!pip install --upgrade pip

"""
Explanation: Preprocessing Data with Simple Example using TensorFlow Transform
Learning objectives

Create a preprocessing function.
Use the resulting transform_fn directory.
Export the model.

In this notebook, you learn a very simple example of how <a target='_blank' href='https://www.tensorflow.org/tfx/transform/get_started/'>TensorFlow Transform</a> (<code>tf.Transform</code>) 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.
Each learning objective will correspond to a #TODO in the student lab notebook -- try to complete that notebook first before reviewing this solution notebook.
Upgrade Pip
End of explanation
"""


!pip install -q -U tensorflow_transform

"""
Explanation: Install TensorFlow Transform
End of explanation
"""


# This cell is only necessary because packages were installed while python was running.
import pkg_resources
import importlib

importlib.reload(pkg_resources)

"""
Explanation: Restart the kernel to use updated packages. (On the Notebook menu, select Kernel > Restart Kernel > Restart).
End of explanation
"""


import pathlib
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 schema_utils

"""
Explanation: Imports
End of explanation
"""


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),
    }))

"""
Explanation: Data: Create some dummy data
You'll create some simple dummy data for your simple example:

raw_data is the initial raw data that you'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.
End of explanation
"""


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,
    }

"""
Explanation: 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 <a target='_blank' href='https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/Tensor'><code>Tensor</code></a> or <a target='_blank' href='https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/SparseTensor'><code>SparseTensor</code></a>. 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, (prior to training, during analysis), and typically makes a full pass over the entire training dataset. They create tf.constant tensors, which are added to your graph. For example, tft.min computes the minimum of a tensor over the training dataset.

Caution: When you apply your preprocessing function to serving inferences, the constants that were created by analyzers during training do not change.  If your data has trend or seasonality components, plan accordingly.
Note: The preprocessing_fn is not directly callable. This means that
calling preprocessing_fn(raw_data) will not work. Instead, it must
be passed to the Transform Beam API as shown in the following cells.
End of explanation
"""


def main(output_dir):
  # 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

  # TODO 1: Save the transform_fn to the output_dir
  _ = (
      transform_fn
      | 'WriteTransformFn' >> tft_beam.WriteTransformFn(output_dir))

  return transformed_data, transformed_metadata

output_dir = pathlib.Path(tempfile.mkdtemp())

transformed_data, transformed_metadata = main(str(output_dir))

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

"""
Explanation: Syntax
You're almost ready to put everything together and use <a target='_blank' href='https://beam.apache.org/'>Apache Beam</a> to run it.
Apache Beam uses a <a target='_blank' href='https://beam.apache.org/documentation/programming-guide/#applying-transforms'>special syntax to define and invoke transforms</a>.  For example, in this line:
result = pass_this | 'name this step' &gt;&gt; to_this_call
The method to_this_call is being invoked and passed the object called pass_this, and <a target='_blank' href='https://stackoverflow.com/questions/50519662/what-does-the-redirection-mean-in-apache-beam-python'>this operation will be referred to as name this step in a stack trace</a>.  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' &gt;&gt; do_this_first() | 'second step' &gt;&gt; do_this_last()
and since that started with a new pipeline, you can continue like this:
next_result = result | 'doing more stuff' &gt;&gt; another_function()
Putting it all together
Now you're ready to transform your data. You'll use Apache Beam with a direct runner, and supply three inputs:

raw_data - The raw input data that you created above
raw_data_metadata - The schema for the raw data
preprocessing_fn - The function that you created to do your transformation
End of explanation
"""


!ls -l {output_dir}

"""
Explanation: Is this the right answer?
Previously, you 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 you subtract it from x to center your x values at 0. So your result of [-1.0, 0.0, 1.0] is correct.
y_normalized - You wanted to scale your y values between 0 and 1.  Your input was [1, 2, 3] so your result of [0.0, 0.5, 1.0] is correct.
s_integerized - You wanted to map your strings to indexes in a vocabulary, and there were only 2 words in your vocabulary ("hello" and "world"). So with input of ["hello", "world", "hello"] your 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 - You 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 your new result of [-0.0, 0.0, 1.0] is correct.

Use the resulting transform_fn directory
End of explanation
"""


# TODO 2: Load a SavedModel from export_dir
loaded = tf.saved_model.load(str(output_dir/'transform_fn'))
loaded.signatures['serving_default']

"""
Explanation: The transform_fn/ directory contains a tf.saved_model implementing with all the constants tensorflow-transform analysis results built into the graph. 
It is possible to load this directly with tf.saved_model.load, but this not easy to use:
End of explanation
"""


tf_transform_output = tft.TFTransformOutput(output_dir)

tft_layer = tf_transform_output.transform_features_layer()
tft_layer

"""
Explanation: A better approach is to load it using tft.TFTransformOutput. The TFTransformOutput.transform_features_layer method returns a tft.TransformFeaturesLayer object that can be used to apply the transformation:
End of explanation
"""


raw_data_batch = {
    's': tf.constant([ex['s'] for ex in raw_data]),
    'x': tf.constant([ex['x'] for ex in raw_data], dtype=tf.float32),
    'y': tf.constant([ex['y'] for ex in raw_data], dtype=tf.float32),
}

"""
Explanation: This tft.TransformFeaturesLayer expects a dictionary of batched features. So create a Dict[str, tf.Tensor] from the List[Dict[str, Any]] in raw_data:
End of explanation
"""


transformed_batch = tft_layer(raw_data_batch)

{key: value.numpy() for key, value in transformed_batch.items()}

"""
Explanation: You can use the tft.TransformFeaturesLayer on it's own:
End of explanation
"""


class StackDict(tf.keras.layers.Layer):
  def call(self, inputs):
    values = [
        tf.cast(v, tf.float32)
        for k,v in sorted(inputs.items(), key=lambda kv: kv[0])]
    return tf.stack(values, axis=1)

class TrainedModel(tf.keras.Model):
  def __init__(self):
    super().__init__(self)
    self.concat = StackDict()
    self.body = tf.keras.Sequential([
        tf.keras.layers.Dense(64, activation='relu'),
        tf.keras.layers.Dense(64, activation='relu'),
        tf.keras.layers.Dense(10),
    ])

  def call(self, inputs, training=None):
    x = self.concat(inputs)
    return self.body(x, training)

trained_model = TrainedModel()

"""
Explanation: Export
A more typical use case would use tf.Transform to apply the transformation to the training and evaluation datasets (see the next tutorial for an example). Then, after training, before exporting the model attach the tft.TransformFeaturesLayer as the first layer so that you can export it as part of your tf.saved_model. For a concrete example, keep reading.
An example training model
Below is a model that:

takes the transformed batch,
stacks them all together into a simple (batch, features) matrix,
runs them through a few dense layers, and
produces 10 linear outputs.

In a real use case you would apply a one-hot to the s_integerized feature.
You could train this model on a dataset transformed by tf.Transform:
End of explanation
"""


trained_model_output = trained_model(transformed_batch)
trained_model_output.shape

"""
Explanation: Imagine you trained the model.
trained_model.compile(loss=..., optimizer='adam')
trained_model.fit(...)
This model runs on the transformed inputs
End of explanation
"""


class ExportModel(tf.Module):
  def __init__(self, trained_model, input_transform):
    self.trained_model = trained_model
    self.input_transform = input_transform

  @tf.function
  def __call__(self, inputs, training=None):
    x = self.input_transform(inputs)
    return self.trained_model(x)

# TODO 3: Export the model
export_model = ExportModel(trained_model=trained_model,
                           input_transform=tft_layer)

"""
Explanation: An example export wrapper
Imagine you've trained the above model and want to export it.
You'll want to include the transform function in the exported model:
End of explanation
"""


export_model_output = export_model(raw_data_batch)
export_model_output.shape

tf.reduce_max(abs(export_model_output - trained_model_output)).numpy()

"""
Explanation: This combined model works on the raw data, and produces exactly the same results as calling the trained model directly:
End of explanation
"""


import tempfile
model_dir = tempfile.mkdtemp(suffix='tft')

tf.saved_model.save(export_model, model_dir)

reloaded = tf.saved_model.load(model_dir)

reloaded_model_output = reloaded(raw_data_batch)
reloaded_model_output.shape

tf.reduce_max(abs(export_model_output - reloaded_model_output)).numpy()

"""
Explanation: This export_model includes the tft.TransformFeaturesLayer and is entierly self-contained. You can save it and restore it in another environment and still get exactly the same result:
End of explanation
"""


