阿里巴巴天池169名

import pandas as pd
import numpy as np
from sklearn.metrics import mean_squared_error
import lightgbm as lgb
import xgboost as xgb
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.model_selection import KFold, RepeatedKFold
from scipy import sparse
#显示所有列
pd.set_option('display.max_columns', None)
#显示所有行
pd.set_option('display.max_rows', None)
from datetime import datetime
#train_abbr=pd.read_csv("datalab/231702/happiness_train_abbr.csv",encoding='ISO-8859-1')
train=pd.read_csv("C:\英雄时刻\Python\天池，赛\happiness_train_complete.csv",encoding='ISO-8859-1')
#test_abbr=pd.read_csv("datalab/231702/happiness_test_abbr.csv",encoding='ISO-8859-1')
test=pd.read_csv(r"C:\英雄时刻\Python\天池，赛\happiness_test_complete.csv",encoding='ISO-8859-1')
#test_sub=pd.read_csv("datalab/231702/happiness_submit.csv",encoding='ISO-8859-1')
data = pd.concat([train,test],axis=0,ignore_index=True)
data.info(verbose=True,null_counts=True)
y_train_=train["happiness"]
y_train_.value_counts()
y_train_=y_train_.map(lambda x:3 if x==-8 else x)
y_train_=y_train_.map(lambda x:x-1)
#处理时间特征
data['survey_time'] = pd.to_datetime(data['survey_time'],format='%Y-%m-%d %H:%M:%S')
data["weekday"]=data["survey_time"].dt.weekday
data["year"]=data["survey_time"].dt.year
data["quarter"]=data["survey_time"].dt.quarter
data["hour"]=data["survey_time"].dt.hour
data["month"]=data["survey_time"].dt.month
#把一天的时间分段
def hour_cut(x):
    if 0<=x<6:
        return 0
    elif  6<=x<8:
        return 1
    elif  8<=x<12:
        return 2
    elif  12<=x<14:
        return 3
    elif  14<=x<18:
        return 4
    elif  18<=x<21:
        return 5
    elif  21<=x<24:
        return 6

    
data["hour_cut"]=data["hour"].map(hour_cut)
data["survey_age"]=data["year"]-data["birth"]
data["happiness"]=data["happiness"].map(lambda x:x-1)
#去掉三个缺失值很多的
data=data.drop(["edu_other"], axis=1)
data=data.drop(["happiness"], axis=1)
data=data.drop(["survey_time"], axis=1)
data["join_party"]=data["join_party"].map(lambda x:0 if pd.isnull(x)  else 1)
#出生的年代
def birth_split(x):
    if 1920<=x<=1930:
        return 0
    elif  1930<x<=1940:
        return 1
    elif  1940<x<=1950:
        return 2
    elif  1950<x<=1960:
        return 3
    elif  1960<x<=1970:
        return 4
    elif  1970<x<=1980:
        return 5
    elif  1980<x<=1990:
        return 6
    elif  1990<x<=2000:
        return 7
    
data["birth_s"]=data["birth"].map(birth_split)
#收入分组
def income_cut(x):
    if x<0:
        return 0
    elif  0<=x<1200:
        return 1
    elif  1200<x<=10000:
        return 2
    elif  10000<x<24000:
        return 3
    elif  24000<x<40000:
        return 4
    elif  40000<=x:
        return 5
 

    
data["income_cut"]=data["income"].map(income_cut)
#填充数据
data["edu_status"]=data["edu_status"].fillna(5)
data["edu_yr"]=data["edu_yr"].fillna(-2)
data["property_other"]=data["property_other"].map(lambda x:0 if pd.isnull(x)  else 1)
data["hukou_loc"]=data["hukou_loc"].fillna(1)
data["social_neighbor"]=data["social_neighbor"].fillna(8)
data["social_friend"]=data["social_friend"].fillna(8)
data["work_status"]=data["work_status"].fillna(0)
data["work_yr"]=data["w
                     ork_yr"].fillna(0)
data["work_type"]=data["work_type"].fillna(0)
data["work_manage"]=data["work_manage"].fillna(0)
data["family_income"]=data["family_income"].fillna(-2)
data["invest_other"]=data["invest_other"].map(lambda x:0 if pd.isnull(x)  else 1)
#填充数据
data["minor_child"]=data["minor_child"].fillna(0)
data["marital_1st"]=data["marital_1st"].fillna(0)
data["s_birth"]=data["s_birth"].fillna(0)
data["marital_now"]=data["marital_now"].fillna(0)
data["s_edu"]=data["s_edu"].fillna(0)
data["s_political"]=data["s_political"].fillna(0)
data["s_hukou"]=data["s_hukou"].fillna(0)
data["s_income"]=data["s_income"].fillna(0)
data["s_work_exper"]=data["s_work_exper"].fillna(0)
data["s_work_status"]=data["s_work_status"].fillna(0)
data["s_work_type"]=data["s_work_type"].fillna(0)
data['income_cut'].fillna(0,inplace=True)
data=data.drop(["id"], axis=1)
X_train_ = data[:train.shape[0]]
X_test_  = data[train.shape[0]:]
X_train = np.array(X_train_)
y_train = np.array(y_train_)
X_test  = np.array(X_test_)
gg=data.iloc[1].index.tolist()  
def myFeval(preds, xgbtrain):
    label = xgbtrain.get_label()
    score = mean_squared_error(label,preds)
    return 'myFeval',score
oof_xgb2 = np.zeros(len(train))
param = {'boosting_type': 'gbdt',
         'num_leaves': 20,
         'min_data_in_leaf': 20, 
         'objective':'regression',
         'max_depth':6,
         'learning_rate': 0.01,
         "min_child_samples": 30,
         
         "feature_fraction": 0.8,
         "bagging_freq": 1,
         "bagging_fraction": 0.8 ,
         "bagging_seed": 11,
         "metric": 'mse',
         "lambda_l1": 0.1,
         "verbosity": -1}
folds = KFold(n_splits=5, shuffle=True, random_state=2018)
oof_lgb = np.zeros(len(X_train_))
predictions_lgb = np.zeros(len(X_test_))

for trn_idx, val_idx in folds.split(X_train):
    print("fold n°{}".format(fold_+1))
   # print(trn_idx)
   # print(".............x_train.........")
   # print(X_train[trn_idx])
  #  print(".............y_train.........")
  #  print(y_train[trn_idx])
    trn_data = lgb.Dataset(X_train[trn_idx], y_train[trn_idx])
    
    val_data = lgb.Dataset(X_train[val_idx], y_train[val_idx])

    num_round = 10000
    clf = lgb.train(param, trn_data, num_round, valid_sets = [trn_data, val_data], verbose_eval=200, early_stopping_rounds = 100)
    oof_lgb[val_idx] = clf.predict(X_train[val_idx], num_iteration=clf.best_iteration)
    
    predictions_lgb += clf.predict(X_test, num_iteration=clf.best_iteration) / folds.n_splits

print("CV score: {:<8.8f}".format(mean_squared_error(oof_lgb, y_train_)))
from catboost import Pool, CatBoostRegressor
from catboost import Pool, CatBoostRegressor
# cat_features=[0,2,3,10,11,13,15,16,17,18,19]
from sklearn.model_selection import train_test_split


#X_train_s, X_test_s, y_train_s, y_test_s = train_test_split(X_train_, y_train_, test_size=0.3, random_state=2019)
# train_pool = Pool(X_train_s, y_train_s,cat_features=[0,2,3,10,11,13,15,16,17,18,19])
# val_pool = Pool(X_test_s, y_test_s,cat_features=[0,2,3,10,11,13,15,16,17,18,19])
# test_pool = Pool(X_test_ ,cat_features=[0,2,3,10,11,13,15,16,17,18,19]) 


kfolder = KFold(n_splits=5, shuffle=True, random_state=2019)
oof_cb = np.zeros(len(X_train_))
predictions_cb = np.zeros(len(X_test_))
kfold = kfolder.split(X_train_, y_train_)
fold_=0
#X_train_s, X_test_s, y_train_s, y_test_s = train_test_split(X_train, y_train, test_size=0.3, random_state=2019)
for train_index, vali_index in kfold:
    print("fold n°{}".format(fold_))
    fold_=fold_+1
    k_x_train = X_train[train_index]
    k_y_train = y_train[train_index]
    k_x_vali = X_train[vali_index]
    k_y_vali = y_train[vali_index]
    cb_params = {
         'n_estimators': 100000,
         'loss_function': 'RMSE',
         'eval_metric':'RMSE',
         'learning_rate': 0.05,
         'depth': 5,
         'use_best_model': True,
         'subsample': 0.6,
         'bootstrap_type': 'Bernoulli',
         'reg_lambda': 3
    }
    model_cb = CatBoostRegressor(**cb_params)
    #train the model
    model_cb.fit(k_x_train, k_y_train,eval_set=[(k_x_vali, k_y_vali)],verbose=100,early_stopping_rounds=50)
    oof_cb[vali_index] = model_cb.predict(k_x_vali, ntree_end=model_cb.best_iteration_)
    predictions_cb += model_cb.predict(X_test_, ntree_end=model_cb.best_iteration_) / kfolder.n_splits



print("CV score: {:<8.8f}".format(mean_squared_error(oof_cb, y_train_)))

本文地址：https://blog.csdn.net/xhygghgja/article/details/107417665