def run_metrics():
"""Creates a pipeline to measure wordpiece vocab metrics over a corpus."""
metrics_pipeline = beam.Pipeline()
with tft_beam.Context(temp_dir=tempfile.mkdtemp()):
# Read raw data and convert to TF Transform encoded dict.
raw_data = (
metrics_pipeline
| 'ReadInputData' >> beam.io.tfrecordio.ReadFromTFRecord(
data_file, coder=beam.coders.ProtoCoder(tf.train.Example))
| 'DecodeInputData' >> beam.Map(example_converter.decode))
# Apply transform to wordpiece-tokenize input.
(metrics_transformed_data, _), _ = (
(raw_data, raw_metadata)
| 'WordpieceTokenizeInput' >> tft_beam.AnalyzeAndTransformDataset(
utils.metrics_preprocessing_fn(FLAGS.vocab_file,
FLAGS.text_key,
FLAGS.language_code_key)))
# Initialize CSV coder. Aggregate values for each lang, calculate metrics,
# and write to output to a CSV file.
csv_converter = tft.coders.CsvCoder(columns, csv_schema)
_ = (
metrics_transformed_data
| 'CompileTokenInfo' >> beam.ParDo(utils.CompileTokenizationInfo())
| 'CombineStatsForLang' >> beam.CombineGlobally(utils.AggregateLang())
| 'CalculateMetrics' >> beam.ParDo(utils.CalculateMetrics())
| 'EncodeMetrics' >> beam.Map(csv_converter.encode)
| 'WriteMetrics' >> beam.io.WriteToText(
metrics_file, shard_name_template='', header=','.join(columns)))
return metrics_pipeline
def setUp(self):
super(AggregateLangTest, self).setUp()
self.aggregator = utils.AggregateLang()
self.sample_input = [{
'lang':
'en',
'count':
1,
'num_preserved_chars':
13,
'num_dropped_chars':
2,
'num_non_unk_wordpieces':
4,
'preserved_ratio': [13 / 4],
'dropped_ratio': [2 / 15],
'wordpieces':
collections.Counter(['the', 'app', '##le', 'sauce'])
}, {
'lang':
'en',
'count':
1,
'num_preserved_chars':
11,
'num_dropped_chars':
0,
'num_non_unk_wordpieces':
4,
'preserved_ratio': [11 / 4],
'dropped_ratio': [0],
'wordpieces':
collections.Counter(['the', 'app', 'st', '##ore'])
}]
def calculate_metrics():
"""Returns a pipeline to compute wordpiece model stats given a vocab and corpus."""
# Schema of input dataset.
raw_metadata = dataset_metadata.DatasetMetadata(
dataset_schema.from_feature_spec({
'text':
tf.FixedLenFeature([], tf.string),
'language_code':
tf.FixedLenFeature([], tf.string),
}))
# Schema to format metrics as CSV.
csv_schema = dataset_schema.from_feature_spec({
'lang':
tf.FixedLenFeature([], tf.string),
'sample_count':
tf.FixedLenFeature([], tf.int64),
'micro_drop_char_percent':
tf.FixedLenFeature([], tf.string),
'macro_drop_char_percent':
tf.FixedLenFeature([], tf.string),
'micro_compress_ratio':
tf.FixedLenFeature([], tf.string),
'macro_compress_ratio':
tf.FixedLenFeature([], tf.string),
'unweighted_en_wp_overlap_percent':
tf.FixedLenFeature([], tf.string),
'weighted_en_wp_overlap_percent':
tf.FixedLenFeature([], tf.string),
})
columns = [
'lang', 'sample_count', 'micro_drop_char_percent',
'macro_drop_char_percent', 'micro_compress_ratio',
'macro_compress_ratio', 'unweighted_en_wp_overlap_percent',
'weighted_en_wp_overlap_percent'
]
# Create pipeline.
pipeline = beam.Pipeline()
with tft_beam.Context(temp_dir=tempfile.mkdtemp()):
example_converter = tft.coders.ExampleProtoCoder(raw_metadata.schema,
serialized=False)
csv_converter = tft.coders.CsvCoder(columns, csv_schema)
# Read raw data and convert to TF Transform encoded dict.
raw_data = (pipeline
| 'ReadInputData' >> beam.io.tfrecordio.ReadFromTFRecord(
FLAGS.input_file,
coder=beam.coders.ProtoCoder(tf.train.Example))
| 'DecodeInputData' >> beam.Map(example_converter.decode))
# Apply transform to wordpiece-tokenize input.
(transformed_data, _), _ = (
(raw_data, raw_metadata)
| 'WordpieceTokenizeInput' >> tft_beam.AnalyzeAndTransformDataset(
utils.metrics_preprocessing_fn(
FLAGS.vocab_file, FLAGS.text_key, FLAGS.language_code_key))
)
# Aggregate values for each lang, calculate metrics, and write to output.
_ = (transformed_data
|
'CompileTokenInfo' >> beam.ParDo(utils.CompileTokenizationInfo())
| 'CombineStatsForLang' >> beam.CombineGlobally(
utils.AggregateLang())
| 'CalculateMetrics' >> beam.ParDo(utils.CalculateMetrics())
| 'EncodeMetrics' >> beam.Map(csv_converter.encode)
| 'WriteMetrics' >> beam.io.WriteToText(FLAGS.output_file,
shard_name_template='',
header=','.join(columns)))
return pipeline