AttributeError：“numpy.ndarray”对象没有属性“lower”

Posted 2023-03-12

【中文标题】AttributeError：“numpy.ndarray”对象没有属性“lower”

【英文标题】：AttributeError: 'numpy.ndarray' object has no attribute 'lower'

【发布时间】：2020-10-06 01:46:03

【问题描述】：

我正在尝试使用 SVM 进行预测，但收到错误消息

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

在执行我的代码的text_clf.fit(X_train,y_train) 行时。如何解决这个问题并使用 SVM 获得我的预测正确的概率？

我正在根据剩余列的值来预测输入文件的第一列（金色）。我的输入文件dataExtended.txt在表单下：

   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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这是我完整的可重现代码：

# 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)

回溯导致错误：

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())

【问题讨论】：

大部分代码与问题无关（因此混乱无益），包括一大堆print 语句。但是你到底为什么要在 numeric 数据上应用 TF-IDF？

如果您发布导致错误的完整回溯可能会有所帮助（Traceback (most recent call last): 之后的所有内容，因为这有助于定位错误的来源）。请（几乎）在从 Python 代码中发布错误时始终这样做，尤其是在涉及非平凡库时。

我只是想获得每个预测正确的概率，而 TF-IDF 似乎是使用 SVM 时这样做的唯一方法。没有错误\

@desertnaut 是对的：Tfidfvectorizer 需要字符串（最好是 - 单词），因此它抱怨它不能小写您的值，因为您的值是数字。

我可以通过使用您的输入文件data = pd.read_csv(filename); X, Y = data.values[:, 1:], data.values[:, 0]; tfidf = TfidfVectorizer(); tfidf.fit(X, Y) 更简单地重现问题。我不太确定您希望这样做，因为X 中的列不包含字符串，而the docs 在这种情况下读取输入（在这种情况下为X）应该是字符串的可迭代，但是您传递给它的是任意 numpy 数组。

【参考方案1】：

您不能对数字数据使用 TF-IDF 相关的方法；该方法专门用于 text 数据，因此它使用诸如 .tolower() 之类的方法，这些方法默认适用于字符串，因此会出现错误。这在documentation 中已经很明显了：

fit(self, raw_documents, y=None)

从训练集中学习词汇和 idf。

参数

raw_documents：可迭代

产生 str、unicode 或文件对象的可迭代对象。

恐怕你的理由，正如 cmets 中所解释的那样：

我只是想获得每个预测正确的概率，而 TF-IDF 似乎是使用 SVM 时这样做的唯一方法

非常弱。对于初学者来说，没有“每个预测正确的概率”之类的东西——我认为你的意思是概率预测，这与硬类预测相反（参见Predict classes or class probabilities?)

为了达到您的实际要求：与您在此处使用的 LinearSVC 相比，SVC 确实提供了 predict_proba 方法，它应该可以完成这项工作（请参阅 docs 和其中的说明）。请注意，LinearSVC 不是实际上是一个 SVM - 有关详细信息，请参阅Under what parameters are SVC and LinearSVC in scikit-learn equivalent? 中的答案。

简而言之，忘记 TF-IDF，改用 SVC 而不是 LinearSVC。