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+import pandas as pd
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+import datetime
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+from sklearn.model_selection import train_test_split
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+from xgboost.sklearn import XGBClassifier
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+from sklearn import metrics
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+
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+
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+now_date = datetime.datetime.today()
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+dt = datetime.datetime.strftime(now_date, '%Y%m%d')
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+# 1. 读取数据
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+data = pd.read_csv(f'./data/train_test_data/train_test_{dt}.csv')
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+print(data.shape)
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+# 2. 划分x和y
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+data_columns = data.columns.values.tolist()
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+x = data[data_columns[:-1]]
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+y = data[data_columns[-1]]
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+print(f"x_shape: {x.shape}, y_shape: {y.shape}")
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+# 3. 训练集和测试集分割
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+x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=1)
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+print(f"x_train_shape: {x_train.shape}")
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+print(f"x_test_shape: {x_test.shape}")
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+# 4. 模型训练
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+xgb_model = XGBClassifier(
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+ objective='binary:logistic',
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+ learning_rate=0.3,
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+ max_depth=10,
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+ eval_metric='auc'
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+)
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+xgb_model.fit(x_train, y_train)
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+# 5. 模型保存
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+xgb_model.save_model('./data/ad_xgb.model')
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+# 6. 测试集预测
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+y_test_pre = xgb_model.predict(x_test)
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+# 7. 模型效果验证
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+test_accuracy = metrics.accuracy_score(y_test, y_test_pre)
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+print("Test Accuracy: %.2f%%" % (test_accuracy * 100.0))
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+test_auc = metrics.roc_auc_score(y_test, y_test_pre)
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+print("auc: %.2f%%" % (test_auc * 100.0))
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