NameError: name 'fit_classifier' is not defined

Question

我正在尝试做一个文本分类器

import pandas as pd
import pandas
from sklearn import svm
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.multiclass import OneVsOneClassifier
from sklearn.svm import SVC
from sklearn import cross_validation
from sklearn.metrics import confusion_matrix

dataset = pd.read_csv('data.csv', encoding = 'utf-8')
data = dataset['text']
labels = dataset['label']

X_train, X_test, y_train, y_test = train_test_split (data, labels, test_size = 0.2, random_state = 0)

count_vector = CountVectorizer()
tfidf = TfidfTransformer()

classifier = OneVsOneClassifier(SVC(kernel = 'linear', random_state = 84))

train_counts = count_vector.fit_transform(X_train)
train_tfidf = tfidf.fit_transform(train_counts)
classifier.fit(train_tfidf, y_train)

test_counts = count_vector.transform(X_test)
test_tfidf = tfidf.transform(test_counts)
classifier.predict(test_tfidf)

fit_classifier(X_train, y_train)
predicted = predict(X_test)

print("confusion matrix")
print(confusion_matrix(X_test, predicted, labels = labels))

print("cross validation")
test_counts = count_vector.fit_transform(data)
test_tfidf = tfidf.fit_transform(test_counts)

scores = cross_validation.cross_val_score(classifier, test_tfidf, labels, cv = 10)
print(scores)
print("Accuracy: {} +/- {}".format(scores.mean(), scores.std() * 2))

但是我有如下错误，无法理解。

Traceback (most recent call last):

File "classificacao.py", line 37, in fit_classifier(X_train, y_train)

NameError: name 'fit_classifier' is not defined

但是 fit 并不总是默认定义的？

Answer 1

您正在调用一个不存在的函数：

fit_classifier(X_train, y_train)

为了适合您的分类器，您将使用

classifier.fit(X_train, y_train)

相反。 尝试预测测试数据时，您会遇到同样的错误。 您需要更改

predicted = predict(X_test)

到

predicted = classifier.predict(X_test)

你的 Confusionmatrix 应该得到你的标签，而不是你的测试数据：

print(confusion_matrix(y_test, predicted, labels = labels))