python-缺失值处理

﹏ヽ暗。殇╰゛Y 2021-12-04 05:09 368阅读 0赞

#coding:utf-8
    import pandas as pd
    import numpy as np
    from sklearn.model_selection import  train_test_split
    from multiprocessing import Pool
    import xgboost as xgb
    
    pool = Pool(8)
    feature = [u'age', u'sex', u'expect_quota', u'max_month_repay', u'occupation', u'education', u'marital_status_min', u'live_info', u'local_hk', u'money_function_mean', u'company_type', u'salary_max', u'school_type', u'flow', u'gross_profit', u'business_type', u'business_year', u'personnel_num', u'pay_type', u'product_id', u'tm_encode', u'info_num', u'time_gap', u'tm_encode_max_max_month_repay_min', u'education_max_info_num', u'num', u'prior_period_bill_amt_max', u'prior_period_bill_amt_min', u'prior_period_bill_amt_mean', u'prior_period_bill_amt_std', u'prior_period_repay_amt_max', u'prior_period_repay_amt_min', u'prior_period_repay_amt_mean', u'prior_period_repay_amt_std', u'credit_lmt_amt_max', u'credit_lmt_amt_min', u'credit_lmt_amt_mean', u'credit_lmt_amt_std', u'curt_jifen_max', u'curt_jifen_min', u'curt_jifen_mean', u'curt_jifen_std', u'current_bill_bal_max', u'current_bill_bal_min', u'current_bill_bal_mean', u'current_bill_bal_std', u'current_bill_min_repay_amt_max', u'current_bill_min_repay_amt_min', u'current_bill_min_repay_amt_mean', u'current_bill_min_repay_amt_std', u'is_cheat_bill_max', u'is_cheat_bill_min', u'is_cheat_bill_mean', u'is_cheat_bill_std', u'cost_cnt_max', u'cost_cnt_min', u'cost_cnt_mean', u'cost_cnt_std', u'current_bill_amt_max', u'current_bill_amt_min', u'current_bill_amt_mean', u'current_bill_amt_std', u'adj_amt_max', u'adj_amt_min', u'adj_amt_mean', u'adj_amt_std', u'circle_interest_max', u'circle_interest_min', u'circle_interest_mean', u'circle_interest_std', u'prior_period_jifen_bal_max', u'prior_period_jifen_bal_min', u'prior_period_jifen_bal_mean', u'prior_period_jifen_bal_std', u'nadd_jifen_max', u'nadd_jifen_min', u'nadd_jifen_mean', u'nadd_jifen_std', u'current_adj_jifen_max', u'current_adj_jifen_min', u'current_adj_jifen_mean', u'current_adj_jifen_std', u'avlb_bal_usd_max', u'avlb_bal_usd_min', u'avlb_bal_usd_mean', u'avlb_bal_usd_std', u'avlb_bal_max', u'avlb_bal_min', u'avlb_bal_mean', u'avlb_bal_std', u'card_type_max', u'card_type_min', u'card_type_mean', u'card_type_std', u'pre_borrow_cash_amt_usd_max', u'pre_borrow_cash_amt_usd_min', u'pre_borrow_cash_amt_usd_mean', u'pre_borrow_cash_amt_usd_std', u'credit_lmt_amt_usd_max', u'credit_lmt_amt_usd_min', u'credit_lmt_amt_usd_mean', u'credit_lmt_amt_usd_std', u'pre_borrow_cash_amt_max', u'pre_borrow_cash_amt_min', u'pre_borrow_cash_amt_mean', u'pre_borrow_cash_amt_std', u'curr_max', u'curr_min', u'curr_mean', u'curr_std', u'repay_stat_max', u'repay_stat_min', u'repay_stat_mean', u'repay_stat_std', u'current_min_repay_amt_usd_max', u'current_min_repay_amt_usd_min', u'current_min_repay_amt_usd_mean', u'current_min_repay_amt_usd_std', u'current_repay_amt_usd_max', u'current_repay_amt_usd_min', u'current_repay_amt_usd_mean', u'current_repay_amt_usd_std', u'current_convert_jifen_max', u'current_convert_jifen_min', u'current_convert_jifen_mean', u'current_convert_jifen_std', u'current_award_jifen_max', u'current_award_jifen_min', u'current_award_jifen_mean', u'current_award_jifen_std', u'relation1_num', u'type0_num', u'type0_sum', u'type1_num', u'type1_sum', u'type2_num', u'type2_sum', u'type3_num', u'type3_sum', u'time_mean', u'time_min', u'time_max', u'time_std',  u'tag_num', u'tag_3', u'tag_4', u'tag_5', u'tag_6', u'tag_7', u'tag_8', u'tag_9', u'300028', u'300196', u'300301', u'300385', u'300469', u'300658', u'301036', u'301211', u'301687']#feature will be used
    
    feature1 = [u'age', u'sex', u'expect_quota', u'max_month_repay', u'occupation', u'education', u'marital_status_min', u'live_info', u'local_hk', u'money_function_mean', u'company_type', u'salary_max', u'school_type', u'flow', u'gross_profit', u'business_type', u'business_year', u'personnel_num', u'pay_type', u'product_id', u'tm_encode', u'info_num', u'time_gap', u'tm_encode_max_max_month_repay_min', u'education_max_info_num'] #features from user_info
    
    feature2 = [u'num', u'prior_period_bill_amt_max', u'prior_period_bill_amt_min', u'prior_period_bill_amt_mean', u'prior_period_bill_amt_std', u'prior_period_repay_amt_max', u'prior_period_repay_amt_min', u'prior_period_repay_amt_mean', u'prior_period_repay_amt_std', u'credit_lmt_amt_max', u'credit_lmt_amt_min', u'credit_lmt_amt_mean', u'credit_lmt_amt_std', u'curt_jifen_max', u'curt_jifen_min', u'curt_jifen_mean', u'curt_jifen_std', u'current_bill_bal_max', u'current_bill_bal_min', u'current_bill_bal_mean', u'current_bill_bal_std', u'current_bill_min_repay_amt_max', u'current_bill_min_repay_amt_min', u'current_bill_min_repay_amt_mean', u'current_bill_min_repay_amt_std', u'is_cheat_bill_max', u'is_cheat_bill_min', u'is_cheat_bill_mean', u'is_cheat_bill_std', u'cost_cnt_max', u'cost_cnt_min', u'cost_cnt_mean', u'cost_cnt_std', u'current_bill_amt_max', u'current_bill_amt_min', u'current_bill_amt_mean', u'current_bill_amt_std', u'adj_amt_max', u'adj_amt_min', u'adj_amt_mean', u'adj_amt_std', u'circle_interest_max', u'circle_interest_min', u'circle_interest_mean', u'circle_interest_std', u'prior_period_jifen_bal_max', u'prior_period_jifen_bal_min', u'prior_period_jifen_bal_mean', u'prior_period_jifen_bal_std', u'nadd_jifen_max', u'nadd_jifen_min', u'nadd_jifen_mean', u'nadd_jifen_std', u'current_adj_jifen_max', u'current_adj_jifen_min', u'current_adj_jifen_mean', u'current_adj_jifen_std', u'avlb_bal_usd_max', u'avlb_bal_usd_min', u'avlb_bal_usd_mean', u'avlb_bal_usd_std', u'avlb_bal_max', u'avlb_bal_min', u'avlb_bal_mean', u'avlb_bal_std', u'card_type_max', u'card_type_min', u'card_type_mean', u'card_type_std', u'pre_borrow_cash_amt_usd_max', u'pre_borrow_cash_amt_usd_min', u'pre_borrow_cash_amt_usd_mean', u'pre_borrow_cash_amt_usd_std', u'credit_lmt_amt_usd_max', u'credit_lmt_amt_usd_min', u'credit_lmt_amt_usd_mean', u'credit_lmt_amt_usd_std', u'pre_borrow_cash_amt_max', u'pre_borrow_cash_amt_min', u'pre_borrow_cash_amt_mean', u'pre_borrow_cash_amt_std', u'curr_max', u'curr_min', u'curr_mean', u'curr_std', u'repay_stat_max', u'repay_stat_min', u'repay_stat_mean', u'repay_stat_std', u'current_min_repay_amt_usd_max', u'current_min_repay_amt_usd_min', u'current_min_repay_amt_usd_mean', u'current_min_repay_amt_usd_std', u'current_repay_amt_usd_max', u'current_repay_amt_usd_min', u'current_repay_amt_usd_mean', u'current_repay_amt_usd_std', u'current_convert_jifen_max', u'current_convert_jifen_min', u'current_convert_jifen_mean', u'current_convert_jifen_std', u'current_award_jifen_max', u'current_award_jifen_min', u'current_award_jifen_mean', u'current_award_jifen_std'] #features from user_consumption
    
    feature3 = [u'relation1_num'] #features from relation1
    
    feature4 = [u'type0_num', u'type0_sum', u'type1_num', u'type1_sum', u'type2_num', u'type2_sum', u'type3_num', u'type3_sum', u'time_mean', u'time_min', u'time_max', u'time_std'] #feature from relation2
    
    feature5 = [u'tag_num', u'tag_3', u'tag_4', u'tag_5', u'tag_6', u'tag_7', u'tag_8', u'tag_9', u'300028', u'300196', u'300301', u'300385', u'300469', u'300658', u'301036', u'301211', u'301687','300028_301687','300196_2'] #feature from tag
    
    df = pd.read_csv('completetrain.csv')
    train = df.ix[:,df.columns!='label']
    df = pd.read_csv('completetest.csv')
    df = df.drop_duplicates(cols = 'user_id')
    test = df.ix[:,df.columns != 'probability']
    
    df = pd.concat([train,test])
    df.loc[df.relation1_num==0,'relation1_num'] = -1
    df = df.replace(-1,np.NAN)
    #check the feature one by one, use the feature in other feature file to predictt the mission one. not using the features from the same file as it almost always miss together
    for label in feature:
        print(label)
        if label in feature1:
            usedfeature = feature2 +feature3 + feature4 + feature5
        elif label in feature2:
            usedfeature = feature1 +feature3 + feature4 + feature5
        elif label in feature3:
            usedfeature = feature2 +feature1 + feature4 + feature5
        elif label in feature4:
            usedfeature = feature2 +feature3 + feature1 + feature5
        elif label in feature5:
            usedfeature = feature2 +feature3 + feature4 + feature1
    
            new = df[~pd.isnull(df[label])]
            new = new.fillna(new.mean())
            X_train, X_test, y_train, y_test = train_test_split(new[usedfeature], new[label], test_size=0.3,
                                                                random_state=200)
            if len(np.unique(new[label])) < 3:
                pass
                estimator = xgb.XGBClassifier(n_estimators=123, max_depth=3, learning_rate=0.05)
                estimator.fit(X_train, y_train)  # ,eval_metric = 'auc', eval_set=[(X_train, y_train), (X_test, y_test)])
                try:
                    df.loc[pd.isnull(df[label]), label] = estimator.predict(
                        df[pd.isnull(df[label])][usedfeature].as_matrix())
                except:
                    df.loc[pd.isnull(df[label]), label] = -100
            else:
                estimator = xgb.XGBRegressor(n_estimators=123, max_depth=3, learning_rate=0.05)
                if estimator.score(X_test,
                                   y_test) < 0.2:  # if the score higher than 0.2, use the algorithm to predict the missing value, if not, use -100 to fill the missing value
                    df.loc[pd.isnull(df[label]), label] = -100
                else:
                    try:
                        df.loc[pd.isnull(df[label]), label] = estimator.predict(
                            df[pd.isnull(df[label])][usedfeature].as_matrix())
                    except:
                        df.loc[pd.isnull(df[label]), label] = -100
            print(estimator.score(X_test, y_test))
    
        df.to_csv('xgbimpute.csv', index=None)

### 方法2 ###

from sklearn.ensemble import RandomForestRegressor
     
    ### 使用 RandomForestClassifier 填补缺失的年龄属性
    def set_missing_ages(df):
        
        # 把已有的数值型特征取出来丢进Random Forest Regressor中
        age_df = df[['Age','Fare', 'Parch', 'SibSp', 'Pclass']]
    
        # 乘客分成已知年龄和未知年龄两部分
        known_age = age_df[age_df.Age.notnull()].as_matrix()
        unknown_age = age_df[age_df.Age.isnull()].as_matrix()
    
        # y即目标年龄
        y = known_age[:, 0]
    
        # X即特征属性值
        X = known_age[:, 1:]
    
        # fit到RandomForestRegressor之中
        rfr = RandomForestRegressor(random_state=0, n_estimators=2000, n_jobs=-1)
        rfr.fit(X, y)
        
        # 用得到的模型进行未知年龄结果预测
        predictedAges = rfr.predict(unknown_age[:, 1::])
        
        # 用得到的预测结果填补原缺失数据
        df.loc[ (df.Age.isnull()), 'Age' ] = predictedAges 
        
        return df, rfr
    
    def set_Cabin_type(df):
        df.loc[ (df.Cabin.notnull()), 'Cabin' ] = "Yes"
        df.loc[ (df.Cabin.isnull()), 'Cabin' ] = "No"
        return df
    
    data_train, rfr = set_missing_ages(data_train)
    data_train = set_Cabin_type(data_train)