易百教程人工智能python修正-人工智能NLTK性别发现器

在这个问题陈述中，将通过提供名字来训练分类器以找到性别(男性或女性)。 我们需要使用启发式构造特征向量并训练分类器。这里使用scikit-learn软件包中的标签数据。 以下是构建性别查找器的Python代码 -

导入必要的软件包 -

import random

from nltk import NaiveBayesClassifier
from nltk.classify import accuracy as nltk_accuracy
from nltk.corpus import names

现在需要从输入字中提取最后的N个字母。 这些字母将作为功能 -

def extract_features(word, N = 2):
   last_n_letters = word[-N:]
   return {'feature': last_n_letters.lower()}

if __name__=='__main__':

 

使用NLTK中提供的标签名称(男性和女性)创建培训数据 -

male_list = [(name, 'male') for name in names.words('male.txt')]
female_list = [(name, 'female') for name in names.words('female.txt')]
data = (male_list + female_list)

random.seed(5)
random.shuffle(data)

 

现在，测试数据将被创建如下 -

namesInput = ['Rajesh', 'Gaurav', 'Swati', 'Shubha']

 

使用以下代码定义用于列车和测试的样本数 -

train_sample = int(0.8 * len(data))

 

现在，需要迭代不同的长度，以便可以比较精度 -

for i in range(1, 6):
   print('\nNumber of end letters:', i)
   features = [(extract_features(n, i), gender) for (n, gender) in data]
   train_data, test_data = features[:train_sample],
features[train_sample:]
   classifier = NaiveBayesClassifier.train(train_data)

 

分类器的准确度可以计算如下 -

accuracy_classifier = round(100 * nltk_accuracy(classifier, test_data), 2)
   print('Accuracy = ' + str(accuracy_classifier) + '%')

 

现在，可以预测输出结果 -

for name in namesInput:
   print(name, '==>', classifier.classify(extract_features(name, i))

 

上述程序将生成以下输出 -

Number of end letters: 1
Accuracy = 74.7%
Rajesh -> female
Gaurav -> male
Swati -> female
Shubha -> female

Number of end letters: 2
Accuracy = 78.79%
Rajesh -> male
Gaurav -> male
Swati -> female
Shubha -> female

Number of end letters: 3
Accuracy = 77.22%
Rajesh -> male
Gaurav -> female
Swati -> female
Shubha -> female

Number of end letters: 4
Accuracy = 69.98%
Rajesh -> female
Gaurav -> female
Swati -> female
Shubha -> female

Number of end letters: 5
Accuracy = 64.63%
Rajesh -> female
Gaurav -> female
Swati -> female
Shubha -> female

在上面的输出中可以看到，结束字母的最大数量的准确性是两个，并且随着结束字母数量的增加而减少。

完整代码

import random

from nltk import NaiveBayesClassifier
from nltk.classify import accuracy as nltk_accuracy
from nltk.corpus import names

def extract_features(word, N=2):
    last_n_letters = word[-N:]
    return {'feature': last_n_letters.lower()}

if __name__ == '__main__':

    male_list = [(name, 'male') for name in names.words('male.txt')]
    female_list = [(name, 'female') for name in names.words('female.txt')]
    data = (male_list + female_list)

    random.seed(5)
    random.shuffle(data)
    namesInput = ['Rajesh', 'Gaurav', 'Swati', 'Shubha']
    train_sample = int(0.8 * len(data))

    for i in range(1, 6):
        print('\nNumber of end letters:', i)
        features = [(extract_features(n, i), gender) for (n, gender) in data]
        train_data, test_data = features[:train_sample], features[train_sample:]

        classifier = NaiveBayesClassifier.train(train_data)

        accuracy_classifier = round(100 * nltk_accuracy(classifier, test_data), 2)
        print('Accuracy = ' + str(accuracy_classifier) + '%')

        for name in namesInput:
            print(name, '==>', classifier.classify(extract_features(name, i)))