import pandas as pd
import datetime
import time
from sklearn.model_selection import train_test_split
from xgboost.sklearn import XGBClassifier
from sklearn import metrics


def xgboost_train():
    now_date = datetime.datetime.today()
    dt = datetime.datetime.strftime(now_date, '%Y%m%d')
    # 1. 读取数据
    # data = pd.read_csv(f'./data/train_test_data/train_test_{dt}.csv')
    # print(data.shape)
    train_data = pd.read_csv(f'./data/train_test_data/train_{dt}.csv')
    print(train_data.shape)
    test_data = pd.read_csv(f'./data/train_test_data/test_{dt}.csv')
    print(test_data.shape)
    # 2. 划分x和y
    # data_columns = data.columns.values.tolist()
    # x = data[data_columns[:-1]]
    # y = data[data_columns[-1]]
    # print(f"x_shape: {x.shape}, y_shape: {y.shape}")
    data_columns = train_data.columns.values.tolist()
    x_train = train_data[data_columns[:-1]]
    y_train = train_data[data_columns[-1]]
    x_test = test_data[data_columns[:-1]]
    y_test = test_data[data_columns[-1]]
    # 3. 训练集和测试集分割
    # x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=1)
    print(f"x_train_shape: {x_train.shape}")
    print(f"x_test_shape: {x_test.shape}")
    # 4. 模型训练
    xgb_model = XGBClassifier(
        objective='binary:logistic',
        learning_rate=0.05,
        max_depth=9,
        min_child_weight=8,
        n_estimators=450,
        eval_metric=['error', 'auc']
    )
    xgb_model.fit(x_train, y_train, eval_set=[(x_train, y_train), (x_test, y_test)])
    # 5. 模型保存
    xgb_model.save_model('./data/ad_xgb.model')
    # 6. 测试集预测
    y_test_pre = xgb_model.predict(x_test)

    # test_df = x_test.copy()
    # test_df['y'] = y_test
    # test_df['y_pre'] = y_test_pre
    # test_df.to_csv('./data/test_pre.csv', index=False)

    # 7. 模型效果验证
    test_accuracy = metrics.accuracy_score(y_test, y_test_pre)
    print("Test Accuracy: %.2f%%" % (test_accuracy * 100.0))
    test_auc = metrics.roc_auc_score(y_test, y_test_pre)
    print("auc: %.2f%%" % (test_auc * 100.0))
    test_recall = metrics.recall_score(y_test, y_test_pre)
    print("recall:%.2f%%"%(test_recall*100.0))
    test_f1 = metrics.f1_score(y_test, y_test_pre)
    print("f1:%.2f%%"%(test_f1*100.0))
    test_precision = metrics.precision_score(y_test, y_test_pre)
    print("precision:%.2f%%"%(test_precision*100.0))


if __name__ == '__main__':
    st_time = time.time()
    xgboost_train()
    print(f"{time.time() - st_time}s")