37全球AI挑战赛之虚拟股票预测，冠军

    import pandas as pd
    from sklearn.linear_model import LogisticRegression
    from sklearn.preprocessing import Imputer
    import numpy as np
    from sklearn.decomposition import PCA
    from sklearn.ensemble import RandomForestClassifier  
    from lightgbm import LGBMClassifier
    from xgboost import XGBClassifier
    from sklearn.metrics import log_loss
    from sklearn.model_selection import cross_val_score
    from sklearn.model_selection import train_test_split
    from sklearn.ensemble import GradientBoostingClassifier
    from sklearn.svm import SVC
    from sklearn.linear_model import LogisticRegression

    def cvPro(train,label):
    x_train,x_test,y_train,y_test = train_test_split(train,label,test_size = 0.3,random_state = 0)
    
    ##xgb
    xgb_clf = XGBClassifier()
    xgb_clf.fit(x_train,y_train)
    xgb_test_y = xgb_clf.predict_proba(x_test)
    xgb_y_lr_1 = [i[1] for i in xgb_test_y]
    
    loss = log_loss(y_test,xgb_y_lr_1)
    print("log_loss is :",loss)

    def main(train_file_name,test_file_name,path):
    '''
    说明：lgb or xgb 默认参数:0.99 44 降维 成绩为：0.67447
    :param train_file_name:
    :param test_file_name:
    :param path:
    :return:
    '''
    
    print('Starting:...')

    last_col = 100
    median_col = 44
    train_data = pd.read_csv(train_file_name)
    test_data = pd.read_csv(test_file_name)
    one_hot_Fea = ['group1','group2','code_id']
    weight = train_data['weight']
    df_one_hot = train_data[one_hot_Fea]
    train_label = train_data['label']
    train_matrix_xgb = train_data.iloc[:,range(1,median_col)]
    test_matrix_xgb = test_data.iloc[:,range(1,median_col)]
    train_matrix_lgb = train_data.iloc[:,range(median_col,last_col)]
    test_matrix_lgb = test_data.iloc[:,range(median_col,last_col)]
    test_id = test_data['id'].astype("int64")
    test_id = [np.int64(i) for i in test_id]

    #数据清洗加归一化操作
    train_matrix_xgb.fillna(0)
    test_matrix_xgb.fillna(0)
    train_matrix_lgb.fillna(0)
    test_matrix_xgb.fillna(0)
    train_xgb_stand = Imputer().fit_transform(train_matrix_xgb)
    test_xgb_stand = Imputer().fit_transform(test_matrix_xgb)
    train_lgb_stand = Imputer().fit_transform(train_matrix_lgb)
    test_lgb_stand = Imputer().fit_transform(test_matrix_lgb)

    ##lgb模型
    lgb_clf = LGBMClassifier()
    lgb_clf.fit(train_lgb_stand,train_label)
    lgb_test_y = lgb_clf.predict_proba(test_lgb_stand)
    lgb_y_lr_1 = [i[1] for i in lgb_test_y]

    ###xgb模型
    xgb_clf = XGBClassifier()
    xgb_clf.fit(train_xgb_stand,train_label)
    xgb_test_y = xgb_clf.predict_proba(test_xgb_stand)
    xgb_y_lr_1 = [i[1] for i in xgb_test_y]
    
    pre_data = [(xgb_y_lr_1[i] * 0.5 + lgb_y_lr_1[i] * 0.5) for i in range(len(xgb_y_lr_1))]

    ##模型评估
    #    cvPro(train_xgb_stand,train_label)

    #生成提交的数据
    subname = "xgb_sub_05.csv"
    sub_data_lr = pd.DataFrame(test_id)
    sub_data_lr.columns = ['id']
    sub_data_lr['proba'] = pd.Series(pre_data)
    # dats = sub_data_lr.loc[1:1000]
    # print(dats.shape)
    sub_data_lr.to_csv(path + subname,encoding='utf8',index = False)
    print('It is Ok.')

    if __name__ == "__main__":
    test_file_name = "../data/stock_test_data_20171125.csv"
    train_file_name = "../data/stock_train_data_20171125.csv"
    path = "../data/"
    main(train_file_name,test_file_name,path)