""" 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 class LightGBM(object): """ LightGBM model for classification """ def __init__(self, flag, dt): self.label_encoder = LabelEncoder() self.my_c = [ "uid", "channel", "fans", "view_count_user_30days", "share_count_user_30days", "return_count_user_30days", "rov_user", "str_user", "tag1", "tag2", "tag3" ] self.str_columns = ["channel", "uid", "tag1", "tag2", "tag3"] self.float_columns = [ "fans", "view_count_user_30days", "share_count_user_30days", "return_count_user_30days", "rov_user", "str_user", ] self.split_c = 0.7 self.yc = 0.8 self.model = "lightgbm_0326_user.bin" self.flag = flag self.dt = dt def bays_params(self, trial): """ Bayesian parameters for :return: best parameters """ # 定义搜索空间 param = { 'objective': 'binary', 'metric': 'binary_logloss', 'verbosity': -1, 'boosting_type': 'gbdt', 'num_leaves': trial.suggest_int('num_leaves', 20, 40), 'learning_rate': trial.suggest_loguniform('learning_rate', 1e-8, 1.0), 'feature_fraction': trial.suggest_uniform('feature_fraction', 0.4, 1.0), 'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.4, 1.0), 'bagging_freq': trial.suggest_int('bagging_freq', 1, 7), 'min_child_samples': trial.suggest_int('min_child_samples', 5, 100), "num_threads": 16, # 线程数量 } X_train, X_test = self.generate_x_data() Y_train, Y_test = self.generate_y_data() train_data = lgb.Dataset( X_train, label=Y_train, categorical_feature=["channel", "uid", "tag1", "tag2", "tag3"], ) test_data = lgb.Dataset(X_test, label=Y_test, reference=train_data) gbm = lgb.train(param, train_data, num_boost_round=100, valid_sets=[test_data]) preds = gbm.predict(X_test) pred_labels = np.rint(preds) accuracy = accuracy_score(Y_test, pred_labels) return accuracy def generate_x_data(self): """ Generate data for feature engineering :return: """ with open("data/produce_data/x_data_total_return_{}_{}_user.json".format(self.flag, self.dt)) as f1: x_list = json.loads(f1.read()) index_t = int(len(x_list) * self.split_c) X_train = pd.DataFrame(x_list[:index_t], columns=self.my_c) for key in self.str_columns: X_train[key] = self.label_encoder.fit_transform(X_train[key]) for key in self.float_columns: X_train[key] = pd.to_numeric(X_train[key], errors="coerce") X_test = pd.DataFrame(x_list[index_t:], 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") return X_train, X_test def generate_y_data(self): """ Generate data for label :return: """ with open("data/produce_data/y_data_total_return_{}_{}_user.json".format(self.flag, self.dt)) as f2: y_list = json.loads(f2.read()) index_t = int(len(y_list) * self.split_c) temp = sorted(y_list) yuzhi = temp[int(len(temp) * self.yc) - 1] print("阈值是: {}".format(yuzhi)) y__list = [0 if i <= yuzhi else 1 for i in y_list] y_train = np.array(y__list[:index_t]) y_test = np.array(y__list[index_t:]) return y_train, y_test def train_model(self): """ Load dataset :return: """ X_train, X_test = self.generate_x_data() Y_train, Y_test = self.generate_y_data() train_data = lgb.Dataset( X_train, label=Y_train, categorical_feature=["channel", "uid", "tag1", "tag2", "tag3"], ) test_data = lgb.Dataset(X_test, label=Y_test, reference=train_data) params = { 'num_leaves': 29, 'learning_rate': 0.0005153812869522004, 'feature_fraction': 0.7460901121756344, 'bagging_fraction': 0.5744390458938479, 'bagging_freq': 1, "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{}_user.txt".format(self.dt), "a+", encoding="utf-8") # 测试数据 with open("data/produce_data/x_data_total_return_predict_{}_user.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_{}_user.json".format(self.dt)) as f2: Y_test = json.loads(f2.read()) Y_test = [0 if i <= 31 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") # 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)