介紹

維基百科上說，“Hyperparameter optimization或tuning是為學(xué)習(xí)算法選擇一組最優(yōu)的hyperparameters的問題”。

超參數(shù)

內(nèi)容

1. 傳統(tǒng)的手工調(diào)參
2. 網(wǎng)格搜索
3. 隨機搜索
4. 貝葉斯搜索

1. 傳統(tǒng)手工搜索

#importing?required?libraries
from?sklearn.neighbors?import?KNeighborsClassifier
from?sklearn.model_selection?import?train_test_split
from?sklearn.model_selection?import?KFold?,?cross_val_score
from?sklearn.datasets?import?load_wine

wine?=?load_wine()
X?=?wine.data
y?=?wine.target

#splitting?the?data?into?train?and?test?set
X_train,X_test,y_train,y_test?=?train_test_split(X,y,test_size?=?0.3,random_state?=?14)

#declaring?parameters?grid
k_value?=?list(range(2,11))
algorithm?=?['auto','ball_tree','kd_tree','brute']
scores?=?[]
best_comb?=?[]
kfold?=?KFold(n_splits=5)

#hyperparameter?tunning
for?algo?in?algorithm:
??for?k?in?k_value:
????knn?=?KNeighborsClassifier(n_neighbors=k,algorithm=algo)
????results?=?cross_val_score(knn,X_train,y_train,cv?=?kfold)

????print(f'Score:{round(results.mean(),4)}?with?algo?=?{algo}?,?K?=?{k}')
????scores.append(results.mean())
????best_comb.append((k,algo))

best_param?=?best_comb[scores.index(max(scores))]
print(f'\nThe?Best?Score?:?{max(scores)}')
print(f"['algorithm':?{best_param[1]}?,'n_neighbors':?{best_param[0]}]")

1. 沒辦法確保得到最佳的參數(shù)組合。
2. 這是一個不斷試錯的過程，所以，非常的耗時。

2. 網(wǎng)格搜索

from?sklearn.model_selection?import?GridSearchCV

knn?=?KNeighborsClassifier()
grid_param?=?{?'n_neighbors'?:?list(range(2,11))?,?
??????????????'algorithm'?:?['auto','ball_tree','kd_tree','brute']?}
??????????????
grid?=?GridSearchCV(knn,grid_param,cv?=?5)
grid.fit(X_train,y_train)

#best?parameter?combination
grid.best_params_

#Score?achieved?with?best?parameter?combination
grid.best_score_

#all?combinations?of?hyperparameters
grid.cv_results_['params']

#average?scores?of?cross-validation
grid.cv_results_['mean_test_score']

3. 隨機搜索

from?sklearn.model_selection?import?RandomizedSearchCV

knn?=?KNeighborsClassifier()

grid_param?=?{?'n_neighbors'?:?list(range(2,11))?,?
??????????????'algorithm'?:?['auto','ball_tree','kd_tree','brute']?}

rand_ser?=?RandomizedSearchCV(knn,grid_param,n_iter=10)
rand_ser.fit(X_train,y_train)

#best?parameter?combination
rand_ser.best_params_

#score?achieved?with?best?parameter?combination
rand_ser.best_score_

#all?combinations?of?hyperparameters
rand_ser.cv_results_['params']

#average?scores?of?cross-validation
rand_ser.cv_results_['mean_test_score']

4. 貝葉斯搜索

1. 使用先前評估的點X1*:n*，計算損失f的后驗期望。
2. 在新的點X的抽樣損失f，從而最大化f的期望的某些方法。該方法指定f域的哪些區(qū)域最適于抽樣。

Installation: pip install scikit-optimize

from?skopt?import?BayesSearchCV

import?warnings
warnings.filterwarnings("ignore")

#?parameter?ranges?are?specified?by?one?of?below
from?skopt.space?import?Real,?Categorical,?Integer

knn?=?KNeighborsClassifier()
#defining?hyper-parameter?grid
grid_param?=?{?'n_neighbors'?:?list(range(2,11))?,?
??????????????'algorithm'?:?['auto','ball_tree','kd_tree','brute']?}

#initializing?Bayesian?Search
Bayes?=?BayesSearchCV(knn?,?grid_param?,?n_iter=30?,?random_state=14)
Bayes.fit(X_train,y_train)

#best?parameter?combination
Bayes.best_params_

#score?achieved?with?best?parameter?combination
Bayes.best_score_

#all?combinations?of?hyperparameters
Bayes.cv_results_['params']

#average?scores?of?cross-validation
Bayes.cv_results_['mean_test_score']

Installation: pip install bayesian-optimization

總結(jié)