Python file_io.read_file_to_string函数代码示例

def local_analysis(args):
  if args.analysis:
    # Already analyzed.
    return

  if not args.schema or not args.features:
    raise ValueError('Either --analysis, or both --schema and --features are provided.')

  tf_config = json.loads(os.environ.get('TF_CONFIG', '{}'))
  cluster_spec = tf_config.get('cluster', {})
  if len(cluster_spec.get('worker', [])) > 0:
    raise ValueError('If "schema" and "features" are provided, local analysis will run and ' +
                     'only BASIC scale-tier (no workers node) is supported.')

  if cluster_spec and not (args.schema.startswith('gs://') and args.features.startswith('gs://')):
    raise ValueError('Cloud trainer requires GCS paths for --schema and --features.')

  print('Running analysis.')
  schema = json.loads(file_io.read_file_to_string(args.schema).decode())
  features = json.loads(file_io.read_file_to_string(args.features).decode())
  args.analysis = os.path.join(args.job_dir, 'analysis')
  args.transform = True
  file_io.recursive_create_dir(args.analysis)
  feature_analysis.run_local_analysis(args.analysis, args.train, schema, features)
  print('Analysis done.')

def main(argv=None):
  args = parse_arguments(sys.argv if argv is None else argv)

  if args.schema:
    schema = json.loads(
        file_io.read_file_to_string(args.schema).decode())
  else:
    import google.datalab.bigquery as bq
    schema = bq.Table(args.bigquery).schema._bq_schema
  features = json.loads(
      file_io.read_file_to_string(args.features).decode())

  file_io.recursive_create_dir(args.output)

  if args.cloud:
    run_cloud_analysis(
        output_dir=args.output,
        csv_file_pattern=args.csv,
        bigquery_table=args.bigquery,
        schema=schema,
        features=features)
  else:
    feature_analysis.run_local_analysis(
        output_dir=args.output,
        csv_file_pattern=args.csv,
        schema=schema,
        features=features)

  def test_categorical(self):
    output_folder = tempfile.mkdtemp()
    input_file_path = tempfile.mkstemp(dir=output_folder)[1]
    try:
      csv_file = ['red,apple', 'red,pepper', 'red,apple', 'blue,grape',
                  'blue,apple', 'green,pepper']
      file_io.write_string_to_file(
        input_file_path,
        '\n'.join(csv_file))

      schema = [{'name': 'color', 'type': 'STRING'},
                {'name': 'type', 'type': 'STRING'}]
      features = {'color': {'transform': 'one_hot', 'source_column': 'color'},
                  'type': {'transform': 'target'}}
      feature_analysis.run_local_analysis(
        output_folder, [input_file_path], schema, features)

      stats = json.loads(
          file_io.read_file_to_string(
              os.path.join(output_folder, analyze.constant.STATS_FILE)).decode())
      self.assertEqual(stats['column_stats']['color']['vocab_size'], 3)

      # Color column.
      vocab_str = file_io.read_file_to_string(
        os.path.join(output_folder, analyze.constant.VOCAB_ANALYSIS_FILE % 'color'))
      vocab = pd.read_csv(six.StringIO(vocab_str),
                          header=None,
                          names=['color', 'count'])
      expected_vocab = pd.DataFrame(
          {'color': ['red', 'blue', 'green'], 'count': [3, 2, 1]},
          columns=['color', 'count'])
      pd.util.testing.assert_frame_equal(vocab, expected_vocab)

    finally:
      shutil.rmtree(output_folder)

  def test_text(self):
    output_folder = tempfile.mkdtemp()
    input_file_path = tempfile.mkstemp(dir=output_folder)[1]
    try:
      csv_file = ['the quick brown fox,raining in kir,cat1|cat2,true',
                  'quick   brown brown chicken,raining in pdx,cat2|cat3|cat4,false']
      file_io.write_string_to_file(
        input_file_path,
        '\n'.join(csv_file))

      schema = [{'name': 'col1', 'type': 'STRING'},
                {'name': 'col2', 'type': 'STRING'},
                {'name': 'col3', 'type': 'STRING'},
                {'name': 'col4', 'type': 'STRING'}]
      features = {'col1': {'transform': 'bag_of_words', 'source_column': 'col1'},
                  'col2': {'transform': 'tfidf', 'source_column': 'col2'},
                  'col3': {'transform': 'multi_hot', 'source_column': 'col3', 'separator': '|'},
                  'col4': {'transform': 'target'}}
      feature_analysis.run_local_analysis(
        output_folder, [input_file_path], schema, features)

      stats = json.loads(
          file_io.read_file_to_string(
              os.path.join(output_folder, analyze.constant.STATS_FILE)).decode())
      self.assertEqual(stats['column_stats']['col1']['vocab_size'], 5)
      self.assertEqual(stats['column_stats']['col2']['vocab_size'], 4)
      self.assertEqual(stats['column_stats']['col3']['vocab_size'], 4)

      vocab_str = file_io.read_file_to_string(
          os.path.join(output_folder,
                       analyze.constant.VOCAB_ANALYSIS_FILE % 'col1'))
      vocab = pd.read_csv(six.StringIO(vocab_str),
                          header=None,
                          names=['col1', 'count'])

      # vocabs are sorted by count only
      col1_vocab = vocab['col1'].tolist()
      self.assertItemsEqual(col1_vocab[:2], ['brown', 'quick'])
      self.assertItemsEqual(col1_vocab[2:], ['chicken', 'fox', 'the'])
      self.assertEqual(vocab['count'].tolist(), [2, 2, 1, 1, 1])

      vocab_str = file_io.read_file_to_string(
          os.path.join(output_folder,
                       analyze.constant.VOCAB_ANALYSIS_FILE % 'col2'))
      vocab = pd.read_csv(six.StringIO(vocab_str),
                          header=None,
                          names=['col2', 'count'])

      # vocabs are sorted by count only
      col2_vocab = vocab['col2'].tolist()
      self.assertItemsEqual(col2_vocab[:2], ['in', 'raining'])
      self.assertItemsEqual(col2_vocab[2:], ['kir', 'pdx'])
      self.assertEqual(vocab['count'].tolist(), [2, 2, 1, 1])
    finally:
      shutil.rmtree(output_folder)

 def testAtomicWriteStringToFileOverwriteFalse(self):
   file_path = os.path.join(self._base_dir, "temp_file")
   file_io.atomic_write_string_to_file(file_path, "old", overwrite=False)
   with self.assertRaises(errors.AlreadyExistsError):
     file_io.atomic_write_string_to_file(file_path, "new", overwrite=False)
   file_contents = file_io.read_file_to_string(file_path)
   self.assertEqual("old", file_contents)
   file_io.delete_file(file_path)
   file_io.atomic_write_string_to_file(file_path, "new", overwrite=False)
   file_contents = file_io.read_file_to_string(file_path)
   self.assertEqual("new", file_contents)

  def __init__(self, *args, **kwargs):
    super(ApiCompatibilityTest, self).__init__(*args, **kwargs)

    golden_update_warning_filename = os.path.join(
        resource_loader.get_root_dir_with_all_resources(), _UPDATE_WARNING_FILE)
    self._update_golden_warning = file_io.read_file_to_string(
        golden_update_warning_filename)

    test_readme_filename = os.path.join(
        resource_loader.get_root_dir_with_all_resources(), _TEST_README_FILE)
    self._test_readme_message = file_io.read_file_to_string(
        test_readme_filename)

def get_model_schema_and_features(model_dir):
  """Get a local model's schema and features config.

  Args:
    model_dir: local or GCS path of a model.
  Returns:
    A tuple of schema (list) and features config (dict).
  """
  schema_file = os.path.join(model_dir, 'assets.extra', 'schema.json')
  schema = json.loads(file_io.read_file_to_string(schema_file))
  features_file = os.path.join(model_dir, 'assets.extra', 'features.json')
  features_config = json.loads(file_io.read_file_to_string(features_file))
  return schema, features_config

 def testCopy(self):
   file_path = os.path.join(self._base_dir, "temp_file")
   file_io.FileIO(file_path, mode="w").write("testing")
   copy_path = os.path.join(self._base_dir, "copy_file")
   file_io.copy(file_path, copy_path)
   self.assertTrue(file_io.file_exists(copy_path))
   self.assertEqual(b"testing", file_io.read_file_to_string(file_path))

  def test_numerics(self):
    output_folder = tempfile.mkdtemp()
    input_file_path = tempfile.mkstemp(dir=output_folder)[1]
    try:
      file_io.write_string_to_file(
        input_file_path,
        '\n'.join(['%s,%s,%s' % (i, 10 * i + 0.5, i + 0.5) for i in range(100)]))

      schema = [{'name': 'col1', 'type': 'INTEGER'},
                {'name': 'col2', 'type': 'FLOAT'},
                {'name': 'col3', 'type': 'FLOAT'}]
      features = {'col1': {'transform': 'scale', 'source_column': 'col1'},
                  'col2': {'transform': 'identity', 'source_column': 'col2'},
                  'col3': {'transform': 'target'}}
      feature_analysis.run_local_analysis(
          output_folder, [input_file_path], schema, features)

      stats = json.loads(
          file_io.read_file_to_string(
              os.path.join(output_folder, analyze.constant.STATS_FILE)).decode())

      self.assertEqual(stats['num_examples'], 100)
      col = stats['column_stats']['col1']
      self.assertAlmostEqual(col['max'], 99.0)
      self.assertAlmostEqual(col['min'], 0.0)
      self.assertAlmostEqual(col['mean'], 49.5)

      col = stats['column_stats']['col2']
      self.assertAlmostEqual(col['max'], 990.5)
      self.assertAlmostEqual(col['min'], 0.5)
      self.assertAlmostEqual(col['mean'], 495.5)
    finally:
      shutil.rmtree(output_folder)

def load_model(saved_model_path):
  """Load a keras.Model from SavedModel.

  load_model reinstantiates model state by:
  1) loading model topology from json (this will eventually come
     from metagraph).
  2) loading model weights from checkpoint.

  Args:
    saved_model_path: a string specifying the path to an existing SavedModel.

  Returns:
    a keras.Model instance.
  """
  # restore model topology from json string
  model_json_filepath = os.path.join(
      compat.as_bytes(saved_model_path),
      compat.as_bytes(constants.ASSETS_DIRECTORY),
      compat.as_bytes(constants.SAVED_MODEL_FILENAME_JSON))
  model_json = file_io.read_file_to_string(model_json_filepath)
  model = model_from_json(model_json)

  # restore model weights
  checkpoint_prefix = os.path.join(
      compat.as_text(saved_model_path),
      compat.as_text(constants.VARIABLES_DIRECTORY),
      compat.as_text(constants.VARIABLES_FILENAME))
  model.load_weights(checkpoint_prefix)
  return model

def _read_file(filename):
  """Reads a file containing `GraphDef` and returns the protocol buffer.

  Args:
    filename: `graph_def` filename including the path.

  Returns:
    A `GraphDef` protocol buffer.

  Raises:
    IOError: If the file doesn't exist, or cannot be successfully parsed.
  """
  graph_def = graph_pb2.GraphDef()
  if not file_io.file_exists(filename):
    raise IOError("File %s does not exist." % filename)
  # First try to read it as a binary file.
  file_content = file_io.read_file_to_string(filename)
  try:
    graph_def.ParseFromString(file_content)
    return graph_def
  except Exception:  # pylint: disable=broad-except
    pass

  # Next try to read it as a text file.
  try:
    text_format.Merge(file_content.decode("utf-8"), graph_def)
  except text_format.ParseError as e:
    raise IOError("Cannot parse file %s: %s." % (filename, str(e)))

  return graph_def

 def testMultipleWrites(self):
   file_path = os.path.join(self._base_dir, "temp_file")
   with file_io.FileIO(file_path, mode="w") as f:
     f.write("line1\n")
     f.write("line2")
   file_contents = file_io.read_file_to_string(file_path)
   self.assertEqual("line1\nline2", file_contents)

def run_analysis(args):
  """Builds an analysis file for training.

  Uses BiqQuery tables to do the analysis.

  Args:
    args: command line args

  Raises:
    ValueError if schema contains unknown types.
  """
  import google.datalab.bigquery as bq
  if args.bigquery_table:
    table = bq.Table(args.bigquery_table)
    schema_list = table.schema._bq_schema
  else:
    schema_list = json.loads(
        file_io.read_file_to_string(args.schema_file).decode())
    table = bq.ExternalDataSource(
        source=args.input_file_pattern,
        schema=bq.Schema(schema_list))

  # Check the schema is supported.
  for col_schema in schema_list:
    col_type = col_schema['type'].lower()
    if col_type != 'string' and col_type != 'integer' and col_type != 'float':
      raise ValueError('Schema contains an unsupported type %s.' % col_type)

  run_numerical_analysis(table, schema_list, args)
  run_categorical_analysis(table, schema_list, args)

  # Save a copy of the schema to the output location.
  file_io.write_string_to_file(
      os.path.join(args.output_dir, SCHEMA_FILE),
      json.dumps(schema_list, indent=2, separators=(',', ': ')))

 def testFileWrite(self):
   file_path = os.path.join(self.get_temp_dir(), "temp_file")
   file_io.write_string_to_file(file_path, "testing")
   self.assertTrue(file_io.file_exists(file_path))
   file_contents = file_io.read_file_to_string(file_path)
   self.assertEqual(b"testing", file_contents)
   file_io.delete_file(file_path)

  def _GetBaseApiMap(self):
    """Get a map from graph op name to its base ApiDef.

    Returns:
      Dictionary mapping graph op name to corresponding ApiDef.
    """
    # Convert base ApiDef in Multiline format to Proto format.
    converted_base_api_dir = os.path.join(
        test.get_temp_dir(), 'temp_base_api_defs')
    subprocess.check_call(
        [os.path.join(resource_loader.get_root_dir_with_all_resources(),
                      _CONVERT_FROM_MULTILINE_SCRIPT),
         _BASE_API_DIR, converted_base_api_dir])

    name_to_base_api_def = {}
    base_api_files = file_io.get_matching_files(
        os.path.join(converted_base_api_dir, 'api_def_*.pbtxt'))
    for base_api_file in base_api_files:
      if file_io.file_exists(base_api_file):
        api_defs = api_def_pb2.ApiDefs()
        text_format.Merge(
            file_io.read_file_to_string(base_api_file), api_defs)
        for api_def in api_defs.op:
          name_to_base_api_def[api_def.graph_op_name] = api_def
    return name_to_base_api_def