def main(argv=None):
parser = argparse.ArgumentParser()
parser.add_argument('--cloud', type=str, help='y' )
args = parser.parse_args(argv) # Parse the arguments
if args.cloud=="y":
pipeline_options = get_cloud_pipeline_options()
else:
pipeline_options = beam.pipeline.PipelineOptions(flags=[],**{'project': "iotpubsub-1536350750202"})
with beam_impl.Context(temp_dir="gs://relation_extraction/beam"):
p = beam.Pipeline(options=pipeline_options)
train_data, test_data = (p | "Read from bigquery" >> ReadBigQuery())
(test_data | "test it" >> beam.Map(printy))
train_data = (train_data, train_metadata)
train_dataset, transform_fn = (train_data
| 'AnalyzeAndTransform' >> beam_impl.AnalyzeAndTransformDataset(preprocessing_fn)
)
test_data = (test_data, train_metadata)
test_data, _ = ((test_data, transform_fn) | 'Transform test data' >> beam_impl.TransformDataset())
train_data, transformed_metadata = train_dataset
transformed_data_coder = tft.coders.ExampleProtoCoder(transformed_metadata.schema)
_ = (train_data
| 'Encode train data to save it' >> beam.Map(transformed_data_coder.encode)
| 'Write the train data to tfrecords' >> tfrecordio.WriteToTFRecord(os.path.join("gs://relation_extraction/beam/Train","TRAIN"))
)
_ = (test_data
| 'Encode test data to save it' >> beam.Map(transformed_data_coder.encode)
| 'Write the test data to tfrecords' >> tfrecordio.WriteToTFRecord(os.path.join("gs://relation_extraction/beam/Test","TEST"))
)
_ = (transform_fn | "WriteTransformFn" >> transform_fn_io.WriteTransformFn("gs://relation_extraction/beam/"))
p.run().wait_until_finish()
def read_and_shuffle_data(train_neg_filepattern, train_pos_filepattern,
test_neg_filepattern, test_pos_filepattern,
working_dir):
"""Read and shuffle the data and write out as a TFRecord of Example protos.
Read in the data from the positive and negative examples on disk, shuffle it
and write it out in TFRecord format.
transform it using a preprocessing pipeline that removes punctuation,
tokenizes and maps tokens to int64 values indices.
Args:
train_neg_filepattern: Filepattern for training data negative examples
train_pos_filepattern: Filepattern for training data positive examples
test_neg_filepattern: Filepattern for test data negative examples
test_pos_filepattern: Filepattern for test data positive examples
working_dir: Directory to write shuffled data to
"""
with beam.Pipeline() as pipeline:
coder = tft.coders.ExampleProtoCoder(RAW_DATA_METADATA.schema)
# pylint: disable=no-value-for-parameter
_ = (pipeline
| 'ReadAndShuffleTrain' >> ReadAndShuffleData(
(train_neg_filepattern, train_pos_filepattern))
| 'EncodeTrainData' >> beam.Map(coder.encode)
| 'WriteTrainData' >> tfrecordio.WriteToTFRecord(
os.path.join(working_dir, SHUFFLED_TRAIN_DATA_FILEBASE)))
_ = (pipeline
| 'ReadAndShuffleTest' >> ReadAndShuffleData(
(test_neg_filepattern, test_pos_filepattern))
| 'EncodeTestData' >> beam.Map(coder.encode)
| 'WriteTestData' >> tfrecordio.WriteToTFRecord(
os.path.join(working_dir, SHUFFLED_TEST_DATA_FILEBASE)))
def read_and_shuffle_data(train_neg_filepattern, train_pos_filepattern,
test_neg_filepattern, test_pos_filepattern,
shuffled_train_filebase, shuffled_test_filebase):
"""Read and shuffle the data and write out as a TFRecord of Example protos.
Read in the data from the positive and negative examples on disk, shuffle it
and write it out in TFRecord format.
transform it using a preprocessing pipeline that removes punctuation,
tokenizes and maps tokens to int64 values indices.
Args:
train_neg_filepattern: Filepattern for training data negative examples
train_pos_filepattern: Filepattern for training data positive examples
test_neg_filepattern: Filepattern for test data negative examples
test_pos_filepattern: Filepattern for test data positive examples
shuffled_train_filebase: Base filename for shuffled training data shards
shuffled_test_filebase: Base filename for shuffled test data shards
"""
with beam.Pipeline() as pipeline:
# pylint: disable=no-value-for-parameter
_ = (pipeline
| 'ReadAndShuffleTrain' >> ReadAndShuffleData(
(train_neg_filepattern, train_pos_filepattern))
| 'WriteTrainData' >> tfrecordio.WriteToTFRecord(
shuffled_train_filebase,
coder=example_proto_coder.ExampleProtoCoder(
RAW_DATA_METADATA.schema)))
_ = (pipeline
| 'ReadAndShuffleTest' >> ReadAndShuffleData(
(test_neg_filepattern, test_pos_filepattern))
| 'WriteTestData' >> tfrecordio.WriteToTFRecord(
shuffled_test_filebase,
coder=example_proto_coder.ExampleProtoCoder(
RAW_DATA_METADATA.schema)))
def expand(self, features):
return (
features
| 'Write to %s' % self._file_path_prefix.replace('/', '_') >>
tfrecordio.WriteToTFRecord(file_path_prefix=self._file_path_prefix,
file_name_suffix='.tfrecord.gz',
coder=ExampleProtoCoder()))
def run_pipeline():
'''
Apache beam pipeline
ARGS: None
'''
args, pipeline_args = get_args()
pipeline_options = PipelineOptions(pipeline_args)
pipeline_options.view_as(SetupOptions).save_main_session = True
read_textline_from_csv = beam.io.ReadFromText(
args.csv_path, skip_header_lines=1)
load_img_from_path = LoadImageDoFn()
augment_data = PreprocessImagesDoFn()
img_to_tfexample = ImageToTfExampleDoFn()
write_to_tf_record = tfrecordio.WriteToTFRecord(
file_path_prefix='gs://bucket_name/Apache_beam_records/Test_records/',
num_shards=20)
with beam.Pipeline(options=pipeline_options) as pipe:
_ = (pipe
| 'ReadCSVFromText' >> read_textline_from_csv
| 'LoadImageData' >> beam.ParDo(load_img_from_path)
| 'PreprocessImages' >> beam.ParDo(augment_data)
| 'ImageToTfExample' >> beam.ParDo(img_to_tfexample)
| 'SerializeProto' >> beam.Map(lambda x: x.SerializeToString())
| 'WriteTfRecord' >> write_to_tf_record)
print('Done running')
def expand(self, features):
return (features
| 'Write to %s' % self._file_path_prefix.replace('/', '_') >>
tfrecordio.WriteToTFRecord(
file_path_prefix=self._file_path_prefix,
file_name_suffix='.tfrecord.gz',
shard_name_template=fileio.DEFAULT_SHARD_NAME_TEMPLATE,
coder=ExampleProtoCoder(),
compression_type=fileio.CompressionTypes.AUTO))
def main(argv=None):
'''Run Preprocessing as a Dataflow pipeline.'''
args = parse_arguments(sys.argv if argv is None else argv)
if args.cloud:
logging.info('Start running in the cloud')
options = {
'runner':
'DataflowRunner',
'job_name': ('mlengine-boilerplate-{}'.format(
datetime.datetime.now().strftime('%Y%m%d%H%M%S'))),
'staging_location':
os.path.join(args.output_dir, 'staging'),
'temp_location':
os.path.join(args.output_dir, 'tmp'),
'project':
args.project_id,
'zone':
'europe-west1-d',
'autoscaling_algorithm':
'THROUGHPUT_BASED',
'save_main_session':
True,
'setup_file':
'./setup.py',
}
pipeline_options = beam.pipeline.PipelineOptions(flags=[], **options)
print(pipeline_options)
else:
pipeline_options = None
train_coder = coders.ExampleProtoCoder(schema)
p = beam.Pipeline(options=pipeline_options)
examples = (p
| 'ReadData' >> beam.io.ReadFromText(DATA_DIR + '/*',
skip_header_lines=1)
| 'buildExamples' >>
beam.FlatMap(lambda raw_input: buildExample(raw_input)))
examples_split = examples | beam.Partition(partition_fn, 3)
example_dict = {
'train': examples_split[0],
'validation': examples_split[1],
'test': examples_split[2]
}
for part, examples in example_dict.items():
_ = examples | part + '_writeExamples' >> tfrecordio.WriteToTFRecord(
file_path_prefix=os.path.join(args.output_dir, part + '_examples'),
compression_type=filesystem.CompressionTypes.GZIP,
coder=train_coder,
file_name_suffix='.gz')
p.run()
def run(argv=None):
"""Runs the revise preprocessed data pipeline.
Args:
argv: Pipeline options as a list of arguments.
"""
pipeline_options = PipelineOptions(flags=argv)
revise_options = pipeline_options.view_as(ReviseOptions)
cloud_options = pipeline_options.view_as(GoogleCloudOptions)
output_dir = os.path.join(
revise_options.output,
datetime.datetime.now().strftime('%Y%m%d-%H%M%S'))
pipeline_options.view_as(SetupOptions).save_main_session = True
pipeline_options.view_as(
WorkerOptions).autoscaling_algorithm = 'THROUGHPUT_BASED'
cloud_options.staging_location = os.path.join(output_dir, 'tmp', 'staging')
cloud_options.temp_location = os.path.join(output_dir, 'tmp')
cloud_options.job_name = 'relabel-examples-%s' % (
datetime.datetime.now().strftime('%y%m%d-%H%M%S'))
metadata_query = str(
Template(open(revise_options.metadata,
'r').read()).render(METADATA_QUERY_REPLACEMENTS))
logging.info('metadata query : %s', metadata_query)
with beam.Pipeline(options=pipeline_options) as p:
# Gather our sample metadata into a python dictionary.
samples_metadata = (
p
| 'ReadSampleMetadata' >> beam.io.Read(
beam.io.BigQuerySource(query=metadata_query,
use_standard_sql=True))
| 'TableToDictionary' >> beam.CombineGlobally(
util.TableToDictCombineFn(key_column=encoder.KEY_COLUMN)))
# Read the tf.Example protos into a PCollection.
examples = p | 'ReadExamples' >> tfrecordio.ReadFromTFRecord(
file_pattern=revise_options.input,
compression_type=CompressionTypes.GZIP)
# Filter the TensorFlow Example Protocol Buffers.
filtered_examples = (examples | 'ReviseExamples' >> beam.FlatMap(
lambda example, samples_metadata: filter_and_revise_example(
example, samples_metadata),
beam.pvalue.AsSingleton(samples_metadata)))
# Write the subset of tf.Example protos to Cloud Storage.
_ = (filtered_examples
| 'SerializeExamples' >>
beam.Map(lambda example: example.SerializeToString())
| 'WriteExamples' >> tfrecordio.WriteToTFRecord(
file_path_prefix=os.path.join(output_dir, 'examples'),
compression_type=CompressionTypes.GZIP,
file_name_suffix='.tfrecord.gz'))
def main(argv=None):
"""Run preprocessing as a Dataflow pipeline.
Args:
argv (list): list of arguments
"""
args = parse_arguments(sys.argv if argv is None else argv)
if args.cloud:
pipeline_options = get_cloud_pipeline_options()
else:
pipeline_options = None
p = beam.Pipeline(options=pipeline_options)
with beam_impl.Context(temp_dir=tempfile.mkdtemp()):
# read data and join by key
raw_data_input = (p
| 'ReadInputData' >> beam.io.ReadFromText(
TRAIN_INPUT_DATA, skip_header_lines=1)
| 'ParseInputCSV' >> beam.Map(converter_input.decode)
| 'ExtractBatchKeyIn' >> beam.Map(extract_batchkey))
raw_data_output = (
p
| 'ReadOutputData' >> beam.io.ReadFromText(TRAIN_OUTPUT_DATA,
skip_header_lines=1)
| 'ParseOutputCSV' >> beam.Map(converter_output.decode)
| 'ExtractBatchKeyOut' >> beam.Map(extract_batchkey))
raw_data = ((raw_data_input, raw_data_output)
| 'JoinData' >> beam.CoGroupByKey()
| 'RemoveKeys' >> beam.FlatMap(remove_keys))
# analyse and transform dataset
raw_dataset = (raw_data, input_metadata)
transformed_dataset, transform_fn = (
raw_dataset
| 'AnalyzeAndTransform' >>
beam_impl.AnalyzeAndTransformDataset(preprocessing_fn))
transformed_data, transformed_metadata = transformed_dataset
# save data and serialize TransformFn
transformed_data_coder = tft.coders.ExampleProtoCoder(
transformed_metadata.schema)
_ = (transformed_data
| 'EncodeData' >> beam.Map(transformed_data_coder.encode)
| 'WriteData' >> tfrecordio.WriteToTFRecord(
os.path.join(TFRECORD_DIR, 'records')))
_ = (transform_fn
|
"WriteTransformFn" >> transform_fn_io.WriteTransformFn(MODEL_DIR))
p.run().wait_until_finish()
def store_transformed_data(data, schema, path, name=''):
"""Stores data from input pipeline into TFRecord in the specified path.
Args:
data: `PCollection`, input pipeline.
schema: `DatasetMetadata` object, describes schema of the input pipeline.
path: string, where to write output.
name: string: name describing pipeline to be written.
Returns:
PCollection
"""
p = (data
| 'WriteData{}'.format(name) >> tfrecordio.WriteToTFRecord(
path, coder=example_proto_coder.ExampleProtoCoder(schema.schema)))
return p
def pipeline(root):
"""Method to pass into flume runner."""
_ = (
root
| 'Read RecordIO TSV' >> beam.io.ReadFromText(FLAGS.input_path)
| 'Validate sentence pair' >> beam.ParDo(
ValidateSentencePair(FLAGS.min_edit_distance))
| 'Select TSV columns' >> beam.ParDo(
SelectTSVColumns(
source_column=FLAGS.tsv_source_column,
target_column=FLAGS.tsv_target_column))
| 'Reshuffle' >> beam.Reshuffle()
| 'Batch elements' >> beam.BatchElements(
min_batch_size=1024, max_batch_size=1024)
| 'Make tf.Examples' >> beam.ParDo(
PrepareTfExamples(
spm_path=FLAGS.spm_path,
packed_length=FLAGS.packed_length,
pad_length=FLAGS.pad_length))
| 'Write to tf.Record' >> tfrecordio.WriteToTFRecord(FLAGS.output_path))
def main(argv=None):
"""Run preprocessing as a Dataflow pipeline.
Args:
argv (list): list of arguments
"""
args = parse_arguments(sys.argv if argv is None else argv)
if args.cloud:
pipeline_options = get_cloud_pipeline_options(args.project_id,
args.output_dir)
else:
pipeline_options = None
pipeline = beam.Pipeline(options=pipeline_options)
examples = (
pipeline
# | 'ReadData' >> beam.Create(open('data/test.csv')
# .readlines()[1:])
|
'ReadData' >> beam.io.ReadFromText(DATA_DIR + '*', skip_header_lines=1)
| 'BuildExamples' >> beam.FlatMap(build_example))
examples_split = examples | beam.Partition(partition_fn, 3)
example_dict = {
'train': examples_split[0],
'validation': examples_split[1],
'test': examples_split[2]
}
for part, examples in example_dict.items():
examples | part + '_writeExamples' >> tfrecordio.WriteToTFRecord(
file_path_prefix=os.path.join(TFRECORD_DIR, part + '_examples'),
compression_type=filesystem.CompressionTypes.GZIP,
coder=coders.ExampleProtoCoder(schema),
file_name_suffix='.tfrecord.gz')
pipeline.run().wait_until_finish()
def run(argv=None):
"""Runs the sparse measurements preprocess pipeline.
Args:
argv: Pipeline options as a list of arguments.
"""
pipeline_options = PipelineOptions(flags=argv)
preprocess_options = pipeline_options.view_as(PreprocessOptions)
cloud_options = pipeline_options.view_as(GoogleCloudOptions)
output_dir = os.path.join(
preprocess_options.output,
datetime.datetime.now().strftime('%Y%m%d-%H%M%S'))
pipeline_options.view_as(SetupOptions).save_main_session = True
pipeline_options.view_as(
WorkerOptions).autoscaling_algorithm = 'THROUGHPUT_BASED'
cloud_options.staging_location = os.path.join(output_dir, 'tmp', 'staging')
cloud_options.temp_location = os.path.join(output_dir, 'tmp')
cloud_options.job_name = 'preprocess-measurements-%s' % (
datetime.datetime.now().strftime('%y%m%d-%H%M%S'))
data_query = str(
Template(open(preprocess_options.input,
'r').read()).render(DATA_QUERY_REPLACEMENTS))
logging.info('data query : %s', data_query)
with beam.Pipeline(options=pipeline_options) as p:
# Read the table rows into a PCollection.
rows = p | 'ReadMeasurements' >> beam.io.Read(
beam.io.BigQuerySource(query=data_query, use_standard_sql=True))
# Convert the data into TensorFlow Example Protocol Buffers.
examples = measurements_to_examples(rows)
# Write the serialized compressed protocol buffers to Cloud Storage.
_ = (examples
| 'EncodeExamples' >>
beam.Map(lambda example: example.SerializeToString())
| 'WriteExamples' >> tfrecordio.WriteToTFRecord(
file_path_prefix=os.path.join(output_dir, 'examples'),
compression_type=CompressionTypes.GZIP,
file_name_suffix='.tfrecord.gz'))
def pipeline(root):
"""Method to pass into flume runner."""
for i, tsv_in in enumerate(
tf.io.gfile.glob(os.path.join(FLAGS.input_path, '*.tsv'))):
print('Processing tsv input: %s' % tsv_in)
tfr_out = tsv_in.replace('.tsv', '.tfr')
num_output_shards = FLAGS.num_train_shards if 'train' in tsv_in else FLAGS.num_guide_shards
_ = (root
| 'Read RecordIO TSV__%s' % i >> beam.io.ReadFromText(tsv_in)
| 'Validate sentence pair__%s' % i >> beam.ParDo(
ValidateSentencePair(FLAGS.min_edit_distance))
| 'Select TSV columns__%s' % i >> beam.ParDo(
SelectTSVColumns(source_column=FLAGS.tsv_source_column,
target_column=FLAGS.tsv_target_column))
| 'Reshuffle__%s' % i >> beam.Reshuffle()
| 'Batch elements__%s' % i >> beam.BatchElements(
min_batch_size=1024, max_batch_size=1024)
| 'Make tf.Examples__%s' % i >> beam.ParDo(
PrepareTfExamples(spm_path=FLAGS.spm_path,
packed_length=FLAGS.packed_length,
pad_length=FLAGS.pad_length))
| 'Write to tf.Record__%s' % i >> tfrecordio.WriteToTFRecord(
tfr_out, num_shards=num_output_shards))
def run(p, params):
"""Defines Beam preprocessing pipeline.
Performs the following:
- Reads text files from pattern.
- Split text files in train and validation sets.
Args:
p: PCollection, initial pipeline.
params: Object holding a set of parameters as name-value pairs.
"""
path_pattern = os.path.join(params.input_dir, '*',
'*{}'.format(constants.FILE_EXTENSION))
data = (p
| 'ListFiles' >> beam.Create(gfile.Glob(path_pattern))
| 'ReadFiles' >> beam.ParDo(ReadFile())
| 'SplitData' >> beam.ParDo(
_SplitData(),
train_size=params.train_size,
val_label=_DatasetType.VAL.name).with_outputs(
_DatasetType.VAL.name, main=_DatasetType.TRAIN.name))
schema = dataset_schema.from_feature_spec(
utils.get_processed_data_schema())
for dataset in _DatasetType:
if not dataset.value:
continue
_ = (
data[dataset.name]
| 'Shuffle{}'.format(dataset.name) >> shuffle() # pylint: disable=no-value-for-parameter
|
'WriteFiles{}'.format(dataset.name) >> tfrecordio.WriteToTFRecord(
os.path.join(params.output_dir,
dataset.name + constants.TFRECORD),
coder=example_proto_coder.ExampleProtoCoder(schema)))
def preprocess(query, in_test_mode):
import os
import os.path
import tempfile
from apache_beam.io import tfrecordio
from tensorflow_transform.coders import example_proto_coder
from tensorflow_transform.tf_metadata import dataset_metadata
from tensorflow_transform.tf_metadata import dataset_schema
from tensorflow_transform.beam.tft_beam_io import transform_fn_io
job_name = 'preprocess-babyweight-features' + '-' + datetime.datetime.now().strftime('%y%m%d-%H%M%S')
if in_test_mode:
import shutil
print 'Launching local job ... hang on'
OUTPUT_DIR = './preproc_tft'
shutil.rmtree(OUTPUT_DIR, ignore_errors=True)
else:
print 'Launching Dataflow job {} ... hang on'.format(job_name)
OUTPUT_DIR = 'gs://{0}/babyweight/preproc_tft/'.format(BUCKET)
import subprocess
subprocess.call('gsutil rm -r {}'.format(OUTPUT_DIR).split())
options = {
'staging_location': os.path.join(OUTPUT_DIR, 'tmp', 'staging'),
'temp_location': os.path.join(OUTPUT_DIR, 'tmp'),
'job_name': job_name,
'project': PROJECT,
'max_num_workers': 24,
'teardown_policy': 'TEARDOWN_ALWAYS',
'no_save_main_session': True,
'requirements_file': 'requirements.txt'
}
opts = beam.pipeline.PipelineOptions(flags=[], **options)
if in_test_mode:
RUNNER = 'DirectRunner'
else:
RUNNER = 'DataflowRunner'
# set up metadata
raw_data_schema = {
colname : dataset_schema.ColumnSchema(tf.string, [], dataset_schema.FixedColumnRepresentation())
for colname in 'key,is_male,mother_race,mother_married,cigarette_use,alcohol_use'.split(',')
}
raw_data_schema.update({
colname : dataset_schema.ColumnSchema(tf.float32, [], dataset_schema.FixedColumnRepresentation())
for colname in 'weight_pounds,mother_age,plurality,gestation_weeks'.split(',')
})
raw_data_metadata = dataset_metadata.DatasetMetadata(dataset_schema.Schema(raw_data_schema))
def read_rawdata(p, step, test_mode):
if step == 'train':
selquery = 'SELECT * FROM ({}) WHERE ABS(MOD(hashmonth, 4)) < 3'.format(query)
else:
selquery = 'SELECT * FROM ({}) WHERE ABS(MOD(hashmonth, 4)) = 3'.format(query)
if in_test_mode:
selquery = selquery + ' LIMIT 100'
#print 'Processing {} data from {}'.format(step, selquery)
return (p
| '{}_read'.format(step) >> beam.io.Read(beam.io.BigQuerySource(query=selquery, use_standard_sql=True))
| '{}_cleanup'.format(step) >> beam.FlatMap(cleanup)
)
# run Beam
with beam.Pipeline(RUNNER, options=opts) as p:
with beam_impl.Context(temp_dir=os.path.join(OUTPUT_DIR, 'tmp')):
# analyze and transform training
raw_data = read_rawdata(p, 'train', in_test_mode)
raw_dataset = (raw_data, raw_data_metadata)
transformed_dataset, transform_fn = (
raw_dataset | beam_impl.AnalyzeAndTransformDataset(preprocess_tft))
transformed_data, transformed_metadata = transformed_dataset
_ = transformed_data | 'WriteTrainData' >> tfrecordio.WriteToTFRecord(
os.path.join(OUTPUT_DIR, 'train'),
coder=example_proto_coder.ExampleProtoCoder(
transformed_metadata.schema))
# transform eval data
raw_test_data = read_rawdata(p, 'eval', in_test_mode)
raw_test_dataset = (raw_test_data, raw_data_metadata)
transformed_test_dataset = (
(raw_test_dataset, transform_fn) | beam_impl.TransformDataset())
transformed_test_data, _ = transformed_test_dataset
_ = transformed_test_data | 'WriteTestData' >> tfrecordio.WriteToTFRecord(
os.path.join(OUTPUT_DIR, 'eval'),
coder=example_proto_coder.ExampleProtoCoder(
transformed_metadata.schema))
_ = (transform_fn
| 'WriteTransformFn' >>
transform_fn_io.WriteTransformFn(os.path.join(OUTPUT_DIR, 'metadata')))
job = p.run()
def transform_data(working_dir):
"""Transform the data and write out as a TFRecord of Example protos.
Read in the data from the positive and negative examples on disk, and
transform it using a preprocessing pipeline that removes punctuation,
tokenizes and maps tokens to int64 values indices.
Args:
working_dir: Directory to read shuffled data from and write transformed data
and metadata to.
"""
with beam.Pipeline() as pipeline:
with beam_impl.Context(temp_dir=tempfile.mkdtemp()):
coder = tft.coders.ExampleProtoCoder(RAW_DATA_METADATA.schema)
train_data = (pipeline
| 'ReadTrain' >> tfrecordio.ReadFromTFRecord(
os.path.join(working_dir,
SHUFFLED_TRAIN_DATA_FILEBASE + '*'))
| 'DecodeTrain' >> beam.Map(coder.decode))
test_data = (pipeline
| 'ReadTest' >> tfrecordio.ReadFromTFRecord(
os.path.join(working_dir,
SHUFFLED_TEST_DATA_FILEBASE + '*'))
| 'DecodeTest' >> beam.Map(coder.decode))
def preprocessing_fn(inputs):
"""Preprocess input columns into transformed columns."""
review = inputs[REVIEW_KEY]
review_tokens = tf.string_split(review, DELIMITERS)
review_indices = tft.string_to_int(review_tokens,
top_k=VOCAB_SIZE)
# Add one for the oov bucket created by string_to_int.
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]
}
(transformed_train_data, transformed_metadata), transform_fn = (
(train_data, RAW_DATA_METADATA)
| 'AnalyzeAndTransform' >>
beam_impl.AnalyzeAndTransformDataset(preprocessing_fn))
transformed_data_coder = tft.coders.ExampleProtoCoder(
transformed_metadata.schema)
transformed_test_data, _ = (
((test_data, RAW_DATA_METADATA), transform_fn)
| 'Transform' >> beam_impl.TransformDataset())
_ = (transformed_train_data
| 'EncodeTrainData' >> beam.Map(transformed_data_coder.encode)
| 'WriteTrainData' >> tfrecordio.WriteToTFRecord(
os.path.join(working_dir,
TRANSFORMED_TRAIN_DATA_FILEBASE)))
_ = (
transformed_test_data
| 'EncodeTestData' >> beam.Map(transformed_data_coder.encode)
| 'WriteTestData' >> tfrecordio.WriteToTFRecord(
os.path.join(working_dir, TRANSFORMED_TEST_DATA_FILEBASE)))
# Will write a 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
| 'WriteTransformFn' >>
transform_fn_io.WriteTransformFn(working_dir))
def run(input_feature_spec,
labels,
feature_extraction,
feature_scaling=None,
eval_percent=20.0,
beam_options=None,
work_dir=None):
"""Runs the whole preprocessing step.
This runs the feature extraction PTransform, validates that the data conforms
to the schema provided, normalizes the features, and splits the dataset into
a training and evaluation dataset.
"""
# Populate optional arguments
if not feature_scaling:
feature_scaling = lambda inputs: inputs
# Type checking
if not isinstance(labels, list):
raise ValueError('`labels` must be list(str). '
'Given: {} {}'.format(labels, type(labels)))
if not isinstance(feature_extraction, beam.PTransform):
raise ValueError('`feature_extraction` must be {}. '
'Given: {} {}'.format(beam.PTransform,
feature_extraction,
type(feature_extraction)))
if not callable(feature_scaling):
raise ValueError('`feature_scaling` must be callable. '
'Given: {} {}'.format(feature_scaling,
type(feature_scaling)))
if beam_options and not isinstance(beam_options, PipelineOptions):
raise ValueError('`beam_options` must be {}. '
'Given: {} {}'.format(PipelineOptions, beam_options,
type(beam_options)))
if not work_dir:
work_dir = tempfile.mkdtemp(prefix='tensorflow-preprocessing')
tft_temp_dir = os.path.join(work_dir, 'tft-temp')
train_dataset_dir = os.path.join(work_dir, 'train-dataset')
eval_dataset_dir = os.path.join(work_dir, 'eval-dataset')
transform_fn_dir = os.path.join(work_dir, transform_fn_io.TRANSFORM_FN_DIR)
# if tf.gfile.Exists(transform_fn_dir):
if tf.io.gfile.exists(transform_fn_dir):
tf.gfile.DeleteRecursively(transform_fn_dir)
# [START dataflow_molecules_create_pipeline]
# Build and run a Beam Pipeline
with beam.Pipeline(options=beam_options) as p, \
beam_impl.Context(temp_dir=tft_temp_dir):
# [END dataflow_molecules_create_pipeline]
# [START dataflow_molecules_feature_extraction]
# Transform and validate the input data matches the input schema
dataset = (
p
| 'Feature extraction' >> feature_extraction
# [END dataflow_molecules_feature_extraction]
# [START dataflow_molecules_validate_inputs]
| 'Validate inputs' >> beam.ParDo(
ValidateInputData(input_feature_spec)))
# [END dataflow_molecules_validate_inputs]
# [START dataflow_molecules_analyze_and_transform_dataset]
# Apply the tf.Transform preprocessing_fn
input_metadata = dataset_metadata.DatasetMetadata(
dataset_schema.from_feature_spec(input_feature_spec))
dataset_and_metadata, transform_fn = (
(dataset, input_metadata)
| 'Feature scaling' >>
beam_impl.AnalyzeAndTransformDataset(feature_scaling))
dataset, metadata = dataset_and_metadata
# [END dataflow_molecules_analyze_and_transform_dataset]
# [START dataflow_molecules_split_to_train_and_eval_datasets]
# Split the dataset into a training set and an evaluation set
assert 0 < eval_percent < 100, 'eval_percent must in the range (0-100)'
train_dataset, eval_dataset = (
dataset
| 'Split dataset' >> beam.Partition(
lambda elem, _: int(random.uniform(0, 100) < eval_percent), 2))
# [END dataflow_molecules_split_to_train_and_eval_datasets]
# [START dataflow_molecules_write_tfrecords]
# Write the datasets as TFRecords
coder = example_proto_coder.ExampleProtoCoder(metadata.schema)
train_dataset_prefix = os.path.join(train_dataset_dir, 'part')
_ = (train_dataset
| 'Write train dataset' >> tfrecordio.WriteToTFRecord(
train_dataset_prefix, coder))
eval_dataset_prefix = os.path.join(eval_dataset_dir, 'part')
_ = (eval_dataset
| 'Write eval dataset' >> tfrecordio.WriteToTFRecord(
eval_dataset_prefix, coder))
# Write the transform_fn
_ = (transform_fn
|
'Write transformFn' >> transform_fn_io.WriteTransformFn(work_dir))
# [END dataflow_molecules_write_tfrecords]
return PreprocessData(input_feature_spec, labels,
train_dataset_prefix + '*',
eval_dataset_prefix + '*')
def transform_data(train_data_file, test_data_file, working_dir):
"""Transform the data and write out as a TFRecord of Example protos.
Read in the data using the CSV reader, and transform it using a
preprocessing pipeline that scales numeric data and converts categorical data
from strings to int64 values indices, by creating a vocabulary for each
category.
Args:
train_data_file: File containing training data
test_data_file: File containing test data
working_dir: Directory to write transformed data and metadata to
"""
def preprocessing_fn(inputs):
"""Preprocess input columns into transformed columns."""
outputs = {}
# Scale numeric columns to have range [0, 1].
for key in NUMERIC_FEATURE_KEYS:
outputs[key] = tft.scale_to_0_1(inputs[key])
# For all categorical columns except the label column, we use
# tft.string_to_int which computes the set of unique values and uses this
# to convert the strings to indices.
for key in CATEGORICAL_FEATURE_KEYS:
outputs[key] = tft.string_to_int(inputs[key])
# For the label column we provide the mapping from string to index.
def convert_label(label):
table = lookup.index_table_from_tensor(['>50K', '<=50K'])
return table.lookup(label)
outputs[LABEL_KEY] = tft.apply_function(convert_label,
inputs[LABEL_KEY])
return outputs
# The "with" block will create a pipeline, and run that pipeline at the exit
# of the block.
with beam.Pipeline() as pipeline:
with tft.Context(temp_dir=tempfile.mkdtemp()):
# Create a coder to read the census data with the schema. To do this we
# need to list all columns in order since the schema doesn't specify the
# order of columns in the csv.
ordered_columns = [
'age', 'workclass', 'fnlwgt', 'education', 'education-num',
'marital-status', 'occupation', 'relationship', 'race', 'sex',
'capital-gain', 'capital-loss', 'hours-per-week',
'native-country', 'label'
]
converter = tft.CsvCoder(ordered_columns, RAW_DATA_METADATA.schema)
# Read in raw data and convert using CSV converter. Note that we apply
# some Beam transformations here, which will not be encoded in the TF
# graph since we don't do the from within tf.Transform's methods
# (AnalyzeDataset, TransformDataset etc.). These transformations are just
# to get data into a format that the CSV converter can read, in particular
# removing empty lines and removing spaces after commas.
raw_data = (pipeline
|
'ReadTrainData' >> textio.ReadFromText(train_data_file)
| 'FilterTrainData' >> beam.Filter(lambda line: line)
| 'FixCommasTrainData' >>
beam.Map(lambda line: line.replace(', ', ','))
| 'DecodeTrainData' >> beam.Map(converter.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.
raw_dataset = (raw_data, RAW_DATA_METADATA)
transformed_dataset, transform_fn = (
raw_dataset | tft.AnalyzeAndTransformDataset(preprocessing_fn))
transformed_data, transformed_metadata = transformed_dataset
_ = transformed_data | 'WriteTrainData' >> tfrecordio.WriteToTFRecord(
os.path.join(working_dir, TRANSFORMED_TRAIN_DATA_FILEBASE),
coder=tft.ExampleProtoCoder(transformed_metadata.schema))
# Now apply transform function to test data. In this case we also remove
# the header line from the CSV file and the trailing period at the end of
# each line.
raw_test_data = (
pipeline
| 'ReadTestData' >> textio.ReadFromText(test_data_file)
| 'FilterTestData' >> beam.Filter(
lambda line: line and line != '|1x3 Cross validator')
| 'FixCommasTestData' >>
beam.Map(lambda line: line.replace(', ', ','))
| 'RemoveTrailingPeriodsTestData' >>
beam.Map(lambda line: line[:-1])
| 'DecodeTestData' >> beam.Map(converter.decode))
raw_test_dataset = (raw_test_data, RAW_DATA_METADATA)
transformed_test_dataset = ((raw_test_dataset, transform_fn)
| tft.TransformDataset())
# Don't need transformed data schema, it's the same as before.
transformed_test_data, _ = transformed_test_dataset
_ = transformed_test_data | 'WriteTestData' >> tfrecordio.WriteToTFRecord(
os.path.join(working_dir, TRANSFORMED_TEST_DATA_FILEBASE),
coder=tft.ExampleProtoCoder(transformed_metadata.schema))
# Will write a SavedModel and metadata to two subdirectories of
# working_dir, given by tft.TRANSFORM_FN_DIR and
# tft.TRANSFORMED_METADATA_DIR respectively.
_ = (transform_fn
| 'WriteTransformFn' >> tft.WriteTransformFn(working_dir))
def write_to_tfrecord(args):
"""
This function is supposed to be called as a script.
"""
# Decode arguments
current_index, num_shards, train_split_fname_out, eval_split_fname_out, \
exp_log_data_file_train_tfrecord, exp_log_data_file_eval_tfrecord, working_dir, data_formatter_module_path = args
# num_shards = "32"
current_index, num_shards = int(current_index), int(num_shards)
split_train_file_pattern = '{}-{:05}-of-{:05}'.format(
train_split_fname_out, current_index, num_shards) + '*'
split_eval_file_pattern = '{}-{:05}-of-{:05}'.format(
eval_split_fname_out, current_index, num_shards)
log.info('exp_log_data_file_train_tfrecord {}'.format(
exp_log_data_file_train_tfrecord))
log.info('exp_log_data_file_eval_tfrecord {}'.format(
exp_log_data_file_eval_tfrecord))
log.info('split_train_file_pattern {}'.format(split_train_file_pattern))
log.info('split_eval_file_pattern {}'.format(split_eval_file_pattern))
data_formatter = import_from_uri(
data_formatter_module_path).DataFormatter()
# Set up the preprocessing pipeline.
pipeline = beam.Pipeline(runner=DirectRunner())
with beam_impl.Context(temp_dir=tempfile.mkdtemp()):
# Read raw data files: CSV format ordered according to the `data_formatter`, that are then converted
# into a cleaned up format.
raw_train_data = (
pipeline
| 'ReadTrainDataFile' >> textio.ReadFromText(
split_train_file_pattern, skip_header_lines=0)
| 'DecodeTrainDataCSV' >> MapAndFilterErrors(
tft_coders.CsvCoder(
data_formatter.get_features_and_targets(),
data_formatter.get_features_metadata().schema).decode))
raw_eval_data = (
pipeline
| 'ReadEvalDataFile' >> textio.ReadFromText(
split_eval_file_pattern, skip_header_lines=0)
| 'DecodeEvalDataCSV' >> MapAndFilterErrors(
tft_coders.CsvCoder(
data_formatter.get_features_and_targets(),
data_formatter.get_features_metadata().schema).decode))
# Examples in tf-example format (for model analysis purposes).
# raw_feature_spec = data_formatter.RAW_DATA_METADATA.schema.as_feature_spec()
# raw_schema = dataset_schema.from_feature_spec(raw_feature_spec)
# coder = example_proto_coder.ExampleProtoCoder(raw_schema)
#
# _ = (
# raw_eval_data
# | 'ToSerializedTFExample' >> beam.Map(coder.encode)
# | 'WriteAnalysisTFRecord' >> tfrecordio.WriteToTFRecord(
# '{}-{:05}-of-{:05}'.format(analysis_fname_out, i, num_shards),
# shard_name_template='', num_shards=1)
# )
# 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 = (pipeline
| 'ReadTransformGraph' >>
transform_fn_io.ReadTransformFn(working_dir))
# Applies the transformation `transform_fn` to the raw eval dataset
(transformed_train_data, transformed_metadata) = (
((raw_train_data, data_formatter.get_features_metadata()),
transform_fn)
| 'TransformTrainData' >> beam_impl.TransformDataset())
# Applies the transformation `transform_fn` to the raw eval dataset
(transformed_eval_data, transformed_metadata) = (
((raw_eval_data, data_formatter.get_features_metadata()),
transform_fn)
| 'TransformEvalData' >> beam_impl.TransformDataset())
# The data schema of the transformed data gets used to build a signature to create
# a TFRecord (tf binary data format). This signature is a wrapper function used to
# encode transformed data.
transformed_data_coder = tft.coders.ExampleProtoCoder(
transformed_metadata.schema)
_ = (transformed_train_data
| 'EncodeTrainDataTransform' >> MapAndFilterErrors(
transformed_data_coder.encode)
| 'WriteTrainDataTFRecord' >>
tfrecordio.WriteToTFRecord('{}-{:05}-of-{:05}'.format(
exp_log_data_file_train_tfrecord, current_index, num_shards),
shard_name_template='',
num_shards=1))
_ = (transformed_eval_data
| 'EncodeEvalDataTransform' >> MapAndFilterErrors(
transformed_data_coder.encode)
| 'WriteEvalDataTFRecord' >>
tfrecordio.WriteToTFRecord('{}-{:05}-of-{:05}'.format(
exp_log_data_file_eval_tfrecord, current_index, num_shards),
shard_name_template='',
num_shards=1))
result = pipeline.run()
result.wait_until_finish()
def transform_data(train_data_file, test_data_file, working_dir):
"""Transform the data and write out as a TFRecord of Example protos.
Read in the data using the CSV reader, and transform it using a
preprocessing pipeline that scales numeric data and converts categorical data
from strings to int64 values indices, by creating a vocabulary for each
category.
Args:
train_data_file: File containing training data
test_data_file: File containing test data
working_dir: Directory to write transformed data and metadata to
"""
def preprocessing_fn(inputs):
"""Preprocess input columns into transformed columns."""
# Since we are modifying some features and leaving others unchanged, we
# start by setting `outputs` to a copy of `inputs.
outputs = inputs.copy()
# Scale numeric columns to have range [0, 1].
for key in NUMERIC_FEATURE_KEYS:
outputs[key] = tft.scale_to_0_1(outputs[key])
# For all categorical columns except the label column, we generate a
# vocabulary but do not modify the feature. This vocabulary is instead
# used in the trainer, by means of a feature column, to convert the feature
# from a string to an integer id.
for key in CATEGORICAL_FEATURE_KEYS:
tft.vocabulary(inputs[key], vocab_filename=key)
# For the label column we provide the mapping from string to index.
table = tf.contrib.lookup.index_table_from_tensor(['>50K', '<=50K'])
outputs[LABEL_KEY] = table.lookup(outputs[LABEL_KEY])
return outputs
# The "with" block will create a pipeline, and run that pipeline at the exit
# of the block.
with beam.Pipeline() as pipeline:
with beam_impl.Context(temp_dir=tempfile.mkdtemp()):
# Create a coder to read the census data with the schema. To do this we
# need to list all columns in order since the schema doesn't specify the
# order of columns in the csv.
ordered_columns = [
'age', 'workclass', 'fnlwgt', 'education', 'education-num',
'marital-status', 'occupation', 'relationship', 'race', 'sex',
'capital-gain', 'capital-loss', 'hours-per-week', 'native-country',
'label'
]
converter = tft.coders.CsvCoder(ordered_columns, RAW_DATA_METADATA.schema)
# Read in raw data and convert using CSV converter. Note that we apply
# some Beam transformations here, which will not be encoded in the TF
# graph since we don't do the from within tf.Transform's methods
# (AnalyzeDataset, TransformDataset etc.). These transformations are just
# to get data into a format that the CSV converter can read, in particular
# removing spaces after commas.
#
# We use MapAndFilterErrors instead of Map to filter out decode errors in
# convert.decode which should only occur for the trailing blank line.
raw_data = (
pipeline
| 'ReadTrainData' >> textio.ReadFromText(train_data_file)
| 'FixCommasTrainData' >> beam.Map(
lambda line: line.replace(', ', ','))
| 'DecodeTrainData' >> MapAndFilterErrors(converter.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.
raw_dataset = (raw_data, RAW_DATA_METADATA)
transformed_dataset, transform_fn = (
raw_dataset | beam_impl.AnalyzeAndTransformDataset(preprocessing_fn))
transformed_data, transformed_metadata = transformed_dataset
transformed_data_coder = tft.coders.ExampleProtoCoder(
transformed_metadata.schema)
_ = (
transformed_data
| 'EncodeTrainData' >> beam.Map(transformed_data_coder.encode)
| 'WriteTrainData' >> tfrecordio.WriteToTFRecord(
os.path.join(working_dir, TRANSFORMED_TRAIN_DATA_FILEBASE)))
# Now apply transform function to test data. In this case we remove the
# trailing period at the end of each line, and also ignore the header line
# that is present in the test data file.
raw_test_data = (
pipeline
| 'ReadTestData' >> textio.ReadFromText(test_data_file,
skip_header_lines=1)
| 'FixCommasTestData' >> beam.Map(
lambda line: line.replace(', ', ','))
| 'RemoveTrailingPeriodsTestData' >> beam.Map(lambda line: line[:-1])
| 'DecodeTestData' >> MapAndFilterErrors(converter.decode))
raw_test_dataset = (raw_test_data, RAW_DATA_METADATA)
transformed_test_dataset = (
(raw_test_dataset, transform_fn) | beam_impl.TransformDataset())
# Don't need transformed data schema, it's the same as before.
transformed_test_data, _ = transformed_test_dataset
_ = (
transformed_test_data
| 'EncodeTestData' >> beam.Map(transformed_data_coder.encode)
| 'WriteTestData' >> tfrecordio.WriteToTFRecord(
os.path.join(working_dir, TRANSFORMED_TEST_DATA_FILEBASE)))
# Will write a 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
| 'WriteTransformFn' >>
transform_fn_io.WriteTransformFn(working_dir))
def data_transformation(EPOCHS: int, STEPS: int, BATCH_SIZE: int, HIDDEN_LAYER_SIZE: str, LEARNING_RATE: float):
import os
import shutil
from kale.utils import pod_utils
from kale.marshal import resource_save as _kale_resource_save
from kale.marshal import resource_load as _kale_resource_load
_kale_data_directory = "/marshal"
if not os.path.isdir(_kale_data_directory):
os.makedirs(_kale_data_directory, exist_ok=True)
# -----------------------DATA LOADING START--------------------------------
_kale_directory_file_names = [
os.path.splitext(f)[0]
for f in os.listdir(_kale_data_directory)
if os.path.isfile(os.path.join(_kale_data_directory, f))
]
if "column_names" not in _kale_directory_file_names:
raise ValueError("column_names" + " does not exists in directory")
_kale_load_file_name = [
f
for f in os.listdir(_kale_data_directory)
if os.path.isfile(os.path.join(_kale_data_directory, f)) and
os.path.splitext(f)[0] == "column_names"
]
if len(_kale_load_file_name) > 1:
raise ValueError("Found multiple files with name " +
"column_names" + ": " + str(_kale_load_file_name))
_kale_load_file_name = _kale_load_file_name[0]
column_names = _kale_resource_load(os.path.join(
_kale_data_directory, _kale_load_file_name))
if "schema" not in _kale_directory_file_names:
raise ValueError("schema" + " does not exists in directory")
_kale_load_file_name = [
f
for f in os.listdir(_kale_data_directory)
if os.path.isfile(os.path.join(_kale_data_directory, f)) and
os.path.splitext(f)[0] == "schema"
]
if len(_kale_load_file_name) > 1:
raise ValueError("Found multiple files with name " +
"schema" + ": " + str(_kale_load_file_name))
_kale_load_file_name = _kale_load_file_name[0]
schema = _kale_resource_load(os.path.join(
_kale_data_directory, _kale_load_file_name))
# -----------------------DATA LOADING END----------------------------------
import os
import shutil
import logging
import apache_beam as beam
import tensorflow as tf
import tensorflow_transform as tft
import tensorflow_model_analysis as tfma
import tensorflow_data_validation as tfdv
from apache_beam.io import textio
from apache_beam.io import tfrecordio
from tensorflow_transform.beam import impl as beam_impl
from tensorflow_transform.beam.tft_beam_io import transform_fn_io
from tensorflow_transform.coders.csv_coder import CsvCoder
from tensorflow_transform.coders.example_proto_coder import ExampleProtoCoder
from tensorflow_transform.tf_metadata import dataset_metadata
from tensorflow_transform.tf_metadata import metadata_io
DATA_DIR = 'data/'
TRAIN_DATA = os.path.join(DATA_DIR, 'taxi-cab-classification/train.csv')
EVALUATION_DATA = os.path.join(
DATA_DIR, 'taxi-cab-classification/eval.csv')
# Categorical features are assumed to each have a maximum value in the dataset.
MAX_CATEGORICAL_FEATURE_VALUES = [24, 31, 12]
CATEGORICAL_FEATURE_KEYS = ['trip_start_hour',
'trip_start_day', 'trip_start_month']
DENSE_FLOAT_FEATURE_KEYS = ['trip_miles', 'fare', 'trip_seconds']
# Number of buckets used by tf.transform for encoding each feature.
FEATURE_BUCKET_COUNT = 10
BUCKET_FEATURE_KEYS = [
'pickup_latitude', 'pickup_longitude', 'dropoff_latitude', 'dropoff_longitude']
# Number of vocabulary terms used for encoding VOCAB_FEATURES by tf.transform
VOCAB_SIZE = 1000
# Count of out-of-vocab buckets in which unrecognized VOCAB_FEATURES are hashed.
OOV_SIZE = 10
VOCAB_FEATURE_KEYS = ['pickup_census_tract', 'dropoff_census_tract', 'payment_type', 'company',
'pickup_community_area', 'dropoff_community_area']
# allow nan values in these features.
OPTIONAL_FEATURES = ['dropoff_latitude', 'dropoff_longitude', 'pickup_census_tract', 'dropoff_census_tract',
'company', 'trip_seconds', 'dropoff_community_area']
LABEL_KEY = 'tips'
FARE_KEY = 'fare'
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
# tf.get_logger().setLevel(logging.ERROR)
def to_dense(tensor):
"""Takes as input a SparseTensor and return a Tensor with correct default value
Args:
tensor: tf.SparseTensor
Returns:
tf.Tensor with default value
"""
if not isinstance(tensor, tf.sparse.SparseTensor):
return tensor
if tensor.dtype == tf.string:
default_value = ''
elif tensor.dtype == tf.float32:
default_value = 0.0
elif tensor.dtype == tf.int32:
default_value = 0
else:
raise ValueError(f"Tensor type not recognized: {tensor.dtype}")
return tf.squeeze(tf.sparse_to_dense(tensor.indices,
[tensor.dense_shape[0], 1],
tensor.values, default_value=default_value), axis=1)
# TODO: Update to below version
# return tf.squeeze(tf.sparse.to_dense(tensor, default_value=default_value), axis=1)
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
trns_output = os.path.join(DATA_DIR, "transformed")
if os.path.exists(trns_output):
shutil.rmtree(trns_output)
tft_input_metadata = dataset_metadata.DatasetMetadata(schema)
runner = 'DirectRunner'
with beam.Pipeline(runner, options=None) as p:
with beam_impl.Context(temp_dir=os.path.join(trns_output, 'tmp')):
converter = CsvCoder(column_names, tft_input_metadata.schema)
# READ TRAIN DATA
train_data = (
p
| 'ReadTrainData' >> textio.ReadFromText(TRAIN_DATA, skip_header_lines=1)
| 'DecodeTrainData' >> beam.Map(converter.decode))
# TRANSFORM TRAIN DATA (and get transform_fn function)
transformed_dataset, transform_fn = (
(train_data, tft_input_metadata) | beam_impl.AnalyzeAndTransformDataset(preprocess_fn))
transformed_data, transformed_metadata = transformed_dataset
# SAVE TRANSFORMED TRAIN DATA
_ = transformed_data | 'WriteTrainData' >> tfrecordio.WriteToTFRecord(
os.path.join(trns_output, 'train'),
coder=ExampleProtoCoder(transformed_metadata.schema))
# READ EVAL DATA
eval_data = (
p
| 'ReadEvalData' >> textio.ReadFromText(EVALUATION_DATA, skip_header_lines=1)
| 'DecodeEvalData' >> beam.Map(converter.decode))
# TRANSFORM EVAL DATA (using previously created transform_fn function)
eval_dataset = (eval_data, tft_input_metadata)
transformed_eval_data, transformed_metadata = (
(eval_dataset, transform_fn) | beam_impl.TransformDataset())
# SAVE EVAL DATA
_ = transformed_eval_data | 'WriteEvalData' >> tfrecordio.WriteToTFRecord(
os.path.join(trns_output, 'eval'),
coder=ExampleProtoCoder(transformed_metadata.schema))
# SAVE transform_fn FUNCTION FOR LATER USE
# TODO: check out what is the transform function (transform_fn) that came from previous step
_ = (transform_fn | 'WriteTransformFn' >>
transform_fn_io.WriteTransformFn(trns_output))
# SAVE TRANSFORMED METADATA
metadata_io.write_metadata(
metadata=tft_input_metadata,
path=os.path.join(trns_output, 'metadata'))
# -----------------------DATA SAVING START---------------------------------
if "trns_output" in locals():
_kale_resource_save(trns_output, os.path.join(
_kale_data_directory, "trns_output"))
else:
print("_kale_resource_save: `trns_output` not found.")
beam.FlatMap(lambda stream: [stream.detrend('demean')])
| 'Remove trend' >>
beam.FlatMap(lambda stream: [stream.detrend('linear')])
| 'Resample to 100 Hz' >>
beam.FlatMap(lambda stream: [stream.resample(100)])
| 'Trim traces' >> beam.ParDo(TrimTrace(points=3001)))
station_location = location | '(sta, loc)' >> beam.FlatMap(
lambda loc: [(loc['station'], loc)])
station_pick = picks | '(sta, pick)' >> beam.FlatMap(
lambda pick: [(pick.waveform_id.station_code, pick)])
station_stream = streams | '(sta, stream)' >> beam.FlatMap(
lambda stream: [(stream[0].stats.station, stream)])
dataset = (
{
'pick': station_pick,
'stream': station_stream,
'location': station_location
}
| 'Join by station' >> beam.CoGroupByKey()
| 'Drop empty station' >> beam.ParDo(DropEmptyStation())
| 'Group stream pick by time' >> GroupStreamPick()
| 'Generate stream PDFs' >> beam.ParDo(GeneratePDF(sigma=0.1))
| 'Extract stream features' >> beam.ParDo(StreamFeatureExtraction()))
transform = (dataset
| 'Feature to Example' >> beam.ParDo(FeatureToExample())
| 'Write dataset' >> tfrecordio.WriteToTFRecord(
tfrecord_dir, coder=beam.coders.ProtoCoder))
def transform_data(train_neg_filepattern, train_pos_filepattern,
test_neg_filepattern, test_pos_filepattern,
transformed_train_filebase, transformed_test_filebase,
transformed_metadata_dir):
"""Transform the data and write out as a TFRecord of Example protos.
Read in the data from the positive and negative examples on disk, and
transform it using a preprocessing pipeline that removes punctuation,
tokenizes and maps tokens to int64 values indices.
Args:
train_neg_filepattern: Filepattern for training data negative examples
train_pos_filepattern: Filepattern for training data positive examples
test_neg_filepattern: Filepattern for test data negative examples
test_pos_filepattern: Filepattern for test data positive examples
transformed_train_filebase: Base filename for transformed training data
shards
transformed_test_filebase: Base filename for transformed test data shards
transformed_metadata_dir: Directory where metadata for transformed data
should be written
"""
with beam.Pipeline() as pipeline:
with beam_impl.Context(temp_dir=tempfile.mkdtemp()):
# pylint: disable=no-value-for-parameter
train_data = pipeline | 'ReadTrain' >> ReadAndShuffleData(
(train_neg_filepattern, train_pos_filepattern))
# pylint: disable=no-value-for-parameter
test_data = pipeline | 'ReadTest' >> ReadAndShuffleData(
(test_neg_filepattern, test_pos_filepattern))
metadata = dataset_metadata.DatasetMetadata(
dataset_schema.Schema({
REVIEW_COLUMN:
dataset_schema.ColumnSchema(
tf.string, [],
dataset_schema.FixedColumnRepresentation()),
LABEL_COLUMN:
dataset_schema.ColumnSchema(
tf.int64, [],
dataset_schema.FixedColumnRepresentation()),
}))
def preprocessing_fn(inputs):
"""Preprocess input columns into transformed columns."""
review = inputs[REVIEW_COLUMN]
review_tokens = tf.string_split(review, DELIMITERS)
review_indices = tft.string_to_int(review_tokens,
top_k=VOCAB_SIZE)
# Add one for the oov bucket created by string_to_int.
review_bow_indices, review_weight = tft.tfidf(
review_indices, VOCAB_SIZE + 1)
return {
REVIEW_COLUMN: review_bow_indices,
REVIEW_WEIGHT: review_weight,
LABEL_COLUMN: inputs[LABEL_COLUMN]
}
(transformed_train_data, transformed_metadata), transform_fn = (
(train_data, metadata)
| 'AnalyzeAndTransform' >>
beam_impl.AnalyzeAndTransformDataset(preprocessing_fn))
transformed_test_data, _ = (
((test_data, metadata), transform_fn)
| 'Transform' >> beam_impl.TransformDataset())
_ = (transformed_train_data
| 'WriteTrainData' >> tfrecordio.WriteToTFRecord(
transformed_train_filebase,
coder=example_proto_coder.ExampleProtoCoder(
transformed_metadata.schema)))
_ = (transformed_test_data
| 'WriteTestData' >> tfrecordio.WriteToTFRecord(
transformed_test_filebase,
coder=example_proto_coder.ExampleProtoCoder(
transformed_metadata.schema)))
_ = (transformed_metadata
| 'WriteMetadata' >> beam_metadata_io.WriteMetadata(
transformed_metadata_dir, pipeline=pipeline))
def preprocess_data(train_neg_file_pattern,
train_pos_file_pattern,
test_neg_file_pattern,
test_pos_file_pattern,
transformed_train_file_pattern,
transformed_test_file_pattern,
transformed_metadata_dir,
raw_metadata_dir,
transform_func_dir,
temp_dir,
vocab_size,
delimiters):
"""Transform the data and write out as a TFRecord of Example protos.
Read in the data from the positive and negative examples on disk, and
transform it using a preprocessing pipeline that removes punctuation,
tokenizes and maps tokens to int64 values indices.
Args:
train_neg_filepattern: Filepattern for training data negative examples
train_pos_filepattern: Filepattern for training data positive examples
test_neg_filepattern: Filepattern for test data negative examples
test_pos_filepattern: Filepattern for test data positive examples
transformed_train_filebase: Base filename for transformed training data shards
transformed_test_filebase: Base filename for transformed test data shards
transformed_metadata_dir: Directory where metadata for transformed data should be written
raw_metadata = dataset_metadata.DatasetMetadata(dataset_schema.Schema({
REVIEW_COLUMN: dataset_schema.ColumnSchema(tf.string, [], dataset_schema.FixedColumnRepresentation()),
LABEL_COLUMN: dataset_schema.ColumnSchema(tf.int64, [], dataset_schema.FixedColumnRepresentation()),
}))
"""
pipeline_name = 'DataflowRunner'
options = {
'job_name': ('cloud-ml-hazmat-preprocess-{}'.format(datetime.datetime.now().strftime('%Y%m%d%H%M%S'))),
'temp_location': temp_dir,
'project': "stone-outpost-636",
'max_num_workers': 8
}
pipeline_options = beam.pipeline.PipelineOptions(flags=[], **options)
#with beam.Pipeline(pipeline_name, options=pipeline_options) as pipeline:
# with beam_impl.Context(temp_dir=temp_dir):
with beam.Pipeline() as pipeline:
with beam_impl.Context(temp_dir=tempfile.mkdtemp()):
train_data = pipeline | 'ReadTrain' >> ReadAndShuffleData((train_neg_file_pattern, train_pos_file_pattern))
test_data = pipeline | 'ReadTest' >> ReadAndShuffleData((test_neg_file_pattern, test_pos_file_pattern))
preprocessing_fn = generate_preprocessing_fn(vocab_size, delimiters)
(transformed_train_data, transformed_metadata), transform_fn = ((train_data, const.RAW_METADATA)
| 'AnalyzeAndTransform' >> beam_impl.AnalyzeAndTransformDataset(preprocessing_fn))
_ = (transform_fn | 'WriteTransformFn' >> tft_beam_io.WriteTransformFn(transform_func_dir))
transformed_test_data, _ = (((test_data, const.RAW_METADATA), transform_fn)
| 'Transform' >> beam_impl.TransformDataset())
_ = (transformed_train_data
| 'WriteTrainData' >> tfrecordio.WriteToTFRecord(transformed_train_file_pattern,
coder=example_proto_coder.ExampleProtoCoder(transformed_metadata.schema)))
_ = (transformed_test_data
| 'WriteTestData' >> tfrecordio.WriteToTFRecord(transformed_test_file_pattern,
coder=example_proto_coder.ExampleProtoCoder(transformed_metadata.schema)))
_ = (transformed_metadata
| 'WriteTransformedMetadata' >> beam_metadata_io.WriteMetadata(transformed_metadata_dir, pipeline=pipeline))
_ = (const.RAW_METADATA
| 'WriteRawMetadata' >> beam_metadata_io.WriteMetadata(raw_metadata_dir, pipeline=pipeline))
def run_transform(output_dir,
schema,
train_data_file,
eval_data_file,
project,
mode,
preprocessing_fn=None):
"""Writes a tft transform fn, and metadata files.
Args:
output_dir: output folder
schema: schema list.
train_data_file: training data file pattern.
eval_data_file: eval data file pattern.
project: the project to run dataflow in.
local: whether the job should be local or cloud.
preprocessing_fn: a function used to preprocess the raw data. If not
specified, a function will be automatically inferred
from the schema.
"""
tft_input_metadata = make_tft_input_metadata(schema)
temp_dir = os.path.join(output_dir, 'tmp')
preprocessing_fn = preprocessing_fn or make_preprocessing_fn(schema)
if mode == 'local':
pipeline_options = None
runner = 'DirectRunner'
elif mode == 'cloud':
options = {
'job_name':
'pipeline-tft-' +
datetime.datetime.now().strftime('%y%m%d-%H%M%S'),
'temp_location':
temp_dir,
'project':
project,
'extra_packages': [
'gs://ml-pipeline-playground/tensorflow-transform-0.6.0.dev0.tar.gz'
]
}
pipeline_options = beam.pipeline.PipelineOptions(flags=[], **options)
runner = 'DataFlowRunner'
else:
raise ValueError("Invalid mode %s." % mode)
with beam.Pipeline(runner, options=pipeline_options) as p:
with beam_impl.Context(temp_dir=temp_dir):
names = [x['name'] for x in schema]
converter = CsvCoder(names, tft_input_metadata.schema)
train_data = (
p
| 'ReadTrainData' >> textio.ReadFromText(train_data_file)
| 'DecodeTrainData' >> beam.Map(converter.decode))
train_dataset = (train_data, tft_input_metadata)
transformed_dataset, transform_fn = (
train_dataset
| beam_impl.AnalyzeAndTransformDataset(preprocessing_fn))
transformed_data, transformed_metadata = transformed_dataset
# Writes transformed_metadata and transfrom_fn folders
_ = (transform_fn | 'WriteTransformFn' >>
transform_fn_io.WriteTransformFn(output_dir))
# Write the raw_metadata
metadata_io.write_metadata(metadata=tft_input_metadata,
path=os.path.join(
output_dir, 'metadata'))
_ = transformed_data | 'WriteTrainData' >> tfrecordio.WriteToTFRecord(
os.path.join(output_dir, 'train'),
coder=ExampleProtoCoder(transformed_metadata.schema))
eval_data = (p
|
'ReadEvalData' >> textio.ReadFromText(eval_data_file)
| 'DecodeEvalData' >> beam.Map(converter.decode))
eval_dataset = (eval_data, tft_input_metadata)
transformed_eval_dataset = ((eval_dataset, transform_fn)
| beam_impl.TransformDataset())
transformed_eval_data, transformed_metadata = transformed_eval_dataset
_ = transformed_eval_data | 'WriteEvalData' >> tfrecordio.WriteToTFRecord(
os.path.join(output_dir, 'eval'),
coder=ExampleProtoCoder(transformed_metadata.schema))
def transform_data(train_neg_filepattern, train_pos_filepattern,
test_neg_filepattern, test_pos_filepattern,
transformed_train_filebase, transformed_test_filebase,
transformed_metadata_dir):
"""Transform the data and write out as a TFRecord of Example protos.
Read in the data from the positive and negative examples on disk, and
transform it using a preprocessing pipeline that removes punctuation,
tokenizes and maps tokens to int64 values indices.
Args:
train_neg_filepattern: Filepattern for training data negative examples
train_pos_filepattern: Filepattern for training data positive examples
test_neg_filepattern: Filepattern for test data negative examples
test_pos_filepattern: Filepattern for test data positive examples
transformed_train_filebase: Base filename for transformed training data
shards
transformed_test_filebase: Base filename for transformed test data shards
transformed_metadata_dir: Directory where metadata for transformed data
should be written
"""
with beam.Pipeline() as pipeline:
with beam_impl.Context(temp_dir=tempfile.mkdtemp()):
# pylint: disable=no-value-for-parameter
train_data = pipeline | 'ReadTrain' >> ReadAndShuffleData(
(train_neg_filepattern, train_pos_filepattern))
# pylint: disable=no-value-for-parameter
test_data = pipeline | 'ReadTest' >> ReadAndShuffleData(
(test_neg_filepattern, test_pos_filepattern))
metadata = dataset_metadata.DatasetMetadata(dataset_schema.Schema({
REVIEW_COLUMN: dataset_schema.ColumnSchema(
tf.string, [], dataset_schema.FixedColumnRepresentation()),
LABEL_COLUMN: dataset_schema.ColumnSchema(
tf.int64, [], dataset_schema.FixedColumnRepresentation()),
}))
def preprocessing_fn(inputs):
"""Preprocess input columns into transformed columns."""
review = inputs[REVIEW_COLUMN]
def remove_character(s, char):
"""Remove a character from a string.
Args:
s: A SparseTensor of rank 1 of type tf.string
char: A string of length 1
Returns:
The string `s` with the given character removed (i.e. replaced by
'')
"""
# Hacky implementation where we split and rejoin.
split = tf.string_split(s, char)
rejoined = tf.reduce_join(
tf.sparse_to_dense(
split.indices, split.dense_shape, split.values, ''),
1)
return rejoined
def remove_punctuation(s):
"""Remove puncuation from a string.
Args:
s: A SparseTensor of rank 1 of type tf.string
Returns:
The string `s` with punctuation removed.
"""
for char in PUNCTUATION_CHARACTERS:
s = remove_character(s, char)
return s
cleaned_review = tft.map(remove_punctuation, review)
review_tokens = tft.map(tf.string_split, cleaned_review)
review_indices = tft.string_to_int(review_tokens, top_k=VOCAB_SIZE)
return {
REVIEW_COLUMN: review_indices,
LABEL_COLUMN: inputs[LABEL_COLUMN]
}
(transformed_train_data, transformed_metadata), transform_fn = (
(train_data, metadata)
| 'AnalyzeAndTransform' >> beam_impl.AnalyzeAndTransformDataset(
preprocessing_fn))
transformed_test_data, _ = (
((test_data, metadata), transform_fn)
| 'Transform' >> beam_impl.TransformDataset())
_ = (
transformed_train_data
| 'WriteTrainData' >> tfrecordio.WriteToTFRecord(
transformed_train_filebase,
coder=example_proto_coder.ExampleProtoCoder(
transformed_metadata.schema)))
_ = (
transformed_test_data
| 'WriteTestData' >> tfrecordio.WriteToTFRecord(
transformed_test_filebase,
coder=example_proto_coder.ExampleProtoCoder(
transformed_metadata.schema)))
_ = (
transformed_metadata
| 'WriteMetadata' >> beam_metadata_io.WriteMetadata(
transformed_metadata_dir, pipeline=pipeline))
def transform_data(train_data_file, test_data_file, transformed_train_filebase,
transformed_test_filebase, transformed_metadata_dir):
"""Transform the data and write out as a TFRecord of Example protos.
Read in the data using the CSV reader, and transform it using a
preprocessing pipeline that scales numeric data and coverts categorical data
from strings to int64 values indices, by creating a vocabulary for each
category.
Args:
train_data_file: File containing training data
test_data_file: File containing test data
transformed_train_filebase: Base filename for transformed training data
shards
transformed_test_filebase: Base filename for transformed test data shards
transformed_metadata_dir: Directory where metadata for transformed data
should be written
"""
raw_data_schema = {
key:
dataset_schema.ColumnSchema(tf.string, [],
dataset_schema.FixedColumnRepresentation())
for key in CATEGORICAL_COLUMNS
}
raw_data_schema.update({
key:
dataset_schema.ColumnSchema(tf.float32, [],
dataset_schema.FixedColumnRepresentation())
for key in NUMERIC_COLUMNS
})
raw_data_schema[LABEL_COLUMN] = dataset_schema.ColumnSchema(
tf.string, [], dataset_schema.FixedColumnRepresentation())
raw_data_schema = dataset_schema.Schema(raw_data_schema)
raw_data_metadata = dataset_metadata.DatasetMetadata(raw_data_schema)
def preprocessing_fn(inputs):
"""Preprocess input columns into transformed columns."""
outputs = {}
# Scale numeric columns to have range [0, 1].
for key in NUMERIC_COLUMNS:
outputs[key] = tft.scale_to_0_1(inputs[key])
# For all categorical columns except the label column, we use
# tft.string_to_int which computes the set of unique values and uses this
# to convert the strings to indices.
for key in CATEGORICAL_COLUMNS:
outputs[key] = tft.string_to_int(inputs[key])
# For the label column we provide the mapping from string to index.
def convert_label(label):
table = lookup.string_to_index_table_from_tensor(['>50K', '<=50K'])
return table.lookup(label)
outputs[LABEL_COLUMN] = tft.map(convert_label, inputs[LABEL_COLUMN])
return outputs
# The "with" block will create a pipeline, and run that pipeline at the exit
# of the block.
with beam.Pipeline() as pipeline:
with beam_impl.Context(temp_dir=tempfile.mkdtemp()):
# Create a coder to read the census data with the schema. To do this we
# need to list all columns in order since the schema doesn't specify the
# order of columns in the csv.
ordered_columns = [
'age', 'workclass', 'fnlwgt', 'education', 'education-num',
'marital-status', 'occupation', 'relationship', 'race', 'sex',
'capital-gain', 'capital-loss', 'hours-per-week',
'native-country', 'label'
]
converter = csv_coder.CsvCoder(ordered_columns, raw_data_schema)
# Read in raw data and convert using CSV converter. Note that we apply
# some Beam transformations here, which will not be encoded in the TF
# graph since we don't do the from within tf.Transform's methods
# (AnalyzeDataset, TransformDataset etc.). These transformations are just
# to get data into a format that the CSV converter can read, in particular
# removing empty lines and removing spaces after commas.
raw_data = (pipeline
|
'ReadTrainData' >> textio.ReadFromText(train_data_file)
| 'FilterTrainData' >> beam.Filter(lambda line: line)
| 'FixCommasTrainData' >>
beam.Map(lambda line: line.replace(', ', ','))
| 'DecodeTrainData' >> beam.Map(converter.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.
raw_dataset = (raw_data, raw_data_metadata)
transformed_dataset, transform_fn = (
raw_dataset
| beam_impl.AnalyzeAndTransformDataset(preprocessing_fn))
transformed_data, transformed_metadata = transformed_dataset
_ = transformed_data | 'WriteTrainData' >> tfrecordio.WriteToTFRecord(
transformed_train_filebase,
coder=example_proto_coder.ExampleProtoCoder(
transformed_metadata.schema))
# Now apply transform function to test data. In this case we also remove
# the header line from the CSV file and the trailing period at the end of
# each line.
raw_test_data = (
pipeline
| 'ReadTestData' >> textio.ReadFromText(test_data_file)
| 'FilterTestData' >> beam.Filter(
lambda line: line and line != '|1x3 Cross validator')
| 'FixCommasTestData' >>
beam.Map(lambda line: line.replace(', ', ','))
| 'RemoveTrailingPeriodsTestData' >>
beam.Map(lambda line: line[:-1])
| 'DecodeTestData' >> beam.Map(converter.decode))
raw_test_dataset = (raw_test_data, raw_data_metadata)
transformed_test_dataset = ((raw_test_dataset, transform_fn)
| beam_impl.TransformDataset())
# Don't need transformed data schema, it's the same as before.
transformed_test_data, _ = transformed_test_dataset
_ = transformed_test_data | 'WriteTestData' >> tfrecordio.WriteToTFRecord(
transformed_test_filebase,
coder=example_proto_coder.ExampleProtoCoder(
transformed_metadata.schema))
_ = (transformed_metadata
| 'WriteMetadata' >> beam_metadata_io.WriteMetadata(
transformed_metadata_dir, pipeline=pipeline))
# split into train and test
train, test = (
transformed_data
| 'Partition train/test' >> beam.Partition(
split_train_test, 2, split_dt=datetime(2020, 1, 1)))
# encoder for TFRecords
transformed_data_coder = tft.coders.ExampleProtoCoder(
transformed_metadata.schema)
# write train dataset
_ = (
train
| 'Encode & write train -> TFRecords' >>
tfrecordio.WriteToTFRecord(
file_path_prefix=os.path.join(args.data_dir, 'tfrecords',
args.output_dir,
TRAIN_FILES_PATTERN),
coder=transformed_data_coder,
file_name_suffix='.gz',
num_shards=4,
compression_type=beam.io.filesystem.CompressionTypes.GZIP))
# write validation dataset
_ = (
test
| 'Encode & write test -> TFRecords' >>
tfrecordio.WriteToTFRecord(
file_path_prefix=os.path.join(args.data_dir, 'tfrecords',
args.output_dir,
EVAL_FILES_PATTERN),
coder=transformed_data_coder,
file_name_suffix='.gz',
num_shards=1,
compression_type=beam.io.filesystem.CompressionTypes.GZIP))
def preprocess(in_test_mode):
import os
import os.path
import tempfile
from apache_beam.io import tfrecordio
from tensorflow_transform.coders import example_proto_coder
from tensorflow_transform.tf_metadata import dataset_metadata
from tensorflow_transform.tf_metadata import dataset_schema
from tensorflow_transform.beam import tft_beam_io
from tensorflow_transform.beam.tft_beam_io import transform_fn_io
job_name = 'preprocess-taxi-features' + '-' + datetime.datetime.now().strftime('%y%m%d-%H%M%S')
if in_test_mode:
import shutil
print 'Launching local job ... hang on'
OUTPUT_DIR = './preproc_tft'
shutil.rmtree(OUTPUT_DIR, ignore_errors=True)
EVERY_N = 100000
else:
print 'Launching Dataflow job {} ... hang on'.format(job_name)
OUTPUT_DIR = 'gs://{0}/taxifare/preproc_tft/'.format(BUCKET)
import subprocess
subprocess.call('gsutil rm -r {}'.format(OUTPUT_DIR).split())
EVERY_N = 10000
options = {
'staging_location': os.path.join(OUTPUT_DIR, 'tmp', 'staging'),
'temp_location': os.path.join(OUTPUT_DIR, 'tmp'),
'job_name': job_name,
'project': PROJECT,
'max_num_workers': 24,
'teardown_policy': 'TEARDOWN_ALWAYS',
'no_save_main_session': True,
'requirements_file': 'requirements.txt'
}
opts = beam.pipeline.PipelineOptions(flags=[], **options)
if in_test_mode:
RUNNER = 'DirectRunner'
else:
RUNNER = 'DataflowRunner'
# set up metadata
raw_data_schema = {
colname : dataset_schema.ColumnSchema(tf.string, [], dataset_schema.FixedColumnRepresentation())
for colname in 'dayofweek,key'.split(',')
}
raw_data_schema.update({
colname : dataset_schema.ColumnSchema(tf.float32, [], dataset_schema.FixedColumnRepresentation())
for colname in 'fare_amount,pickuplon,pickuplat,dropofflon,dropofflat'.split(',')
})
raw_data_schema.update({
colname : dataset_schema.ColumnSchema(tf.int64, [], dataset_schema.FixedColumnRepresentation())
for colname in 'hourofday,passengers'.split(',')
})
raw_data_metadata = dataset_metadata.DatasetMetadata(dataset_schema.Schema(raw_data_schema))
# run Beam
with beam.Pipeline(RUNNER, options=opts) as p:
with beam_impl.Context(temp_dir=os.path.join(OUTPUT_DIR, 'tmp')):
# save the raw data metadata
_ = (raw_data_metadata
| 'WriteInputMetadata' >> tft_beam_io.WriteMetadata(
os.path.join(OUTPUT_DIR, 'metadata/rawdata_metadata'),
pipeline=p))
# analyze and transform training
raw_data = (p
| 'train_read' >> beam.io.Read(beam.io.BigQuerySource(query=create_query(1, EVERY_N), use_standard_sql=True))
| 'train_filter' >> beam.Filter(is_valid))
raw_dataset = (raw_data, raw_data_metadata)
transformed_dataset, transform_fn = (
raw_dataset | beam_impl.AnalyzeAndTransformDataset(preprocess_tft))
transformed_data, transformed_metadata = transformed_dataset
_ = transformed_data | 'WriteTrainData' >> tfrecordio.WriteToTFRecord(
os.path.join(OUTPUT_DIR, 'train'),
file_name_suffix='.gz',
coder=example_proto_coder.ExampleProtoCoder(
transformed_metadata.schema))
# transform eval data
raw_test_data = (p
| 'eval_read' >> beam.io.Read(beam.io.BigQuerySource(query=create_query(2, EVERY_N), use_standard_sql=True))
| 'eval_filter' >> beam.Filter(is_valid))
raw_test_dataset = (raw_test_data, raw_data_metadata)
transformed_test_dataset = (
(raw_test_dataset, transform_fn) | beam_impl.TransformDataset())
transformed_test_data, _ = transformed_test_dataset
_ = transformed_test_data | 'WriteTestData' >> tfrecordio.WriteToTFRecord(
os.path.join(OUTPUT_DIR, 'eval'),
file_name_suffix='.gz',
coder=example_proto_coder.ExampleProtoCoder(
transformed_metadata.schema))
_ = (transform_fn
| 'WriteTransformFn' >>
transform_fn_io.WriteTransformFn(os.path.join(OUTPUT_DIR, 'metadata')))
