By:HEHE

本实例是基于：混凝土抗压强度的回归分析

# 导包
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
import seaborn as sns%matplotlib inlineimport warnings
warnings.filterwarnings('ignore')import os

1. 数据基本面分析

# path
path_dir = os.path.dirname(os.path.dirname(os.getcwd()))path_data = path_dir +  r'\concrete_data.xls'

# load_data
data = pd.read_excel(path_data)

# 查看数据基本面
data.head()

# 修改列名
data.columns = ['cement_component', 'furnace_slag', 'flay_ash', 'water_component', 'superplasticizer', \'coarse_aggregate', 'fine_aggregate', 'age', 'concrete_strength']

data.head()

# 查看数据基本面
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1030 entries, 0 to 1029
Data columns (total 9 columns):
cement_component     1030 non-null float64
furnace_slag         1030 non-null float64
flay_ash             1030 non-null float64
water_component      1030 non-null float64
superplasticizer     1030 non-null float64
coarse_aggregate     1030 non-null float64
fine_aggregate       1030 non-null float64
age                  1030 non-null int64
concrete_strength    1030 non-null float64
dtypes: float64(8), int64(1)
memory usage: 72.5 KB

# 查看数据基本面
data.describe()

数据基本面总结如下：

1. 数据集共1030条数据，特征8个，目标为concrete_strength
2. 数据集无缺失值，数据类型全为数值

2. EDA（数据探索性分析）

2.1 concrete_strength

sns.distplot(data['concrete_strength'], bins = 20, color = 'red')

<matplotlib.axes._subplots.AxesSubplot at 0x213da2c2080>

concrete_strength:数据分布正常，稍微有点右偏

2.2 features

plt.figure(figsize = (15,10.5))
plot_count = 1for feature in list(data.columns)[:-1]:plt.subplot(3,3, plot_count)plt.scatter(data[feature], data['concrete_strength'])plt.xlabel(feature.replace('_',' ').title())plt.ylabel('Concrete strength')plot_count +=1plt.show()

plt.figure(figsize=(9,9))
corrmat = data.corr()
sns.heatmap(corrmat, vmax= 0.8, square = True, )

<matplotlib.axes._subplots.AxesSubplot at 0x213ddc4e7b8>

EDA总结：

1. 数据相关性都不强，
2. cement_component，water_component，superplasticizer，age似乎相关性高一点
3. 由于特征都不多，可以分别用这四个特征以及所有特征尝试一遍
4. 没有发现异常值
5. 还没决定数据要不要标准化

3. model

实验内容：分别使用上面得到的特征，以及所有特征对混凝土强度做预测，同时使用不同的回归算法

from sklearn.model_selection import train_test_split

# 按数据集特征切割训练集测试集
def split_train_test(data, features=None, test_ratio=0.2):y = data['concrete_strength']if features != None:x = data[features]else:x = data.drop(['concrete_strength'], axis=1)train_x, test_x, train_y, test_y = train_test_split(x, y, test_size = test_ratio)return train_x, test_x, train_y, test_y

# 训练集，测试集
train_x, test_x, train_y, test_y = split_train_test(data, test_ratio = 0)

from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_scorefrom sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.linear_model import Lasso
from sklearn.linear_model import ElasticNet
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.svm import SVRfrom sklearn.metrics import r2_score

def data_cross_val(x,y, clfs, clfs_name, cv= 5):for i,clf in enumerate(clfs):scores = cross_val_score(estimator=clf, X= x, y= y, cv=cv, scoring ='r2')print(clfs_name[i])print('the R2 score: %f' %  np.mean(scores))

3.1 所有特征做回归

clfs = [LinearRegression(), Ridge(), Lasso(), ElasticNet(), GradientBoostingRegressor(), SVR()]
clfs_name = ['LinearRegression', 'Ridge', 'Lasso', 'ElasticNet', 'GradientBoostingRegressor', 'SVR']
data_cross_val(train_x, train_y, clfs,clfs_name, cv = 5)

LinearRegression
the R2 score: 0.604974
Ridge
the R2 score: 0.604974
Lasso
the R2 score: 0.605090
ElasticNet
the R2 score: 0.605220
GradientBoostingRegressor
the R2 score: 0.908837
SVR
the R2 score: 0.023249

结论：单一的回归器还是没有梯度提升机好，可以尝试用bagging和stacking的方式再实验一下，或者增加特征。

3.2 部分相关特征做回归

# 训练集，测试集
features = ['cement_component','water_component','superplasticizer','age']
train_x, test_x, train_y, test_y = split_train_test(data, features, test_ratio = 0)

clfs = [LinearRegression(), Ridge(), Lasso(), ElasticNet(), GradientBoostingRegressor(), SVR()]
clfs_name = ['LinearRegression', 'Ridge', 'Lasso', 'ElasticNet', 'GradientBoostingRegressor', 'SVR']
data_cross_val(train_x, train_y, clfs,clfs_name, cv = 5)

LinearRegression
the R2 score: 0.485046
Ridge
the R2 score: 0.485045
Lasso
the R2 score: 0.484828
ElasticNet
the R2 score: 0.484840
GradientBoostingRegressor
the R2 score: 0.830816
SVR
the R2 score: 0.043992

总结：目前来说使用部分相关的特征来做回归，由于特征数目太少，还不如用所有特征来的比较好

3.3 单线性回归

plt.figure(figsize=(15,7))
plot_count = 1for feature in ['cement_component', 'flay_ash', 'water_component', 'superplasticizer', 'coarse_aggregate']:data_tr = data[['concrete_strength', feature]]x_train, x_test, y_train, y_test = split_train_test(data_tr, [feature])# Create linear regression objectregr = LinearRegression()# Train the model using the training setsregr.fit(x_train, y_train)y_pred = regr.predict(x_test)# Plot outputsplt.subplot(2,3,plot_count)plt.scatter(x_test, y_test,  color='black')plt.plot(x_test, y_pred, color='blue',linewidth=3)plt.xlabel(feature.replace('_',' ').title())plt.ylabel('Concrete strength')print(feature, r2_score(y_test, y_pred))plot_count+=1plt.show()

cement_component 0.24550132796330282
flay_ash 0.012228585601186226
water_component 0.09828887425075417
superplasticizer 0.11471267678235075
coarse_aggregate 0.02046823335033021

features = ['cement_component', 'flay_ash', 'water_component', 'superplasticizer', 'coarse_aggregate']data_tr = data
data_tr=data_tr[(data_tr.T != 0).all()]x_train, x_test, y_train, y_test = split_train_test(data_tr, features)# Create linear regression object
regr = LinearRegression()# Train the model using the training sets
regr.fit(x_train, y_train)
y_pred = regr.predict(x_test)plt.scatter(range(len(y_test)), y_test,  color='black')
plt.plot(y_pred, color='blue', linewidth=3)print('Features: %s'%str(features))
print('R2 score: %f'%r2_score(y_test, y_pred))
print('Intercept: %f'%regr.intercept_)
print('Coefficients: %s'%str(regr.coef_))

Features: ['cement_component', 'flay_ash', 'water_component', 'superplasticizer', 'coarse_aggregate']
R2 score: 0.155569
Intercept: 84.481913
Coefficients: [ 0.04304209 -0.02577486 -0.1747249   0.15980663 -0.02633656]

alphas = np.arange(0.1,5,0.1)model = Ridge()
cv = GridSearchCV(estimator=model, param_grid=dict(alpha=alphas))y_pred = cv.fit(x_train, y_train).predict(x_test)plt.scatter(range(len(y_test)), y_test,  color='black')
plt.plot(y_pred, color='blue', linewidth=3)print('Features: %s'%str(features))
print('R2 score: %f'%r2_score(y_test, y_pred))
print('Intercept: %f'%regr.intercept_)
print('Coefficients: %s'%str(regr.coef_))

Features: ['cement_component', 'flay_ash', 'water_component', 'superplasticizer', 'coarse_aggregate']
R2 score: 0.155562
Intercept: 84.481913
Coefficients: [ 0.04304209 -0.02577486 -0.1747249   0.15980663 -0.02633656]

model = Lasso()
cv = GridSearchCV(estimator=model, param_grid=dict(alpha=alphas))y_pred = cv.fit(x_train, y_train).predict(x_test)plt.scatter(range(len(y_test)), y_test,  color='black')
plt.plot(y_pred, color='blue', linewidth=3)print('Features: %s'%str(features))
print('R2 score: %f'%r2_score(y_test, y_pred))
print('Intercept: %f'%regr.intercept_)
print('Coefficients: %s'%str(regr.coef_))

Features: ['cement_component', 'flay_ash', 'water_component', 'superplasticizer', 'coarse_aggregate']
R2 score: 0.151682
Intercept: 84.481913
Coefficients: [ 0.04304209 -0.02577486 -0.1747249   0.15980663 -0.02633656]

model = ElasticNet()
cv = GridSearchCV(estimator=model, param_grid=dict(alpha=alphas))y_pred = cv.fit(x_train, y_train).predict(x_test)plt.scatter(range(len(y_test)), y_test,  color='black')
plt.plot(y_pred, color='blue', linewidth=3)print('Features: %s'%str(features))
print('R2 score: %f'%r2_score(y_test, y_pred))
print('Intercept: %f'%regr.intercept_)
print('Coefficients: %s'%str(regr.coef_))

Features: ['cement_component', 'flay_ash', 'water_component', 'superplasticizer', 'coarse_aggregate']
R2 score: 0.151796
Intercept: 84.481913
Coefficients: [ 0.04304209 -0.02577486 -0.1747249   0.15980663 -0.02633656]

plt.figure(figsize=(15,7))
plot_count = 1for feature in ['cement_component', 'flay_ash', 'water_component', 'superplasticizer', 'coarse_aggregate']:data_tr = data[['concrete_strength', feature]]data_tr=data_tr[(data_tr.T != 0).all()]x_train, x_test, y_train, y_test = split_train_test(data_tr, [feature])# Create linear regression objectregr = GradientBoostingRegressor()# Train the model using the training setsregr.fit(x_train, y_train)y_pred = regr.predict(x_test)# Plot outputsplt.subplot(2,3,plot_count)plt.scatter(x_test, y_test,  color='black')plt.plot(x_test, y_pred, color='blue',linewidth=3)plt.xlabel(feature.replace('_',' ').title())plt.ylabel('Concrete strength')print(feature, r2_score(y_test, y_pred))plot_count+=1plt.show()

cement_component 0.35248985320039705
flay_ash 0.17319875701989795
water_component 0.285023360910455
superplasticizer 0.19306275412216778
coarse_aggregate 0.17712532312647877

model = GradientBoostingRegressor()y_pred = model.fit(x_train, y_train).predict(x_test)plt.scatter(range(len(y_test)), y_test,  color='black')
plt.plot(y_pred, color='blue',linewidth=3)print('Features: %s'%str(features))
print('R2 score: %f'%r2_score(y_test, y_pred))
#print('Intercept: %f'%regr.intercept_)
#print('Coefficients: %s'%str(regr.coef_))

Features: ['cement_component', 'flay_ash', 'water_component', 'superplasticizer', 'coarse_aggregate']
R2 score: 0.177125

plt.figure(figsize=(15,7))
plot_count = 1for feature in ['cement_component', 'flay_ash', 'water_component', 'superplasticizer', 'coarse_aggregate']:data_tr = data[['concrete_strength', feature]]data_tr=data_tr[(data_tr.T != 0).all()]x_train, x_test, y_train, y_test = split_train_test(data_tr, [feature])# Create linear regression objectregr = SVR(kernel='linear')# Train the model using the training setsregr.fit(x_train, y_train)y_pred = regr.predict(x_test)# Plot outputsplt.subplot(2,3,plot_count)plt.scatter(x_test, y_test,  color='black')plt.plot(x_test, y_pred, color='blue', linewidth=3)plt.xlabel(feature.replace('_',' ').title())plt.ylabel('Concrete strength')print(feature, r2_score(y_test, y_pred))plot_count+=1plt.show()

cement_component 0.2054832593541437
flay_ash -0.044636249705873654
water_component 0.07749271320026574
superplasticizer 0.0671220299245393
coarse_aggregate 0.016036478490831563

model = SVR(kernel='linear')y_pred = model.fit(x_train, y_train).predict(x_test)plt.scatter(range(len(y_test)), y_test,  color='black')
plt.plot(y_pred, color='blue', linewidth=3)print('Features: %s'%str(features))
print('R2 score: %f'%r2_score(y_test, y_pred))

Features: ['cement_component', 'flay_ash', 'water_component', 'superplasticizer', 'coarse_aggregate']
R2 score: 0.016036

4. 使用 cement_component和 water_component预测concrete_strength

feature = 'cement_component'
cc_new_data = np.array([[213.5]])data_tr = data[['concrete_strength', feature]]
data_tr=data_tr[(data_tr.T != 0).all()]x_train, x_test, y_train, y_test = split_train_test(data_tr, [feature])regr = GradientBoostingRegressor()# Train the model using the training setsregr.fit(x_train, y_train)
cs_pred = regr.predict(cc_new_data)
print('Predicted value of concrete strength: %f'%cs_pred)

Predicted value of concrete strength: 36.472380

feature = 'water_component'
wc_new_data = np.array([[200]])data_tr = data[['concrete_strength', feature]]
data_tr=data_tr[(data_tr.T != 0).all()]x_train, x_test, y_train, y_test = split_train_test(data_tr, [feature])regr = GradientBoostingRegressor()# Train the model using the training sets
regr.fit(x_train, y_train)
cs_pred = regr.predict(wc_new_data)
print('Predicted value of concrete strength: %f'%cs_pred)

Predicted value of concrete strength: 32.648425