问题
I am trying to predict using SVM but I receive the error
AttributeError: 'numpy.ndarray' object has no attribute 'lower'
when executing line text_clf.fit(X_train,y_train) of my code. How to fix this and get the probability that my prediction is correct using SVM?
I am predicting the first column (gold) of my input file based on the values of the remaining columns. My input file dataExtended.txtis under the form:
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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Here is my full reproducible code:
# 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)
Traceback leading to error:
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())
回答1:
You cannot use TF-IDF-related methods for numeric data; the method is exclusively for use with text data, hence it uses methods such as .tolower(), which are by default applicable to strings, hence the error. This is already apparent from the documentation:
fit(self, raw_documents, y=None)Learn vocabulary and idf from training set.
Parameters
raw_documents: iterable
An iterable which yields either str, unicode or file objects.
I am afraid that your rationale, as explained in the comments:
I'm just trying to get the probability that each prediction is correct and TF-IDF seems to be the only way to do so when using SVM
is extremely weak. For starters, there is no such thing as "the probability that each prediction is correct" - I take it that you mean probabilistic predictions, in contrast to hard class predictions (see Predict classes or class probabilities?)
To get to the point of your actual requirement: in contrast to LinearSVC, which you are using here, SVC does indeed provide a predict_proba method, which should do the job (see the docs and the instructions therein). Notice that LinearSVC is not actually an SVM - see answer in Under what parameters are SVC and LinearSVC in scikit-learn equivalent? for details.
In short, forget about TF-IDF and switch to SVC instead of LinearSVC.
来源：https://stackoverflow.com/questions/62408697/attributeerror-numpy-ndarray-object-has-no-attribute-lower