Scikit Learn：将朴素贝叶斯模型预测纳入逻辑回归？

Question

我有关于各种客户属性（自我描述和年龄）的数据，以及这些客户是否会购买特定产品的二元结果

  {"would_buy": "No", 
  "self_description": "I'm a college student studying biology", 
  "Age": 19}, 

我想在self-description 上使用MultinomialNB 来预测would_buy，然后将这些预测合并到would_buy 上的逻辑回归模型中，该模型也将age 作为协变量。

到目前为止的文本模型代码（我是 SciKit 的新手！），带有一个简化的数据集。

from sklearn.naive_bayes import MultinomialNB
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

#Customer data that includes whether a customer would buy an item (what I'm interested), their self-description, and their age. 
data = [
  {"would_buy": "No", "self_description": "I'm a college student studying biology", "Age": 19}, 
  {"would_buy": "Yes", "self_description": "I'm a blue-collar worker", "Age": 20},
  {"would_buy": "No", "self_description": "I'm a Stack Overflow denzien", "Age": 56}, 
  {"would_buy": "No", "self_description": "I'm a college student studying economics", "Age": 20}, 
  {"would_buy": "Yes", "self_description": "I'm a UPS worker", "Age": 35}, 
  {"would_buy": "No", "self_description": "I'm a Stack Overflow denzien", "Age": 56}
  ]

def naive_bayes_model(customer_data):
  self_descriptions = [customer['self_description'] for customer in customer_data]
  decisions = [customer['would_buy'] for customer in customer_data]

  vectorizer = TfidfVectorizer(stop_words='english', ngram_range=(1,2))
  X = vectorizer.fit_transform(self_descriptions, decisions)
  naive_bayes = MultinomialNB(alpha=0.01)
  naive_bayes.fit(X, decisions)
  train(naive_bayes, X, decisions)

def train(classifier, X, y):
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=22)
    classifier.fit(X_train, y_train)

    print(classification_report(classifier.predict(X_test), y_test))


def main():
  naive_bayes_model(data)



main()

Answer 1

简短的回答是在经过训练的naive_bayes 上使用predict_proba 或predict_log_proba 方法来为逻辑回归模型创建输入。这些可以与 Age 值连接，为您的 LogisticRegression 模型创建训练和测试集。

但是，我确实想指出，您编写的代码在训练后无法访问您的 naive_bayes 模型。所以你肯定需要重构你的代码。

抛开这个问题不谈，这就是我将naive_bayes 的输出合并到 LogisticRegression 中的方式：

descriptions = np.array([customer['self_description'] for customer in data])
decisions = np.array([customer['would_buy'] for customer in data])
ages = np.array([customer['Age'] for customer in data])

vectorizer = TfidfVectorizer(stop_words='english', ngram_range=(1,2))
desc_vec = vectorizer.fit_transform(descriptions, decisions)
naive_bayes = MultinomialNB(alpha=0.01)
desc_train, desc_test, age_train, age_test, dec_train, dec_test = train_test_split(desc_vec, ages, decisions, test_size=0.25, random_state=22)

naive_bayes.fit(desc_train, dec_train)
nb_train_preds = naive_bayes.predict_proba(desc_train)
lr = LogisticRegression()
lr_X_train = np.hstack((nb_tarin_preds, age_train.reshape(-1, 1)))
lr.fit(lr_X_train, dec_train)

lr_X_test = np.hstack((naive_bayes.predict_proba(desc_test), age_test.reshape(-1, 1)))
lr.score(lr_X_test, dec_test)