机器学习-超参调整-网格搜索(Grid Search)

Section I: Code and Analyses

第一部分：代码

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.pipeline import make_pipeline
import numpy as np
from sklearn.model_selection import GridSearchCV
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings("ignore")

plt.rcParams['figure.dpi']=200
plt.rcParams['savefig.dpi']=200
font = {'family': 'Times New Roman',
        'weight': 'light'}
plt.rc("font", **font)

#Section 1: Load Breast data, i.e., Benign and Malignant
breast=datasets.load_breast_cancer()
X=breast.data
y=breast.target
X_train,X_test,y_train,y_test=\
    train_test_split(X,y,test_size=0.2,stratify=y,random_state=1)

#Section 2: Construct model optimized via GridSearch
pipe_svc=make_pipeline(StandardScaler(),
                       SVC(random_state=1))

param_range=[0.0001,0.001,0.01,0.1,1.0,10.0,100.0,1000]
param_grid=[{'svc__C':param_range,'svc__kernel':['linear']},
            {'svc__C':param_range,'svc__kernel':['rbf'],
             'svc__gamma':param_range}]
gs=GridSearchCV(estimator=pipe_svc,
                param_grid=param_grid,
                scoring='accuracy',
                cv=10,
                n_jobs=-1)
gs.fit(X_train,y_train)
print("Best score: %.3f" % gs.best_score_)
print("Best parameters: ",gs.best_params_)

clf=gs.best_estimator_
clf.fit(X_train,y_train)
print("Test Accuracy: ",clf.score(X_test,y_test))
plt.savefig('./fig1.png')
plt.show()

第二部分：结果

Best score: 0.976
Best parameters:  {'svc__C': 10.0, 'svc__gamma': 0.001, 'svc__kernel': 'rbf'}
Test Accuracy:  0.9824561403508771

测试集预测精度相比已较好的训练精度，亦相对较好。如此说明调优后模型的精度

参考文献
Sebastian Raschka, Vahid Mirjalili. Python机器学习第二版. 南京：东南大学出版社，2018.