TFIDF向量器出现错误

Question

我正在尝试使用TFIDF和SVM对某些文件进行文本分类。这些特征一次选择3个单词。我的数据文件已经是这样的格式: angel eyes，每个文件都有自己的格式。没有停止的单词，也不能做词干或词干。我希望功能被选择为:天使的眼睛有…我写的代码如下：

import os
import sys
import numpy
from sklearn.svm import LinearSVC
from sklearn.metrics import confusion_matrix
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn import metrics
from sklearn.datasets import load_files
from sklearn.cross_validation import train_test_split

dt=load_files('C:/test4',load_content=True)
d= len(dt)
print dt.target_names
X, y = dt.data, dt.target
print y
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
print y_train
vectorizer = CountVectorizer()
z= vectorizer.fit_transform(X_train)
tfidf_vect= TfidfVectorizer(lowercase= True, tokenizer=',', max_df=1.0, min_df=1, max_features=None, norm=u'l2', use_idf=True, smooth_idf=True, sublinear_tf=False)


X_train_tfidf = tfidf_vect.fit_transform(z)

print tfidf_vect.get_feature_names()
svm_classifier = LinearSVC().fit(X_train_tfidf, y_train)

不幸的是，我在“X_train_tfidf = tfidf_vect.fit_transform(z)”：AttributeError: lower not found得到了一个错误。

如果我修改代码来做

tfidf_vect= TfidfVectorizer( tokenizer=',', use_idf=True, smooth_idf=True, sublinear_tf=False)
print "okay2"
#X_train_tfidf = tfidf_transformer.fit_transform(z)
X_train_tfidf = tfidf_vect.fit_transform(X_train)
print X_train_tfidf.getfeature_names()

我收到错误: TypeError：'str‘对象不可调用可以请某人告诉我哪里出错了吗

Answer 1

标记器参数的输入是一个可调用的。尝试定义一个函数来适当地标记化您的数据。如果它是逗号分隔的，则：

def tokens(x):
return x.split(',')

应该行得通。

from sklearn.feature_extraction.text import TfidfVectorizer
tfidf_vect= TfidfVectorizer( tokenizer=tokens ,use_idf=True, smooth_idf=True, sublinear_tf=False)

创建由,分隔的随机字符串

 a=['cat on the,angel eyes has,blue red angel,one two blue,blue whales eat,hot tin roof']

tfidf_vect.fit_transform(a)
tfidf_vect.get_feature_names()

返回

Out[73]:

[u'angel eyes has',
 u'blue red angel',
 u'blue whales eat',
 u'cat on the',
 u'hot tin roof',
 u'one two blue']