BrandCultivation/models/rank/gbdt_lr.py

#!/usr/bin/env python3
# -*- coding:utf-8 -*-
import numpy as np
from models.rank.data import DataLoader
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.linear_model import LogisticRegression

class Trainer:
    def __init__(self, path):
        self._load_data(path)
        
    def _load_data(self, path):
        dataloader = DataLoader(path)
        self._train_dataset, self._val_dataset, self._test_dataset = dataloader.split_dataset()
        
    def train_gbdt(self):
        self._gbdt_model = GradientBoostingClassifier(
            n_estimators=100,
            learning_rate=0.1,
            max_depth=3,
            random_state=42,
        )
        
        # 模型训练
        self._gbdt_model.fit(self._train_dataset["data"], self._train_dataset["label"])
        
    def train_lr(self):
        gbdt_train_prdes = self._gbdt_model.predict_proba(self._train_dataset["data"])[:, 1] # 获取正类概率
        gbdt_val_prdes = self._gbdt_model.predict_proba(self._val_dataset["data"])[:, 1]
        
        # 将GBDT的预测结果作为额外特征来训练LR
        lr_train_data = np.column_stack([self._train_dataset["data"], gbdt_train_prdes])
        lr_val_data = np.column_stack([self._val_dataset["data"], gbdt_val_prdes])
        
        # 训练LR模型
        self.lr_model = LogisticRegression(solver='saga', max_iter=1000)
        self.lr_model.fit(lr_train_data, self._train_dataset["label"])
        
if __name__ == "__main__":
    gbdt_data_path = "./models/rank/data/gbdt_data.csv"
    trainer = Trainer(gbdt_data_path)
    trainer.train_gbdt()
    trainer.train_lr()