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437 lines
12 KiB
437 lines
12 KiB
#!/bin/env python2
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# Copyright (c) 2016 Baidu, Inc. All Rights Reserved
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Preprocess Movielens dataset, to get movie/user object.
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Usage:
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./preprocess.py <dataset_dir> <binary_filename> [--config=<config_file>]
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./preprocess.py -h | --help
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Options:
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-h --help Show this screen.
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--version Show version.
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--config=<config_file> Get MetaData config file [default: config.json].
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"""
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import docopt
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import os
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import sys
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import re
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import collections
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try:
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import cPickle as pickle
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except ImportError:
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import pickle
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class UniqueIDGenerator(object):
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def __init__(self):
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self.pool = collections.defaultdict(self.__next_id__)
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self.next_id = 0
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def __next_id__(self):
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tmp = self.next_id
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self.next_id += 1
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return tmp
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def __call__(self, k):
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return self.pool[k]
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def to_list(self):
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ret_val = [None] * len(self.pool)
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for k in self.pool.keys():
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ret_val[self.pool[k]] = k
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return ret_val
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class SortedIDGenerator(object):
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def __init__(self):
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self.__key_set__ = set()
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self.dict = None
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def scan(self, key):
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self.__key_set__.add(key)
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def finish_scan(self, compare=None, key=None, reverse=False):
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self.__key_set__ = sorted(list(self.__key_set__), cmp=compare,
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key=key, reverse=reverse)
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self.dict = dict()
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for idx, each_key in enumerate(self.__key_set__):
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self.dict[each_key] = idx
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def __call__(self, key):
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return self.dict[key]
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def to_list(self):
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return self.__key_set__
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class SplitFileReader(object):
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def __init__(self, work_dir, config):
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assert isinstance(config, dict)
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self.filename = config['name']
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self.delimiter = config.get('delimiter', ',')
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self.work_dir = work_dir
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def read(self):
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with open(os.path.join(self.work_dir, self.filename), 'r') as f:
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for line in f:
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line = line.strip()
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if isinstance(self.delimiter, unicode):
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self.delimiter = str(self.delimiter)
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yield line.split(self.delimiter)
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@staticmethod
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def create(work_dir, config):
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assert isinstance(config, dict)
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if config['type'] == 'split':
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return SplitFileReader(work_dir, config)
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class IFileReader(object):
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READERS = [SplitFileReader]
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def read(self):
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raise NotImplementedError()
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@staticmethod
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def create(work_dir, config):
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for reader_cls in IFileReader.READERS:
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val = reader_cls.create(work_dir, config)
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if val is not None:
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return val
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class IDFieldParser(object):
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TYPE = 'id'
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def __init__(self, config):
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self.__max_id__ = -sys.maxint - 1
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self.__min_id__ = sys.maxint
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self.__id_count__ = 0
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def scan(self, line):
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idx = int(line)
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self.__max_id__ = max(self.__max_id__, idx)
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self.__min_id__ = min(self.__min_id__, idx)
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self.__id_count__ += 1
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def parse(self, line):
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return int(line)
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def meta_field(self):
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return {
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"is_key": True,
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'max': self.__max_id__,
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'min': self.__min_id__,
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'count': self.__id_count__,
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'type': 'id'
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}
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class SplitEmbeddingDict(object):
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def __init__(self, delimiter):
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self.__id__ = UniqueIDGenerator()
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self.delimiter = delimiter
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def scan(self, multi):
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for val in multi.split(self.delimiter):
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self.__id__(val)
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def parse(self, multi):
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return map(self.__id__, multi.split(self.delimiter))
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def meta_field(self):
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return self.__id__.to_list()
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class EmbeddingFieldParser(object):
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TYPE = 'embedding'
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NO_SEQUENCE = "no_sequence"
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SEQUENCE = "sequence"
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class CharBasedEmbeddingDict(object):
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def __init__(self, is_seq=True):
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self.__id__ = UniqueIDGenerator()
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self.is_seq = is_seq
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def scan(self, s):
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for ch in s:
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self.__id__(ch)
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def parse(self, s):
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return map(self.__id__, s) if self.is_seq else self.__id__(s[0])
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def meta_field(self):
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return self.__id__.to_list()
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class WholeContentDict(object):
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def __init__(self, need_sort=True):
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assert need_sort
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self.__id__ = SortedIDGenerator()
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self.__has_finished__ = False
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def scan(self, txt):
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self.__id__.scan(txt)
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def meta_field(self):
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if not self.__has_finished__:
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self.__id__.finish_scan()
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self.__has_finished__ = True
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return self.__id__.to_list()
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def parse(self, txt):
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return self.__id__(txt)
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def __init__(self, config):
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try:
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self.seq_type = config['type']['seq_type']
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except TypeError:
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self.seq_type = EmbeddingFieldParser.NO_SEQUENCE
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if config['dict']['type'] == 'char_based':
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self.dict = EmbeddingFieldParser.CharBasedEmbeddingDict(
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self.seq_type == EmbeddingFieldParser.SEQUENCE)
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elif config['dict']['type'] == 'split':
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self.dict = SplitEmbeddingDict(
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config['dict'].get('delimiter', ','))
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elif config['dict']['type'] == 'whole_content':
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self.dict = EmbeddingFieldParser.WholeContentDict(
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config['dict']['sort'])
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else:
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print config
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assert False
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self.name = config['name']
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def scan(self, s):
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self.dict.scan(s)
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def meta_field(self):
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return {
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'name': self.name,
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'dict': self.dict.meta_field(),
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'type': 'embedding',
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'seq': self.seq_type
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}
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def parse(self, s):
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return self.dict.parse(s)
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class OneHotDenseFieldParser(object):
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TYPE = 'one_hot_dense'
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def __init__(self, config):
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if config['dict']['type'] == 'split':
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self.dict = SplitEmbeddingDict(config['dict']['delimiter'])
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self.name = config['name']
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def scan(self, s):
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self.dict.scan(s)
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def meta_field(self):
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# print self.dict.meta_field()
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return {
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'dict': self.dict.meta_field(),
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'name': self.name,
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'type': 'one_hot_dense'
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}
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def parse(self, s):
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ids = self.dict.parse(s)
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retv = [0.0] * len(self.dict.meta_field())
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for idx in ids:
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retv[idx] = 1.0
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# print retv
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return retv
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class FieldParserFactory(object):
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PARSERS = [IDFieldParser, EmbeddingFieldParser, OneHotDenseFieldParser]
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@staticmethod
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def create(config):
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if isinstance(config['type'], basestring):
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config_type = config['type']
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elif isinstance(config['type'], dict):
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config_type = config['type']['name']
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assert config_type is not None
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for each_parser_cls in FieldParserFactory.PARSERS:
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if config_type == each_parser_cls.TYPE:
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return each_parser_cls(config)
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print config
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class CompositeFieldParser(object):
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def __init__(self, parser, extractor):
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self.extractor = extractor
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self.parser = parser
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def scan(self, *args, **kwargs):
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self.parser.scan(self.extractor.extract(*args, **kwargs))
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def parse(self, *args, **kwargs):
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return self.parser.parse(self.extractor.extract(*args, **kwargs))
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def meta_field(self):
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return self.parser.meta_field()
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class PositionContentExtractor(object):
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def __init__(self, pos):
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self.pos = pos
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def extract(self, line):
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assert isinstance(line, list)
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return line[self.pos]
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class RegexPositionContentExtractor(PositionContentExtractor):
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def __init__(self, pos, pattern, group_id, strip=True):
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PositionContentExtractor.__init__(self, pos)
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pattern = pattern.strip()
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self.pattern = re.compile(pattern)
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self.group_id = group_id
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self.strip = strip
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def extract(self, line):
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line = PositionContentExtractor.extract(self, line)
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match = self.pattern.match(line)
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# print line, self.pattern.pattern, match
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assert match is not None
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txt = match.group(self.group_id)
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if self.strip:
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txt.strip()
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return txt
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class ContentExtractorFactory(object):
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def extract(self, line):
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pass
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@staticmethod
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def create(config):
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if 'pos' in config:
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if 'regex' not in config:
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return PositionContentExtractor(config['pos'])
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else:
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extra_args = config['regex']
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return RegexPositionContentExtractor(pos=config['pos'],
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**extra_args)
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class MetaFile(object):
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def __init__(self, work_dir):
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self.work_dir = work_dir
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self.obj = dict()
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def parse(self, config):
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config = config['meta']
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ret_obj = dict()
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for key in config.keys():
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val = config[key]
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assert 'file' in val
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reader = IFileReader.create(self.work_dir, val['file'])
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assert reader is not None
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assert 'fields' in val and isinstance(val['fields'], list)
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fields_config = val['fields']
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field_parsers = map(MetaFile.__field_config_mapper__, fields_config)
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for each_parser in field_parsers:
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assert each_parser is not None
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for each_block in reader.read():
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for each_parser in field_parsers:
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each_parser.scan(each_block)
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metas = map(lambda x: x.meta_field(), field_parsers)
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# print metas
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key_index = filter(lambda x: x is not None, map(
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lambda (idx, meta): idx if 'is_key' in meta and meta['is_key']
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else None, enumerate(metas)))[0]
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key_map = []
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for i in range(min(key_index, len(metas))):
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key_map.append(i)
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for i in range(key_index + 1, len(metas)):
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key_map.append(i)
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obj = {
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'__meta__': {
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'raw_meta': metas,
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'feature_map': key_map
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}
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}
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for each_block in reader.read():
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idx = field_parsers[key_index].parse(each_block)
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val = []
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for i, each_parser in enumerate(field_parsers):
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if i != key_index:
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val.append(each_parser.parse(each_block))
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obj[idx] = val
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ret_obj[key] = obj
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self.obj = ret_obj
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return ret_obj
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@staticmethod
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def __field_config_mapper__(conf):
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assert isinstance(conf, dict)
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extrator = ContentExtractorFactory.create(conf)
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field_parser = FieldParserFactory.create(conf)
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assert extrator is not None
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assert field_parser is not None
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return CompositeFieldParser(field_parser, extrator)
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def dump(self, fp):
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pickle.dump(self.obj, fp, pickle.HIGHEST_PROTOCOL)
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def preprocess(binary_filename, dataset_dir, config, **kwargs):
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assert isinstance(config, str)
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with open(config, 'r') as config_file:
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file_loader = None
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if config.lower().endswith('.yaml'):
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import yaml
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file_loader = yaml
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elif config.lower().endswith('.json'):
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import json
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file_loader = json
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config = file_loader.load(config_file)
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meta = MetaFile(dataset_dir)
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meta.parse(config)
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with open(binary_filename, 'wb') as outf:
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meta.dump(outf)
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if __name__ == '__main__':
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args = docopt.docopt(__doc__, version='0.1.0')
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kwargs = dict()
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for key in args.keys():
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if key != '--help':
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param_name = key
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assert isinstance(param_name, str)
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param_name = param_name.replace('<', '')
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param_name = param_name.replace('>', '')
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param_name = param_name.replace('--', '')
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kwargs[param_name] = args[key]
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preprocess(**kwargs)
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