# -*- coding: utf-8 -*-
"""
Created on Tue Aug 11 10:12:48 2020

@author: Admin
"""
## 我們使用葡萄酒數(shù)據(jù)集進(jìn)行建模(數(shù)據(jù)處理)
import pandas as pd 
df_wine = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data',header=None)
df_wine.columns = ['Class label', 'Alcohol','Malic acid', 'Ash','Alcalinity of ash','Magnesium', 'Total phenols',
                   'Flavanoids', 'Nonflavanoid phenols','Proanthocyanins','Color intensity', 'Hue','OD280/OD315 of diluted wines','Proline']
df_wine['Class label'].value_counts()
'''
2    71
1    59
3    48
Name: Class label, dtype: int64
'''
df_wine = df_wine[df_wine['Class label'] != 1]  # drop 1 class
y = df_wine['Class label'].values
X = df_wine[['Alcohol','OD280/OD315 of diluted wines']].values
from sklearn.model_selection import train_test_split  # 切分訓(xùn)練集與測試集
from sklearn.preprocessing import LabelEncoder   # 標(biāo)簽化分類變量
le = LabelEncoder()
y = le.fit_transform(y)  #吧y值改為0和1 ，原來是2和3 
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=1,stratify=y)  #2、8分

## 我們使用單一決策樹分類：
from sklearn.tree import DecisionTreeClassifier
tree = DecisionTreeClassifier(criterion='entropy',random_state=1,max_depth=None)   #選擇決策樹為基本分類器
from sklearn.metrics import accuracy_score  #計(jì)算準(zhǔn)確率
tree = tree.fit(X_train,y_train)
y_train_pred = tree.predict(X_train)
y_test_pred = tree.predict(X_test)
tree_train = accuracy_score(y_train,y_train_pred)  #訓(xùn)練集準(zhǔn)確率
tree_test = accuracy_score(y_test,y_test_pred)  #測試集準(zhǔn)確率
print('Decision tree train/test accuracies %.3f/%.3f' % (tree_train,tree_test))
#Decision tree train/test accuracies 1.000/0.833


## 我們使用BaggingClassifier分類：
from sklearn.ensemble import BaggingClassifier
tree = DecisionTreeClassifier(criterion='entropy',random_state=1,max_depth=None)   #選擇決策樹為基本分類器
bag = BaggingClassifier(base_estimator=tree,n_estimators=500,max_samples=1.0,max_features=1.0,bootstrap=True,
                        bootstrap_features=False,n_jobs=1,random_state=1)
from sklearn.metrics import accuracy_score
bag = bag.fit(X_train,y_train)
y_train_pred = bag.predict(X_train)
y_test_pred = bag.predict(X_test)
bag_train = accuracy_score(y_train,y_train_pred)
bag_test = accuracy_score(y_test,y_test_pred)
print('Bagging train/test accuracies %.3f/%.3f' % (bag_train,bag_test))
#Bagging train/test accuracies 1.000/0.917

'''
我們可以對(duì)比兩個(gè)準(zhǔn)確率，測試準(zhǔn)確率較之決策樹得到了顯著的提高

我們來對(duì)比下這兩個(gè)分類方法上的差異
'''
## 我們來對(duì)比下這兩個(gè)分類方法上的差異
x_min = X_train[:, 0].min() - 1
x_max = X_train[:, 0].max() + 1
y_min = X_train[:, 1].min() - 1
y_max = X_train[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),np.arange(y_min, y_max, 0.1))
f, axarr = plt.subplots(nrows=1, ncols=2,sharex='col',sharey='row',figsize=(12, 6))
for idx, clf, tt in zip([0, 1],[tree, bag],['Decision tree', 'Bagging']):
    clf.fit(X_train, y_train)
    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])  #ravel()方法將數(shù)組維度拉成一維數(shù)組,np.c_是按行連接兩個(gè)矩陣，就是把兩矩陣左右相加，要求行數(shù)相等
    Z = Z.reshape(xx.shape)
    axarr[idx].contourf(xx, yy, Z, alpha=0.3)
    axarr[idx].scatter(X_train[y_train==0, 0],X_train[y_train==0, 1],c='blue', marker='^')
    axarr[idx].scatter(X_train[y_train==1, 0],X_train[y_train==1, 1],c='green', marker='o')
    axarr[idx].set_title(tt)
axarr[0].set_ylabel('Alcohol', fontsize=12)
plt.tight_layout()
plt.text(0, -0.2,s='OD280/OD315 of diluted wines',ha='center',va='center',fontsize=12,transform=axarr[1].transAxes)
plt.show()
'''
從結(jié)果圖看起來，三個(gè)節(jié)點(diǎn)深度的決策樹分段線性決策邊界在Bagging集成中看起來更加平滑
'''

class sklearn.ensemble.BaggingClassifier(base_estimator=None, n_estimators=10, max_samples=1.0, max_features=1.0, bootstrap=True, bootstrap_features=False, oob_score=False, warm_start=False, n_jobs=None, random_state=None, verbose=0)

# -*- coding: utf-8 -*-
"""
Created on Tue Aug 11 10:12:48 2020

@author: Admin
"""
# 從 sklearn.datasets 導(dǎo)入波士頓房價(jià)數(shù)據(jù)讀取器。
from sklearn.datasets import load_boston
# 從讀取房價(jià)數(shù)據(jù)存儲(chǔ)在變量 boston 中。
boston = load_boston()

# 從sklearn.cross_validation 導(dǎo)入數(shù)據(jù)分割器。
from sklearn.model_selection import train_test_split
X = boston.data
y = boston.target
# 隨機(jī)采樣 25% 的數(shù)據(jù)構(gòu)建測試樣本，其余作為訓(xùn)練樣本。
X_train, X_test, y_train, y_test = train_test_split(X,
                                                    y,
                                                    random_state=33,
                                                    test_size=0.25)
                                                    
# 從 sklearn.preprocessing 導(dǎo)入數(shù)據(jù)標(biāo)準(zhǔn)化模塊。
from sklearn.preprocessing import StandardScaler
# 分別初始化對(duì)特征和目標(biāo)值的標(biāo)準(zhǔn)化器。
ss_X = StandardScaler()
ss_y = StandardScaler()
# 分別對(duì)訓(xùn)練和測試數(shù)據(jù)的特征以及目標(biāo)值進(jìn)行標(biāo)準(zhǔn)化處理。
X_train = ss_X.fit_transform(X_train)
X_test = ss_X.transform(X_test)
y_train = ss_y.fit_transform(y_train.reshape(-1,1))
y_test = ss_y.transform(y_test.reshape(-1,1))                                                


#BaggingRegressor
from sklearn.ensemble import BaggingRegressor
bagr = BaggingRegressor(n_estimators=500,max_samples=1.0,max_features=1.0,bootstrap=True,
                        bootstrap_features=False,n_jobs=1,random_state=1)
bagr.fit(X_train, y_train)
bagr_y_predict = bagr.predict(X_test)

from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error
# 使用 R-squared、MSE 以及 MAE 指標(biāo)對(duì)默認(rèn)配置的隨機(jī)回歸森林在測試集上進(jìn)行性能評(píng)估。
print('R-squared value of BaggingRegressor:', bagr.score(X_test, y_test))

print('The mean squared error of BaggingRegressor:', mean_squared_error(
    ss_y.inverse_transform(y_test), ss_y.inverse_transform(bagr_y_predict)))

print('The mean absoluate error of BaggingRegressor:', mean_absolute_error(
    ss_y.inverse_transform(y_test), ss_y.inverse_transform(bagr_y_predict)))

'''
R-squared value of BaggingRegressor: 0.8417369323817341
The mean squared error of BaggingRegressor: 12.27192314456692
The mean absoluate error of BaggingRegressor: 2.2523244094488195
'''

#隨機(jī)森林實(shí)現(xiàn)
from sklearn.ensemble import RandomForestRegressor
rfr = RandomForestRegressor(random_state=1)

rfr.fit(X_train, y_train)

rfr_y_predict = rfr.predict(X_test)


# 使用 R-squared、MSE 以及 MAE 指標(biāo)對(duì)默認(rèn)配置的隨機(jī)回歸森林在測試集上進(jìn)行性能評(píng)估。
print('R-squared value of RandomForestRegressor:', rfr.score(X_test, y_test))

print('The mean squared error of RandomForestRegressor:', mean_squared_error(
    ss_y.inverse_transform(y_test), ss_y.inverse_transform(rfr_y_predict)))

print('The mean absoluate error of RandomForestRegressor:', mean_absolute_error(
    ss_y.inverse_transform(y_test), ss_y.inverse_transform(rfr_y_predict)))

'''
R-squared value of RandomForestRegressor: 0.8083674472512408
The mean squared error of RandomForestRegressor: 14.859436220472439
The mean absoluate error of RandomForestRegressor: 2.4732283464566924
'''

#用Bagging集成隨機(jī)森林
bagr = BaggingRegressor(base_estimator=rfr,n_estimators=500,max_samples=1.0,max_features=1.0,bootstrap=True,
                        bootstrap_features=False,n_jobs=1,random_state=1)
bagr.fit(X_train, y_train)
bagr_y_predict = bagr.predict(X_test)

# 使用 R-squared、MSE 以及 MAE 指標(biāo)對(duì)默認(rèn)配置的隨機(jī)回歸森林在測試集上進(jìn)行性能評(píng)估。
print('R-squared value of BaggingRegressor:', bagr.score(X_test, y_test))

print('The mean squared error of BaggingRegressor:', mean_squared_error(
    ss_y.inverse_transform(y_test), ss_y.inverse_transform(bagr_y_predict)))

print('The mean absoluate error of BaggingRegressor:', mean_absolute_error(
    ss_y.inverse_transform(y_test), ss_y.inverse_transform(bagr_y_predict)))

'''
R-squared value of BaggingRegressor: 0.8226953069216255
The mean squared error of BaggingRegressor: 13.748435433319694
The mean absoluate error of BaggingRegressor: 2.3744811023622048
'''