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龙珠机器学习训练营-竞赛练习笔记

发布于2021-04-26 00:36     阅读(910)     评论(0)     点赞(28)     收藏(1)


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本学习笔记为阿里云天池龙珠计划机器学习训练营的学习内容,学习链接为:添加链接描述

1.学习知识点概要

  • 参与新手练习赛:快来一起挖掘幸福感!
    幸福感是一个古老而深刻的话题,是人类世代追求的方向。与幸福感相关的因素成千上万、因人而异,大如国计民生,小如路边烤红薯,都会对幸福感产生影响。这些错综复杂的因素中,我们能找到其中的共性,一窥幸福感的要义吗?

2. 学习主要内容

2.1 数据预处理

  • 导入库和数据读取
#导入库
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

#数据读取
train_abbr=pd.read_csv("datalab/231702/happiness_train_abbr.csv",encoding='ISO-8859-1')
train=pd.read_csv("datalab/231702/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("datalab/231702/happiness_test_complete.csv",encoding='ISO-8859-1')
test_sub=pd.read_csv("datalab/231702/happiness_submit.csv",encoding='ISO-8859-1')
  • 查看数据
#观察数据大小
test.shape
test_sub.shape
train.shape
#简单查看数据
train.head()

在这里插入图片描述

#查看数据是否缺失
train.info(verbose=True,null_counts=True)
#查看label分布
y_train_=train["happiness"]
y_train_.value_counts()

在这里插入图片描述

  • 数据预处理
#将-8换成3
y_train_=y_train_.map(lambda x:3 if x==-8 else x)
#让label从0开始
y_train_=y_train_.map(lambda x:x-1)
#train和test连在一起
data = pd.concat([train,test], sort=False)
#全部数据大小
data.shape

在这里插入图片描述

#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)
#做问卷时候的年龄
data["survey_age"]=data["year"]-data["birth"]
#让label从0开始
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)
#是否入党
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["work_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=data.drop(["id"], axis=1)
X_train_ = data[:train.shape[0]]
X_test_  = data[train.shape[0]:]
target_column = 'happiness'
feature_columns=list(X_test_.columns) 
feature_columns
X_train = np.array(X_train_)
y_train = np.array(y_train_)
X_test  = np.array(X_test_)

2.2 算法建模

  • 定义评价函数
#自定义评价函数
def myFeval(preds, xgbtrain):
    label = xgbtrain.get_label()
    score = mean_squared_error(label,preds)
    return 'myFeval',score
  • 建立模型
##### xgb
xgb_params = {"booster":'gbtree','eta': 0.005, 'max_depth': 5, 'subsample': 0.7, 
              'colsample_bytree': 0.8, 'objective': 'reg:linear', 'eval_metric': 'rmse', 'silent': True, 'nthread': 8}
folds = KFold(n_splits=5, shuffle=True, random_state=2018)
oof_xgb = np.zeros(len(train))
predictions_xgb = np.zeros(len(test))

for fold_, (trn_idx, val_idx) in enumerate(folds.split(X_train, y_train)):
    print("fold n°{}".format(fold_+1))
    trn_data = xgb.DMatrix(X_train[trn_idx], y_train[trn_idx])
    val_data = xgb.DMatrix(X_train[val_idx], y_train[val_idx])
    
    watchlist = [(trn_data, 'train'), (val_data, 'valid_data')]
    clf = xgb.train(dtrain=trn_data, num_boost_round=20000, evals=watchlist, early_stopping_rounds=200, verbose_eval=100, params=xgb_params,feval = myFeval)
    oof_xgb[val_idx] = clf.predict(xgb.DMatrix(X_train[val_idx]), ntree_limit=clf.best_ntree_limit)
    predictions_xgb += clf.predict(xgb.DMatrix(X_test), ntree_limit=clf.best_ntree_limit) / folds.n_splits    
print("CV score: {:<8.8f}".format(mean_squared_error(oof_xgb, y_train_)))

在这里插入图片描述

##### lgb
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 fold_, (trn_idx, val_idx) in enumerate(folds.split(X_train, y_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_)))

在这里插入图片描述

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_)))

在这里插入图片描述

from sklearn import linear_model
# 将lgb和xgb和ctb的结果进行stacking
train_stack = np.vstack([oof_lgb,oof_xgb,oof_cb]).transpose()
test_stack = np.vstack([predictions_lgb, predictions_xgb,predictions_cb]).transpose()
folds_stack = RepeatedKFold(n_splits=5, n_repeats=2, random_state=2018)
oof_stack = np.zeros(train_stack.shape[0])
predictions = np.zeros(test_stack.shape[0])

for fold_, (trn_idx, val_idx) in enumerate(folds_stack.split(train_stack,y_train)):
    print("fold {}".format(fold_))
    trn_data, trn_y = train_stack[trn_idx], y_train[trn_idx]
    val_data, val_y = train_stack[val_idx], y_train[val_idx]
    
    clf_3 = linear_model.BayesianRidge()
    #clf_3 =linear_model.Ridge()
    clf_3.fit(trn_data, trn_y)
    
    oof_stack[val_idx] = clf_3.predict(val_data)
    predictions += clf_3.predict(test_stack) / 10    
print("CV score: {:<8.8f}".format(mean_squared_error(oof_stack, y_train_)))

在这里插入图片描述

  • 生成预测结果
result=list(predictions)
result=list(map(lambda x: x + 1, result))
test_sub["happiness"]=result
test_sub.to_csv("submit_20210424.csv", index=False)

在这里插入图片描述

学习问题与解答

  • Q:对数据中的缺失值如何处理?
    A:需要根据具体确实值的数据类型进行处理,本课题中对缺失值非常多的特征选择了丢弃,其余特征的缺失值大多采用“0”值填充。

学习思考与总结

在数据特征工程领域,实际生产中机器学习的整个过程应该是如下的几步:
1.场景选择(算法选择)——根据实际问题选择合适的算法,是分类还是回归等,是否需要做特征抽象,或者特征缩放;
2.数据预处理——缺失值的处理,数据清洗等等;
3.特征工程——包括特征构建、特征提取、特征选择等;
4.模型训练——判断过拟合和欠拟合,通过交叉验证和grid research来选择参数,调整模型复杂度;
5.模型评估及优化——测试模型,分析误差,找到误差来源,进行改进;
6.模型融合——提升算法主要方法是模型的前端(如特征工程、清洗、预处理、采样等环节)和后端(如模型融合),成熟的机器算法只有有限的几种,往往通过几种算法的融合来提升模型的性能;
在本课题中对于数据可视化的部分涉及较少,后面可以多进行该方面工作,进一步探索特征的重要性分布和相关性,根据结果构建新特征,来尝试提升模型精度。

原文链接:https://blog.csdn.net/chkay399/article/details/82494048

原文链接:https://blog.csdn.net/p_j_t/article/details/116107260

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作者:来一碗蛋炒饭

链接: https://www.pythonheidong.com/blog/article/953643/03c83fb5bad575f898b8/

来源: python黑洞网

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