数据挖掘-Task5：模型融合

目录前言一、二、1.2.总结前言一、二、1.2.总结

Abro.

727人浏览 · 2021-03-29 00:46:27

Abro. · 2021-03-29 00:46:27 发布

前言

内容介绍

stacking 原理详解

一、模型融合的方式

1.1 简单加权融合

回归（分类概论）：算术平均融合（ $A r i t h m e t i c$ $m e a n$ ），几何平均融合（ $G e o m e t r i c$ $m e a n$ ）;
分类：投票（ $V o t i n g$ ）；
综合：排序融合（ $R a n k$ $a v e r a g i n g$ ）， $l o g$ 融合。

1.2 $s t a c k i n g$ $/$ $b l e n d i n g$

构建多层模型，并利用预测结果再拟合预测。

1.3 $b o o s t i n g$ $/$ $b a g g i n g$

多树提升方法

二、代码示例

2.1 回归/分类概率-融合

2.1.1 简单加权平均，结果直接融合

# In[1]: 简单加权平均，结果直接融合

import numpy as np
import pandas as pd
from sklearn import metrics


## 生成一些简单的样本数据，test_prei 代表 第i个模型的预测值
test_pre1 = [1.2, 3.2, 2.1, 6.2]
test_pre2 = [0.9, 3.1, 2.0, 5.9]
test_pre3 = [1.1, 2.9, 2.2, 6.0]
## y_test_true 代表第i个模型的真实值
y_test_true = [1, 3, 2, 6]

## 定义结果的加权平均函数
def Weighted_method(test_pre1,test_pre2,test_pre3,w=[1/3,1/3,1/3]):
    Weighted_result = w[0]*pd.Series(test_pre1)+w[1]*pd.Series(test_pre2)+w[2]*pd.Series(test_pre3)
    return Weighted_result

# 各模型的预测结果计算 MAE
print('Pred1 MAE:',metrics.mean_absolute_error(y_test_true, test_pre1))
print('Pred2 MAE:',metrics.mean_absolute_error(y_test_true, test_pre2))
print('Pred3 MAE:',metrics.mean_absolute_error(y_test_true, test_pre3))

## 根据加权计算MAE
w = [0.3,0.4,0.3] # 定义比重权值
Weighted_pre = Weighted_method(test_pre1,test_pre2,test_pre3,w)
print('Weighted_pre MAE:',metrics.mean_absolute_error(y_test_true, Weighted_pre))

# 可以发现加权结果相对于之前的结果是有提升的，这种我们称其为简单的加权平均

在这里插入图片描述

可以发现加权结果相对于之前的结果是有提升的，这种称其为简单的加权平均

还有一些特殊的形式，比如 $m e a n$ 平均， $m e d i a n$ 平均：

# In[2]: 特殊的形式，比如 mean平均，median 平均

## 定义结果的加权平均函数
def Mean_method(test_pre1,test_pre2,test_pre3):
    Mean_result = pd.concat([pd.Series(test_pre1),pd.Series(test_pre2),pd.Series(test_pre3)],axis=1).mean(axis=1)
    return Mean_result

Mean_pre = Mean_method(test_pre1,test_pre2,test_pre3)
print('Mean_pre MAE:',metrics.mean_absolute_error(y_test_true, Mean_pre))

## 定义结果的加权平均函数
def Median_method(test_pre1,test_pre2,test_pre3):
    Median_result = pd.concat([pd.Series(test_pre1),pd.Series(test_pre2),pd.Series(test_pre3)],axis=1).median(axis=1)
    return Median_result

Median_pre = Median_method(test_pre1,test_pre2,test_pre3)
print('Median_pre MAE:',metrics.mean_absolute_error(y_test_true, Median_pre))

2.1.2 $S t a c k i n g$ 融合(回归)

# In[3]: Stacking 融合(回归)

from sklearn import linear_model

def Stacking_method(train_reg1,train_reg2,train_reg3,y_train_true,test_pre1,test_pre2,test_pre3,model_L2= linear_model.LinearRegression()):
    model_L2.fit(pd.concat([pd.Series(train_reg1),pd.Series(train_reg2),pd.Series(train_reg3)],axis=1).values,y_train_true)
    Stacking_result = model_L2.predict(pd.concat([pd.Series(test_pre1),pd.Series(test_pre2),pd.Series(test_pre3)],axis=1).values)
    return Stacking_result

## 生成一些简单的样本数据，test_prei 代表第i个模型的预测值
train_reg1 = [3.2, 8.2, 9.1, 5.2]
train_reg2 = [2.9, 8.1, 9.0, 4.9]
train_reg3 = [3.1, 7.9, 9.2, 5.0]
# y_test_true 代表第模型的真实值
y_train_true = [3, 8, 9, 5] 

test_pre1 = [1.2, 3.2, 2.1, 6.2]
test_pre2 = [0.9, 3.1, 2.0, 5.9]
test_pre3 = [1.1, 2.9, 2.2, 6.0]

# y_test_true 代表第模型的真实值
y_test_true = [1, 3, 2, 6] 

model_L2= linear_model.LinearRegression()
Stacking_pre = Stacking_method(train_reg1,train_reg2,train_reg3,y_train_true,
                               test_pre1,test_pre2,test_pre3,model_L2)
print('Stacking_pre MAE:',metrics.mean_absolute_error(y_test_true, Stacking_pre))

2.2 分类模型融合

# In[4]:  导包 

import numpy as np
import lightgbm as lgb
from sklearn.datasets import make_blobs
from sklearn import datasets
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import VotingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.datasets import make_moons
from sklearn.metrics import accuracy_score,roc_auc_score
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import StratifiedKFold

2.2.1 $V o t i n g$ 投票机制

$V o t i n g$ 即投票机制，分为软投票和硬投票两种，其原理采用少数服从多数的思想。

# In[5]: Voting 投票机制

'''
硬投票：对多个模型直接进行投票，不区分模型结果的相对重要度，最终投票数最多的类为最终被预测的类。
'''
iris = datasets.load_iris()

x=iris.data
y=iris.target
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3)

clf1 = lgb.LGBMClassifier(learning_rate=0.1, n_estimators=150, max_depth=3, min_child_weight=2, subsample=0.7,
                     colsample_bytree=0.6, objective='binary:logistic')
clf2 = RandomForestClassifier(n_estimators=200, max_depth=10, min_samples_split=10,
                              min_samples_leaf=63,oob_score=True)
clf3 = SVC(C=0.1)

# 硬投票
eclf = VotingClassifier(estimators=[('lgb', clf1), ('rf', clf2), ('svc', clf3)], voting='hard')
for clf, label in zip([clf1, clf2, clf3, eclf], ['LGB', 'Random Forest', 'SVM', 'Ensemble']):
    scores = cross_val_score(clf, x, y, cv=5, scoring='accuracy')
    print("Accuracy: %0.2f (+/- %0.2f) [%s]" % (scores.mean(), scores.std(), label))

2.2.2 分类的 $S t a c k i n g$ $/$ $B l e n d i n g$ 融合

# In[6]: 分类的Stacking/Blending融合

'''
5-Fold Stacking
'''
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import ExtraTreesClassifier,GradientBoostingClassifier
import pandas as pd
#创建训练的数据集
data_0 = iris.data
data = data_0[:100,:]

target_0 = iris.target
target = target_0[:100]

#模型融合中使用到的各个单模型
clfs = [LogisticRegression(solver='lbfgs'),
        RandomForestClassifier(n_estimators=5, n_jobs=-1, criterion='gini'),
        ExtraTreesClassifier(n_estimators=5, n_jobs=-1, criterion='gini'),
        ExtraTreesClassifier(n_estimators=5, n_jobs=-1, criterion='entropy'),
        GradientBoostingClassifier(learning_rate=0.05, subsample=0.5, max_depth=6, n_estimators=5)]
 
#切分一部分数据作为测试集
X, X_predict, y, y_predict = train_test_split(data, target, test_size=0.3, random_state=2020)

dataset_blend_train = np.zeros((X.shape[0], len(clfs)))
dataset_blend_test = np.zeros((X_predict.shape[0], len(clfs)))

#5折stacking
n_splits = 5
skf = StratifiedKFold(n_splits)
skf = skf.split(X, y)

for j, clf in enumerate(clfs):
    #依次训练各个单模型
    dataset_blend_test_j = np.zeros((X_predict.shape[0], 5))
    for i, (train, test) in enumerate(skf):
        #5-Fold交叉训练，使用第i个部分作为预测，剩余的部分来训练模型，获得其预测的输出作为第i部分的新特征。
        X_train, y_train, X_test, y_test = X[train], y[train], X[test], y[test]
        clf.fit(X_train, y_train)
        y_submission = clf.predict_proba(X_test)[:, 1]
        dataset_blend_train[test, j] = y_submission
        dataset_blend_test_j[:, i] = clf.predict_proba(X_predict)[:, 1]
    #对于测试集，直接用这k个模型的预测值均值作为新的特征。
    dataset_blend_test[:, j] = dataset_blend_test_j.mean(1)
    print("val auc Score: %f" % roc_auc_score(y_predict, dataset_blend_test[:, j]))

clf = LogisticRegression(solver='lbfgs')
clf.fit(dataset_blend_train, y)
y_submission = clf.predict_proba(dataset_blend_test)[:, 1]

print("Val auc Score of Stacking: %f" % (roc_auc_score(y_predict, y_submission)))

在这里插入图片描述

# In[7]: 分类的 Stacking\Blending 融合：
'''
Blending
'''
 
#创建训练的数据集
#创建训练的数据集
data_0 = iris.data
data = data_0[:100,:]

target_0 = iris.target
target = target_0[:100]
 
#模型融合中使用到的各个单模型
clfs = [LogisticRegression(solver='lbfgs'),
        RandomForestClassifier(n_estimators=5, n_jobs=-1, criterion='gini'),
        RandomForestClassifier(n_estimators=5, n_jobs=-1, criterion='entropy'),
        ExtraTreesClassifier(n_estimators=5, n_jobs=-1, criterion='gini'),
        #ExtraTreesClassifier(n_estimators=5, n_jobs=-1, criterion='entropy'),
        GradientBoostingClassifier(learning_rate=0.05, subsample=0.5, max_depth=6, n_estimators=5)]

#切分一部分数据作为测试集
X, X_predict, y, y_predict = train_test_split(data, target, test_size=0.3, random_state=2020)

#切分训练数据集为d1,d2两部分
X_d1, X_d2, y_d1, y_d2 = train_test_split(X, y, test_size=0.5, random_state=2020)
dataset_d1 = np.zeros((X_d2.shape[0], len(clfs)))
dataset_d2 = np.zeros((X_predict.shape[0], len(clfs)))
 
for j, clf in enumerate(clfs):
    #依次训练各个单模型
    clf.fit(X_d1, y_d1)
    y_submission = clf.predict_proba(X_d2)[:, 1]
    dataset_d1[:, j] = y_submission
    #对于测试集，直接用这k个模型的预测值作为新的特征。
    dataset_d2[:, j] = clf.predict_proba(X_predict)[:, 1]
    print("val auc Score: %f" % roc_auc_score(y_predict, dataset_d2[:, j]))

#融合使用的模型
clf = GradientBoostingClassifier(learning_rate=0.02, subsample=0.5, max_depth=6, n_estimators=30)
clf.fit(dataset_d1, y_d2)
y_submission = clf.predict_proba(dataset_d2)[:, 1]
print("Val auc Score of Blending: %f" % (roc_auc_score(y_predict, y_submission)))

在这里插入图片描述
一些其它的方法：

# In[8]: 其他的方法
        
def Ensemble_add_feature(train,test,target,clfs):
    
    # n_flods = 5
    # skf = list(StratifiedKFold(y, n_folds=n_flods))

    train_ = np.zeros((train.shape[0],len(clfs*2)))
    test_ = np.zeros((test.shape[0],len(clfs*2)))

    for j,clf in enumerate(clfs):
        '''依次训练各个单模型'''
        # print(j, clf)
        '''使用第1个部分作为预测，第2部分来训练模型，获得其预测的输出作为第2部分的新特征。'''
        # X_train, y_train, X_test, y_test = X[train], y[train], X[test], y[test]

        clf.fit(train,target)
        y_train = clf.predict(train)
        y_test = clf.predict(test)

        ## 新特征生成
        train_[:,j*2] = y_train**2
        test_[:,j*2] = y_test**2
        train_[:, j+1] = np.exp(y_train)
        test_[:, j+1] = np.exp(y_test)
        # print("val auc Score: %f" % r2_score(y_predict, dataset_d2[:, j]))
        print('Method ',j)

    train_ = pd.DataFrame(train_)
    test_ = pd.DataFrame(test_)
    return train_,test_

# In[9]:

from sklearn.model_selection import cross_val_score, train_test_split
from sklearn.linear_model import LogisticRegression
clf = LogisticRegression()

data_0 = iris.data
data = data_0[:100,:]

target_0 = iris.target
target = target_0[:100]

x_train,x_test,y_train,y_test=train_test_split(data,target,test_size=0.3)
x_train = pd.DataFrame(x_train) ; x_test = pd.DataFrame(x_test)

#模型融合中使用到的各个单模型
clfs = [LogisticRegression(),
        RandomForestClassifier(n_estimators=5, n_jobs=-1, criterion='gini'),
        ExtraTreesClassifier(n_estimators=5, n_jobs=-1, criterion='gini'),
        ExtraTreesClassifier(n_estimators=5, n_jobs=-1, criterion='entropy'),
        GradientBoostingClassifier(learning_rate=0.05, subsample=0.5, max_depth=6, n_estimators=5)]

New_train,New_test = Ensemble_add_feature(x_train,x_test,y_train,clfs)

clf = LogisticRegression()
# clf = GradientBoostingClassifier(learning_rate=0.02, subsample=0.5, max_depth=6, n_estimators=30)
clf.fit(New_train, y_train)
y_emb = clf.predict_proba(New_test)[:, 1]

print("Val auc Score of stacking: %f" % (roc_auc_score(y_test, y_emb)))

在这里插入图片描述

三、心跳预测赛题示例

赛题地址

3.1 准备工作

导入数据集并进行简单的预处理
将数据集划分成训练集和验证集
构建单模： $R a n d o m$ $F o r e s t$ ， $L G B$ ， $N N$
读取并演示如何利用融合模型生成可提交预测数据

# In[1]:导包

import pandas as pd
import numpy as np
import warnings
#import matplotlib
#import matplotlib.pyplot as plt
#import seaborn as sns

warnings.filterwarnings('ignore')
#%matplotlib inline

#import itertools
#import matplotlib.gridspec as gridspec
#from sklearn import datasets
#from sklearn.linear_model import LogisticRegression
#from sklearn.neighbors import KNeighborsClassifier
#from sklearn.naive_bayes import GaussianNB 
#from sklearn.ensemble import RandomForestClassifier,RandomForestRegressor
# from mlxtend.classifier import StackingClassifier
#from sklearn.model_selection import cross_val_score, train_test_split
# from mlxtend.plotting import plot_learning_curves
# from mlxtend.plotting import plot_decision_regions

#from sklearn.model_selection import StratifiedKFold
#from sklearn.model_selection import train_test_split
#from sklearn.model_selection import StratifiedKFold
#from sklearn.model_selection import train_test_split
import lightgbm as lgb
#from sklearn.neural_network import MLPClassifier,MLPRegressor
#from sklearn.metrics import mean_squared_error, mean_absolute_error

引入一个降内存的函数：

# In[2]: 这里引入一个降内存的函数

def reduce_mem_usage(df):
    start_mem = df.memory_usage().sum() / 1024**2 
    print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))
    
    for col in df.columns:
        col_type = df[col].dtype
        
        if col_type != object:
            c_min = df[col].min()
            c_max = df[col].max()
            if str(col_type)[:3] == 'int':
                if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:
                    df[col] = df[col].astype(np.int8)
                elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:
                    df[col] = df[col].astype(np.int16)
                elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:
                    df[col] = df[col].astype(np.int32)
                elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:
                    df[col] = df[col].astype(np.int64)  
            else:
                if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:
                    df[col] = df[col].astype(np.float16)
                elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:
                    df[col] = df[col].astype(np.float32)
                else:
                    df[col] = df[col].astype(np.float64)
        else:
            df[col] = df[col].astype('category')

    end_mem = df.memory_usage().sum() / 1024**2 
    print('Memory usage after optimization is: {:.2f} MB'.format(end_mem))
    print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem))
    
    return df

读取数据及数据预处理：

# In[3]:

train = pd.read_csv('D:/Cadabra_tools002/tianqi_file/train.csv')
test = pd.read_csv('D:/Cadabra_tools002/tianqi_file/testA.csv')

# 简单预处理
train_list = []
for items in train.values:
    train_list.append([items[0]] + [float(i) for i in items[1].split(',')] + [items[2]])
    
test_list = []
for items in test.values:
    test_list.append([items[0]] + [float(i) for i in items[1].split(',')])

train = pd.DataFrame(np.array(train_list))
test = pd.DataFrame(np.array(test_list))

# id列不算入特征
features = ['s_'+str(i) for i in range(len(train_list[0])-2)] 
train.columns = ['id'] + features + ['label']
test.columns = ['id'] + features

train = reduce_mem_usage(train)
test = reduce_mem_usage(test)

在这里插入图片描述

# In[4]:
# 根据8：2划分训练集和校验集
X_train = train.drop(['id','label'], axis=1)
y_train = train['label']

# 测试集
X_test = test.drop(['id'], axis=1)

# 第一次运行可以先用一个subdata，这样速度会快些
X_train = X_train.iloc[:50000,:20]
y_train = y_train.iloc[:50000]
X_test = X_test.iloc[:,:20]

# 划分训练集和测试集
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2)

# In[5]:
# 单模函数
def build_model_rf(X_train,y_train):
    model = RandomForestRegressor(n_estimators = 100)
    model.fit(X_train, y_train)
    return model


def build_model_lgb(X_train,y_train):
    model = lgb.LGBMRegressor(num_leaves=63,learning_rate = 0.1,n_estimators = 100)
    model.fit(X_train, y_train)
    return model


def build_model_nn(X_train,y_train):
    model = MLPRegressor(alpha=1e-05, hidden_layer_sizes=(5, 2), random_state=1,solver='lbfgs')
    model.fit(X_train, y_train)
    return model

# In[6]:
# 这里针对三个单模进行训练，其中subA_rf/lgb/nn都是可以提交的模型
# 单模没有进行调参，因此是弱分类器，效果可能不是很好。

print('predict rf...')
model_rf = build_model_rf(X_train,y_train)
val_rf = model_rf.predict(X_val)
subA_rf = model_rf.predict(X_test)


print('predict lgb...')
model_lgb = build_model_lgb(X_train,y_train)
val_lgb = model_lgb.predict(X_val)
subA_lgb = model_rf.predict(X_test)


print('predict NN...')
model_nn = build_model_nn(X_train,y_train)
val_nn = model_nn.predict(X_val)
subA_nn = model_rf.predict(X_test)

在这里插入图片描述

3.2 加权融合

# In[7]: 加权融合
# 加权融合模型，如果w没有变，就是均值融合

def Weighted_method(test_pre1,test_pre2,test_pre3,w=[1/3,1/3,1/3]):
    Weighted_result = w[0]*pd.Series(test_pre1)+w[1]*pd.Series(test_pre2)+w[2]*pd.Series(test_pre3)
    return Weighted_result

# 初始权重，可以进行自定义，这里我们随便设置一个权重
w = [0.2, 0.3, 0.5]

val_pre = Weighted_method(val_rf,val_lgb,val_nn,w)
MAE_Weighted = mean_absolute_error(y_val,val_pre)
print('MAE of Weighted of val:',MAE_Weighted)

在这里插入图片描述

# In[8]: Stacking融合

## 预测数据部分
subA = Weighted_method(subA_rf,subA_lgb,subA_nn,w)

## 生成提交文件
sub = pd.DataFrame()
sub['SaleID'] = X_test.index
sub['price'] = subA
sub.to_csv('D:/Cadabra_tools002/tianqi_file/sub_Weighted.csv',index=False)

在这里插入图片描述

# In[9]:
## Stacking

## 第一层
train_rf_pred = model_rf.predict(X_train)
train_lgb_pred = model_lgb.predict(X_train)
train_nn_pred = model_nn.predict(X_train)

stacking_X_train = pd.DataFrame()
stacking_X_train['Method_1'] = train_rf_pred
stacking_X_train['Method_2'] = train_lgb_pred
stacking_X_train['Method_3'] = train_nn_pred

stacking_X_val = pd.DataFrame()
stacking_X_val['Method_1'] = val_rf
stacking_X_val['Method_2'] = val_lgb
stacking_X_val['Method_3'] = val_nn

stacking_X_test = pd.DataFrame()
stacking_X_test['Method_1'] = subA_rf
stacking_X_test['Method_2'] = subA_lgb
stacking_X_test['Method_3'] = subA_nn

# In[9-1]: 查看前五行的数据
stacking_X_test.head()

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# In[10]:
# 第二层是用random forest

model_lr_stacking = build_model_rf(stacking_X_train,y_train)

## 训练集
train_pre_Stacking = model_lr_stacking.predict(stacking_X_train)
print('MAE of stacking:',mean_absolute_error(y_train,train_pre_Stacking))

## 验证集
val_pre_Stacking = model_lr_stacking.predict(stacking_X_val)
print('MAE of stacking:',mean_absolute_error(y_val,val_pre_Stacking))

## 预测集
print('Predict stacking...')
subA_Stacking = model_lr_stacking.predict(stacking_X_test)

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附录

数据集下载

源码及数据集下载

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