我正在使用 Scikt-Learn 包从语料库中提取特征。我的代码如下：
#! /usr/bin/python -tt
from __future__ import division
import re
import random
import numpy as np
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from nltk.cluster.util import cosine_distance
from operator import itemgetter
def preprocess(fnin, fnout):
fin = open(fnin, 'rb')
fout = open(fnout, 'wb')
buf = []
id = ""
category = ""
for line in fin:
line = line.strip()
if line.find("-- Document Separator --") > -1:
if len(buf) > 0:
# write out body,
body = re.sub("\s+", " ", " ".join(buf))
fout.write("%s\t%s\t%s\n" % (id, category, body))
# process next header and init buf
id, category, rest = map(lambda x: x.strip(), line.split(": "))
buf = []
else:
# process body
buf.append(line)
fin.close()
fout.close()
def train(fnin):
docs = []
cats = []
fin = open(fnin, 'rb')
for line in fin:
id, category, body = line.strip().split("\t")
docs.append(body)
cats.append(category)
fin.close()
v=CountVectorizer(min_df=1,stop_words="english")
pipeline = Pipeline([
("vect", v),
("tfidf", TfidfTransformer(use_idf=False))])
tdMatrix = pipeline.fit_transform(docs, cats)
return tdMatrix, cats
def main():
preprocess("corpus.txt", "sccpp.txt")
tdMatrix, cats = train("sccpp.txt")
if __name__ == "__main__":
main()
我的语料库是（简要形式）：corpus.txt
0: sugar: -- Document Separator -- reut2-021.sgm
British Sugar Plc was forced to shut its
Ipswich sugar factory on Sunday afternoon due to an acute
shortage of beet supplies, a spokesman said, responding to a
Reuter inquiry
Beet supplies have dried up at Ipswich due to a combination
of very wet weather, which has prevented most farmers in the
factory's catchment area from harvesting, and last week's
hurricane which blocked roads.
The Ipswich factory will remain closed until roads are
cleared and supplies of beet build up again.
This is the first time in many years that a factory has
been closed in mid-campaign, the spokesman added.
Other factories are continuing to process beet normally,
but harvesting remains very difficult in most areas.
Ipswich is one of 13 sugar factories operated by British
Sugar. It processes in excess of 500,000 tonnes of beet a year
out of an annual beet crop of around eight mln tonnes.
Despite the closure of Ipswich and the severe harvesting
problems in other factory areas, British Sugar is maintaining
its estimate of sugar production this campaign at around
错误信息是：
v=CountVectorizer(min_df=1,stop_words="english")
TypeError: __init__() got an unexpected keyword argument 'min_df'
我在 Linux Mint 中使用 python2.7.4。谁能建议我如何解决这个问题？先感谢您。