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stacking.py
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#--coding=utf-8--
# coding=utf8
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt # for plotting
from scipy.stats import mode
import warnings # to ignore warnings
from sklearn import preprocessing
from sklearn.externals import joblib
from sklearn.metrics import mean_squared_error #均方误差
from sklearn.metrics import mean_absolute_error #平方绝对误差
from sklearn.metrics import r2_score#R square
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV
import re
import seaborn as sns
from xgboost import XGBRegressor
from sklearn import datasets
from sklearn.ensemble import RandomForestRegressor, ExtraTreesRegressor, GradientBoostingRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import StratifiedKFold
import numpy as np
from sklearn.metrics import roc_auc_score
from sklearn.datasets.samples_generator import make_blobs
from xgboost import XGBRegressor
from sklearn.linear_model import LogisticRegression
'''创建训练的数据集'''
warnings.filterwarnings('ignore')
plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签
plt.rcParams['axes.unicode_minus']=False #用来正常显示负号
data = pd.read_csv('train_dataset.csv',encoding='utf-8')
test=pd.read_csv('test_dataset.csv',encoding='utf-8')
data=data
data=data.fillna(0)
data_hot=data.drop(columns=["用户编码","信用分","是否大学生客户","用户实名制是否通过核实","当月是否到过福州山姆会员店","用户最近一次缴费距今时长（月）",
"当月是否逛过福州仓山万达","是否黑名单客户","当月物流快递类应用使用次数",
"是否经常逛商场的人","是否4G不健康客户","当月火车类应用使用次数","当月是否看电影","当月是否体育场馆消费"])
data_hot=pd.DataFrame(data_hot,dtype=float)
train_columns=data_hot.columns.values
test_data=test.drop(columns=["用户编码","是否大学生客户","用户实名制是否通过核实","当月是否到过福州山姆会员店","用户最近一次缴费距今时长（月）",
"当月是否逛过福州仓山万达","是否黑名单客户","当月物流快递类应用使用次数",
"是否经常逛商场的人","是否4G不健康客户","当月火车类应用使用次数","当月是否看电影","当月是否体育场馆消费"])
test_data=pd.DataFrame(test_data,dtype=float)
target_data=data["信用分"]
train_data = preprocessing.scale(data_hot)
test_data=preprocessing.scale(test_data)
ran_params={'criterion':'mae', 'max_depth':3,'n_estimators': 200,'verbose':1}
logistic_params={'penalty':'l2','solver':'lbfgs','verbose':1}
'''模型融合中使用到的各个单模型'''
clfs = [LinearRegression(),
ExtraTreesRegressor(**ran_params,bootstrap=True),
RandomForestRegressor(**ran_params),
GradientBoostingRegressor(**ran_params),
LogisticRegression(**logistic_params)]
'''5折stacking'''
from sklearn.model_selection import KFold
n_folds = 5
train_set=[]
test_set=[]
target_set=[]
new_target_value=[]
new_feature_value=[]
new_feature_all=[]
new_train_value=[]
test_data_set=[]
test_id=[]
skf = KFold(n_splits=5, shuffle=False).split(train_data)
print(train_data[3].shape)
print(train_data[3])
for train, test in skf:
for j, clf in enumerate(clfs):
train_set=list(map(lambda a: list(train_data[a,:]), train))
test_set=list(map(lambda a: list(train_data[a,:]), test))
target_set=list(map(lambda a: target_data[a], train))
train_set=pd.DataFrame(train_set)
print(train_set.shape)
print(len(target_set))
print("model",clf)
clf.fit(train_set,target_set)
new_feature_target=clf.predict(test_set)
new_feature_value.append((j,new_feature_target))
test_id.append((j,test))
test_data_set.append((j,clf.predict(test_data)))
print("新的特征",test_id)
print("新的特征序列",new_feature_value)
print("测试数据集",test_data_set)
print(len(test_data_set[1][1]))
new_feature_0=[]
new_feature_1=[]
new_feature_2=[]
new_feature_3=[]
new_feature_4=[]
new_feature_matrix=[]
for i in new_feature_value:
if i[0]==0:
new_feature_0=new_feature_0+list(i[1])
elif i[0]==1:
new_feature_1= new_feature_1+ list(i[1])
elif i[0]==2:
new_feature_2= new_feature_2+ list(i[1])
elif i[0]==3:
new_feature_3= new_feature_3+ list(i[1])
elif i[0]==4:
new_feature_4= new_feature_4+ list(i[1])
print(new_feature_1)
new_feature_matrix.append(new_feature_0)
new_feature_matrix.append(new_feature_1)
new_feature_matrix.append(new_feature_2)
new_feature_matrix.append(new_feature_3)
new_feature_matrix.append(new_feature_4)
new_feature_matrix=pd.DataFrame(new_feature_matrix)
new_feature_matrix=new_feature_matrix.T
print(new_feature_matrix)
new_test_0=[]
new_test_1=[]
new_test_2=[]
new_test_3=[]
new_test_4=[]
new_test_matrix=[]
for i in test_data_set:
if i[0]==0:
new_test_0.append(list(i[1]))
elif i[0]==1:
new_test_1.append(list(i[1]))
elif i[0]==2:
new_test_2.append(list(i[1]))
elif i[0]==3:
new_test_3.append(list(i[1]))
elif i[0]==4:
new_test_4.append(list(i[1]))
new_test_0=(pd.DataFrame(new_test_0)).T
new_test_1=(pd.DataFrame(new_test_1)).T
new_test_2=(pd.DataFrame(new_test_2)).T
new_test_3=(pd.DataFrame(new_test_3)).T
new_test_4=(pd.DataFrame(new_test_4)).T
j=0
B0=[]
B1=[]
B2=[]
B3=[]
B4=[]
new_test=[new_test_0,new_test_1,new_test_2,new_test_3,new_test_4]
for i in range(len(new_test)):
for j in range(len(new_test[i])):
if i ==0:
B0.append(np.mean(new_test[i].iloc[j,:]))
elif i==1:
B1.append(np.mean(new_test[i].iloc[j,:]))
elif i==2:
B2.append(np.mean(new_test[i].iloc[j,:]))
elif i==3:
B3.append(np.mean(new_test[i].iloc[j,:]))
elif i==4:
B4.append(np.mean(new_test[i].iloc[j,:]))
new_test_matrix.append(B0)
new_test_matrix.append(B1)
new_test_matrix.append(B2)
new_test_matrix.append(B3)
new_test_matrix.append(B4)
new_test_matrix=pd.DataFrame(new_test_matrix)
new_test_matrix=new_test_matrix.T
print(new_test_matrix)
print(1)
other_params = {'learning_rate': 0.1, 'n_estimators': 200, 'max_depth': 3,'min_child_weight': 5,'seed': 0,
'subsample': 0.8, 'colsample_bytree': 0.8,'gamma': 0.1,'reg_alpha': 0,'reg_lambda': 1,'eval_metric':"mae",'verbose':1}
clf_2 = XGBRegressor(**other_params)
clf_2.fit(new_feature_matrix, target_data)
test_pre=clf_2.predict(new_test_matrix)
test_pre=[int(a)for a in test_pre]
print(len(test_pre))
dataframe = pd.DataFrame({'id': id, 'score': test_pre})
dataframe.to_csv("result_ensemble_stack.csv", index=False, sep=',')