Python 属性错误：'；numpy.ndarray和#x27；对象没有属性'；下'；

我试图使用SVM进行预测，但我收到了错误

AttributeError: 'numpy.ndarray' object has no attribute 'lower'

执行我的代码的行

text\u clf.fit（X\u train，y\u train）

时。如何解决这个问题，并使用SVM获得我的预测正确的概率

我根据其余列的值预测输入文件的第一列（gold）。我的输入文件

dataExtended.txt

的格式如下：

   gold,T-x-T,T-x-N,T-x-U,T-x-NT,T-x-UT,T-x-UN,T-x-UNT,N-x-T,N-x-N,N-x-U,N-x-NT,N-x-UT,N-x-UN,N-x-UNT,U-x-T,U-x-N,U-x-U,U-x-NT,U-x-UT,U-x-UN,U-x-UNT,NT-x-T,NT-x-N,NT-x-U,NT-x-NT,NT-x-UT,NT-x-UN,NT-x-UNT,UT-x-T,UT-x-N,UT-x-U,UT-x-NT,UT-x-UT,UT-x-UN,UT-x-UNT,UN-x-T,UN-x-N,UN-x-U,UN-x-NT,UN-x-UT,UN-x-UN,UN-x-UNT,UNT-x-T,UNT-x-N,UNT-x-U,UNT-x-NT,UNT-x-UT,UNT-x-UN,UNT-x-UNT,T-T-x,T-N-x,T-U-x,T-NT-x,T-UT-x,T-UN-x,T-UNT-x,N-T-x,N-N-x,N-U-x,N-NT-x,N-UT-x,N-UN-x,N-UNT-x,U-T-x,U-N-x,U-U-x,U-NT-x,U-UT-x,U-UN-x,U-UNT-x,NT-T-x,NT-N-x,NT-U-x,NT-NT-x,NT-UT-x,NT-UN-x,NT-UNT-x,UT-T-x,UT-N-x,UT-U-x,UT-NT-x,UT-UT-x,UT-UN-x,UT-UNT-x,UN-T-x,UN-N-x,UN-U-x,UN-NT-x,UN-UT-x,UN-UN-x,UN-UNT-x,UNT-T-x,UNT-N-x,UNT-U-x,UNT-NT-x,UNT-UT-x,UNT-UN-x,UNT-UNT-x,x-T-T,x-T-N,x-T-U,x-T-NT,x-T-UT,x-T-UN,x-T-UNT,x-N-T,x-N-N,x-N-U,x-N-NT,x-N-UT,x-N-UN,x-N-UNT,x-U-T,x-U-N,x-U-U,x-U-NT,x-U-UT,x-U-UN,x-U-UNT,x-NT-T,x-NT-N,x-NT-U,x-NT-NT,x-NT-UT,x-NT-UN,x-NT-UNT,x-UT-T,x-UT-N,x-UT-U,x-UT-NT,x-UT-UT,x-UT-UN,x-UT-UNT,x-UN-T,x-UN-N,x-UN-U,x-UN-NT,x-UN-UT,x-UN-UN,x-UN-UNT,x-UNT-T,x-UNT-N,x-UNT-U,x-UNT-NT,x-UNT-UT,x-UNT-UN,x-UNT-UNT,callersAtLeast1T,CalleesAtLeast1T,callersAllT,calleesAllT,CallersAtLeast1N,CalleesAtLeast1N,CallersAllN,CalleesAllN,childrenAtLeast1T,parentsAtLeast1T,childrenAtLeast1N,parentsAtLeast1N,childrenAllT,parentsAllT,childrenAllN,ParentsAllN,ParametersatLeast1T,FieldMethodsAtLeast1T,ReturnTypeAtLeast1T,ParametersAtLeast1N,FieldMethodsAtLeast1N,ReturnTypeN,ParametersAllT,FieldMethodsAllT,ParametersAllN,FieldMethodsAllN,ClassGoldN,ClassGoldT,Inner,Leaf,Root,Isolated,EmptyCallers,EmptyCallees,EmptyCallersCallers,EmptyCalleesCallees,Program,Requirement,MethodID
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以下是我的完整可复制代码：

# Make Predictions with Naive Bayes On The Iris Dataset
from sklearn.cross_validation import train_test_split 
from sklearn import metrics
import pandas as pd 
import numpy as np
import seaborn as sns; sns.set()
from sklearn.metrics import confusion_matrix 
from sklearn.metrics import accuracy_score 
from sklearn.metrics import classification_report 
import seaborn as sns
from sklearn import svm
from sklearn.svm import LinearSVC
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.pipeline import Pipeline



data = pd.read_csv( 'dataExtended.txt', sep= ',') 
row_count, column_count = data.shape

    # Printing the dataswet shape 
print ("Dataset Length: ", len(data)) 
print ("Dataset Shape: ", data.shape) 
print("Number of columns ", column_count)

    # Printing the dataset obseravtions 
print ("Dataset: ",data.head()) 
data['gold'] = data['gold'].astype('category').cat.codes
data['Program'] = data['Program'].astype('category').cat.codes
    # Building Phase Separating the target variable 
X = data.values[:, 1:column_count] 
Y = data.values[:, 0] 

    # Splitting the dataset into train and test 
X_train, X_test, y_train, y_test = train_test_split( 
X, Y, test_size = 0.3, random_state = 100) 
    #Create a svm Classifier
svclassifier = svm.LinearSVC()

print('Before fitting')
svclassifier.fit(X_train, y_train)
predicted = svclassifier.predict(X_test)


text_clf = Pipeline([('tfidf',TfidfVectorizer()),('clf',LinearSVC())])
text_clf.fit(X_train,y_train)

导致错误的回溯：

Traceback (most recent call last):

  File "<ipython-input-9-8e85a0a9f81c>", line 1, in <module>
    runfile('C:/Users/mouna/ownCloud/Mouna Hammoudi/dumps/Python/Paper4SVM.py', wdir='C:/Users/mouna/ownCloud/Mouna Hammoudi/dumps/Python')

  File "C:\Users\mouna\Anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 668, in runfile
    execfile(filename, namespace)

  File "C:\Users\mouna\Anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 108, in execfile
    exec(compile(f.read(), filename, 'exec'), namespace)

  File "C:/Users/mouna/ownCloud/Mouna Hammoudi/dumps/Python/Paper4SVM.py", line 53, in <module>
    text_clf.fit(X_train,y_train)

  File "C:\Users\mouna\Anaconda3\lib\site-packages\sklearn\pipeline.py", line 248, in fit
    Xt, fit_params = self._fit(X, y, **fit_params)

  File "C:\Users\mouna\Anaconda3\lib\site-packages\sklearn\pipeline.py", line 213, in _fit
    **fit_params_steps[name])

  File "C:\Users\mouna\Anaconda3\lib\site-packages\sklearn\externals\joblib\memory.py", line 362, in __call__
    return self.func(*args, **kwargs)

  File "C:\Users\mouna\Anaconda3\lib\site-packages\sklearn\pipeline.py", line 581, in _fit_transform_one
    res = transformer.fit_transform(X, y, **fit_params)

  File "C:\Users\mouna\Anaconda3\lib\site-packages\sklearn\feature_extraction\text.py", line 1381, in fit_transform
    X = super(TfidfVectorizer, self).fit_transform(raw_documents)

  File "C:\Users\mouna\Anaconda3\lib\site-packages\sklearn\feature_extraction\text.py", line 869, in fit_transform
    self.fixed_vocabulary_)

  File "C:\Users\mouna\Anaconda3\lib\site-packages\sklearn\feature_extraction\text.py", line 792, in _count_vocab
    for feature in analyze(doc):

  File "C:\Users\mouna\Anaconda3\lib\site-packages\sklearn\feature_extraction\text.py", line 266, in <lambda>
    tokenize(preprocess(self.decode(doc))), stop_words)

  File "C:\Users\mouna\Anaconda3\lib\site-packages\sklearn\feature_extraction\text.py", line 232, in <lambda>
    return lambda x: strip_accents(x.lower())

回溯（最近一次呼叫最后一次）：
文件“”，第1行，在
runfile（'C:/Users/mouna/ownCloud/mouna-Hammoudi/dumps/Python/Paper4SVM.py'，wdir='C:/Users/mouna/ownCloud/mouna-Hammoudi/dumps/Python'）
文件“C:\Users\mouna\Anaconda3\lib\site packages\spyder\u kernels\customize\spyderrcustomize.py”，第668行，在runfile中
execfile（文件名、命名空间）
文件“C:\Users\mouna\Anaconda3\lib\site packages\spyder\u kernels\customize\spyderrcustomize.py”，第108行，在execfile中
exec（编译（f.read（），文件名，'exec'），命名空间）
文件“C:/Users/mouna/ownCloud/mouna-Hammoudi/dumps/Python/Paper4SVM.py”，第53行，在
文本匹配（X列、y列）
文件“C:\Users\mouna\Anaconda3\lib\site packages\sklearn\pipeline.py”，第248行，格式为fit
Xt，拟合参数=自拟合（X，y，**拟合参数）
文件“C:\Users\mouna\Anaconda3\lib\site packages\sklearn\pipeline.py”，第213行，以\u-fit格式
**拟合参数步骤[名称]）
文件“C:\Users\mouna\Anaconda3\lib\site packages\sklearn\externals\joblib\memory.py”，第362行，在调用中__
返回self.func（*args，**kwargs）
文件“C:\Users\mouna\Anaconda3\lib\site packages\sklearn\pipeline.py”，第581行，在fit\u transform\u one中
res=变换器。拟合变换（X，y，**拟合参数）
文件“C:\Users\mouna\Anaconda3\lib\site packages\sklearn\feature\u extraction\text.py”，第1381行，在fit\u transform中
X=super（TfidfVectorizer，self）.fit\u转换（原始文档）
文件“C:\Users\mouna\Anaconda3\lib\site packages\sklearn\feature\u extraction\text.py”，第869行，在fit\u transform中
自我修复（词汇）
文件“C:\Users\mouna\Anaconda3\lib\site packages\sklearn\feature\u extraction\text.py”，第792行，在\u count\u vocab中
对于分析中的功能（文档）：
文件“C:\Users\mouna\Anaconda3\lib\site packages\sklearn\feature\u extraction\text.py”，第266行，在
标记化（预处理（自解码（doc））、停止字）
文件“C:\Users\mouna\Anaconda3\lib\site packages\sklearn\feature\u extraction\text.py”，第232行，在
返回lambda x:strip_重音（x.lower（））

您不能对数值数据使用TF IDF相关方法；该方法专门用于文本数据，因此它使用的方法如

.tolower（）

，默认情况下适用于字符串，因此会出现错误。这一点在以下方面已经很明显：

fit

（自我，原始文档，y=None）

从培训集中学习词汇和idf

参数

原始文档：可编辑

生成str、unicode或file对象的iterable

我恐怕你的理由，正如评论中所解释的：

---

我只是想得到每个预测都是正确的概率，而TF-IDF似乎是使用SVM时实现这一点的唯一方法

他非常虚弱。首先，没有“每个预测都是正确的概率”这样的东西——我认为你指的是概率预测，而不是硬类预测（参见）

要了解您的实际需求：与您在此处使用的

LinearSVC

相比，

SVC

确实提供了一种

predict\u proba

方法，该方法应该可以完成这项工作（请参阅和其中的说明）。请注意，LinearSVC不是实际上的SVM-有关详细信息，请参阅中的答案

---

简言之，忘记TF-IDF，切换到SVC而不是LinearSVC。

大部分代码与问题无关（因此没有帮助），包括一大堆

打印

语句。但你到底为什么要在数字数据上应用TF-IDF呢？如果你发布了导致错误的完整回溯（回溯之后的所有内容（最近一次调用）：，这可能会有所帮助，因为这有助于定位错误的来源）。当发布Python代码中的错误时，请（几乎）总是这样做，特别是当它涉及到非平凡的库时。我只是试图获得每个预测都是正确的概率，而TF-IDF似乎是使用SVM时唯一的方法。如果没有错误\@desertnaut是对的：TfidVectorizer需要字符串（最好是-words），因此它抱怨它不能将您的值小写，因为您的值是数字的。我可以通过运行输入文件

data=pd.read\u csv（filename）来更简单地重现问题；十、 Y=data.values[：，1:，data.values[：，0]；tfidf=TfidfVectorizer（）；tfidf.fit（X，Y）

。我不确定您希望它做什么，因为

X

中的列不包含字符串，在本例中读取的输入（

X

在本例中）应该是一个字符串的iterable，但您传递它的是任意numpy数组。