""" 针对爬虫类型数据单独训练模型 """ import os import sys import json import optuna from sklearn.linear_model import LogisticRegression sys.path.append(os.getcwd()) import numpy as np import pandas as pd import lightgbm as lgb from sklearn.preprocessing import LabelEncoder from sklearn.metrics import accuracy_score from sklearn.model_selection import train_test_split, StratifiedKFold from sklearn.datasets import load_breast_cancer from sklearn.metrics import roc_auc_score from bayes_opt import BayesianOptimization class LightGBM(object): """ LightGBM model for classification """ def __init__(self, flag, dt): self.label_encoder = LabelEncoder() self.my_c = [ "channel", "out_user_id", "mode", "out_play_cnt", "out_like_cnt", "out_share_cnt", "lop", "duration", "tag1", "tag2", "tag3" ] self.str_columns = ["channel", "mode", "out_user_id", "tag1", "tag2", "tag3"] self.float_columns = [ "out_play_cnt", "out_like_cnt", "out_share_cnt", "lop", "duration" ] self.split_c = 0.7 self.yc = 0.8 self.model = "models/lightgbm_0401_spider.bin" self.flag = flag self.dt = dt def read_data(self): """ Read data from local :return: """ path = "data/train_data/spider_data_240401.json" df = pd.read_json(path) df = df.dropna(subset=['label']) labels = df['label'] features = df.drop("label", axis=1) for key in self.float_columns: features[key] = pd.to_numeric(features[key], errors="coerce") for key in self.str_columns: features[key] = self.label_encoder.fit_transform(features[key]) return features, labels def bays(self): # 创建LightGBM数据集，注意不要在这里指定categorical_feature，因为我们使用的是玩具数据集 x, y = self.read_data() train_size = int(len(x) * 0.9) X_train, X_test = x[:train_size], x[train_size:] Y_train, Y_test = y[:train_size], y[train_size:] train_data = lgb.Dataset(X_train, label=Y_train) def lgbm_eval(num_leaves, learning_rate, feature_fraction, bagging_fraction, bagging_freq, min_child_samples): params = { 'objective': 'binary', 'metric': 'auc', 'verbose': -1, 'num_leaves': int(num_leaves), 'learning_rate': learning_rate, 'feature_fraction': feature_fraction, 'bagging_fraction': bagging_fraction, 'bagging_freq': int(bagging_freq), 'min_child_samples': int(min_child_samples), } cv_result = lgb.cv(params, train_data, nfold=5, seed=42, stratified=True, metrics=['auc'], early_stopping_rounds=10) return max(cv_result['auc-mean']) param_bounds = { 'num_leaves': (20, 40), 'learning_rate': (1e-4, 1e-2), 'feature_fraction': (0.5, 0.8), 'bagging_fraction': (0.5, 0.8), 'bagging_freq': (1, 10), 'min_child_samples': (20, 100), } optimizer = BayesianOptimization(f=lgbm_eval, pbounds=param_bounds, random_state=42) optimizer.maximize(init_points=5, n_iter=25) print("Best Parameters:", optimizer.max['params']) def train_model(self): """ Load dataset :return: """ x, y = self.read_data() train_size = int(len(x) * self.split_c) X_train, X_test = x[:train_size], x[train_size:] Y_train, Y_test = y[:train_size], y[train_size:] train_data = lgb.Dataset( X_train, label=Y_train, categorical_feature=["channel", "mode", "out_user_id", "tag1", "tag2", "tag3"], ) test_data = lgb.Dataset(X_test, label=Y_test, reference=train_data) params = { 'num_leaves': 31, 'learning_rate': 0.00020616904432655601, 'feature_fraction': 0.6508847259863764, 'bagging_fraction': 0.7536774652478249, 'bagging_freq': 6, 'min_child_samples': 99, 'num_threads': 16 } # 训练模型 num_round = 100 print("开始训练......") bst = lgb.train(params, train_data, num_round, valid_sets=[test_data]) bst.save_model(self.model) print("模型训练完成✅") def evaluate_model(self): """ 评估模型性能 :return: """ fw = open("summary_tag_03{}_spider.txt".format(self.dt), "a+", encoding="utf-8") # 测试数据 with open("data/produce_data/x_data_total_return_predict_{}_spider.json".format(self.dt)) as f1: x_list = json.loads(f1.read()) # 测试 label with open("data/produce_data/y_data_total_return_predict_{}_spider.json".format(self.dt)) as f2: Y_test = json.loads(f2.read()) Y_test = [0 if i <= 19 else 1 for i in Y_test] X_test = pd.DataFrame(x_list, columns=self.my_c) for key in self.str_columns: X_test[key] = self.label_encoder.fit_transform(X_test[key]) for key in self.float_columns: X_test[key] = pd.to_numeric(X_test[key], errors="coerce") bst = lgb.Booster(model_file=self.model) y_pred = bst.predict(X_test, num_iteration=bst.best_iteration) temp = sorted(list(y_pred)) yuzhi = temp[int(len(temp) * 0.7) - 1] y_pred_binary = [0 if i <= yuzhi else 1 for i in list(y_pred)] # 转换为二进制输出 score_list = [] for index, item in enumerate(list(y_pred)): real_label = Y_test[index] score = item prid_label = y_pred_binary[index] print(real_label, "\t", prid_label, "\t", score) fw.write("{}\t{}\t{}\n".format(real_label, prid_label, score)) score_list.append(score) print("预测样本总量: {}".format(len(score_list))) data_series = pd.Series(score_list) print("统计 score 信息") print(data_series.describe()) # 评估模型 accuracy = accuracy_score(Y_test, y_pred_binary) print(f"Accuracy: {accuracy}") fw.close() def feature_importance(self): """ Get the importance of each feature :return: """ lgb_model = lgb.Booster(model_file=self.model) importance = lgb_model.feature_importance(importance_type='split') feature_name = lgb_model.feature_name() feature_importance = sorted(zip(feature_name, importance), key=lambda x: x[1], reverse=True) # 打印特征重要性 for name, imp in feature_importance: print(name, imp) if __name__ == "__main__": # i = int(input("输入 1 训练， 输入 2 预测:\n")) # if i == 1: # f = "train" # dt = "whole" # L = LightGBM(flag=f, dt=dt) # L.train_model() # elif i == 2: # f = "predict" # dt = int(input("输入日期， 16-21:\n")) # L = LightGBM(flag=f, dt=dt) # L.evaluate_model() # L.feature_importance() L = LightGBM("train", "whole") L.bays() # study = optuna.create_study(direction='maximize') # study.optimize(L.bays_params, n_trials=100) # print('Number of finished trials:', len(study.trials)) # print('Best trial:', study.best_trial.params)