模型评估、选择与验证——参数优化

暴力搜索寻优GridSearchCV

from sklearn.datasets import load_digits
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split

#加载数据
digits=load_digits()
X_train,X_test,y_train,y_test=train_test_split(digits.data,digits.target,test_size=0.25,
                                               random_state=0,stratify=digits.target)

#参数优化
tuned_parameters=[{'penalty':['l1','l2'],
                  'C':[0.01,0.05,0.1,0.5,1,5,10,50,100],
                  'solver':['liblinear'],
                  'multi_class':['ovr']},

                 {'penalty':['l2'],
                  'C':[0.01,0.05,0.1,0.5,1,5,10,50,100],
                  'solver':['lbfgs'],
                  'multi_class':['ovr','multinomial']}]
clf=GridSearchCV(LogisticRegression(tol=1e-6),tuned_parameters,cv=10)
clf.fit(X_train,y_train)
print('Best parameters set found:',clf.best_params_)
print('Grid scores:')
for params,mean_score,scores in clf.grid_scores_:
    print('\t%0.3f(+/-%0.03f) for %s'%(mean_score,scores.std()*2,params))
print('Optimized Score:',clf.score(X_test,y_test))
print('Detailed classification report:')
y_true,y_pred=y_test,clf.predict(X_test)
print(classification_report(y_true,y_pred))

随机搜索寻优RandomizedSearchCV

from sklearn.datasets import load_digits
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import RandomizedSearchCV
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
import scipy

#加载数据
digits=load_digits()
X_train,X_test,y_train,y_test=train_test_split(digits.data,digits.target,test_size=0.25,
                                               random_state=0,stratify=digits.target)

#参数优化
tuned_parameters={
                  'C':scipy.stats.expon(scale=100),
                  'multi_class':['ovr','multinomial']}
clf=RandomizedSearchCV(LogisticRegression(penalty='l2',solver='lbfgs',tol=1e-6),tuned_parameters,
                       cv=10,scoring='accuracy',n_iter=100)
clf.fit(X_train,y_train)
print('Best parameters set found:',clf.best_params_)
print('Grid scores:')
for params,mean_score,scores in clf.grid_scores_:
    print('\t%0.3f(+/-%0.03f) for %s'%(mean_score,scores.std()*2,params))
print('Optimized Score:',clf.score(X_test,y_test))
print('Detailed classification report:')
y_true,y_pred=y_test,clf.predict(X_test)
print(classification_report(y_true,y_pred))