NameError：名称“ fit_classifier”未定义

Question

我正在尝试使文本分类

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))

但是我有以下错误，我无法理解。

追溯（最近一次通话）：

在fit_classifier（X_train，y_train）中的文件“ classificacao.py”，第37行

NameError：名称“ fit_classifier”未定义

但是默认情况下并不总是适合吗？

Answer 1

您正在调用一个不存在的函数：

fit_classifier（X_train，y_train）

适合您的分类器

classifier.fit（X_train，y_train）

代替。尝试预测测试数据时，您会得到相同的错误。你需要改变

预测=预测（X_test）

至

预测= classifier.predict（X_test）

您的Confusionmatrix应该得到标签，而不是测试数据：

打印（confusion_matrix（y_test，预测，标签=标签））