)
outputs[LABEL_KEY] = table.lookup(outputs[LABEL_KEY])
return outputs
One difference from the previous example is the label column manually specifies
the mapping from the string to an index. So `'>50'` is mapped to `0` and
`'<=50K'` is mapped to `1` because it's useful to know which index in the
trained model corresponds to which label.
The `record_batches` variable represents a `PCollection` of
`pyarrow.RecordBatch`es. The `tensor_adapter_config` is given by `csv_tfxio`,
which is inferred from `SCHEMA` (and ultimately, in this example, from the TF
parsing specs).
The final stage is to write the transformed data to disk and has a similar form
to reading the raw data. The schema used to do this is part of the output of
[`tft_beam.AnalyzeAndTransformDataset`](https://www.tensorflow.org/tfx/transform/api_docs/python/tft_beam/AnalyzeAndTransformDataset) which infers a schema for the output data. The code to write to disk is shown below. The schema is a part of the metadata but uses the two interchangeably in the `tf.Transform` API (i.e. pass the metadata to the [`tft.coders.ExampleProtoCoder`](https://www.tensorflow.org/tfx/transform/api_docs/python/tft/coders/ExampleProtoCoder)). Be aware that this writes to a different format. Instead of `textio.WriteToText`, use Beam's built-in support for the `TFRecord` format and use a coder to encode the data as `Example` protos. This is a better format to use for training, as shown in the next section. `transformed_eval_data_base` provides the base filename for the individual shards that are written.
raw_dataset = (raw_data, csv_tfxio.TensorAdapterConfig())
working_dir = tempfile.mkdtemp()
with tft_beam.Context(temp_dir=working_dir):
transformed_dataset, transform_fn = (
raw_dataset | tft_beam.AnalyzeAndTransformDataset(
preprocessing_fn, output_record_batches=True))
output_dir = tempfile.mkdtemp()
transformed_data, _ = transformed_dataset
_ = (
transformed_data
| 'EncodeTrainData' >>
beam.FlatMapTuple(lambda batch, _: RecordBatchToExamples(batch))
| 'WriteTrainData' >> beam.io.WriteToTFRecord(
os.path.join(output_dir , 'transformed.tfrecord')))
In addition to the training data, `transform_fn` is also written out with the
metadata:
_ = (
transform_fn
| 'WriteTransformFn' >> tft_beam.WriteTransformFn(output_dir))
Run the entire Beam pipeline with `pipeline.run().wait_until_finish()`. Up until this point, the Beam pipeline represents a deferred, distributed computation. It provides instructions for what will be done, but the instructions have not been executed. This final call executes the specified pipeline.
result = pipeline.run().wait_until_finish()
```
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmphiyrst4f/tftransform_tmp/c633cd0eb0c14a2bba2bc6f7ba556ce3/assets
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmphiyrst4f/tftransform_tmp/c633cd0eb0c14a2bba2bc6f7ba556ce3/assets
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmphiyrst4f/tftransform_tmp/9080e8c73e2443fea34d6505feed4129/assets
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmphiyrst4f/tftransform_tmp/9080e8c73e2443fea34d6505feed4129/assets
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_text is not available.
WARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be.
```
After running the pipeline the output directory contains two artifacts.
- The transformed data, and the metadata describing it.
- The [`tf.saved_model`](https://www.tensorflow.org/api_docs/python/tf/saved_model) containing the resulting `preprocessing_fn`
ls {output_dir}
```
transform_fn transformed.tfrecord-00000-of-00001 transformed_metadata
```
To see how to use these artifacts refer to the [Advanced preprocessing tutorial](https://www.tensorflow.org/tfx/tutorials/transform/census).