def preprocessing_fn(inputs):
"""tf.transform's callback function for preprocessing inputs.
Args:
inputs: map from feature keys to raw not-yet-transformed features.
Returns:
Map from string feature key to transformed feature operations.
"""
outputs = {}
for key in _DENSE_FLOAT_FEATURE_KEYS:
# Preserve this feature as a dense float, setting nan's to the mean.
outputs[_transformed_name(key)] = tft.scale_to_z_score(
_fill_in_missing(inputs[key]))
for key in _VOCAB_FEATURE_KEYS:
# Build a vocabulary for this feature.
outputs[_transformed_name(key)] = tft.compute_and_apply_vocabulary(
_fill_in_missing(inputs[key]),
top_k=_VOCAB_SIZE,
num_oov_buckets=_OOV_SIZE)
for key in _BUCKET_FEATURE_KEYS:
outputs[_transformed_name(key)] = tft.bucketize(
_fill_in_missing(inputs[key]),
_FEATURE_BUCKET_COUNT)
for key in _CATEGORICAL_FEATURE_KEYS:
outputs[_transformed_name(key)] = _fill_in_missing(inputs[key])
# TODO(b/157064428): Support label transformation for Keras.
# Do not apply label transformation as it will result in wrong evaluation.
outputs[_transformed_name(_LABEL_KEY)] = inputs[_LABEL_KEY]
return outputs
def _tokenize_review(review):
"""Tokenize the reviews by spliting the reviews.
Constructing a vocabulary. Map the words to their frequency index in the
vocabulary.
Args:
review: tensors containing the reviews. (batch_size/None, 1)
Returns:
Tokenized and padded review tensors. (batch_size/None, _MAX_LEN)
"""
review_sparse = tf.strings.split(tf.reshape(review, [-1])).to_sparse()
# tft.apply_vocabulary doesn't reserve 0 for oov words. In order to comply
# with convention and use mask_zero in keras.embedding layer, set oov value
# to _VOCAB_SIZE and padding value to -1. Then add 1 to all the tokens.
review_indices = tft.compute_and_apply_vocabulary(
review_sparse, default_value=_VOCAB_SIZE, top_k=_VOCAB_SIZE)
dense = tf.sparse.to_dense(review_indices, default_value=-1)
# TFX transform expects the transform result to be FixedLenFeature.
padding_config = [[0, 0], [0, _MAX_LEN]]
dense = tf.pad(dense, padding_config, 'CONSTANT', -1)
padded = tf.slice(dense, [0, 0], [-1, _MAX_LEN])
padded += 1
return padded
def preprocessing_fn(inputs):
"""Preprocesses Covertype Dataset.
Scales numerical features and generates vocabularies
and mappings for categorical features.
Args:
inputs: A map from feature keys to raw not-yet-transformed features
Returns:
A map from transformed feature keys to transformation operations
"""
outputs = {}
# Scale numerical features
for key in NUMERIC_FEATURE_KEYS:
outputs[_transformed_name(key)] = tft.scale_to_z_score(
_fill_in_missing(inputs[key]))
# Generate vocabularies and maps categorical features
for key in CATEGORICAL_FEATURE_KEYS:
outputs[_transformed_name(key)] = tft.compute_and_apply_vocabulary(
x=_fill_in_missing(inputs[key]),
num_oov_buckets=1,
vocab_filename=key)
# Convert Cover_Type from 1-7 to 0-6
outputs[_transformed_name(LABEL_KEY)] = _fill_in_missing(
inputs[LABEL_KEY]) - 1
return outputs
def compute_vocab_fn(inputs):
"""Preprocessing fn for sparse features.
This function computes unique IDs for the sparse features. We rely on implicit
behavior which writes the vocab files to the vocab_filename specified in
tft.compute_and_apply_vocabulary.
Pre-condition: Sparse features have been converted to integer and mod'ed with
MAX_IND_RANGE.
Args:
inputs: Input features to transform.
Returns:
Output dict with transformed features.
"""
outputs = {}
outputs[LABEL_KEY] = inputs[LABEL_KEY]
for key in NUMERIC_FEATURE_KEYS:
outputs[key] = inputs[key]
for idx, key in enumerate(CATEGORICAL_FEATURE_KEYS):
outputs[key] = tft.compute_and_apply_vocabulary(
x=inputs[key], vocab_filename="feature_{}_vocab".format(idx))
return outputs
def _preprocess_tft(raw_data, user_freq, item_freq):
"""Creates vocabularies for users and items and maps their ids to ints.
Args:
raw_data: a dict of shape {$user_key: tensor, $item_key: tensor, ...}.
user_freq: minimum frequency of a user to include it in the user vocab.
item_freq: minimum frequency of an item to include it in the item vocab.
Returns:
A dict containing int ids cooresponding to a user_id and item_id and other
features: {$user_key: $user_id, $item_key: $item_id, ...}.
"""
features = {
feature: raw_data[feature]
for feature in constants.BQ_FEATURES
}
item_vocab = tft.vocabulary(raw_data[constants.ITEM_KEY],
vocab_filename=constants.ITEM_VOCAB_NAME,
frequency_threshold=item_freq)
tft_features = {
constants.TFT_USER_KEY:
tft.compute_and_apply_vocabulary(
raw_data[constants.USER_KEY],
vocab_filename=constants.USER_VOCAB_NAME,
frequency_threshold=user_freq,
default_value=constants.TFT_DEFAULT_ID),
constants.TFT_ITEM_KEY:
tft.apply_vocabulary(raw_data[constants.ITEM_KEY],
item_vocab,
default_value=constants.TFT_DEFAULT_ID),
constants.TFT_ARTIST_KEY:
tft.compute_and_apply_vocabulary(
raw_data[constants.ARTIST_KEY],
vocab_filename=constants.ARTIST_VOCAB_NAME,
default_value=constants.TFT_DEFAULT_ID),
constants.TFT_TAGS_KEY:
tft.compute_and_apply_vocabulary(
raw_data[constants.TAGS_KEY],
vocab_filename=constants.TAG_VOCAB_NAME,
default_value=constants.TFT_DEFAULT_ID),
constants.TFT_TOP_10_KEY:
tft.apply_vocabulary(raw_data[constants.TOP_10_KEY],
item_vocab,
default_value=constants.TFT_DEFAULT_ID),
}
features.update(tft_features)
return features
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(inputs[key])
for key in OPTIONAL_NUMERIC_FEATURE_KEYS:
# This is a SparseTensor because it is optional. Here we fill in a default
# value when it is missing.
sparse = tf.sparse.SparseTensor(inputs[key].indices,
inputs[key].values,
[inputs[key].dense_shape[0], 1])
dense = tf.sparse.to_dense(sp_input=sparse, default_value=0.)
# Reshaping from a batch of vectors of size 1 to a batch to scalars.
dense = tf.squeeze(dense, axis=1)
outputs[key] = tft.scale_to_0_1(dense)
# For all categorical columns except the label column, we generate a
# vocabulary, and convert the string feature to a one-hot encoding.
for key in CATEGORICAL_FEATURE_KEYS:
integerized = tft.compute_and_apply_vocabulary(
tf.strings.strip(inputs[key]),
num_oov_buckets=NUM_OOV_BUCKETS,
vocab_filename=key)
depth = (tft.experimental.get_vocabulary_size_by_name(key) +
NUM_OOV_BUCKETS)
one_hot_encoded = tf.one_hot(integerized,
depth=tf.cast(depth, tf.int32),
on_value=1.0,
off_value=0.0)
# This output is now one-hot encoded. If saving transformed data to disk,
# this can incur significant memory cost.
outputs[key] = tf.reshape(one_hot_encoded, [-1, depth])
# For the label column we provide the mapping from string to index.
table_keys = ['>50K', '<=50K']
with tf.init_scope():
initializer = tf.lookup.KeyValueTensorInitializer(
keys=table_keys,
values=tf.cast(tf.range(len(table_keys)), tf.int64),
key_dtype=tf.string,
value_dtype=tf.int64)
table = tf.lookup.StaticHashTable(initializer, default_value=-1)
# Remove trailing periods for test data when the data is read with tf.data.
label_str = tf.strings.regex_replace(inputs[LABEL_KEY], r'\.', '')
label_str = tf.strings.strip(label_str)
data_labels = table.lookup(label_str)
transformed_label = tf.one_hot(indices=data_labels,
depth=len(table_keys),
on_value=1.0,
off_value=0.0)
outputs[LABEL_KEY] = tf.reshape(transformed_label,
[-1, len(table_keys)])
return outputs
def preprocessing_fn(inputs):
#end::entry_point[]
#tag::logic[]
outputs = {}
# TFT business logic goes here
outputs["body_stuff"] = tft.compute_and_apply_vocabulary(inputs["body"],
top_k=1000)
return outputs
def preprocess_fn(inputs):
"""tf.transform's callback function for preprocessing inputs.
Args:
inputs: map from feature keys to raw not-yet-transformed features.
Returns:
Map from string feature key to transformed feature operations.
"""
outputs = {}
for key in DENSE_FLOAT_FEATURE_KEYS:
# Preserve this feature as a dense float, setting nan's to the mean.
outputs[key] = tft.scale_to_z_score(to_dense(inputs[key]))
for key in VOCAB_FEATURE_KEYS:
# Build a vocabulary for this feature.
if inputs[key].dtype == tf.string:
vocab_tensor = to_dense(inputs[key])
else:
vocab_tensor = tf.as_string(to_dense(inputs[key]))
outputs[key] = tft.compute_and_apply_vocabulary(
vocab_tensor,
vocab_filename='vocab_' + key,
top_k=VOCAB_SIZE,
num_oov_buckets=OOV_SIZE)
for key in BUCKET_FEATURE_KEYS:
outputs[key] = tft.bucketize(to_dense(inputs[key]),
FEATURE_BUCKET_COUNT)
for key in CATEGORICAL_FEATURE_KEYS:
outputs[key] = tf.cast(to_dense(inputs[key]), tf.int64)
taxi_fare = to_dense(inputs[FARE_KEY])
taxi_tip = to_dense(inputs[LABEL_KEY])
# Test if the tip was > 20% of the fare.
tip_threshold = tf.multiply(taxi_fare, tf.constant(0.2))
outputs[LABEL_KEY] = tf.logical_and(
tf.logical_not(tf.math.is_nan(taxi_fare)),
tf.greater(taxi_tip, tip_threshold))
for key in outputs:
if outputs[key].dtype == tf.bool:
outputs[key] = tft.compute_and_apply_vocabulary(
tf.as_string(outputs[key]), vocab_filename='vocab_' + key)
return outputs
def preprocess(inputs): # inputs is a batch of input features
median_age = inputs["housing_median_age"]
ocean_proximity = inputs["ocean_proximity"]
standardized_age = tft.scale_to_z_score(median_age - tft.mean(median_age))
ocean_proximity_id = tft.compute_and_apply_vocabulary(ocean_proximity)
return {
"standardized_median_age": standardized_age,
"ocean_proximity_id": ocean_proximity_id
}
def preprocessing_fn(inputs: Dict[str, Tensor],
custom_config=Dict[str, Any]) -> Dict[str, Tensor]:
"""tf.transform's callback function for preprocessing inputs.
Args:
inputs: map from feature keys to raw not-yet-transformed features.
custom_config: Custom configuration dictionary for passing the task's
ProblemStatement as a text proto, since custom_config must be
JSON-serializable.
Returns:
Map from string feature key to transformed feature operations.
"""
problem_statement = ps_pb2.ProblemStatement()
text_format.Parse(
text=custom_config[BasicPreprocessor.PROBLEM_STATEMENT_KEY],
message=problem_statement)
outputs = {}
for key in [k for k, v in inputs.items() if v.dtype == tf.float32]:
# TODO(weill): Handle case when an int field can actually represents numeric
# rather than categorical values.
task_type = problem_statement.tasks[0].type
if task_type.HasField('one_dimensional_regression') and (
key == task_type.one_dimensional_regression.label):
outputs[key] = inputs[key]
# Skip normalizing regression tasks.
continue
# Preserve this feature as a dense float, setting nan's to the mean.
outputs[_sanitize_feature_name(key)] = tft.scale_to_z_score(
_fill_in_missing(inputs[key]))
for key in [k for k, v in inputs.items() if v.dtype != tf.float32]:
# Build a vocabulary for this feature.
# TODO(weill): Risk here to blow up computation needlessly.
output = tft.compute_and_apply_vocabulary(_fill_in_missing(
inputs[key]),
top_k=None,
num_oov_buckets=1)
# Don't sanitize the label key name.
task_type = problem_statement.tasks[0].type
if task_type.HasField('multi_class_classification') and (
key == task_type.multi_class_classification.label):
outputs[key] = output
continue
if task_type.HasField('binary_classification') and (
key == task_type.binary_classification.label):
outputs[key] = output
continue
# Do sanitize feature key names.
outputs[_sanitize_feature_name(key)] = output
return outputs
def _preprocessing_fn(inputs, integer_label: bool = False):
"""TensorFlow Transform preprocessing function."""
outputs = inputs.copy()
if not integer_label:
# Integerize string labels, if present.
outputs[constants.LABEL_KEY] = tft.compute_and_apply_vocabulary(
outputs[constants.LABEL_KEY])
return outputs
def _preprocessing_fn(inputs: Dict[str, Any],
schema_map: Dict[str, collections.namedtuple]):
"""TensorFlow Transform preprocessing function."""
outputs = {}
for name, supported_type in schema_map.items():
if supported_type.type_name == 'string_label':
outputs[name] = tft.compute_and_apply_vocabulary(inputs[name])
else:
outputs[name] = inputs[name]
return outputs
def preprocessing_fn(inputs):
""" This is the preprocessing functions use by the tensorflow transform
Paramters:
inputs -- the tensorflow parset input tensors in a dict, defined by the metadata input
Returns:
inputs -- dict wit the now appended output values, the added word representation is a sparse tensor
"""
words = tf.string_split(inputs['text'],DELIMITERS_WORDS)
word_representation = tft.compute_and_apply_vocabulary(words,default_value=0,top_k=10000)
inputs["word_representation"] = word_representation
return inputs
def preprocessing_fn(inputs):
"""Preprocesses Titanic Dataset."""
outputs = {}
# Scale numerical features
for key in features.NUMERIC_FEATURE_KEYS:
mean_value = compute_mean_ignore_nan(inputs[key].values)
absl.logging.info(f'TFT preprocessing. Mean value for {key} = {mean_value}')
outputs[features.transformed_name(key)] = tft.scale_to_z_score(
_fill_in_missing_with_impute(inputs[key], mean_value))
for key in features.VOCAB_FEATURE_KEYS:
# Build a vocabulary for this feature.
outputs[features.transformed_name(key)] = tft.compute_and_apply_vocabulary(
_fill_in_missing(inputs[key]),
top_k=features.VOCAB_SIZE_MAP.get(key, features.VOCAB_SIZE),
num_oov_buckets=features.OOV_SIZE)
for key in features.BUCKET_FEATURE_KEYS:
if key in features.FEATURE_BUCKET_BOUNDARIES:
bucket_boundaries = tf.constant(features.FEATURE_BUCKET_BOUNDARIES.get(key))
# tf.print("bucket_boundaries:", bucket_boundaries, output_stream=absl.logging.info)
outputs[features.transformed_name(key)] = tft.apply_buckets(_fill_in_missing(inputs[key]),
bucket_boundaries)
else:
outputs[features.transformed_name(key)] = tft.bucketize(
_fill_in_missing(inputs[key]),
features.FEATURE_BUCKET_COUNT_MAP.get(key, features.FEATURE_BUCKET_COUNT))
# Generate vocabularies and maps categorical features
for key in features.CATEGORICAL_FEATURE_KEYS:
outputs[features.transformed_name(key)] = tft.compute_and_apply_vocabulary(
x=_fill_in_missing(inputs[key]), num_oov_buckets=1, vocab_filename=key)
# Convert Cover_Type to dense tensor
outputs[features.transformed_name(features.LABEL_KEY)] = _fill_in_missing(
inputs[features.LABEL_KEY])
return outputs
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
}
def preprocessing_fn(inputs):
"""tf.transform's callback function for preprocessing inputs.
Args:
inputs: map from feature keys to raw not-yet-transformed features.
Returns:
Map from string feature key to transformed feature operations.
"""
outputs = {}
outputs["id"] = inputs["id"]
tokens = tokenize_reviews(_fill_in_missing(inputs["text"], ''))
outputs["text_xf"] = tft.compute_and_apply_vocabulary(
tokens, top_k=VOCAB_SIZE, num_oov_buckets=OOV_SIZE)
outputs["label_xf"] = _fill_in_missing(inputs["label"], -1)
return outputs
def preprocessing_fn(inputs):
text_fields = []
# Keep the original data and add more to it.
result = inputs.copy()
# Figure out the vocabulary for our text fields.
for field_name in text_fields:
field = inputs[field_name]
tokens = tf.strings.split(text, " ")
bag_of_words = tft.bag_of_words(tokens, range(1,3), seperator=" ")
indices = tft.compute_and_apply_vocabulary(bag_of_words)
bow_indices, weights = tft.tfidf(line_indices)
outputs[f"{field_name}_bow_indices"] = bow_indices
outputs[f"{field_name}_weight"] weights
return result
def preprocessing_fn(inputs):
"""Preprocess input columns into transformed columns."""
review = inputs[REVIEW_KEY]
review_tokens = tf.string_split(review, DELIMITERS)
review_indices = tft.compute_and_apply_vocabulary(
review_tokens, top_k=VOCAB_SIZE)
# Add one for the oov bucket created by compute_and_apply_vocabulary.
review_bow_indices, review_weight = tft.tfidf(review_indices,
VOCAB_SIZE + 1)
return {
REVIEW_KEY: review_bow_indices,
REVIEW_WEIGHT_KEY: review_weight,
LABEL_KEY: inputs[LABEL_KEY]
}
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 preprocessing_fn(inputs):
"""tf.transform's callback function for preprocessing inputs.
Args:
inputs: map from feature keys to raw not-yet-transformed features.
Returns:
Map from string feature key to transformed feature operations.
"""
outputs = {}
for key in features.DENSE_FLOAT_FEATURE_KEYS:
# Preserve this feature as a dense float, setting nan's to the mean.
outputs[features.transformed_name(key)] = tft.scale_to_z_score(
_fill_in_missing(inputs[key]))
for key in features.VOCAB_FEATURE_KEYS:
# Build a vocabulary for this feature.
outputs[features.transformed_name(
key)] = tft.compute_and_apply_vocabulary(
_fill_in_missing(inputs[key]),
top_k=features.VOCAB_SIZE,
num_oov_buckets=features.OOV_SIZE)
for key, num_buckets in zip(features.BUCKET_FEATURE_KEYS,
features.BUCKET_FEATURE_BUCKET_COUNT):
outputs[features.transformed_name(key)] = tft.bucketize(
_fill_in_missing(inputs[key]),
num_buckets,
always_return_num_quantiles=False)
for key in features.CATEGORICAL_FEATURE_KEYS:
outputs[features.transformed_name(key)] = _fill_in_missing(inputs[key])
# Was this passenger a big tipper?
fare_key = 'fare'
taxi_fare = _fill_in_missing(inputs[fare_key])
tips = _fill_in_missing(inputs[features.LABEL_KEY])
outputs[features.transformed_name(
features.LABEL_KEY)] = tf.compat.v1.where(
tf.math.is_nan(taxi_fare),
tf.cast(tf.zeros_like(taxi_fare), tf.int64),
# Test if the tip was > 20% of the fare.
tf.cast(tf.greater(tips, tf.multiply(taxi_fare, tf.constant(0.2))),
tf.int64))
return outputs
def preprocessing_fn(inputs):
"""tf.transform's callback function for preprocessing inputs.
Args:
inputs: map from feature keys to raw not-yet-transformed features.
Returns:
Map from string feature key to transformed feature operations.
"""
outputs = {}
for key in my_metadata.NUMERIC_FEATURE_KEYS:
# Preserve this feature as a dense float, setting nan's to the mean.
outputs[my_metadata.transformed_name(key)] = transform.scale_to_z_score(_fill_in_missing(inputs[key]))
for key in my_metadata.VOCAB_FEATURE_KEYS:
# Build a vocabulary for this feature.
outputs[my_metadata.transformed_name(key)] = transform.compute_and_apply_vocabulary(
_fill_in_missing(inputs[key]),
vocab_filename=my_metadata.transformed_name(key),
num_oov_buckets=my_metadata.OOV_SIZE,
top_k=my_metadata.VOCAB_SIZE
)
for key, hash_buckets in my_metadata.HASH_STRING_FEATURE_KEYS.items():
outputs[my_metadata.transformed_name(key)] = transform.hash_strings(
_fill_in_missing(inputs[key]),
hash_buckets=hash_buckets
)
for key, nb_buckets in my_metadata.TO_BE_BUCKETIZED_FEATURE.items():
outputs[my_metadata.transformed_name(key +'_bucketized')] = transform.bucketize(
_fill_in_missing(inputs[key]), nb_buckets)
# Was this passenger a big tipper?
taxi_fare = _fill_in_missing(inputs[my_metadata.FARE_KEY])
tips = _fill_in_missing(inputs[my_metadata.LABEL_KEY])
outputs[my_metadata.transformed_name(my_metadata.LABEL_KEY)] = tf.where(
tf.is_nan(taxi_fare),
tf.cast(tf.zeros_like(taxi_fare), tf.int64),
# Test if the tip was > 20% of the fare.
tf.cast(
tf.greater(tips, tf.multiply(taxi_fare, tf.constant(0.2))),
tf.int64))
return outputs
def preprocessing_fn(inputs):
"""
Perform feature reduction via `compute_and_apply_vocabulary`. An
`indices` tensor should come in with values in (0,5e7) and should
be transformed to (0,40000).
"""
outputs = inputs
outputs['indices'] = (
tft.compute_and_apply_vocabulary(x=inputs['indices'],
top_k=(MAX_IDX-5),
num_oov_buckets=5,
vocab_filename='my_vocab')
)
return outputs
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 key in OPTIONAL_NUMERIC_FEATURE_KEYS:
# This is a SparseTensor because it is optional. Here we fill in a default
# value when it is missing.
sparse = tf.sparse.SparseTensor(inputs[key].indices,
inputs[key].values,
[inputs[key].dense_shape[0], 1])
dense = tf.sparse.to_dense(sp_input=sparse, default_value=0.)
# Reshaping from a batch of vectors of size 1 to a batch to scalars.
dense = tf.squeeze(dense, axis=1)
outputs[key] = tft.scale_to_0_1(dense)
# 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(inputs[key],
vocab_filename=key)
# For the label column we provide the mapping from string to index.
table_keys = ['>50K', '<=50K']
initializer = tf.lookup.KeyValueTensorInitializer(
keys=table_keys,
values=tf.cast(tf.range(len(table_keys)), tf.int64),
key_dtype=tf.string,
value_dtype=tf.int64)
table = tf.lookup.StaticHashTable(initializer, default_value=-1)
data_labels = table.lookup(inputs[LABEL_KEY])
outputs[LABEL_KEY] = tf.one_hot(indices=data_labels,
depth=len(table_keys),
on_value=1.0,
off_value=0.0)
return outputs
def preprocessing_fn(inputs):
integerized_s = tft.compute_and_apply_vocabulary(inputs['s'])
_ = tft.bucketize(inputs['x'], 2, name='bucketize')
return {
'integerized_s':
integerized_s,
'x_min':
tft.min(inputs['x'], name='x') + tf.zeros_like(inputs['x']),
'x_mean':
tft.mean(inputs['x'], name='x') + tf.zeros_like(inputs['x']),
'y_min':
tft.min(inputs['y'], name='y') + tf.zeros_like(inputs['y']),
'y_mean':
tft.mean(inputs['y'], name='y') + tf.zeros_like(inputs['y']),
}
def preprocessing_fn(inputs):
"""
Preprocess input columns into transformed columns.
Args:
inputs (dict): dict of input columns
Returns:
output dict of transformed columns
"""
outputs = {}
# Encode categorical column:
outputs['MixingSpeed'] = tft.compute_and_apply_vocabulary(
inputs['MixingSpeed'])
outputs['ButterMass'] = inputs['ButterMass']
# Calculate Derived Features:
outputs['TotalMass'] = inputs['ButterMass'] + inputs['SugarMass'] + inputs[
'FlourMass']
for ingredient in ['Butter', 'Sugar', 'Flour']:
ingredient_percentage = inputs['{}Mass'.format(
ingredient)] / outputs['TotalMass']
outputs['Norm{}perc'.format(ingredient)] = tft.scale_to_z_score(
ingredient_percentage)
# Keep absolute numeric columns
for key in ['TotalVolume', 'Energy']:
outputs[key] = inputs[key]
# Normalize other numeric columns
for key in [
'ButterTemperature',
'SugarHumidity',
'FlourHumidity',
'HeatingTime',
'MixingTime',
'Density',
'Temperature',
'Humidity',
]:
outputs[key] = tft.scale_to_z_score(inputs[key])
# Extract Specific Problems
chunks_detected_str = tf.regex_replace(input=inputs['Problems'],
pattern='.*chunk.*',
rewrite='chunk',
name='DetectChunk')
outputs['Chunks'] = tf.cast(tf.equal(chunks_detected_str, 'chunk'),
tf.float32)
return outputs
def wide_preprocessing_fn(inputs):
"""TFT preprocessing function.
Args:
inputs: Map from feature keys to raw not-yet-transformed features.
Returns:
Map from string feature key to transformed feature operations.
"""
outputs = {}
# pylint: disable=protected-access
for idx, key in enumerate(
itertools.islice(
itertools.cycle(taxi_utils._BUCKET_FEATURE_KEYS),
self._num_bucketize)):
outputs["bucketized" + str(idx)] = tft.bucketize(
taxi_utils._fill_in_missing(inputs[key]),
taxi_utils._FEATURE_BUCKET_COUNT)
for idx, key in enumerate(
itertools.islice(
itertools.cycle(taxi_utils._DENSE_FLOAT_FEATURE_KEYS),
self._num_scale)):
# Preserve this feature as a dense float, setting nan's to the mean.
outputs["scaled" + str(idx)] = tft.scale_to_z_score(
taxi_utils._fill_in_missing(inputs[key]))
for idx, key in enumerate(
itertools.islice(
itertools.cycle(taxi_utils._VOCAB_FEATURE_KEYS),
self._num_vocabs)):
outputs["vocab" + str(idx)] = tft.compute_and_apply_vocabulary(
taxi_utils._fill_in_missing(inputs[key]),
top_k=taxi_utils._VOCAB_SIZE,
num_oov_buckets=taxi_utils._OOV_SIZE)
# Pass-through features.
for key in taxi_utils._CATEGORICAL_FEATURE_KEYS + [
taxi_utils._LABEL_KEY
]:
outputs[key] = inputs[key]
return outputs
def def_preprocessing_fn(inputs):
"""tf.transform's callback function for preprocessing inputs.
Args:
inputs: map from feature keys to raw not-yet-transformed features.
Returns:
Map from string feature key to transformed feature operations.
"""
outputs = {}
for key in taxi.DENSE_FLOAT_FEATURE_KEYS:
# Preserve this feature as a dense float, setting nan's to the mean.
outputs[taxi.transformed_name(key)] = transform.scale_to_z_score(
_fill_in_missing(inputs[key]))
for key in taxi.VOCAB_FEATURE_KEYS:
# Build a vocabulary for this feature.
outputs[
taxi.transformed_name(key)] = transform.compute_and_apply_vocabulary(
_fill_in_missing(inputs[key]),
top_k=taxi.VOCAB_SIZE,
num_oov_buckets=taxi.OOV_SIZE)
for key in taxi.BUCKET_FEATURE_KEYS:
outputs[taxi.transformed_name(key)] = transform.bucketize(
_fill_in_missing(inputs[key]), taxi.FEATURE_BUCKET_COUNT)
for key in taxi.CATEGORICAL_FEATURE_KEYS:
outputs[taxi.transformed_name(key)] = _fill_in_missing(inputs[key])
# Was this passenger a big tipper?
taxi_fare = _fill_in_missing(inputs[taxi.FARE_KEY])
tips = _fill_in_missing(inputs[taxi.LABEL_KEY])
outputs[taxi.transformed_name(taxi.LABEL_KEY)] = tf.where(
tf.is_nan(taxi_fare),
tf.cast(tf.zeros_like(taxi_fare), tf.int64),
# Test if the tip was > 20% of the fare.
tf.cast(
tf.greater(tips, tf.multiply(taxi_fare, tf.constant(0.2))),
tf.int64))
return outputs
def preprocessing_fn(inputs):
"""Preprocesses Covertype Dataset."""
outputs = {}
# Scale numerical features
for key in features.NUMERIC_FEATURE_KEYS:
outputs[features.transformed_name(key)] = tft.scale_to_z_score(
_fill_in_missing(inputs[key]))
# Generate vocabularies and maps categorical features
for key in features.CATEGORICAL_FEATURE_KEYS:
outputs[features.transformed_name(key)] = tft.compute_and_apply_vocabulary(
x=_fill_in_missing(inputs[key]), num_oov_buckets=1, vocab_filename=key)
# Convert Cover_Type to dense tensor
outputs[features.transformed_name(features.LABEL_KEY)] = _fill_in_missing(
inputs[features.LABEL_KEY])
return outputs
def preprocessing_fn(inputs):
"""tf.transform's callback function for preprocessing inputs.
Args:
inputs: map from feature keys to raw not-yet-transformed features.
Returns:
Map from string feature key to transformed feature operations.
"""
outputs = {}
for key in _DENSE_FLOAT_FEATURE_KEYS:
# Preserve this feature as a dense float, setting nan's to the mean.
outputs[_transformed_name(key)] = tft.scale_to_z_score(inputs[key])
for key in _VOCAB_FEATURE_KEYS:
# Build a vocabulary for this feature.
outputs[_transformed_name(key)] = tft.compute_and_apply_vocabulary(
inputs[key], top_k=_VOCAB_SIZE, num_oov_buckets=_OOV_SIZE)
outputs[_transformed_name(_LABEL_KEY)] = inputs[_LABEL_KEY]
return outputs
def preprocessing_fn(inputs, custom_config):
"""tf.transform's callback function for preprocessing inputs.
Args:
inputs: map from feature keys to raw not-yet-transformed features.
custom_config: additional properties for pre-processing.
Returns:
Map from string feature key to transformed features.
"""
outputs = {}
for key in _DENSE_FLOAT_FEATURE_KEYS:
# Preserve this feature as a dense float, setting nan's to the mean.
outputs[_transformed_name(key)] = tft.scale_to_z_score(
_fill_in_missing(_identity(inputs[key])))
for key in _VOCAB_FEATURE_KEYS:
# Build a vocabulary for this feature.
outputs[_transformed_name(key)] = tft.compute_and_apply_vocabulary(
_fill_in_missing(inputs[key]),
top_k=custom_config.get('VOCAB_SIZE', _VOCAB_SIZE),
num_oov_buckets=custom_config.get('OOV_SIZE', _OOV_SIZE))
for key in _BUCKET_FEATURE_KEYS:
outputs[_transformed_name(key)] = tft.bucketize(
_fill_in_missing(inputs[key]), _FEATURE_BUCKET_COUNT)
for key in _CATEGORICAL_FEATURE_KEYS:
outputs[_transformed_name(key)] = _fill_in_missing(inputs[key])
# Was this passenger a big tipper?
taxi_fare = _fill_in_missing(inputs[_FARE_KEY])
tips = _fill_in_missing(inputs[_LABEL_KEY])
outputs[_transformed_name(_LABEL_KEY)] = tf.compat.v1.where(
tf.math.is_nan(taxi_fare),
tf.cast(tf.zeros_like(taxi_fare), tf.int64),
# Test if the tip was > 20% of the fare.
tf.cast(tf.greater(tips, tf.multiply(taxi_fare, tf.constant(0.2))),
tf.int64))
return outputs
def preprocessing_fn(inputs: tf.Tensor) -> tf.Tensor:
"""tf.transform's callback function for preprocessing inputs.
Args:
inputs: map from feature keys to raw not-yet-transformed features.
Returns:
Map from string feature key to transformed feature operations.
"""
outputs = {}
for key in ONE_HOT_FEATURES.keys():
dim = ONE_HOT_FEATURES[key]
#calling the fill_in_missing function
int_value = tft.compute_and_apply_vocabulary(
fill_in_missing(inputs[key]), top_k = dim + 1
)
outputs[transformed_name(key)] = convert_num_to_one_hot(
int_value, num_labels = dim + 1
)
for key, bucket_count in BUCKET_FEATURES.items():
dense_feature = fill_in_missing(inputs[key])
if key == 'zip_code' and dense_feature.dtype == tf.string:
dense_feature = convert_zip_code(dense_feature)
else:
dense_feature = tf.cast(dense_feature, tf.float32)
temp_feature = tft.bucketize(dense_feature, bucket_count,
always_return_num_quantiles= False)
outputs[transformed_name(key)] = convert_num_to_one_hot(
temp_feature, num_labels = bucket_count + 1
)
for key in TEXT_FEATURES.keys():
#it's probably clearer to separate function from dict key
outputs[transformed_name(key)] = fill_in_missing(inputs[key])
outputs[transformed_name(LABEL_KEY)] = fill_in_missing(inputs[LABEL_KEY])
return outputs
