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吴裕雄 python 机器学习——模型选择参数优化随机搜索寻优RandomizedSearchCV模型
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import scipyfrom sklearn.datasets import load_digits
from sklearn.metrics import classification_report
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV,RandomizedSearchCV#模型选择参数优化随机搜索寻优RandomizedSearchCV模型
def test_RandomizedSearchCV():
'''
测试 RandomizedSearchCV 的用法。使用 LogisticRegression 作为分类器,主要优化 C、multi_class 等参数。其中 C 的分布函数为指数分布
'''
### 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), # 指数分布
'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("Randomized Grid scores:")
# for params, mean_score, scores in clf.fit_params,clf.mean_score,clf.score:
# print("\t%0.3f (+/-%0.03f) for %s" % (mean_score, scores() * 2, params))
# print("\t%0.3f (+/-%0.03f) for %s" % (clf.mean_score,clf.score * 2, clf.fit_params))
print(clf) 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))#调用RandomizedSearchCV()
test_RandomizedSearchCV()