设计朴素贝叶斯分类器时的AttributeError

您的代码试图做的是基于名称特性构建一个非常简单的分类器。根据其名称,项目将被分类为 'fruit' 或作为 'veggie' . 训练集包含几个名称及其各自的类。

您遇到的错误是由于训练集和测试集的格式错误造成的。培训集是 功能集 (每个培训示例一个功能集),并应具有以下形式的结构:

training_set = [featureset1, featureset2, ...]

每个功能集都是 一对 (features, class) 哪里 features 是一本字典

{'f1': value1, 'f2': value2, ...}

和 class 是有价值的。例如,在分类器中 'Apple' 是:

({'Apple': True,
  'Banana': False,
  'Broccoli': False,
  'Cabbage': False,
  'Carrot': False,
  'Cherry': False,
  'Cucumber': False,
  'Eggplant': False,
  'Grape': False,
  'Guava': False,
  'Lemon': False,
  'Mangos': False,
  'Onion': False,
  'Orange': False,
  'Pea': False,
  'Potato': False,
  'Spinach': False,
  'Strawberry': False,
  'Tomato': False,
  'Watermelon': False},
 'fruit')

以下是更正的代码:

from nltk.classify import NaiveBayesClassifier, accuracy

dataFruits = ['Apple', 'Banana', 'Cherry', 'Grape', 'Guava', 
              'Lemon', 'Mangos', 'Orange', 'Strawberry', 'Watermelon']

dataVeggies = ['Potato', 'Spinach', 'Carrot', 'Onion', 'Cabbage', 
               'Broccoli', 'Tomato', 'Pea', 'Cucumber', 'Eggplant']

def create_features(word, featureNames):
    my_dict = dict([(w, False) for w in featureNames])    
    my_dict[word] = True
    return my_dict

def test(word):
    lst = create_features(word, allFeatures)
    prob_dist = classifier.prob_classify(lst)
    print('{}'.format(word))
    print('Fruit probability: {:.2f}\tVeggie probability: {:.2f}'.format( prob_dist.prob('fruit'), prob_dist.prob('veggie')))
    return prob_dist

allFeatures = dataFruits + dataVeggies
class1= [(create_features(item, allFeatures), 'fruit') for item in dataFruits]

class2 = [(create_features(item, allFeatures), 'veggie') for item in dataVeggies]

train_set = class1[:] + class2
test_set = [(create_features(item, allFeatures), 'fruit') for item in ['Apple','Banana']]

# Train
classifier = NaiveBayesClassifier.train(train_set)


# Predict
test('Strawberry')
test('Strawby')

# Accuracy on test set
print('Accuracy on test set: {:.2f}'.format(accuracy(classifier, test_set)))  

一个稍微好一点的分类器,也许这就是您所想的(按照 http://www.nltk.org/book/ch06.html (文件分类)。在这里,分类器只是预测篮子中是否包含更多的水果或蔬菜。基于此,您可以构造更复杂的分类器(具有更好的特征和更多的训练数据)。

from nltk.classify import NaiveBayesClassifier, accuracy

dataFruits = ['Apple', 'Banana', 'Cherry', 'Grape', 'Guava', 
              'Lemon', 'Mangos', 'Orange', 'Strawberry', 'Watermelon']

dataVeggies = ['Potato', 'Spinach', 'Carrot', 'Onion', 'Cabbage', 
               'Broccoli', 'Tomato', 'Pea', 'Cucumber', 'Eggplant']


def basket_features(basket): 
    basket_items = set(basket) 
    features = {}
    for item in allFeatures:
        features['contains({})'.format(item)] = (item in basket_items)
    return features

def test(basket):
    lst = basket_features(basket)
    prob_dist = classifier.prob_classify(lst)
    print('Basket: {}'.format(basket))
    print('Fruit probability: {:.2f}\tVeggie probability: {:.2f}'.format(prob_dist.prob('fruit'), prob_dist.prob('veggie')))
    return prob_dist

allFeatures = dataFruits + dataVeggies
class1= [(basket_features([item]), 'fruit') for item in dataFruits]

class2 = [(basket_features([item]), 'veggie') for item in dataVeggies]

train_set = class1[:] + class2

# Train
classifier = NaiveBayesClassifier.train(train_set)


# Predict
test(['Apple', 'Banana', 'Cherry', 'Carrot', 'Eggplant', 'Cabbage','Pea'])
test(['Apple', 'Banana',  'Mangos', 'Carrot', 'Eggplant', 'Cabbage','Pea', 'Cucumber'])
test(['Apple', 'Banana'])
test(['Apple', 'Banana', 'Grape'])

classifier.show_most_informative_features(5)