一.题目背景
kaggle上这三道题目都是和文本相似度相关的，要求建模评估两个文本内容的相关度。
两个电商场景：
https://www.kaggle.com/c/crowdflower-search-relevance
https://www.kaggle.com/c/home-depot-product-search-relevance
要求评估用户输入的搜索项（query）和查询的商品之间的相似度
一个论坛场景：
https://www.kaggle.com/c/quora-question-pairs
要求评估两个问题之间的相似度
虽然应用场景不同， 但我们可以有一些共同的套路来解决这些问题。
二.第三名代码解析
文章中出现的代码都来自这个github地址: https://github.com/ChenglongChen/Kaggle_HomeDepot
三.代码解析
1. pattern-replace基类
初始化: 传一个pattern_replace_pair_list, 把满足pattern的部分转换成replace
转换: 按每一对pattern, replace(tuple格式)做替换, 去掉开头结尾空格(strip())
class BaseReplacer:
def __init__(self, pattern_replace_pair_list=[]):
self.pattern_replace_pair_list = pattern_replace_pair_list
def transform(self, text):
for pattern, replace in self.pattern_replace_pair_list:
try:
text = regex.sub(pattern, replace, text)
except:
pass
return regex.sub(r"\s+", " ", text).strip()
2. 首字母大写变小写
覆盖父类transform方法
class LowerCaseConverter(BaseReplacer):
def transform(self, text):
return text.lower()
3. 本应该分开的两个单词,第二个单词首字母大写
把:hidden from viewDurable rich finishLimited lifetime warrantyEncapsulated panels
变成: hidden from view Durable rich finish limited lifetime warranty Encapsulated panels
class LowerUpperCaseSplitter(BaseReplacer):
def __init__(self):
self.pattern_replace_pair_list = [
(r"(\w)[\.?!]([A-Z])", r"\1 \2"),
(r"(?<=( ))([a-z]+)([A-Z]+)", r"\2 \3"),
]
正则表达式: ()表示分组, \1 \2 \3可以取第几个分组
4. 单词替换
给一个词典, 一一对应替换, 用于改正typo
子类初始化时只积累pattern_replace_pair_list, 并没有完成替换.
调用transform方法才替换
class WordReplacer(BaseReplacer):
def __init__(self, replace_fname):
self.replace_fname = replace_fname # 词典文件路径
self.pattern_replace_pair_list = []
for line in csv.reader(open(self.replace_fname)):
if len(line) == 1 and line[0].startswith("#"):
continue
try:
pattern = r"(?<=\W|^)%s(?=\W|$)"%line[0]
replace = line[1]
self.pattern_replace_pair_list.append( (pattern, replace) )
except:
print(line)
pass
5. 分割连接符连接的单词
类似上面()正则的用法
class LetterLetterSplitter(BaseReplacer):
"""
For letter and letter
/:
Cleaner/Conditioner -> Cleaner Conditioner
-:
Vinyl-Leather-Rubber -> Vinyl Leather Rubber
For digit and digit, we keep it as we will generate some features via math operations,
such as approximate height/width/area etc.
/:
3/4 -> 3/4
-:
1-1/4 -> 1-1/4
"""
def __init__(self):
self.pattern_replace_pair_list = [
(r"([a-zA-Z]+)[/\-]([a-zA-Z]+)", r"\1 \2"),
]
6. 数字加字母
数字.或-字母
字母.或-数字
class DigitLetterSplitter(BaseReplacer):
"""
x:
1x1x1x1x1 -> 1 x 1 x 1 x 1 x 1
19.875x31.5x1 -> 19.875 x 31.5 x 1
-:
1-Gang -> 1 Gang
48-Light -> 48 Light
.:
includes a tile flange to further simplify installation.60 in. L x 36 in. W x 20 in. ->
includes a tile flange to further simplify installation. 60 in. L x 36 in. W x 20 in.
"""
def __init__(self):
self.pattern_replace_pair_list = [
(r"(\d+)[\.\-]*([a-zA-Z]+)", r"\1 \2"),
(r"([a-zA-Z]+)[\.\-]*(\d+)", r"\1 \2"),
]
7. 去掉数字中的逗号
class DigitCommaDigitMerger(BaseReplacer):
"""
1,000,000 -> 1000000
"""
def __init__(self):
self.pattern_replace_pair_list = [
(r"(?<=\d+),(?=000)", r""),
]
8. 单词的数字换成阿拉伯数字
class NumberDigitMapper(BaseReplacer):
"""
one -> 1
two -> 2
"""
def __init__(self):
numbers = [
"zero", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine", "ten",
"eleven", "twelve", "thirteen", "fourteen", "fifteen", "sixteen", "seventeen", "eighteen",
"nineteen", "twenty", "thirty", "forty", "fifty", "sixty", "seventy", "eighty", "ninety"
]
digits = [
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 30, 40, 50, 60, 70, 80, 90
]
self.pattern_replace_pair_list = [
(r"(?<=\W|^)%s(?=\W|$)"%n, str(d)) for n,d in zip(numbers, digits)
]
9. 统一单位
class UnitConverter(BaseReplacer):
"""
shadeMature height: 36 in. - 48 in.Mature width
PUT one UnitConverter before LowerUpperCaseSplitter
"""
def __init__(self):
self.pattern_replace_pair_list = [
(r"([0-9]+)( *)(inches|inch|in|in.|')\.?", r"\1 in. "),
(r"([0-9]+)( *)(pounds|pound|lbs|lb|lb.)\.?", r"\1 lb. "),
(r"([0-9]+)( *)(foot|feet|ft|ft.|'')\.?", r"\1 ft. "),
(r"([0-9]+)( *)(square|sq|sq.) ?\.?(inches|inch|in|in.|')\.?", r"\1 sq.in. "),
(r"([0-9]+)( *)(square|sq|sq.) ?\.?(feet|foot|ft|ft.|'')\.?", r"\1 sq.ft. "),
(r"([0-9]+)( *)(cubic|cu|cu.) ?\.?(inches|inch|in|in.|')\.?", r"\1 cu.in. "),
(r"([0-9]+)( *)(cubic|cu|cu.) ?\.?(feet|foot|ft|ft.|'')\.?", r"\1 cu.ft. "),
(r"([0-9]+)( *)(gallons|gallon|gal)\.?", r"\1 gal. "),
(r"([0-9]+)( *)(ounces|ounce|oz)\.?", r"\1 oz. "),
(r"([0-9]+)( *)(centimeters|cm)\.?", r"\1 cm. "),
(r"([0-9]+)( *)(milimeters|mm)\.?", r"\1 mm. "),
(r"([0-9]+)( *)(minutes|minute)\.?", r"\1 min. "),
(r"([0-9]+)( *)(°|degrees|degree)\.?", r"\1 deg. "),
(r"([0-9]+)( *)(v|volts|volt)\.?", r"\1 volt. "),
(r"([0-9]+)( *)(wattage|watts|watt)\.?", r"\1 watt. "),
(r"([0-9]+)( *)(amperes|ampere|amps|amp)\.?", r"\1 amp. "),
(r"([0-9]+)( *)(qquart|quart)\.?", r"\1 qt. "),
(r"([0-9]+)( *)(hours|hour|hrs.)\.?", r"\1 hr "),
(r"([0-9]+)( *)(gallons per minute|gallon per minute|gal per minute|gallons/min.|gallons/min)\.?", r"\1 gal. per min. "),
(r"([0-9]+)( *)(gallons per hour|gallon per hour|gal per hour|gallons/hour|gallons/hr)\.?", r"\1 gal. per hr "),
]
10. 去掉html标签
class HtmlCleaner:
def __init__(self, parser):
self.parser = parser
def transform(self, text):
bs = BeautifulSoup(text, self.parser)
text = bs.get_text(separator=" ")
return text
11. 标点乱码替换成原始标点
class QuartetCleaner(BaseReplacer):
def __init__(self):
self.pattern_replace_pair_list = [
(r"<.+?>", r""),
# html codes
(r" ", r" "),
(r"&", r"&"),
(r"'", r"'"),
(r"/>/Agt/>", r""),
(r"</a<gt/", r""),
(r"gt/>", r""),
(r"/>", r""),
(r"<br", r""),
# do not remove [".", "/", "-", "%"] as they are useful in numbers, e.g., 1.97, 1-1/2, 10%, etc.
(r"[ &<>)(_,;:!?\+^~@#\$]+", r" "),
("'s\\b", r""),
(r"[']+", r""),
(r"[\"]+", r""),
]
12. 提取词干Lemma和词型归一stemmer
区别:Lemma更短,更"根本".比如
Stemmers: having->hav
Lemmatizers: having->have
class Lemmatizer:
def __init__(self):
self.Tokenizer = nltk.tokenize.TreebankWordTokenizer()
self.Lemmatizer = nltk.stem.wordnet.WordNetLemmatizer()
def transform(self, text):
tokens = [self.Lemmatizer.lemmatize(token) for token in self.Tokenizer.tokenize(text)]
return " ".join(tokens)
## stemming
class Stemmer:
def __init__(self, stemmer_type="snowball"):
self.stemmer_type = stemmer_type
if self.stemmer_type == "porter":
self.stemmer = nltk.stem.PorterStemmer()
elif self.stemmer_type == "snowball":
self.stemmer = nltk.stem.SnowballStemmer("english")
def transform(self, text):
tokens = [self.stemmer.stem(token) for token in text.split(" ")]
return " ".join(tokens)
13. Query拓展
因为title和search term属于短文本， 做一些处理丰富这部分内容。
title_ngram： 把商品的title部分做ngram处理，连续的n个单词用一个连接符连在一起。
search_term_alt： 搜索项部分找每个搜索项中最常见的title_ngram。
class QueryExpansion:
def __init__(self, df, ngram=3, stopwords_threshold=0.9, base_stopwords=set()):
self.df = df[["search_term", "product_title"]].copy()
self.ngram = ngram
self.stopwords_threshold = stopwords_threshold
self.stopwords = set(base_stopwords).union(self._get_customized_stopwords())
def _get_customized_stopwords(self):
words = " ".join(list(self.df["product_title"].values)).split(" ")
counter = Counter(words)
num_uniq = len(list(counter.keys()))
num_stop = int((1.-self.stopwords_threshold)*num_uniq)
stopwords = set()
for e,(w,c) in enumerate(sorted(counter.items(), key=lambda x: x[1])):
if e == num_stop:
break
stopwords.add(w)
return stopwords
def _ngram(self, text):
tokens = text.split(" ")
tokens = [token for token in tokens if token not in self.stopwords]
return ngram_utils._ngrams(tokens, self.ngram, " ")
def _get_alternative_query(self, df):
res = []
for v in df:
res += v
c = Counter(res)
value, count = c.most_common()[0]
return value
def build(self):
self.df["title_ngram"] = self.df["product_title"].apply(self._ngram)
corpus = self.df.groupby("search_term").apply(lambda df: self._get_alternative_query(df["title_ngram"]))
corpus = corpus.reset_index()
corpus.columns = ["search_term", "search_term_alt"]
self.df = pd.merge(self.df, corpus, on="search_term", how="left")
return self.df["search_term_alt"].values
其中的ngram，以n=2为例：
def _bigrams(words, join_string, skip=0):
"""
Input: a list of words, e.g., ["I", "am", "Denny"]
Output: a list of bigram, e.g., ["I_am", "am_Denny"]
I use _ as join_string for this example.
"""
assert type(words) == list
L = len(words)
if L > 1:
lst = []
for i in range(L-1):
for k in range(1,skip+2):
if i+k < L:
lst.append( join_string.join([words[i], words[i+k]]) )
else:
# set it as unigram
lst = _unigrams(words)
return lst
14. 处理商品名称
transform前需要先用前面的类统一格式
class ProductNameExtractor(BaseReplacer):
def __init__(self):
self.pattern_replace_pair_list = [
# Remove descriptions (text between paranthesis/brackets)
("[ ]?[[(].+?[])]", r""),
# Remove "made in..."
("made in [a-z]+\\b", r""),
# Remove descriptions (hyphen or comma followed by space then at most 2 words, repeated)
("([,-]( ([a-zA-Z0-9]+\\b)){1,2}[ ]?){1,}$", r""),
# Remove descriptions (prepositions staring with: with, for, by, in )
("\\b(with|for|by|in|w/) .+$", r""),
# colors & sizes
("size: .+$", r""),
("size [0-9]+[.]?[0-9]+\\b", r""),
(COLORS_PATTERN, r""),
# dimensions
(DIM_PATTERN_NxNxN, r""),
(DIM_PATTERN_NxN, r""),
# measurement units
(UNITS_PATTERN, r""),
# others
("(value bundle|warranty|brand new|excellent condition|one size|new in box|authentic|as is)", r""),
# stop words
("\\b(in)\\b", r""),
# hyphenated words
("([a-zA-Z])-([a-zA-Z])", r"\1\2"),
# special characters
("[ &<>)(_,.;:!?/+#*-]+", r" "),
# numbers that are not part of a word
("\\b[0-9]+\\b", r""),
]
def preprocess(self, text):
pattern_replace_pair_list = [
# Remove single & double apostrophes
("[\"]+", r""),
# Remove product codes (long words (>5 characters) that are all caps, numbers or mix pf both)
# don't use raw string format
("[ ]?\\b[0-9A-Z-]{5,}\\b", ""),
]
text = BaseReplacer(pattern_replace_pair_list).transform(text)
text = LowerCaseConverter().transform(text)
text = DigitLetterSplitter().transform(text)
text = UnitConverter().transform(text)
text = DigitCommaDigitMerger().transform(text)
text = NumberDigitMapper().transform(text)
text = UnitConverter().transform(text)
return text
def transform(self, text):
text = super().transform(self.preprocess(text))
text = Lemmatizer().transform(text)
text = Stemmer(stemmer_type="snowball").transform(text)
# last two words in product
text = " ".join(text.split(" ")[-2:])
return text
15. 处理商品属性
根据输出的不同格式写两个方法
def _split_attr_to_text(text):
attrs = text.split(config.ATTR_SEPARATOR)
return " ".join(attrs)
def _split_attr_to_list(text):
attrs = text.split(config.ATTR_SEPARATOR)
if len(attrs) == 1:
# missing
return [[attrs[0], attrs[0]]]
else:
return [[n,v] for n,v in zip(attrs[::2], attrs[1::2])]
16. 处理不同数据结构的输入输出
待处理的数据可能放在list里，也可能放在dataframe里， 处理方法稍有不同。
class ListProcessor:
"""
WARNING: This class will operate on the original input list itself
"""
def __init__(self, processors):
self.processors = processors
def process(self, lst):
for i in range(len(lst)):
for processor in self.processors:
lst[i] = ProcessorWrapper(processor).transform(lst[i])
return lst
class DataFrameProcessor:
"""
WARNING: This class will operate on the original input dataframe itself
"""
def __init__(self, processors):
self.processors = processors
def process(self, df):
for processor in self.processors:
df = df.apply(ProcessorWrapper(processor).transform)
return df
class DataFrameParallelProcessor:
"""
WARNING: This class will operate on the original input dataframe itself
https://stackoverflow.com/questions/26520781/multiprocessing-pool-whats-the-difference-between-map-async-and-imap
"""
def __init__(self, processors, n_jobs=4):
self.processors = processors
self.n_jobs = n_jobs
def process(self, dfAll, columns):
df_processor = DataFrameProcessor(self.processors)
p = multiprocessing.Pool(self.n_jobs)
dfs = p.imap(df_processor.process, [dfAll[col] for col in columns])
for col,df in zip(columns, dfs):
dfAll[col] = df
return dfAll
四. 总结
把上面的代码都用户合并到一起就是main函数咯。总结一下大神的代码有几点很值得学习：
- 重复的内容用类，和类的继承减少代码重复（各种replacer），并且放到一起使得逻辑清晰。
- 用config文件中的字段控制部分代码是否运行（各种if config.大写单词）， 比反复注释，取消注释节省时间。
- 各种设置统一到一个config文件中，避免调试的时候到处找，浪费时间。
def main():
###########
## Setup ##
###########
logname = "data_processor_%s.log"%now
logger = logging_utils._get_logger(config.LOG_DIR, logname)
# put product_attribute_list, product_attribute and product_description first as they are
# quite time consuming to process
columns_to_proc = [
# # product_attribute_list is very time consuming to process
# # so we just process product_attribute which is of the form
# # attr_name1 | attr_value1 | attr_name2 | attr_value2 | ...
# # and split it into a list afterwards
# "product_attribute_list",
"product_attribute_concat",
"product_description",
"product_brand",
"product_color",
"product_title",
"search_term",
]
if config.PLATFORM == "Linux":
config.DATA_PROCESSOR_N_JOBS = len(columns_to_proc)
# clean using a list of processors
processors = [
LowerCaseConverter(),
# See LowerUpperCaseSplitter and UnitConverter for why we put UnitConverter here
UnitConverter(),
LowerUpperCaseSplitter(),
WordReplacer(replace_fname=config.WORD_REPLACER_DATA),
LetterLetterSplitter(),
DigitLetterSplitter(),
DigitCommaDigitMerger(),
NumberDigitMapper(),
UnitConverter(),
QuartetCleaner(),
HtmlCleaner(parser="html.parser"),
Lemmatizer(),
]
stemmers = [
Stemmer(stemmer_type="snowball"),
Stemmer(stemmer_type="porter")
][0:1]
## simple test
text = "1/2 inch rubber lep tips Bullet07"
print("Original:")
print(text)
list_processor = ListProcessor(processors)
print("After:")
print(list_processor.process([text]))
#############
## Process ##
#############
## load raw data
dfAll = pkl_utils._load(config.ALL_DATA_RAW)
columns_to_proc = [col for col in columns_to_proc if col in dfAll.columns]
## extract product name from search_term and product_title
ext = ProductNameExtractor()
dfAll["search_term_product_name"] = dfAll["search_term"].apply(ext.transform)
dfAll["product_title_product_name"] = dfAll["product_title"].apply(ext.transform)
if config.TASK == "sample":
print(dfAll[["search_term", "search_term_product_name", "product_title_product_name"]])
## clean using GoogleQuerySpellingChecker
# MUST BE IN FRONT OF ALL THE PROCESSING
if config.GOOGLE_CORRECTING_QUERY:
logger.info("Run GoogleQuerySpellingChecker at search_term")
checker = GoogleQuerySpellingChecker()
dfAll["search_term"] = dfAll["search_term"].apply(checker.correct)
## clean uisng a list of processors
df_processor = DataFrameParallelProcessor(processors, config.DATA_PROCESSOR_N_JOBS)
df_processor.process(dfAll, columns_to_proc)
# split product_attribute_concat into product_attribute and product_attribute_list
dfAll["product_attribute"] = dfAll["product_attribute_concat"].apply(_split_attr_to_text)
dfAll["product_attribute_list"] = dfAll["product_attribute_concat"].apply(_split_attr_to_list)
if config.TASK == "sample":
print(dfAll[["product_attribute", "product_attribute_list"]])
# query expansion
if config.QUERY_EXPANSION:
list_processor = ListProcessor(processors)
base_stopwords = set(list_processor.process(list(config.STOP_WORDS)))
qe = QueryExpansion(dfAll, ngram=3, stopwords_threshold=0.9, base_stopwords=base_stopwords)
dfAll["search_term_alt"] = qe.build()
if config.TASK == "sample":
print(dfAll[["search_term", "search_term_alt"]])
# save data
logger.info("Save to %s"%config.ALL_DATA_LEMMATIZED)
columns_to_save = [col for col in dfAll.columns if col != "product_attribute_concat"]
pkl_utils._save(config.ALL_DATA_LEMMATIZED, dfAll[columns_to_save])
## auto correcting query
if config.AUTO_CORRECTING_QUERY:
logger.info("Run AutoSpellingChecker at search_term")
checker = AutoSpellingChecker(dfAll, exclude_stopwords=False, min_len=4)
dfAll["search_term_auto_corrected"] = list(dfAll["search_term"].apply(checker.correct))
columns_to_proc += ["search_term_auto_corrected"]
if config.TASK == "sample":
print(dfAll[["search_term", "search_term_auto_corrected"]])
# save query_correction_map and spelling checker
fname = "%s/auto_spelling_checker_query_correction_map_%s.log"%(config.LOG_DIR, now)
checker.save_query_correction_map(fname)
# save data
logger.info("Save to %s"%config.ALL_DATA_LEMMATIZED)
columns_to_save = [col for col in dfAll.columns if col != "product_attribute_concat"]
pkl_utils._save(config.ALL_DATA_LEMMATIZED, dfAll[columns_to_save])
## clean using stemmers
df_processor = DataFrameParallelProcessor(stemmers, config.DATA_PROCESSOR_N_JOBS)
df_processor.process(dfAll, columns_to_proc)
# split product_attribute_concat into product_attribute and product_attribute_list
dfAll["product_attribute"] = dfAll["product_attribute_concat"].apply(_split_attr_to_text)
dfAll["product_attribute_list"] = dfAll["product_attribute_concat"].apply(_split_attr_to_list)
# query expansion
if config.QUERY_EXPANSION:
list_processor = ListProcessor(stemmers)
base_stopwords = set(list_processor.process(list(config.STOP_WORDS)))
qe = QueryExpansion(dfAll, ngram=3, stopwords_threshold=0.9, base_stopwords=base_stopwords)
dfAll["search_term_alt"] = qe.build()
if config.TASK == "sample":
print(dfAll[["search_term", "search_term_alt"]])
# save data
logger.info("Save to %s"%config.ALL_DATA_LEMMATIZED_STEMMED)
columns_to_save = [col for col in dfAll.columns if col != "product_attribute_concat"]
pkl_utils._save(config.ALL_DATA_LEMMATIZED_STEMMED, dfAll[columns_to_save])
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