AttributeError: объект 'numpy .ndarray' не имеет атрибута 'lower' - PullRequest
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AttributeError: объект 'numpy .ndarray' не имеет атрибута 'lower'

1 голос
/ 16 июня 2020

Я пытаюсь предсказать, используя SVM, но получаю ошибку 

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

при выполнении строки text_clf.fit(X_train,y_train) моего кода. Как исправить это и получить вероятность того, что мой прогноз верен с помощью SVM?

Я прогнозирую первый столбец (золотой) моего входного файла на основе значений остальных столбцов. Мой входной файл dataExtended.txt находится в форме: 

   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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Вот мой полный воспроизводимый код: 

# 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)

Трассировка, приводящая к ошибке: 

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())

1 Ответ

2 голосов
/ 16 июня 2020

Вы не можете использовать методы, связанные с TF-IDF, для данных numeri c; метод предназначен исключительно для использования с данными text , поэтому он использует такие методы, как .tolower(), которые по умолчанию применимы к строкам, отсюда и ошибка. Это уже очевидно из документации :

fit (self, raw_documents, y = None)

Изучите словарь и idf из обучающего набора.

Параметры

raw_documents: iterable

Итерируемый объект, который дает либо str, unicode, либо объекты файла .

Боюсь, что ваше обоснование, как объяснено в комментариях:

Я просто пытаюсь получить вероятность того, что каждый прогноз верен и TF-IDF кажется, это единственный способ сделать это, когда использование SVM

чрезвычайно слабое. Во-первых, не существует такой вещи, как « вероятность того, что каждое предсказание является правильным » - я так понимаю, вы имеете в виду вероятности c предсказания , в отличие от предсказаний жесткого класса ( см. Предсказать классы или вероятности классов? )

Чтобы понять ваши фактические требования: в отличие от LinearSVC, которое вы здесь используете, SVC действительно обеспечивает predict_proba метод, который должен выполнить эту работу (см. docs и инструкции в нем). Обратите внимание, что LinearSV C на самом деле является не SVM - см. Ответ в По каким параметрам SV C и LinearSV C в эквиваленте scikit-learn? для подробностей.

Короче забудьте про TF-IDF и переключитесь на SV C вместо LinearSV C.

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