def testNumericAnalyzersWithSparseInputs(self):
def repeat(in_tensor, value):
batch_size = tf.shape(in_tensor)[0]
return tf.ones([batch_size], value.dtype) * value
input_data = [{'a': [4, 5, 6]}, {'a': [1, 2]}]
input_metadata = self.toMetadata({'a': tf.VarLenFeature(tf.int64)})
input_dataset = (input_data, input_metadata)
with beam_impl.Context(temp_dir=self.get_temp_dir()):
with self.assertRaises(TypeError):
def min_fn(inputs):
return {
'min': tft.map(repeat, inputs['a'],
tft.min(inputs['a']))
}
_ = input_dataset | beam_impl.AnalyzeDataset(min_fn)
with self.assertRaises(TypeError):
def max_fn(inputs):
return {
'max': tft.map(repeat, inputs['a'],
tft.max(inputs['a']))
}
_ = input_dataset | beam_impl.AnalyzeDataset(max_fn)
with self.assertRaises(TypeError):
def sum_fn(inputs):
return {
'sum': tft.map(repeat, inputs['a'],
tft.sum(inputs['a']))
}
_ = input_dataset | beam_impl.AnalyzeDataset(sum_fn)
with self.assertRaises(TypeError):
def size_fn(inputs):
return {
'size': tft.map(repeat, inputs['a'],
tft.size(inputs['a']))
}
_ = input_dataset | beam_impl.AnalyzeDataset(size_fn)
with self.assertRaises(TypeError):
def mean_fn(inputs):
return {
'mean': tft.map(repeat, inputs['a'],
tft.mean(inputs['a']))
}
_ = input_dataset | beam_impl.AnalyzeDataset(mean_fn)
def build_pipeline(p, flags):
"""Sets up Apache Beam pipeline for execution."""
raw_data = (
p | 'QueryTable' >> beam.io.Read(
beam.io.BigQuerySource(query=query.get_query(flags.bq_table),
project=flags.project_id,
use_standard_sql=True))
# omit 'Generate data' step if working with real data
| 'Generate data' >> beam.Map(_generate_fake_data)
| 'Extract lifetime ' >> beam.Map(append_lifetime_duration)
| 'Extract label' >> beam.Map(append_label)
| 'Generate label array' >> beam.Map(combine_censorship_duration))
raw_train, raw_eval, raw_test = (
raw_data | 'RandomlySplitData' >> randomly_split(
train_size=.7, validation_size=.15, test_size=.15))
raw_metadata = features.get_raw_dataset_metadata()
preprocess_fn = features.preprocess_fn
transform_fn = ((raw_train, raw_metadata)
| 'AnalyzeTrain' >> tft_beam.AnalyzeDataset(preprocess_fn))
(transform_fn
| 'WriteTransformFn' >> tft_beam_io.WriteTransformFn(flags.output_dir))
for dataset_type, dataset in [('Train', raw_train), ('Eval', raw_eval),
('Test', raw_test)]:
transform_label = 'Transform{}'.format(dataset_type)
t, metadata = (((dataset, raw_metadata), transform_fn)
| transform_label >> tft_beam.TransformDataset())
if dataset_type == 'Train':
(metadata | 'WriteMetadata' >> tft_beam_io.WriteMetadata(
os.path.join(flags.output_dir, 'transformed_metadata'),
pipeline=p))
write_label = 'Write{}TFRecord'.format(dataset_type)
t | write_label >> write_tfrecord(dataset_type, flags.output_dir,
metadata)
def testTransformWithExcludedOutputs(self):
def preprocessing_fn(inputs):
return {
'x_scaled': tft.scale_to_0_1(inputs['x']),
'y_scaled': tft.scale_to_0_1(inputs['y'])
}
# Run AnalyzeAndTransform on some input data and compare with expected
# output.
input_data = [{'x': 5, 'y': 1}, {'x': 1, 'y': 2}]
input_metadata = self.toMetadata({
'x':
tf.FixedLenFeature((), tf.float32, 0),
'y':
tf.FixedLenFeature((), tf.float32, 0)
})
with beam_impl.Context(temp_dir=self.get_temp_dir()):
transform_fn = ((input_data, input_metadata)
| beam_impl.AnalyzeDataset(preprocessing_fn))
# Take the transform function and use TransformDataset to apply it to
# some eval data, with missing 'y' column.
eval_data = [{'x': 6}]
eval_metadata = self.toMetadata(
{'x': tf.FixedLenFeature((), tf.float32, 0)})
transformed_eval_dataset = (
((eval_data, eval_metadata), transform_fn)
| beam_impl.TransformDataset(exclude_outputs=['y_scaled']))
expected_transformed_eval_data = [{'x_scaled': 1.25}]
expected_transformed_eval_schema = self.toMetadata(
{'x_scaled': tf.FixedLenFeature((), tf.float32, None)})
self.assertDatasetsEqual(
transformed_eval_dataset,
(expected_transformed_eval_data, expected_transformed_eval_schema))
def testAnalyzeBeforeTransform(self):
def preprocessing_fn(inputs):
return {'x_scaled': tft.scale_to_0_1(inputs['x'])}
# Run AnalyzeAndTransform on some input data and compare with expected
# output.
input_data = [{'x': 4}, {'x': 1}, {'x': 5}, {'x': 2}]
input_metadata = self.toMetadata(
{'x': tf.FixedLenFeature((), tf.float32, 0)})
transformed_dataset, transform_fn = (
(input_data, input_metadata)
| beam_impl.AnalyzeAndTransformDataset(
preprocessing_fn,
os.path.join(self.get_temp_dir(),
'analyze_before_transform_at')))
expected_transformed_data = [{
'x_scaled': 0.75
}, {
'x_scaled': 0.0
}, {
'x_scaled': 1.0
}, {
'x_scaled': 0.25
}]
expected_transformed_metadata = self.toMetadata(
{'x_scaled': tf.FixedLenFeature((), tf.float32, None)})
self.assertDatasetsEqual(
transformed_dataset,
(expected_transformed_data, expected_transformed_metadata))
# Take the transform function and use TransformDataset to apply it to
# some eval data, and compare with expected output.
eval_data = [{'x': 6}, {'x': 3}]
transformed_eval_dataset = (((eval_data, input_metadata), transform_fn)
| beam_impl.TransformDataset())
expected_transformed_eval_data = [{
'x_scaled': 1.25
}, {
'x_scaled': 0.5
}]
self.assertDatasetsEqual(
transformed_eval_dataset,
(expected_transformed_eval_data, expected_transformed_metadata))
# Redo test with eval data, using AnalyzeDataset instead of
# AnalyzeAndTransformDataset to genereate transform_fn.
transform_fn = ((input_data, input_metadata)
| beam_impl.AnalyzeDataset(
preprocessing_fn,
os.path.join(self.get_temp_dir(),
'analyze_before_transform_a')))
transformed_eval_dataset = (((eval_data, input_metadata), transform_fn)
| beam_impl.TransformDataset())
self.assertDatasetsEqual(
transformed_eval_dataset,
(expected_transformed_eval_data, expected_transformed_metadata))
def run(pipeline_options, known_args):
pipeline = beam.Pipeline(options=pipeline_options)
with impl.Context(known_args.transform_temp_dir):
articles = (
pipeline
| 'Get Paths' >> beam.Create(get_paths(known_args.file_pattern))
| 'Get Articles' >> beam.Map(get_articles)
| 'Get Article' >> beam.FlatMap(lambda x: x)
)
dataset = (articles, get_metadata())
transform_fn = (
dataset
| 'Analyse dataset' >> impl.AnalyzeDataset(preprocess_fn)
)
transformed_data_with_meta = (
(dataset, transform_fn)
| 'Transform dataset' >> impl.TransformDataset()
)
transformed_data, transformed_metadata = transformed_data_with_meta
transform_fn | 'Export Transform Fn' >> transform_fn_io.WriteTransformFn(
known_args.transform_export_dir)
(
transformed_data
| 'Convert to Insertable data' >> beam.Map(to_bq_row)
| 'Write to BigQuery table' >> beam.io.WriteToBigQuery(
project=known_args.bq_project,
dataset=known_args.bq_dataset,
table=known_args.bq_table,
schema=get_bigquery_schema(),
create_disposition=beam.io.BigQueryDisposition.CREATE_IF_NEEDED,
write_disposition=beam.io.BigQueryDisposition.WRITE_TRUNCATE)
)
if known_args.enable_tfrecord:
transformed_data | 'Write TFRecords' >> beam.io.tfrecordio.WriteToTFRecord(
file_path_prefix='{0}/{1}'.format(known_args.tfrecord_export_dir, 'reuter'),
file_name_suffix='.tfrecords',
coder=tft_coders.example_proto_coder.ExampleProtoCoder(transformed_metadata.schema))
if known_args.enable_debug:
transformed_data | 'Debug Output' >> beam.io.textio.WriteToText(
file_path_prefix=known_args.debug_output_prefix, file_name_suffix='.txt')
job = pipeline.run()
if pipeline_options.get_all_options()['runner'] == 'DirectRunner':
job.wait_until_finish()
def main():
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.string_to_int(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
}
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.Schema({
's':
dataset_schema.ColumnSchema(
tf.string, [], dataset_schema.FixedColumnRepresentation()),
'y':
dataset_schema.ColumnSchema(
tf.float32, [], dataset_schema.FixedColumnRepresentation()),
'x':
dataset_schema.ColumnSchema(
tf.float32, [], dataset_schema.FixedColumnRepresentation())
}))
with beam_impl.Context(temp_dir=tempfile.mkdtemp()):
transform_fn = ((raw_data, raw_data_metadata)
| beam_impl.AnalyzeDataset(preprocessing_fn))
transformed_dataset = (((raw_data, raw_data_metadata), transform_fn)
| beam_impl.TransformDataset())
# pylint: disable=unused-variable
transformed_data, transformed_metadata = transformed_dataset
pprint.pprint(transformed_data)
def build_graph(self):
# Move percentage of train data to .`PPGRAPH_EXT` files, used for graph building.
# num_lines = 0
# for i in range(DATASET_NUM_SHARDS):
# _fname = '{}-{:05}-of-{:05}'.format(self.train_fname_out, i, self.config.DATASET_NUM_SHARDS)
# num_lines += sum(1 for _ in open(_fname))
# _fname_marked = '{}-{:05}-of-{:05}.{}'.format(self.train_fname_out, i, self.config.DATASET_NUM_SHARDS,
# PPGRAPH_EXT)
# shutil.move(_fname, _fname_marked)
# if num_lines >= self.config.PPGRAPH_MAX_SAMPLES:
# break
# Set up the preprocessing pipeline for analyzing the dataset. The analyze call is not combined with the
# transform call because we will parallelize the transform call later. We had the issue that this process
# runs on a single core and tends to cause OOM issues.
pipeline = beam.Pipeline(runner=DirectRunner())
with beam_impl.Context(temp_dir=tempfile.mkdtemp()):
# todo: maybe, I should only use train data (or percentage of train data) to build the graph
raw_train_data = (
pipeline
| 'ReadTrainDataFile' >> textio.ReadFromText(
'data/features' + '*' + 'shard' + '*', skip_header_lines=0)
| 'DecodeTrainDataCSV' >> MapAndFilterErrors(
tft_coders.CsvCoder(
self.data_formatter.get_ordered_columns(),
self.data_formatter.get_raw_data_metadata().schema).
decode))
# Combine data and schema into a dataset tuple. Note that we already used
# the schema to read the CSV data, but we also need it to interpret
# raw_data.
# That is when to use vocabulary, scale_to_0_1 or sparse_to_dense ...
transform_fn = (
(raw_train_data, self.data_formatter.get_raw_data_metadata())
| beam_impl.AnalyzeDataset(
PreprocessingFunction().transform_to_tfrecord))
# Write SavedModel and metadata to two subdirectories of working_dir, given by
# `transform_fn_io.TRANSFORM_FN_DIR` and `transform_fn_io.TRANSFORMED_METADATA_DIR` respectively.
_ = (transform_fn
| 'WriteTransformGraph' >>
transform_fn_io.WriteTransformFn(TARGET_DIR)) # working dir
# Run the Beam preprocessing pipeline.
st = time.time()
result = pipeline.run()
result.wait_until_finish()
self.logger.info(
'Transformation graph built and written in {:.2f} sec'.format(
time.time() - st))
def make_transform_graph(output_dir, schema, features):
"""Writes a tft transform fn, and metadata files.
Args:
output_dir: output folder
schema: schema list
features: features dict
"""
tft_input_schema = make_tft_input_schema(
schema, os.path.join(output_dir, STATS_FILE))
tft_input_metadata = dataset_metadata.DatasetMetadata(
schema=tft_input_schema)
preprocessing_fn = make_preprocessing_fn(output_dir, features)
# preprocessing_fn does not use any analyzer, so we can run a local beam job
# to properly make and write the transform function.
temp_dir = os.path.join(output_dir, 'tmp')
with beam.Pipeline('DirectRunner', options=None) as p:
with tft_impl.Context(temp_dir=temp_dir):
# Not going to transform, so no data is needed.
train_data = p | beam.Create([])
transform_fn = (
(train_data, tft_input_metadata)
| 'BuildTransformFn' # noqa
>> tft_impl.AnalyzeDataset(preprocessing_fn)) # noqa
# Writes transformed_metadata and transfrom_fn folders
_ = (transform_fn |
'WriteTransformFn' >> tft_beam_io.WriteTransformFn(output_dir)
) # noqa
# Write the raw_metadata
metadata_io.write_metadata(metadata=tft_input_metadata,
path=os.path.join(
output_dir, RAW_METADATA_DIR))
def preprocess(p, output_dir, check_path, data_size, bq_table, split_data_path,
project_id):
"""Main processing pipeline reading, processing and storing processed data.
Performs the following operations:
- reads data from BigQuery
- adds hash key value to each row
- scales data
- shuffles and splits data in train / validation / test sets
- oversamples train data
- stores data as TFRecord
- splits and stores test data into labels and features files
Args:
p: PCollection, initial pipeline.
output_dir: string, path to directory to store output.
check_path: string, path to directory to store data checks.
data_size: tuple of float, ratio of data going respectively to train,
validation and test sets.
bq_table: string, name of table to read data from.
split_data_path: string, path to directory to store train, validation and
test raw datasets.
project_id: string, GCP project id.
Raises:
ValueError: No test dataset found in pipeline output.
"""
train_size, validation_size, test_size = data_size
data = (p |
'ReadData' >> read_data(bq_table=bq_table, project_id=project_id))
_ = data | 'StoreData' >> beam.io.WriteToText(
posixpath.join(output_dir, check_path, 'processed_data.txt'))
split_data = (
data |
'RandomlySplitData' >> randomly_split(train_size=train_size,
validation_size=validation_size,
test_size=test_size))
for k in split_data:
split_data[k] |= 'AddHash_{}'.format(k.name) >> beam.ParDo(
AddHash(),
label_column=constants.LABEL_COLUMN,
key_column=constants.KEY_COLUMN,
dtype=k)
# Splits test data into features pipeline and labels pipeline.
if DatasetType.TEST not in split_data:
raise ValueError('No test dataset found in pipeline output.')
test_data = (split_data.pop(DatasetType.TEST)
| 'SplitFeaturesLabels' >> split_features_labels(
constants.LABEL_COLUMN, constants.KEY_COLUMN))
# Stores test data features and labels pipeline separately.
for k in test_data:
_ = (test_data[k]
| 'ParseJsonToString_{}'.format(k) >> beam.Map(json.dumps)
| 'StoreSplitData_{}'.format(k) >> beam.io.WriteToText(
posixpath.join(
output_dir, split_data_path,
'split_data_{}_{}.txt'.format(DatasetType.TEST.name, k))))
meta_data = dataset_metadata.DatasetMetadata(make_input_schema())
transform_fn = (
(split_data[DatasetType.TRAIN], meta_data)
| 'AnalyzeTrainDataset' >> beam_impl.AnalyzeDataset(preprocessing_fn))
_ = (transform_fn
| 'WriteTransformFn' >> tft.beam.tft_beam_io.WriteTransformFn(
posixpath.join(output_dir, constants.PATH_INPUT_TRANSFORMATION)))
_ = (meta_data
| 'WriteInputMetadata' >> tft.beam.tft_beam_io.WriteMetadata(
posixpath.join(output_dir, constants.PATH_INPUT_SCHEMA),
pipeline=p))
transformed_metadata, transformed_data = {}, {}
for k in [DatasetType.TRAIN, DatasetType.VAL]:
transformed_data[k], transformed_metadata[k] = (
((split_data[k], meta_data), transform_fn)
| 'Transform{}'.format(k) >> beam_impl.TransformDataset())
transformed_data[DatasetType.TRAIN] = (
transformed_data[DatasetType.TRAIN]
| 'OverSampleTraining' >> oversampling())
for k in transformed_data:
_ = (transformed_data[k]
| 'ShuffleData{}'.format(k) >> shuffle_data()
| 'StoreData{}'.format(k) >> store_transformed_data(
schema=transformed_metadata[k],
path=posixpath.join(output_dir,
constants.PATH_TRANSFORMED_DATA_SPLIT[k]),
name=DatasetType(k).name))
for k in transformed_data:
_ = (transformed_data[k] | 'CheckSize{}'.format(k.name) >> check_size(
name=DatasetType(k).name,
path=posixpath.join(output_dir, check_path, k.name)))
def preprocess(pipeline, training_data, eval_data, predict_data, output_dir,
frequency_threshold):
"""Run pre-processing step as a pipeline.
Args:
pipeline: beam pipeline
training_data: the name of the table to train on.
eval_data: the name of the table to evaluate on.
predict_data: the name of the table to predict on.
output_dir: file path to where to write all the output files.
frequency_threshold: frequency threshold to use for categorical values.
"""
# 1) The schema can be either defined in-memory or read from a configuration
# file, in this case we are creating the schema in-memory.
input_schema = reddit.make_input_schema()
# 2) Read from BigQuery or from CSV.
train_data = pipeline | 'ReadTrainingData' >> _ReadData(training_data)
evaluate_data = pipeline | 'ReadEvalData' >> _ReadData(eval_data)
input_metadata = dataset_metadata.DatasetMetadata(schema=input_schema)
_ = (input_metadata
| 'WriteInputMetadata' >> tft_beam_io.WriteMetadata(
os.path.join(output_dir, path_constants.RAW_METADATA_DIR),
pipeline=pipeline))
preprocessing_fn = reddit.make_preprocessing_fn(frequency_threshold)
transform_fn = ((train_data, input_metadata)
| 'Analyze' >> tft.AnalyzeDataset(preprocessing_fn))
# WriteTransformFn writes transform_fn and metadata to fixed subdirectories
# of output_dir, which are given by path_constants.TRANSFORM_FN_DIR and
# path_constants.TRANSFORMED_METADATA_DIR.
_ = (transform_fn
| 'WriteTransformFn' >> tft_beam_io.WriteTransformFn(output_dir))
@beam.ptransform_fn
def TransformAndWrite(pcoll, path): # pylint: disable=invalid-name
pcoll |= 'Shuffle' >> _Shuffle() # pylint: disable=no-value-for-parameter
(dataset, metadata) = (((pcoll, input_metadata), transform_fn)
| 'Transform' >> tft.TransformDataset())
coder = coders.ExampleProtoCoder(metadata.schema)
_ = (dataset
| 'SerializeExamples' >> beam.Map(coder.encode)
| 'WriteExamples' >> beam.io.WriteToTFRecord(
os.path.join(output_dir, path),
file_name_suffix='.tfrecord.gz'))
_ = train_data | 'TransformAndWriteTraining' >> TransformAndWrite( # pylint: disable=no-value-for-parameter
path_constants.TRANSFORMED_TRAIN_DATA_FILE_PREFIX)
_ = evaluate_data | 'TransformAndWriteEval' >> TransformAndWrite( # pylint: disable=no-value-for-parameter
path_constants.TRANSFORMED_EVAL_DATA_FILE_PREFIX)
# TODO(b/35300113) Remember to eventually also save the statistics.
if predict_data:
predict_mode = tf.contrib.learn.ModeKeys.INFER
predict_schema = reddit.make_input_schema(mode=predict_mode)
predict_coder = coders.ExampleProtoCoder(predict_schema)
serialized_examples = (
pipeline
| 'ReadPredictData' >> _ReadData(predict_data, mode=predict_mode)
# TODO(b/35194257) Obviate the need for this explicit
# serialization.
| 'EncodePredictData' >> beam.Map(predict_coder.encode))
_ = (serialized_examples
| 'WritePredictDataAsTFRecord' >> beam.io.WriteToTFRecord(
os.path.join(
output_dir,
path_constants.TRANSFORMED_PREDICT_DATA_FILE_PREFIX),
file_name_suffix='.tfrecord.gz'))
_ = (serialized_examples
| 'EncodePredictAsB64Json' >> beam.Map(_encode_as_b64_json)
| 'WritePredictDataAsText' >> beam.io.WriteToText(
os.path.join(
output_dir,
path_constants.TRANSFORMED_PREDICT_DATA_FILE_PREFIX),
file_name_suffix='.txt'))
def assertAnalyzeAndTransformResults(self,
input_data,
input_metadata,
preprocessing_fn,
expected_data=None,
expected_metadata=None,
expected_vocab_file_contents=None,
expected_asset_file_contents=None,
test_data=None,
desired_batch_size=None,
beam_pipeline=None,
temp_dir=None,
use_tfxio=False,
input_data_is_tfxio_format=False):
"""Assert that input data and metadata is transformed as expected.
This methods asserts transformed data and transformed metadata match
with expected_data and expected_metadata.
Args:
input_data: A sequence of dicts whose values are
either strings, lists of strings, numeric types or a pair of those.
input_metadata: DatasetMetadata describing input_data.
preprocessing_fn: A function taking a dict of tensors and returning
a dict of tensors.
expected_data: (optional) A dataset with the same type constraints as
input_data, but representing the output after transformation.
If supplied, transformed data is asserted to be equal.
expected_metadata: (optional) DatasetMetadata describing the transformed
data. If supplied, transformed metadata is asserted to be equal.
expected_vocab_file_contents: (optional) A dictionary from vocab filenames
to their expected content as a list of text lines or a list of tuples
of frequency and text. Values should be the expected result of calling
f.readlines() on the given asset files.
expected_asset_file_contents: deprecated. Use
expected_vocab_file_contents.
test_data: (optional) If this is provided then instead of calling
AnalyzeAndTransformDataset with input_data, this function will call
AnalyzeDataset with input_data and TransformDataset with test_data.
Note that this is the case even if input_data and test_data are equal.
test_data should also conform to input_metadata.
desired_batch_size: (optional) A batch size to batch elements by. If not
provided, a batch size will be computed automatically.
beam_pipeline: (optional) A Beam Pipeline to use in this test.
temp_dir: If set, it is used as output directory, else a new unique
directory is created.
use_tfxio: If True, invoke AnalyzeAndTransformDataset using the new API
that accepts standardized inputs (Arrow `RecordBatch`es). Otherwise
use the old API that accepts Dicts.
input_data_is_tfxio_format: If True, `input_data` and `test_data` are
Arrow `RecordBatch`es and the `input_metadata` is
`tfxio.tensor_adapter.TensorAdapterConfig`. Otherwise the input data
is a list of Dicts and input_metadata is a `DatasetMetadata`.
Raises:
AssertionError: if the expected data does not match the results of
transforming input_data according to preprocessing_fn, or
(if provided) if the expected metadata does not match.
ValueError: if expected_vocab_file_contents and
expected_asset_file_contents are both set.
"""
if (expected_vocab_file_contents is not None
and expected_asset_file_contents is not None):
raise ValueError('only one of expected_asset_file_contents and '
'expected_asset_file_contents should be set')
elif expected_asset_file_contents is not None:
tf.compat.v1.logging.warn(
'expected_asset_file_contents is deprecated, use '
'expected_vocab_file_contents')
expected_vocab_file_contents = (expected_vocab_file_contents
or expected_asset_file_contents or {})
del expected_asset_file_contents
if not use_tfxio and input_data_is_tfxio_format:
raise ValueError('Unable to feed TFXIO input format to the old, '
'non-TFXIO API.')
compatibility_tfxio_needed = use_tfxio and not input_data_is_tfxio_format
# Note: we don't separately test AnalyzeDataset and TransformDataset as
# AnalyzeAndTransformDataset currently simply composes these two
# transforms. If in future versions of the code, the implementation
# differs, we should also run AnalyzeDataset and TransformDataset composed.
temp_dir = temp_dir or tempfile.mkdtemp(prefix=self._testMethodName,
dir=self.get_temp_dir())
with beam_pipeline or self._makeTestPipeline() as pipeline:
with beam_impl.Context(temp_dir=temp_dir,
desired_batch_size=desired_batch_size,
use_tfxio=use_tfxio):
input_data = pipeline | 'CreateInput' >> beam.Create(
input_data, reshuffle=False)
if compatibility_tfxio_needed:
legacy_input_metadata = input_metadata
input_data, input_metadata = self.convert_to_tfxio_api_inputs(
input_data, input_metadata, label='input_data')
if test_data is None:
(transformed_data, transformed_metadata), transform_fn = (
(input_data, input_metadata)
|
beam_impl.AnalyzeAndTransformDataset(preprocessing_fn))
else:
transform_fn = ((input_data, input_metadata)
|
beam_impl.AnalyzeDataset(preprocessing_fn))
test_data = pipeline | 'CreateTest' >> beam.Create(
test_data)
if compatibility_tfxio_needed:
test_data, _ = self.convert_to_tfxio_api_inputs(
test_data,
legacy_input_metadata,
label='test_data')
transformed_data, transformed_metadata = (
((test_data, input_metadata), transform_fn)
| beam_impl.TransformDataset())
# Write transform_fn so we can test its assets
_ = transform_fn | transform_fn_io.WriteTransformFn(temp_dir)
if expected_data is not None:
transformed_data_coder = tft.coders.ExampleProtoCoder(
transformed_metadata.schema)
transformed_data_path = os.path.join(
temp_dir, 'transformed_data')
_ = (transformed_data
| beam.Map(transformed_data_coder.encode)
| beam.io.tfrecordio.WriteToTFRecord(
transformed_data_path, shard_name_template=''))
# TODO(ebreck) Log transformed_data somewhere.
if expected_data is not None:
examples = tf.compat.v1.python_io.tf_record_iterator(
path=transformed_data_path)
transformed_data = [
transformed_data_coder.decode(x) for x in examples
]
self.assertDataCloseOrEqual(expected_data, transformed_data)
tf_transform_output = tft.TFTransformOutput(temp_dir)
if expected_metadata:
# Make a copy with no annotations.
transformed_schema = schema_pb2.Schema()
transformed_schema.CopyFrom(
tf_transform_output.transformed_metadata.schema)
transformed_schema.ClearField('annotation')
for feature in transformed_schema.feature:
feature.ClearField('annotation')
self.assertEqual(expected_metadata.schema, transformed_schema)
for filename, file_contents in six.iteritems(
expected_vocab_file_contents):
full_filename = tf_transform_output.vocabulary_file_by_name(
filename)
self.AssertVocabularyContents(full_filename, file_contents)
def assertAnalyzeAndTransformResults(self,
input_data,
input_metadata,
preprocessing_fn,
expected_data=None,
expected_metadata=None,
expected_vocab_file_contents=None,
expected_asset_file_contents=None,
test_data=None,
desired_batch_size=None,
beam_pipeline=None,
temp_dir=None,
force_tf_compat_v1=True):
"""Assert that input data and metadata is transformed as expected.
This methods asserts transformed data and transformed metadata match
with expected_data and expected_metadata.
Args:
input_data: Input data formatted in one of two ways:
* A sequence of dicts whose values are one of:
strings, lists of strings, numeric types or a pair of those.
Must have at least one key so that we can infer the batch size, or
* A sequence of pa.RecordBatch.
input_metadata: One of -
* DatasetMetadata describing input_data if `input_data` are dicts.
* TensorAdapterConfig otherwise.
preprocessing_fn: A function taking a dict of tensors and returning
a dict of tensors.
expected_data: (optional) A dataset with the same type constraints as
input_data, but representing the output after transformation.
If supplied, transformed data is asserted to be equal.
expected_metadata: (optional) DatasetMetadata describing the transformed
data. If supplied, transformed metadata is asserted to be equal.
expected_vocab_file_contents: (optional) A dictionary from vocab filenames
to their expected content as a list of text lines or a list of tuples
of frequency and text. Values should be the expected result of calling
f.readlines() on the given asset files.
expected_asset_file_contents: deprecated. Use
expected_vocab_file_contents.
test_data: (optional) If this is provided then instead of calling
AnalyzeAndTransformDataset with input_data, this function will call
AnalyzeDataset with input_data and TransformDataset with test_data.
Note that this is the case even if input_data and test_data are equal.
test_data should also conform to input_metadata.
desired_batch_size: (optional) A batch size to batch elements by. If not
provided, a batch size will be computed automatically.
beam_pipeline: (optional) A Beam Pipeline to use in this test.
temp_dir: If set, it is used as output directory, else a new unique
directory is created.
force_tf_compat_v1: A `Boolean`. If `True`, TFT's public APIs use
Tensorflow in compat.v1 mode.
Raises:
AssertionError: if the expected data does not match the results of
transforming input_data according to preprocessing_fn, or
(if provided) if the expected metadata does not match.
ValueError: if expected_vocab_file_contents and
expected_asset_file_contents are both set.
"""
if (expected_vocab_file_contents is not None
and expected_asset_file_contents is not None):
raise ValueError('only one of expected_asset_file_contents and '
'expected_asset_file_contents should be set')
elif expected_asset_file_contents is not None:
tf.compat.v1.logging.warn(
'expected_asset_file_contents is deprecated, use '
'expected_vocab_file_contents')
expected_vocab_file_contents = (expected_vocab_file_contents
or expected_asset_file_contents or {})
del expected_asset_file_contents
# Note: we don't separately test AnalyzeDataset and TransformDataset as
# AnalyzeAndTransformDataset currently simply composes these two
# transforms. If in future versions of the code, the implementation
# differs, we should also run AnalyzeDataset and TransformDataset composed.
temp_dir = temp_dir or tempfile.mkdtemp(prefix=self._testMethodName,
dir=self.get_temp_dir())
with beam_pipeline or self._makeTestPipeline() as pipeline:
with beam_impl.Context(temp_dir=temp_dir,
desired_batch_size=desired_batch_size,
force_tf_compat_v1=force_tf_compat_v1):
input_data = pipeline | 'CreateInput' >> beam.Create(
input_data, reshuffle=False)
if test_data is None:
(transformed_data, transformed_metadata), transform_fn = (
(input_data, input_metadata)
|
beam_impl.AnalyzeAndTransformDataset(preprocessing_fn))
else:
transform_fn = ((input_data, input_metadata)
|
beam_impl.AnalyzeDataset(preprocessing_fn))
test_data = pipeline | 'CreateTest' >> beam.Create(
test_data)
transformed_data, transformed_metadata = (
((test_data, input_metadata), transform_fn)
| beam_impl.TransformDataset())
# Write transform_fn so we can test its assets
_ = transform_fn | transform_fn_io.WriteTransformFn(temp_dir)
if expected_data is not None:
transformed_data_coder = tft.coders.ExampleProtoCoder(
transformed_metadata.schema)
transformed_data_path = os.path.join(
temp_dir, 'transformed_data')
_ = (transformed_data
| beam.Map(transformed_data_coder.encode)
| beam.io.tfrecordio.WriteToTFRecord(
transformed_data_path, shard_name_template=''))
# TODO(ebreck) Log transformed_data somewhere.
if expected_data is not None:
examples = tf.compat.v1.python_io.tf_record_iterator(
path=transformed_data_path)
shapes = {
f.name:
[s.size for s in f.shape.dim] if f.HasField('shape') else [-1]
for f in transformed_metadata.schema.feature
}
transformed_data = [
_format_example_as_numpy_dict(e, shapes) for e in examples
]
self.assertDataCloseOrEqual(expected_data, transformed_data)
tf_transform_output = tft.TFTransformOutput(temp_dir)
if expected_metadata:
# Make a copy with no annotations.
transformed_schema = schema_pb2.Schema()
transformed_schema.CopyFrom(
tf_transform_output.transformed_metadata.schema)
transformed_schema.ClearField('annotation')
for feature in transformed_schema.feature:
feature.ClearField('annotation')
self.assertEqual(expected_metadata.schema, transformed_schema)
for filename, file_contents in six.iteritems(
expected_vocab_file_contents):
full_filename = tf_transform_output.vocabulary_file_by_name(
filename)
self.AssertVocabularyContents(full_filename, file_contents)
def transform_data(input_handle,
outfile_prefix,
working_dir,
schema_file,
transform_dir=None,
max_rows=None,
pipeline_args=None):
"""The main tf.transform method which analyzes and transforms data.
Args:
input_handle: BigQuery table name to process specified as DATASET.TABLE or
path to csv file with input data.
outfile_prefix: Filename prefix for emitted transformed examples
working_dir: Directory in which transformed examples and transform function
will be emitted.
schema_file: An file path that contains a text-serialized TensorFlow
metadata schema of the input data.
transform_dir: Directory in which the transform output is located. If
provided, this will load the transform_fn from disk instead of computing
it over the data. Hint: this is useful for transforming eval data.
max_rows: Number of rows to query from BigQuery
pipeline_args: additional DataflowRunner or DirectRunner args passed to the
beam pipeline.
"""
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
schema = taxi.read_schema(schema_file)
raw_feature_spec = taxi.get_raw_feature_spec(schema)
raw_schema = dataset_schema.from_feature_spec(raw_feature_spec)
raw_data_metadata = dataset_metadata.DatasetMetadata(raw_schema)
with beam.Pipeline(argv=pipeline_args) as pipeline:
with beam_impl.Context(temp_dir=working_dir):
if input_handle.lower().endswith('csv'):
csv_coder = taxi.make_csv_coder(schema)
raw_data = (pipeline
| 'ReadFromText' >> beam.io.ReadFromText(
input_handle, skip_header_lines=1)
| 'ParseCSV' >> beam.Map(csv_coder.decode))
else:
query = taxi.make_sql(input_handle, max_rows, for_eval=False)
raw_data = (
pipeline
| 'ReadBigQuery' >> beam.io.Read(
beam.io.BigQuerySource(query=query,
use_standard_sql=True))
|
'CleanData' >> beam.Map(taxi.clean_raw_data_dict,
raw_feature_spec=raw_feature_spec))
if transform_dir is None:
transform_fn = (
(raw_data, raw_data_metadata)
|
('Analyze' >> beam_impl.AnalyzeDataset(preprocessing_fn)))
_ = (transform_fn
| ('WriteTransformFn' >>
transform_fn_io.WriteTransformFn(working_dir)))
else:
transform_fn = pipeline | transform_fn_io.ReadTransformFn(
transform_dir)
# Shuffling the data before materialization will improve Training
# effectiveness downstream.
shuffled_data = raw_data | 'RandomizeData' >> beam.transforms.Reshuffle(
)
(transformed_data, transformed_metadata) = (
((shuffled_data, raw_data_metadata), transform_fn)
| 'Transform' >> beam_impl.TransformDataset())
coder = example_proto_coder.ExampleProtoCoder(
transformed_metadata.schema)
_ = (transformed_data
| 'SerializeExamples' >> beam.Map(coder.encode)
| 'WriteExamples' >> beam.io.WriteToTFRecord(
os.path.join(working_dir, outfile_prefix),
file_name_suffix='.gz'))
def test_non_frequency_vocabulary_merge(self):
"""This test compares vocabularies produced with and without cache."""
mi_vocab_name = 'mutual_information_vocab'
adjusted_mi_vocab_name = 'adjusted_mutual_information_vocab'
weighted_frequency_vocab_name = 'weighted_frequency_vocab'
def preprocessing_fn(inputs):
_ = tft.vocabulary(
inputs['s'],
labels=inputs['label'],
store_frequency=True,
vocab_filename=mi_vocab_name,
min_diff_from_avg=0.1,
use_adjusted_mutual_info=False)
_ = tft.vocabulary(
inputs['s'],
labels=inputs['label'],
store_frequency=True,
vocab_filename=adjusted_mi_vocab_name,
min_diff_from_avg=1.0,
use_adjusted_mutual_info=True)
_ = tft.vocabulary(
inputs['s'],
weights=inputs['weight'],
store_frequency=True,
vocab_filename=weighted_frequency_vocab_name,
use_adjusted_mutual_info=False)
return inputs
span_0_key = 'span-0'
span_1_key = 'span-1'
input_data = [
dict(s='a', weight=1, label=1),
dict(s='a', weight=0.5, label=1),
dict(s='b', weight=0.75, label=1),
dict(s='b', weight=1, label=0),
]
input_metadata = dataset_metadata.DatasetMetadata(
dataset_schema.from_feature_spec({
's': tf.io.FixedLenFeature([], tf.string),
'label': tf.io.FixedLenFeature([], tf.int64),
'weight': tf.io.FixedLenFeature([], tf.float32),
}))
input_data_dict = {
span_0_key: input_data,
span_1_key: input_data,
}
with _TestPipeline() as p:
flat_data = p | 'CreateInputData' >> beam.Create(
list(itertools.chain(*input_data_dict.values())))
# wrap each value in input_data_dict as a pcoll.
input_data_pcoll_dict = {}
for a, b in six.iteritems(input_data_dict):
input_data_pcoll_dict[a] = p | a >> beam.Create(b)
transform_fn_with_cache, output_cache = (
(flat_data, input_data_pcoll_dict, {}, input_metadata) |
(beam_impl.AnalyzeDatasetWithCache(preprocessing_fn)))
transform_fn_with_cache_dir = os.path.join(self.base_test_dir,
'transform_fn_with_cache')
_ = transform_fn_with_cache | tft_beam.WriteTransformFn(
transform_fn_with_cache_dir)
expected_accumulators = {
b'__v0__VocabularyAccumulate[vocabulary]-\xd3\xe0p\x82\xb1\xa0z\xa3S\xd7N8@\x8f\xa2\xd7\xa1\x9e\xac;':
[
b'["a", [2, [0.0, 1.0], [0.0, 0.0], 1.0]]',
b'["b", [2, [0.5, 0.5], [0.0, 0.0], 1.0]]'
],
b'__v0__VocabularyAccumulate[vocabulary_1]-A\xc7_0\xee\xff\x88@E<\xde\xcb\x8d\xff5\xebyZZ\x8d':
[
b'["a", [2, [0.0, 1.0], [0.0, 0.0], 1.0]]',
b'["b", [2, [0.5, 0.5], [0.0, 0.0], 1.0]]'
],
b"__v0__VocabularyAccumulate[vocabulary_2]-\x97\x1c>\x851\x94'\xdc\xdf\xfd\xcc\x86\xb7\xb8\xe1\xe8*\x89B\t":
[b'["a", 1.5]', b'["b", 1.75]'],
}
spans = [span_0_key, span_1_key]
self.assertCountEqual(output_cache.keys(), spans)
for span in spans:
self.assertCountEqual(output_cache[span].keys(),
expected_accumulators.keys())
for idx, (key,
value) in enumerate(six.iteritems(expected_accumulators)):
beam_test_util.assert_that(
output_cache[span][key],
beam_test_util.equal_to(value),
label='AssertCache[{}][{}]'.format(span, idx))
# 4 from analysis on each of the input spans.
self.assertEqual(_get_counter_value(p.metrics, 'num_instances'), 8)
self.assertEqual(_get_counter_value(p.metrics, 'cache_entries_decoded'), 0)
self.assertEqual(_get_counter_value(p.metrics, 'cache_entries_encoded'), 6)
self.assertEqual(_get_counter_value(p.metrics, 'saved_models_created'), 2)
with _TestPipeline() as p:
flat_data = p | 'CreateInputData' >> beam.Create(input_data * 2)
transform_fn_no_cache = ((flat_data, input_metadata) |
(beam_impl.AnalyzeDataset(preprocessing_fn)))
transform_fn_no_cache_dir = os.path.join(self.base_test_dir,
'transform_fn_no_cache')
_ = transform_fn_no_cache | tft_beam.WriteTransformFn(
transform_fn_no_cache_dir)
# 4 from analysis on each of the input spans.
self.assertEqual(_get_counter_value(p.metrics, 'num_instances'), 8)
self.assertEqual(_get_counter_value(p.metrics, 'cache_entries_decoded'), 0)
self.assertEqual(_get_counter_value(p.metrics, 'cache_entries_encoded'), 0)
self.assertEqual(_get_counter_value(p.metrics, 'saved_models_created'), 2)
tft_output_cache = tft.TFTransformOutput(transform_fn_with_cache_dir)
tft_output_no_cache = tft.TFTransformOutput(transform_fn_no_cache_dir)
for vocab_filename in (mi_vocab_name, adjusted_mi_vocab_name,
weighted_frequency_vocab_name):
cache_path = tft_output_cache.vocabulary_file_by_name(vocab_filename)
no_cache_path = tft_output_no_cache.vocabulary_file_by_name(
vocab_filename)
with tf.io.gfile.GFile(cache_path, 'rb') as f1, tf.io.gfile.GFile(
no_cache_path, 'rb') as f2:
self.assertEqual(
f1.readlines(), f2.readlines(),
'vocab with cache != vocab without cache for: {}'.format(
vocab_filename))
def transform_data(input_handle,
outfile_prefix,
working_dir,
setup_file,
ts1,
ts2,
project=None,
max_rows=None,
mode=None,
stage=None,
preprocessing_fn=None):
"""The main tf.transform method which analyzes and transforms data.
Args:
input_handle: BigQuery table name to process specified as
DATASET.TABLE or path to csv file with input data.
outfile_prefix: Filename prefix for emitted transformed examples
working_dir: Directory in which transformed examples and transform
function will be emitted.
max_rows: Number of rows to query from BigQuery
pipeline_args: additional DataflowRunner or DirectRunner args passed to the
beam pipeline.
"""
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 ts.DENSE_FLOAT_FEATURE_KEYS:
# Preserve this feature as a dense float, setting nan's to the mean.
outputs[key] = transform.scale_to_z_score(inputs[key])
for key in ts.VOCAB_FEATURE_KEYS:
# Build a vocabulary for this feature.
outputs[key] = transform.string_to_int(inputs[key],
top_k=ts.VOCAB_SIZE,
num_oov_buckets=ts.OOV_SIZE)
for key in ts.BUCKET_FEATURE_KEYS:
outputs[key] = transform.bucketize(inputs[key],
ts.FEATURE_BUCKET_COUNT)
for key in ts.CATEGORICAL_FEATURE_KEYS:
outputs[key] = inputs[key]
# Was this passenger a big tipper?
def convert_label(label):
taxi_fare = inputs[ts.FARE_KEY]
return 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(label, tf.multiply(taxi_fare,
tf.constant(0.2))),
tf.int64))
outputs[ts.LABEL_KEY] = transform.apply_function(
convert_label, inputs[ts.LABEL_KEY])
return outputs
preprocessing_fn = preprocessing_fn or def_preprocessing_fn
print('ts1 %s, ts2 %s' % (ts1, ts2))
raw_feature_spec = ts.get_raw_feature_spec()
raw_schema = dataset_schema.from_feature_spec(raw_feature_spec)
raw_data_metadata = dataset_metadata.DatasetMetadata(raw_schema)
temp_dir = os.path.join(working_dir, 'tmp')
if stage is None:
stage = 'train'
if mode == 'local':
options = {'project': project}
pipeline_options = beam.pipeline.PipelineOptions(flags=[], **options)
runner = 'DirectRunner'
elif mode == 'cloud':
options = {
'job_name': 'tft-' + stage + '-' + str(uuid.uuid4()),
'temp_location': temp_dir,
'project': project,
'save_main_session': True,
'setup_file': setup_file
}
pipeline_options = beam.pipeline.PipelineOptions(flags=[], **options)
runner = 'DataFlowRunner'
else:
raise ValueError("Invalid mode %s." % mode)
with beam.Pipeline(runner, options=pipeline_options) as pipeline:
with beam_impl.Context(temp_dir=temp_dir):
csv_coder = ts.make_csv_coder()
if 'csv' in input_handle.lower():
raw_data = (pipeline
| 'ReadFromText' >> beam.io.ReadFromText(
input_handle, skip_header_lines=1)
| 'ParseCSV' >> beam.Map(csv_coder.decode))
else:
query = make_sql(input_handle,
ts1,
ts2,
stage,
max_rows=max_rows,
for_eval=False)
raw_data = (pipeline
| 'ReadBigQuery' >> beam.io.Read(
beam.io.BigQuerySource(query=query,
use_standard_sql=True)))
raw_data |= 'CleanData' >> beam.Map(ts.clean_raw_data_dict)
transform_fn = (
(raw_data, raw_data_metadata)
| 'Analyze' >> beam_impl.AnalyzeDataset(preprocessing_fn))
_ = (transform_fn
| 'WriteTransformFn' >>
transform_fn_io.WriteTransformFn(working_dir))
# Shuffling the data before materialization will improve training
# effectiveness downstream.
shuffled_data = raw_data | 'RandomizeData' >> beam.transforms.Reshuffle(
)
(transformed_data, transformed_metadata) = (
((shuffled_data, raw_data_metadata), transform_fn)
| 'Transform' >> beam_impl.TransformDataset())
if 'csv' not in input_handle.lower(): # if querying BQ
_ = (raw_data
| beam.Map(csv_coder.encode)
| beam.io.WriteToText(os.path.join(
working_dir, '{}.csv'.format(stage)),
num_shards=1))
coder = example_proto_coder.ExampleProtoCoder(
transformed_metadata.schema)
_ = (
transformed_data
| 'SerializeExamples' >> beam.Map(coder.encode)
| 'WriteExamples' >> beam.io.WriteToTFRecord(
os.path.join(working_dir, outfile_prefix),
compression_type=beam.io.filesystem.CompressionTypes.GZIP))
def preprocess(pipeline, training_data, eval_data, predict_data, output_dir,
frequency_threshold, delimiter):
"""Run pre-processing step as a pipeline.
Args:
pipeline: beam pipeline
training_data: file paths to input csv files.
eval_data: file paths to input csv files.
predict_data: file paths to input csv files.
output_dir: file path to where to write all the output files.
frequency_threshold: frequency threshold to use for categorical values.
delimiter: the column delimiter for the CSV format.
"""
# 1) The schema can be either defined in-memory or read from a configuration
# file, in this case we are creating the schema in-memory.
input_schema = criteo.make_input_schema()
# 2) Configure the coder to map the source file column names to a dictionary
# of key -> tensor_proto with the appropiate type derived from the
# input_schema.
coder = criteo.make_csv_coder(input_schema, delimiter)
# 3) Read from text using the coder.
train_data = (pipeline
| 'ReadTrainingData' >> beam.io.ReadFromText(training_data)
| 'ParseTrainingCsv' >> beam.Map(coder.decode))
evaluate_data = (pipeline
| 'ReadEvalData' >> beam.io.ReadFromText(eval_data)
| 'ParseEvalCsv' >> beam.Map(coder.decode))
input_metadata = dataset_metadata.DatasetMetadata(schema=input_schema)
_ = (input_metadata
| 'WriteInputMetadata' >> tft_beam_io.WriteMetadata(
os.path.join(output_dir, path_constants.RAW_METADATA_DIR),
pipeline=pipeline))
preprocessing_fn = criteo.make_preprocessing_fn(frequency_threshold)
transform_fn = ((train_data, input_metadata)
| 'Analyze' >> tft.AnalyzeDataset(preprocessing_fn))
# WriteTransformFn writes transform_fn and metadata to fixed subdirectories
# of output_dir, which are given by path_constants.TRANSFORM_FN_DIR and
# path_constants.TRANSFORMED_METADATA_DIR.
_ = (transform_fn
| 'WriteTransformFn' >> tft_beam_io.WriteTransformFn(output_dir))
@beam.ptransform_fn
def TransformAndWrite(pcoll, path): # pylint: disable=invalid-name
pcoll |= 'Shuffle' >> _Shuffle() # pylint: disable=no-value-for-parameter
(dataset, metadata) = (((pcoll, input_metadata), transform_fn)
| 'Transform' >> tft.TransformDataset())
#coder = criteo.make_csv_coder(input_schema, delimiter)
#coder = coders.ExampleProtoCoder(metadata.schema)
column_names = ['clicked']
#for name in INTEGER_COLUMN_NAMES:
# column_names.append(name)
#for name in CATEGORICAL_COLUMN_NAMES:
# column_names.append(name)
#coder = coders.CsvCoder(column_names, metadata.schema, delimiter=",")
coder = coders.ExampleProtoCoder(metadata.schema)
_ = (dataset
| 'SerializeExamples' >> beam.Map(coder.encode)
| 'WriteExamples' >> beam.io.WriteToTFRecord(
os.path.join(output_dir, path),
file_name_suffix='.tfrecord.gz'))
_ = train_data | 'TransformAndWriteTraining' >> TransformAndWrite( # pylint: disable=no-value-for-parameter
path_constants.TRANSFORMED_TRAIN_DATA_FILE_PREFIX)
_ = evaluate_data | 'TransformAndWriteEval' >> TransformAndWrite( # pylint: disable=no-value-for-parameter
path_constants.TRANSFORMED_EVAL_DATA_FILE_PREFIX)
# TODO(b/35300113) Remember to eventually also save the statistics.
if predict_data:
predict_mode = tf.contrib.learn.ModeKeys.INFER
predict_schema = criteo.make_input_schema(mode=predict_mode)
csv_coder = criteo.make_csv_coder(predict_schema, mode=predict_mode)
predict_coder = coders.ExampleProtoCoder(predict_schema)
serialized_examples = (
pipeline
| 'ReadPredictData' >> beam.io.ReadFromText(predict_data)
| 'ParsePredictCsv' >> beam.Map(csv_coder.decode)
# TODO(b/35194257) Obviate the need for this explicit serialization.
| 'EncodePredictData' >> beam.Map(predict_coder.encode))
_ = (serialized_examples
| 'WritePredictDataAsTFRecord' >> beam.io.WriteToTFRecord(
os.path.join(
output_dir,
path_constants.TRANSFORMED_PREDICT_DATA_FILE_PREFIX),
file_name_suffix='.tfrecord.gz'))
_ = (serialized_examples
| 'EncodePredictAsB64Json' >> beam.Map(_encode_as_b64_json)
| 'WritePredictDataAsText' >> beam.io.WriteToText(
os.path.join(
output_dir,
path_constants.TRANSFORMED_PREDICT_DATA_FILE_PREFIX),
file_name_suffix='.txt'))
movies_data=movies_sideinput,
rating_threshold=args.eval_score_threshold,
is_ranking_problem=(args.eval_type == RANKING),
is_train=False,
num_ranking_candidate_movie_ids=args.num_ranking_candidate_movie_ids)
# TFTransform based preprocessing.
raw_metadata = dataset_metadata.DatasetMetadata(
schema=movielens.make_examples_schema())
_ = (raw_metadata
| 'WriteRawMetadata' >> tft_beam_io.WriteMetadata(
os.path.join(args.output_dir, 'raw_metadata'), pipeline))
preprocessing_fn = movielens.make_preprocessing_fn()
transform_fn = ((train_data, raw_metadata)
| 'Analyze' >> tft.AnalyzeDataset(preprocessing_fn))
_ = (transform_fn
| 'WriteTransformFn' >> tft_beam_io.WriteTransformFn(args.output_dir))
@beam.ptransform_fn
def TransformAndWrite(pcoll, path): # pylint: disable=invalid-name
pcoll |= 'Shuffle' >> _Shuffle() # pylint: disable=no-value-for-parameter
(dataset, metadata) = (((pcoll, raw_metadata), transform_fn)
| 'Transform' >> tft.TransformDataset())
coder = tft_coders.ExampleProtoCoder(metadata.schema)
_ = (dataset
| 'SerializeExamples' >> beam.Map(coder.encode)
| 'WriteExamples' >> beam.io.WriteToTFRecord(
os.path.join(args.output_dir, path),
file_name_suffix='.tfrecord.gz'))
def run(flags, pipeline_args):
"""Run Apache Beam pipeline to generate TFRecords for Survival Analysis"""
options = PipelineOptions(flags=[], **pipeline_args)
options.view_as(WorkerOptions).machine_type = flags.machine_type
temp_dir = os.path.join(flags.output_dir, 'tmp')
runner = 'DataflowRunner' if flags.cloud else 'DirectRunner'
files = tf.gfile.Glob(flags.input_dir + "*")
if not flags.cloud:
files = files[0:
20] # if running locally for testing, process less files
logging.warning("Number of files: " + str(len(files)))
labels = get_labels_array(
"gs://columbia-dl-storage-bucket/ADNI_t1_list_with_fsstatus_20190111.csv"
)
with beam.Pipeline(runner, options=options) as p:
with tft_beam.Context(temp_dir=temp_dir):
input_metadata = dataset_metadata.DatasetMetadata(
dataset_schema.from_feature_spec(features.RAW_FEATURE_SPEC))
filenames = (p | 'Create filenames' >> beam.Create(files))
nii = (filenames | 'Read NII' >> beam.Map(read_nii))
nii_with_labels = (
nii
| 'Get Label' >> beam.FlatMap(lambda x: read_label(x, labels)))
raw_train, raw_eval, raw_test = (
nii_with_labels | 'RandomlySplitData' >> randomly_split(
train_size=.7, validation_size=.15, test_size=.15))
raw_train = raw_train | 'FlattenTrain' >> beam.FlatMap(
lambda x: x[1])
raw_eval = (raw_eval
| 'FlattenEval' >> beam.FlatMap(lambda x: x[1]))
raw_test = (raw_test
| 'FlattenTest' >> beam.FlatMap(lambda x: x[1]))
raw_train | 'CountLabelFreq' >> extractAndCount(flags.output_dir)
dataset_and_metadata, transform_fn = (
(raw_train, input_metadata)
| 'TransformData' >> tft_beam.AnalyzeAndTransformDataset(
features.preprocess))
transform_fn = (
(raw_train, input_metadata)
|
'AnalyzeTrain' >> tft_beam.AnalyzeDataset(features.preprocess))
_ = (transform_fn
| 'WriteTransformFn' >> tft_beam_io.WriteTransformFn(
flags.output_dir))
for dataset_type, dataset in [('Train', raw_train),
('Eval', raw_eval),
('Predict', raw_test)]:
transform_label = 'Transform{}'.format(dataset_type)
t, metadata = (((dataset, input_metadata), transform_fn)
|
transform_label >> tft_beam.TransformDataset())
if dataset_type == 'Train':
_ = (metadata
| 'WriteMetadata' >>
tft_beam_io.WriteMetadata(os.path.join(
flags.output_dir, 'transformed_metadata'),
pipeline=p))
write_label = 'Write{}TFRecord'.format(dataset_type)
_ = t | write_label >> WriteTFRecord(
dataset_type, flags.output_dir, metadata)
def assertAnalyzeAndTransformResults(self,
input_data,
input_metadata,
preprocessing_fn,
expected_data=None,
expected_metadata=None,
only_check_core_metadata=False,
expected_vocab_file_contents=None,
expected_asset_file_contents=None,
test_data=None,
desired_batch_size=None):
"""Assert that input data and metadata is transformed as expected.
This methods asserts transformed data and transformed metadata match
with expected_data and expected_metadata.
Args:
input_data: A sequence of dicts whose values are
either strings, lists of strings, numeric types or a pair of those.
input_metadata: DatasetMetadata describing input_data.
preprocessing_fn: A function taking a dict of tensors and returning
a dict of tensors.
expected_data: (optional) A dataset with the same type constraints as
input_data, but representing the output after transformation.
If supplied, transformed data is asserted to be equal.
expected_metadata: (optional) DatasetMetadata describing the transformed
data. If supplied, transformed metadata is asserted to be equal.
only_check_core_metadata: A boolean to indicate if all elements in
the transformed metadata is asserted to be equal to expected metadata.
If True, only transformed feature names, dtypes and representations
are asserted.
expected_vocab_file_contents: (optional) A dictionary from vocab filenames
to their expected content as a list of text lines. Values should be
the expected result of calling f.readlines() on the given asset files.
expected_asset_file_contents: deprecated. Use
expected_vocab_file_contents.
test_data: (optional) If this is provided then instead of calling
AnalyzeAndTransformDataset with input_data, this function will call
AnalyzeDataset with input_data and TransformDataset with test_data.
Note that this is the case even if input_data and test_data are equal.
test_data should also conform to input_metadata.
desired_batch_size: (optional) A batch size to batch elements by. If not
provided, a batch size will be computed automatically.
Raises:
AssertionError: if the expected data does not match the results of
transforming input_data according to preprocessing_fn, or
(if provided) if the expected metadata does not match.
ValueError: if expected_vocab_file_contents and
expected_asset_file_contents are both set.
"""
if (expected_vocab_file_contents is not None and
expected_asset_file_contents is not None):
raise ValueError('only one of expected_asset_file_contents and '
'expected_asset_file_contents should be set')
elif expected_asset_file_contents is not None:
tf.logging.warn('expected_asset_file_contents is deprecated, use '
'expected_vocab_file_contents')
expected_vocab_file_contents = (
expected_vocab_file_contents or expected_asset_file_contents or {})
del expected_asset_file_contents
# Note: we don't separately test AnalyzeDataset and TransformDataset as
# AnalyzeAndTransformDataset currently simply composes these two
# transforms. If in future versions of the code, the implementation
# differs, we should also run AnalyzeDataset and TransformDatset composed.
temp_dir = self.get_temp_dir()
with beam_impl.Context(
temp_dir=temp_dir, desired_batch_size=desired_batch_size):
if test_data is None:
(transformed_data, transformed_metadata), transform_fn = (
(input_data, input_metadata)
| beam_impl.AnalyzeAndTransformDataset(preprocessing_fn))
else:
transform_fn = ((input_data, input_metadata)
| beam_impl.AnalyzeDataset(preprocessing_fn))
transformed_data, transformed_metadata = (
((test_data, input_metadata), transform_fn)
| beam_impl.TransformDataset())
# Write transform_fn so we can test its assets
if expected_vocab_file_contents:
_ = transform_fn | transform_fn_io.WriteTransformFn(temp_dir)
if expected_data is not None:
self.assertDataCloseOrEqual(expected_data, transformed_data)
if expected_metadata:
# Now that the pipeline has run, transformed_metadata.deferred_metadata
# should be a list containing a single DatasetMetadata with the full
# metadata.
assert len(transformed_metadata.deferred_metadata) == 1
transformed_metadata = transformed_metadata.deferred_metadata[0]
if only_check_core_metadata:
# preprocessing_fn may add metadata to column schema only relevant to
# internal implementation such as vocabulary_file. As such, only check
# feature names, dtypes and representations are as expected.
self.assertSameElements(
transformed_metadata.schema.column_schemas.keys(),
expected_metadata.schema.column_schemas.keys())
for k, v in transformed_metadata.schema.column_schemas.iteritems():
expected_schema = expected_metadata.schema.column_schemas[k]
self.assertEqual(expected_schema.representation, v.representation,
"representation doesn't match for feature '%s'" % k)
self.assertEqual(expected_schema.domain.dtype, v.domain.dtype,
"dtype doesn't match for feature '%s'" % k)
else:
# Check the entire DatasetMetadata is as expected.
# Use extra assertEqual for schemas, since full metadata assertEqual
# error message is not conducive to debugging.
self.assertEqual(expected_metadata.schema.column_schemas,
transformed_metadata.schema.column_schemas)
self.assertEqual(expected_metadata, transformed_metadata)
tf_transform_output = tft.TFTransformOutput(temp_dir)
for filename, file_contents in six.iteritems(expected_vocab_file_contents):
full_filename = tf_transform_output.vocabulary_file_by_name(filename)
with tf.gfile.Open(full_filename) as f:
self.assertEqual(f.readlines(), file_contents)
def assertAnalyzeAndTransformResults(self,
input_data,
input_metadata,
preprocessing_fn,
expected_data=None,
expected_metadata=None,
only_check_core_metadata=False):
"""Assert that input data and metadata is transformed as expected.
This methods asserts transformed data and transformed metadata match
with expected_data and expected_metadata.
Args:
input_data: A sequence of dicts whose values are
either strings, lists of strings, numeric types or a pair of those.
input_metadata: DatasetMetadata describing input_data.
preprocessing_fn: A function taking a dict of tensors and returning
a dict of tensors.
expected_data: (optional) A dataset with the same type constraints as
input_data, but representing the output after transformation.
If supplied, transformed data is asserted to be equal.
expected_metadata: (optional) DatasetMetadata describing the transformed
data. If supplied, transformed metadata is asserted to be equal.
only_check_core_metadata: A boolean to indicate if all elements in
the transformed metadata is asserted to be equal to expected metadata.
If True, only transformed feature names, dtypes and representations
are asserted.
Raises:
AssertionError: if the expected data does not match the results of
transforming input_data according to preprocessing_fn, or
(if provided) if the expected metadata does not match.
"""
# Note: we don't separately test AnalyzeDataset and TransformDataset as
# AnalyzeAndTransformDataset currently simply composes these two
# transforms. If in future versions of the code, the implementation
# differs, we should also run AnalyzeDataset and TransformDatset composed.
#
# Also, the dataset_metadata that is returned along with
# `transformed_data` is incomplete as it does not contain the deferred
# components, so we instead inspect the metadata returned along with the
# transform function.
temp_dir = self.get_temp_dir()
with beam_impl.Context(temp_dir=temp_dir):
transform_fn, transformed_metadata = (
(input_data, input_metadata)
|
'AnalyzeDataset' >> beam_impl.AnalyzeDataset(preprocessing_fn))
transformed_data, _ = (
((input_data, input_metadata),
(transform_fn, transformed_metadata))
| 'TransformDataset' >> beam_impl.TransformDataset())
if expected_data:
self.assertDataCloseOrEqual(expected_data, transformed_data)
if not expected_metadata:
return
transformed_metadata = self._resolveDeferredMetadata(
transformed_metadata)
if only_check_core_metadata:
# preprocessing_fn may add metadata to column schema only relevant to
# internal implementation such as vocabulary_file. As such, only check
# feature names, dtypes and representations are as expected.
self.assertSameElements(
transformed_metadata.schema.column_schemas.keys(),
expected_metadata.schema.column_schemas.keys())
for k, v in transformed_metadata.schema.column_schemas.iteritems():
expected_schema = expected_metadata.schema.column_schemas[k]
self.assertEqual(
expected_schema.representation, v.representation,
"representation doesn't match for feature '%s'" % k)
self.assertEqual(expected_schema.domain.dtype, v.domain.dtype,
"dtype doesn't match for feature '%s'" % k)
else:
# Check the entire DatasetMetadata is as expected.
# Use extra assertEqual for schemas, since full metadata assertEqual
# error message is not conducive to debugging.
self.assertEqual(expected_metadata.schema.column_schemas,
transformed_metadata.schema.column_schemas)
self.assertEqual(expected_metadata, transformed_metadata)
def test_non_frequency_vocabulary_merge(self):
"""This test compares vocabularies produced with and without cache."""
mi_vocab_name = 'mutual_information_vocab'
adjusted_mi_vocab_name = 'adjusted_mutual_information_vocab'
weighted_frequency_vocab_name = 'weighted_frequency_vocab'
def preprocessing_fn(inputs):
_ = tft.vocabulary(inputs['s'],
labels=inputs['label'],
store_frequency=True,
vocab_filename=mi_vocab_name,
min_diff_from_avg=0.1,
use_adjusted_mutual_info=False)
_ = tft.vocabulary(inputs['s'],
labels=inputs['label'],
store_frequency=True,
vocab_filename=adjusted_mi_vocab_name,
min_diff_from_avg=1.0,
use_adjusted_mutual_info=True)
_ = tft.vocabulary(inputs['s'],
weights=inputs['weight'],
store_frequency=True,
vocab_filename=weighted_frequency_vocab_name,
use_adjusted_mutual_info=False)
return inputs
span_0_key = 'span-0'
span_1_key = 'span-1'
input_data = [
dict(s='a', weight=1, label=1),
dict(s='a', weight=0.5, label=1),
dict(s='b', weight=0.75, label=1),
dict(s='b', weight=1, label=0),
]
input_metadata = dataset_metadata.DatasetMetadata(
dataset_schema.from_feature_spec({
's':
tf.io.FixedLenFeature([], tf.string),
'label':
tf.io.FixedLenFeature([], tf.int64),
'weight':
tf.io.FixedLenFeature([], tf.float32),
}))
input_data_dict = {
span_0_key: input_data,
span_1_key: input_data,
}
with beam_impl.Context(temp_dir=self.get_temp_dir()):
flat_data = input_data_dict.values() | 'Flatten' >> beam.Flatten()
transform_fn_with_cache, output_cache = (
(flat_data, input_data_dict, {}, input_metadata) |
(beam_impl.AnalyzeDatasetWithCache(preprocessing_fn)))
expected_accumulators = {
'__v0__VocabularyAccumulate--vocabulary--':
[b'["a", [2, 1.0, 0.0, 1.0]]', b'["b", [2, 0.5, 0.0, 1.0]]'],
'__v0__VocabularyAccumulate--vocabulary_1--':
[b'["a", [2, 1.0, 0.0, 1.0]]', b'["b", [2, 0.5, 0.0, 1.0]]'],
'__v0__VocabularyAccumulate--vocabulary_2--':
[b'["a", 1.5]', b'["b", 1.75]'],
}
spans = [span_0_key, span_1_key]
self.assertCountEqual(output_cache.keys(), spans)
for span in spans:
self.assertCountEqual(output_cache[span].keys(),
expected_accumulators.keys())
for key, value in six.iteritems(expected_accumulators):
self.assertCountEqual(output_cache[span][key], value)
transform_fn_no_cache = (
(input_data * 2, input_metadata) |
(beam_impl.AnalyzeDataset(preprocessing_fn)))
transform_fn_with_cache_dir = os.path.join(self.base_test_dir,
'transform_fn_with_cache')
_ = transform_fn_with_cache | tft_beam.WriteTransformFn(
transform_fn_with_cache_dir)
transform_fn_no_cache_dir = os.path.join(self.base_test_dir,
'transform_fn_no_cache')
_ = transform_fn_no_cache | tft_beam.WriteTransformFn(
transform_fn_no_cache_dir)
tft_output_cache = tft.TFTransformOutput(transform_fn_with_cache_dir)
tft_output_no_cache = tft.TFTransformOutput(transform_fn_no_cache_dir)
for vocab_filename in (mi_vocab_name, adjusted_mi_vocab_name,
weighted_frequency_vocab_name):
cache_path = tft_output_cache.vocabulary_file_by_name(
vocab_filename)
no_cache_path = tft_output_no_cache.vocabulary_file_by_name(
vocab_filename)
with tf.io.gfile.GFile(cache_path, 'rb') as f1, tf.io.gfile.GFile(
no_cache_path, 'rb') as f2:
self.assertEqual(
f1.readlines(), f2.readlines(),
'vocab with cache != vocab without cache for: {}'.format(
vocab_filename))
def assertAnalyzeAndTransformResults(self,
input_data,
input_metadata,
preprocessing_fn,
expected_data=None,
expected_metadata=None,
only_check_core_metadata=False,
expected_vocab_file_contents=None,
expected_asset_file_contents=None,
test_data=None,
desired_batch_size=None,
beam_pipeline=None):
"""Assert that input data and metadata is transformed as expected.
This methods asserts transformed data and transformed metadata match
with expected_data and expected_metadata.
Args:
input_data: A sequence of dicts whose values are
either strings, lists of strings, numeric types or a pair of those.
input_metadata: DatasetMetadata describing input_data.
preprocessing_fn: A function taking a dict of tensors and returning
a dict of tensors.
expected_data: (optional) A dataset with the same type constraints as
input_data, but representing the output after transformation.
If supplied, transformed data is asserted to be equal.
expected_metadata: (optional) DatasetMetadata describing the transformed
data. If supplied, transformed metadata is asserted to be equal.
only_check_core_metadata: A boolean to indicate if all elements in
the transformed metadata is asserted to be equal to expected metadata.
If True, only transformed feature names, dtypes and representations
are asserted.
expected_vocab_file_contents: (optional) A dictionary from vocab filenames
to their expected content as a list of text lines or a list of tuples
of frequency and text. Values should be the expected result of calling
f.readlines() on the given asset files.
expected_asset_file_contents: deprecated. Use
expected_vocab_file_contents.
test_data: (optional) If this is provided then instead of calling
AnalyzeAndTransformDataset with input_data, this function will call
AnalyzeDataset with input_data and TransformDataset with test_data.
Note that this is the case even if input_data and test_data are equal.
test_data should also conform to input_metadata.
desired_batch_size: (optional) A batch size to batch elements by. If not
provided, a batch size will be computed automatically.
beam_pipeline: (optional) A Beam Pipeline to use in this test.
Raises:
AssertionError: if the expected data does not match the results of
transforming input_data according to preprocessing_fn, or
(if provided) if the expected metadata does not match.
ValueError: if expected_vocab_file_contents and
expected_asset_file_contents are both set.
"""
if (expected_vocab_file_contents is not None
and expected_asset_file_contents is not None):
raise ValueError('only one of expected_asset_file_contents and '
'expected_asset_file_contents should be set')
elif expected_asset_file_contents is not None:
tf.logging.warn('expected_asset_file_contents is deprecated, use '
'expected_vocab_file_contents')
expected_vocab_file_contents = (expected_vocab_file_contents
or expected_asset_file_contents or {})
del expected_asset_file_contents
# Note: we don't separately test AnalyzeDataset and TransformDataset as
# AnalyzeAndTransformDataset currently simply composes these two
# transforms. If in future versions of the code, the implementation
# differs, we should also run AnalyzeDataset and TransformDatset composed.
temp_dir = tempfile.mkdtemp(prefix=self._testMethodName,
dir=self.get_temp_dir())
with beam_pipeline or beam.Pipeline(
runner=self._makeRunner()) as pipeline:
with beam_impl.Context(temp_dir=temp_dir,
desired_batch_size=desired_batch_size):
input_data = pipeline | 'CreateInput' >> beam.Create(
input_data)
if test_data is None:
(transformed_data, transformed_metadata), transform_fn = (
(input_data, input_metadata)
|
beam_impl.AnalyzeAndTransformDataset(preprocessing_fn))
else:
transform_fn = ((input_data, input_metadata)
|
beam_impl.AnalyzeDataset(preprocessing_fn))
test_data = pipeline | 'CreateTest' >> beam.Create(
test_data)
transformed_data, transformed_metadata = (
((test_data, input_metadata), transform_fn)
| beam_impl.TransformDataset())
# Write transform_fn so we can test its assets
_ = transform_fn | transform_fn_io.WriteTransformFn(temp_dir)
if expected_data is not None:
transformed_data_coder = tft.coders.ExampleProtoCoder(
transformed_metadata.schema)
transformed_data_path = os.path.join(
temp_dir, 'transformed_data')
_ = (transformed_data
| beam.Map(transformed_data_coder.encode)
| beam.io.tfrecordio.WriteToTFRecord(
transformed_data_path, shard_name_template=''))
if expected_data is not None:
examples = tf.python_io.tf_record_iterator(
path=transformed_data_path)
transformed_data = [
transformed_data_coder.decode(x) for x in examples
]
self.assertDataCloseOrEqual(expected_data, transformed_data)
tf_transform_output = tft.TFTransformOutput(temp_dir)
if expected_metadata:
transformed_metadata = tf_transform_output.transformed_metadata
if only_check_core_metadata:
# preprocessing_fn may add metadata to column schema only relevant to
# internal implementation such as vocabulary_file. As such, only check
# feature names, dtypes and representations are as expected.
self.assertSameElements(
transformed_metadata.schema.column_schemas.keys(),
expected_metadata.schema.column_schemas.keys())
for k, v in transformed_metadata.schema.column_schemas.iteritems(
):
expected_schema = expected_metadata.schema.column_schemas[
k]
self.assertEqual(
expected_schema.representation, v.representation,
"representation doesn't match for feature '%s'" % k)
self.assertEqual(
expected_schema.domain.dtype, v.domain.dtype,
"dtype doesn't match for feature '%s'" % k)
else:
# Check the entire DatasetMetadata is as expected.
# Use extra assertEqual for schemas, since full metadata assertEqual
# error message is not conducive to debugging.
self.assertEqual(expected_metadata.schema.column_schemas,
transformed_metadata.schema.column_schemas)
self.assertEqual(expected_metadata, transformed_metadata)
for filename, file_contents in six.iteritems(
expected_vocab_file_contents):
full_filename = tf_transform_output.vocabulary_file_by_name(
filename)
with tf.gfile.Open(full_filename) as f:
file_lines = f.readlines()
# Store frequency case.
if isinstance(file_contents[0], tuple):
word_and_frequency_list = []
for content in file_lines:
frequency, word = content.split(' ', 1)
word_and_frequency_list.append(
(word.strip('\n'), float(frequency.strip('\n'))))
self.assertAllEqual(
zip(*word_and_frequency_list)[0],
zip(*file_contents)[0])
np.testing.assert_almost_equal(
zip(*word_and_frequency_list)[1],
zip(*file_contents)[1])
else:
file_lines = [
content.strip('\n') for content in file_lines
]
self.assertAllEqual(file_lines, file_contents)
def assertAnalyzeAndTransformResults(self,
input_data,
input_metadata,
preprocessing_fn,
expected_data=None,
expected_metadata=None,
expected_vocab_file_contents=None,
expected_asset_file_contents=None,
test_data=None,
desired_batch_size=None,
beam_pipeline=None,
temp_dir=None):
"""Assert that input data and metadata is transformed as expected.
This methods asserts transformed data and transformed metadata match
with expected_data and expected_metadata.
Args:
input_data: A sequence of dicts whose values are
either strings, lists of strings, numeric types or a pair of those.
input_metadata: DatasetMetadata describing input_data.
preprocessing_fn: A function taking a dict of tensors and returning
a dict of tensors.
expected_data: (optional) A dataset with the same type constraints as
input_data, but representing the output after transformation.
If supplied, transformed data is asserted to be equal.
expected_metadata: (optional) DatasetMetadata describing the transformed
data. If supplied, transformed metadata is asserted to be equal.
expected_vocab_file_contents: (optional) A dictionary from vocab filenames
to their expected content as a list of text lines or a list of tuples
of frequency and text. Values should be the expected result of calling
f.readlines() on the given asset files.
expected_asset_file_contents: deprecated. Use
expected_vocab_file_contents.
test_data: (optional) If this is provided then instead of calling
AnalyzeAndTransformDataset with input_data, this function will call
AnalyzeDataset with input_data and TransformDataset with test_data.
Note that this is the case even if input_data and test_data are equal.
test_data should also conform to input_metadata.
desired_batch_size: (optional) A batch size to batch elements by. If not
provided, a batch size will be computed automatically.
beam_pipeline: (optional) A Beam Pipeline to use in this test.
temp_dir: If set, it is used as output directory, else a new unique
directory is created.
Raises:
AssertionError: if the expected data does not match the results of
transforming input_data according to preprocessing_fn, or
(if provided) if the expected metadata does not match.
ValueError: if expected_vocab_file_contents and
expected_asset_file_contents are both set.
"""
if (expected_vocab_file_contents is not None
and expected_asset_file_contents is not None):
raise ValueError('only one of expected_asset_file_contents and '
'expected_asset_file_contents should be set')
elif expected_asset_file_contents is not None:
tf.compat.v1.logging.warn(
'expected_asset_file_contents is deprecated, use '
'expected_vocab_file_contents')
expected_vocab_file_contents = (expected_vocab_file_contents
or expected_asset_file_contents or {})
del expected_asset_file_contents
# Note: we don't separately test AnalyzeDataset and TransformDataset as
# AnalyzeAndTransformDataset currently simply composes these two
# transforms. If in future versions of the code, the implementation
# differs, we should also run AnalyzeDataset and TransformDatset composed.
temp_dir = temp_dir or tempfile.mkdtemp(prefix=self._testMethodName,
dir=self.get_temp_dir())
with beam_pipeline or self._makeTestPipeline() as pipeline:
with beam_impl.Context(temp_dir=temp_dir,
desired_batch_size=desired_batch_size):
input_data = pipeline | 'CreateInput' >> beam.Create(
input_data)
if test_data is None:
(transformed_data, transformed_metadata), transform_fn = (
(input_data, input_metadata)
|
beam_impl.AnalyzeAndTransformDataset(preprocessing_fn))
else:
transform_fn = ((input_data, input_metadata)
|
beam_impl.AnalyzeDataset(preprocessing_fn))
test_data = pipeline | 'CreateTest' >> beam.Create(
test_data)
transformed_data, transformed_metadata = (
((test_data, input_metadata), transform_fn)
| beam_impl.TransformDataset())
# Write transform_fn so we can test its assets
_ = transform_fn | transform_fn_io.WriteTransformFn(temp_dir)
if expected_data is not None:
transformed_data_coder = tft.coders.ExampleProtoCoder(
transformed_metadata.schema)
transformed_data_path = os.path.join(
temp_dir, 'transformed_data')
_ = (transformed_data
| beam.Map(transformed_data_coder.encode)
| beam.io.tfrecordio.WriteToTFRecord(
transformed_data_path, shard_name_template=''))
# TODO(ebreck) Log transformed_data somewhere.
if expected_data is not None:
examples = tf.compat.v1.python_io.tf_record_iterator(
path=transformed_data_path)
transformed_data = [
transformed_data_coder.decode(x) for x in examples
]
self.assertDataCloseOrEqual(expected_data, transformed_data)
tf_transform_output = tft.TFTransformOutput(temp_dir)
if expected_metadata:
self.assertEqual(expected_metadata,
tf_transform_output.transformed_metadata)
for filename, file_contents in six.iteritems(
expected_vocab_file_contents):
full_filename = tf_transform_output.vocabulary_file_by_name(
filename)
with tf.io.gfile.GFile(full_filename, 'rb') as f:
file_lines = f.readlines()
# Store frequency case.
if isinstance(file_contents[0], tuple):
word_and_frequency_list = []
for content in file_lines:
frequency, word = content.split(b' ', 1)
word_and_frequency_list.append(
(word.strip(b'\n'), float(frequency.strip(b'\n'))))
expected_words, expected_frequency = zip(
*word_and_frequency_list)
actual_words, actual_frequency = zip(*file_contents)
self.assertAllEqual(expected_words, actual_words)
np.testing.assert_almost_equal(expected_frequency,
actual_frequency)
else:
file_lines = [
content.strip(b'\n') for content in file_lines
]
self.assertAllEqual(file_lines, file_contents)
def assertAnalyzeAndTransformResults(self,
input_data,
input_metadata,
preprocessing_fn,
expected_data=None,
expected_metadata=None,
expected_vocab_file_contents=None,
test_data=None,
desired_batch_size=None,
beam_pipeline=None,
temp_dir=None,
force_tf_compat_v1=False,
output_record_batches=False):
"""Assert that input data and metadata is transformed as expected.
This methods asserts transformed data and transformed metadata match
with expected_data and expected_metadata.
Args:
input_data: Input data formatted in one of two ways:
* A sequence of dicts whose values are one of:
strings, lists of strings, numeric types or a pair of those.
Must have at least one key so that we can infer the batch size, or
* A sequence of pa.RecordBatch.
input_metadata: One of -
* DatasetMetadata describing input_data if `input_data` are dicts.
* TensorAdapterConfig otherwise.
preprocessing_fn: A function taking a dict of tensors and returning
a dict of tensors.
expected_data: (optional) A dataset with the same type constraints as
input_data, but representing the output after transformation.
If supplied, transformed data is asserted to be equal.
expected_metadata: (optional) DatasetMetadata describing the transformed
data. If supplied, transformed metadata is asserted to be equal.
expected_vocab_file_contents: (optional) A dictionary from vocab filenames
to their expected content as a list of text lines or a list of tuples
of frequency and text. Values should be the expected result of calling
f.readlines() on the given asset files.
test_data: (optional) If this is provided then instead of calling
AnalyzeAndTransformDataset with input_data, this function will call
AnalyzeDataset with input_data and TransformDataset with test_data.
Note that this is the case even if input_data and test_data are equal.
test_data should also conform to input_metadata.
desired_batch_size: (optional) A batch size to batch elements by. If not
provided, a batch size will be computed automatically.
beam_pipeline: (optional) A Beam Pipeline to use in this test.
temp_dir: If set, it is used as output directory, else a new unique
directory is created.
force_tf_compat_v1: A bool. If `True`, TFT's public APIs use Tensorflow
in compat.v1 mode.
output_record_batches: (optional) A bool. If `True`, `TransformDataset`
and `AnalyzeAndTransformDataset` output `pyarrow.RecordBatch`es;
otherwise, they output instance dicts.
Raises:
AssertionError: if the expected data does not match the results of
transforming input_data according to preprocessing_fn, or
(if provided) if the expected metadata does not match.
"""
expected_vocab_file_contents = expected_vocab_file_contents or {}
# Note: we don't separately test AnalyzeDataset and TransformDataset as
# AnalyzeAndTransformDataset currently simply composes these two
# transforms. If in future versions of the code, the implementation
# differs, we should also run AnalyzeDataset and TransformDataset composed.
temp_dir = temp_dir or tempfile.mkdtemp(
prefix=self._testMethodName, dir=self.get_temp_dir())
with beam_pipeline or self._makeTestPipeline() as pipeline:
with beam_impl.Context(
temp_dir=temp_dir,
desired_batch_size=desired_batch_size,
force_tf_compat_v1=force_tf_compat_v1):
input_data = pipeline | 'CreateInput' >> beam.Create(input_data,
reshuffle=False)
if test_data is None:
(transformed_data, transformed_metadata), transform_fn = (
(input_data, input_metadata)
| beam_impl.AnalyzeAndTransformDataset(
preprocessing_fn,
output_record_batches=output_record_batches))
else:
transform_fn = ((input_data, input_metadata)
| beam_impl.AnalyzeDataset(preprocessing_fn))
test_data = pipeline | 'CreateTest' >> beam.Create(test_data)
transformed_data, transformed_metadata = (
((test_data, input_metadata), transform_fn)
| beam_impl.TransformDataset(
output_record_batches=output_record_batches))
# Write transform_fn so we can test its assets
_ = transform_fn | transform_fn_io.WriteTransformFn(temp_dir)
transformed_data_path = os.path.join(temp_dir, 'transformed_data')
if expected_data is not None:
if isinstance(transformed_metadata,
beam_metadata_io.BeamDatasetMetadata):
deferred_schema = (
transformed_metadata.deferred_metadata
| 'GetDeferredSchema' >> beam.Map(lambda m: m.schema))
else:
deferred_schema = (
self.pipeline | 'CreateDeferredSchema' >> beam.Create(
[transformed_metadata.schema]))
if output_record_batches:
# Since we are using a deferred schema, obtain a pcollection
# containing the data coder that will be created from it.
transformed_data_coder_pcol = (
deferred_schema | 'RecordBatchToExamplesEncoder' >> beam.Map(
example_coder.RecordBatchToExamplesEncoder))
encode_ptransform = 'EncodeRecordBatches' >> beam.FlatMap(
_encode_transformed_data_batch,
coder=beam.pvalue.AsSingleton(transformed_data_coder_pcol))
else:
# Since we are using a deferred schema, obtain a pcollection
# containing the data coder that will be created from it.
transformed_data_coder_pcol = (
deferred_schema
| 'ExampleProtoCoder' >> beam.Map(tft.coders.ExampleProtoCoder))
encode_ptransform = 'EncodeExamples' >> beam.Map(
lambda data, data_coder: data_coder.encode(data),
data_coder=beam.pvalue.AsSingleton(transformed_data_coder_pcol))
_ = (
transformed_data
| encode_ptransform
| beam.io.tfrecordio.WriteToTFRecord(
transformed_data_path, shard_name_template=''))
# TODO(ebreck) Log transformed_data somewhere.
tf_transform_output = tft.TFTransformOutput(temp_dir)
if expected_data is not None:
examples = tf.compat.v1.python_io.tf_record_iterator(
path=transformed_data_path)
shapes = {
f.name:
[s.size for s in f.shape.dim] if f.HasField('shape') else [-1]
for f in tf_transform_output.transformed_metadata.schema.feature
}
transformed_data = [
_format_example_as_numpy_dict(e, shapes) for e in examples
]
self.assertDataCloseOrEqual(expected_data, transformed_data)
if expected_metadata:
# Make a copy with no annotations.
transformed_schema = schema_pb2.Schema()
transformed_schema.CopyFrom(
tf_transform_output.transformed_metadata.schema)
transformed_schema.ClearField('annotation')
for feature in transformed_schema.feature:
feature.ClearField('annotation')
# assertProtoEqual has a size limit on the length of the
# serialized as text strings. Therefore, we first try to use
# assertProtoEqual, if that fails we try to use assertEqual, if that fails
# as well then we raise the exception from assertProtoEqual.
try:
compare.assertProtoEqual(self, expected_metadata.schema,
transformed_schema)
except AssertionError as compare_exception:
try:
self.assertEqual(expected_metadata.schema, transformed_schema)
except AssertionError:
raise compare_exception
for filename, file_contents in expected_vocab_file_contents.items():
full_filename = tf_transform_output.vocabulary_file_by_name(filename)
self.AssertVocabularyContents(full_filename, file_contents)
