Question

多项式朴素贝叶斯分类器给出了正确的结果，但另外两个 - 高斯NB和二项式NB不是。它给出的错误是：

TypeError：传递了稀疏矩阵，但需要密集数据。使用X.toarray（）转换为密集的numpy数组。

但即使添加该函数（train_set.toarray()），错误仍然是

AttributeError：＆＃39; list＆＃39;对象没有属性＆＃39; toarray＆＃39;

代码是

import pickle
from nltk.corpus import names
import random
import nltk
from sklearn.naive_bayes import MultinomialNB, GaussianNB, BernoulliNB
from sklearn.linear_model import SGDClassifier, LogisticRegression
from sklearn.svm import SVC, LinearSVC, NuSVC
from nltk.classify.scikitlearn import SklearnClassifier
import numpy as np
import scipy as sc

def gender_features(word):
    return {'last_letter': word[-1]}

labeled_names = ([(name, 'male') for name in names.words('male.txt')] + [(name, 'female') for name in names.words('female.txt')])
random.shuffle(labeled_names)

featuresets = [(gender_features(n), gender) for (n, gender) in labeled_names]
train_set, test_set = featuresets[500:], featuresets[:500]
classifier = nltk.NaiveBayesClassifier.train(train_set)

print(nltk.classify.accuracy(classifier, test_set)*100)
classifier.show_most_informative_features(5)

MNB_classifier = SklearnClassifier(MultinomialNB())
MNB_classifier.train(train_set)
print ("MNB classifier accuracy: ", (nltk.classify.accuracy(MNB_classifier, test_set))*100)


G_classifier = SklearnClassifier(GaussianNB())
G_classifier.train(train_set)
print ("Gaussian classifier accuracy: ", (nltk.classify.accuracy(G_classifier, test_set))*100)

B_classifier = SklearnClassifier(BernoulliNB())
B_classifier.train(train_set)
print ("Bernoulli classifier accuracy: ", (nltk.classify.accuracy(B_classifier, test_set))*100)

Answer 1

在培训期间尝试使用时，我遇到了同样的问题：

train_set.todense()

对我有用：

Answer 2

也许你可以做：numpy.array（train_set），使列表密集m

TypeError：传递了稀疏矩阵，但需要密集数据。使用X.toarray（）转换为密集的numpy数组。与NaiveBayes分类器

2 个答案: