我目前正在使用Tensorflow转换库来转换并保存转换，虽然它之前很好用，但目前我面临的一些问题类似于下面的
我一直得到像 -
这样的错误'BeamDatasetMetadata'对象在运行时没有属性'schema' 'AnalyzeAndTransformDataset / TransformDataset / ConvertAndUnbatch']
有人熟悉上述错误，我们如何解决？
我的转换功能如下所示 -
# ### Transformation Function
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:
tft.uniques(inputs[key], vocab_filename=key)
""" We would use the lookup table when the label is a string value
In our case here Creative_id = 0/1 so we can direclty assign output as is
"""
outputs[LABEL_KEY] = 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 beam_impl.Context(temp_dir=tempfile.mkdtemp()):
# Create a coder to read the 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 = [
'app_category', 'connection_type', 'creative_id', 'day_of_week',
'device_size', 'geo', 'hour_of_day', 'num_of_connects',
'num_of_conversions', 'opt_bid', 'os_version'
]
converter = csv_coder.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 and line != 'app_category,connection_type,creative_id,day_of_week,device_size,geo,hour_of_day,num_of_connects,num_of_conversions,opt_bid,os_version')
| '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 = example_proto_coder.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 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, skip_header_lines=1)
| 'FixCommasTestData' >> beam.Map(
lambda line: line.replace(', ', ','))
| '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
| 'EncodeTestData' >> beam.Map(transformed_data_coder.encode)
| 'WriteTestData' >> tfrecordio.WriteToTFRecord(
os.path.join(working_dir, TRANSFORMED_TEST_DATA_FILEBASE)))
_ = (
transform_fn
| 'WriteTransformFn' >>
transform_fn_io.WriteTransformFn(working_dir))
输出堆栈 -
pip show tensorflow-transform apache-beam
Name: tensorflow-transform
Version: 0.4.0
Summary: A library for data preprocessing with TensorFlow
Home-page: UNKNOWN
Author: Google Inc.
Author-email: tf-transform-feedback@google.com
License: Apache 2.0
Location: /usr/local/lib/python2.7/dist-packages
Requires: six, apache-beam, protobuf
---
Name: apache-beam
Version: 2.4.0
Summary: Apache Beam SDK for Python
Home-page: https://beam.apache.org
Author: Apache Software Foundation
Author-email: dev@beam.apache.org
License: Apache License, Version 2.0
Location: /usr/local/lib/python2.7/dist-packages
Requires: oauth2client, httplib2, mock, crcmod, grpcio, futures, pyvcf, avro, typing, pyyaml, dill, six, hdfs, protobuf
You are using pip version 9.0.1, however version 10.0.1 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.
上述问题似乎并不是一直发生的！看起来它与其他软件包有一些冲突。
答案 0 :(得分：0)
看不到，但此行看起来不完整：
```
converter = csv_coder.CsvCoder(ordered_columns, RAW_DATA_METADATA.schema)
```
一种可行的方法：
```
INPUT_SCHEMA = dataset_schema.from_feature_spec({
'label':tf.FixedLenFeature(shape=[], dtype=tf.float32),
'id': tf.FixedLenFeature(shape=[], dtype=tf.float32),
'date': tf.FixedLenFeature(shape=[], dtype=tf.string),
'random': tf.FixedLenFeature(shape=[], dtype=tf.string),
'name': tf.FixedLenFeature(shape=[], dtype=tf.string),
'tweet': tf.FixedLenFeature(shape=[], dtype=tf.string),
})
```
```
converter_input = coders.CsvCoder(
['label','id','date','random','name','tweet'],
INPUT_SCHEMA,
delimiter=delimiter)
```
然后在转换步骤中像实际问题一样出现在这里也是一个示例。
```
input_metadata =
dataset_metadata.DatasetMetadata(schema=TRANSFORM_INPUT_SCHEMA)
TRANSFORM_INPUT_SCHEMA = dataset_schema.from_feature_spec({
'id': tf.FixedLenFeature(shape=[], dtype=tf.float32),
'label': tf.FixedLenFeature(shape=[], dtype=tf.float32),
'tweet': tf.FixedLenFeature(shape=[], dtype=tf.string),
'answer_to_nbr': tf.FixedLenFeature(shape=[], dtype=tf.float32),
'nbr_of_tags': tf.FixedLenFeature(shape=[], dtype=tf.float32),
})
train_dataset = (train_dataset, input_metadata)
transformed_dataset, transform_fn = (train_dataset
| 'AnalyzeAndTransform' >>
beam_impl.AnalyzeAndTransformDataset(
preprocessing_fn))
```
希望它对您有帮助:)如果您发布到github仓库，我可以查看完整的代码，看看我是否可以提供帮助！祝好运！