Grid Search 網格搜索
GridSearchCV:一種調參的方法,當你算法模型效果不是很好時,可以通過該方法來調整參數,通過循環遍歷,嘗試每一種參數組合,返回最好的得分值的參數組合
比如支持向量機中的參數 C 和 gamma ,當我們不知道哪個參數效果更好時,可以通過該方法來選擇參數,我們把C 和gamma 的選擇範圍定位[0.001,0.01,0.1,1,10,100]
每個參數都能組合在一起,循環過程就像是在網格中遍歷,所以叫網格搜索
c=0.001 | c=0.01 | c=0.1 | c=1 | c=10 | c=100 | |
---|---|---|---|---|---|---|
gamma =0.001 | SVC( gamma=0.001,C=0.001) | … | … | … | … | … |
gamma =0.01 | SVC( gamma=0.01,C=0.001) | … | … | … | … | … |
… | … | … | … | … | … | … |
… | … | … | … | … | … | … |
gamma= 10 | SVC( gamma=10,C=0.001) | … | … | … | … | … |
gamma=100 | SVC( gamma=100,C=0.001) | … | … | … | … | … |
下面來通過具體代碼看看怎麼調優:
from sklearn.datasets import load_iris
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
iris = load_iris()
X_train,X_test,y_train,y_test = train_test_split(iris.data,iris.target,random_state=0)
print("訓練集個數:%d 測試集個數:%d "%((len(X_train)),len(X_test)))
#開始進行網格搜索
best_score = 0
for gamma in [0.001,0.01,0.1,1,10,100]:
for C in [0.001,0.01,0.1,1,10,100]:
svm = SVC(gamma = gamma ,C = C)
svm.fit(X_train,y_train)
score = svm.score(X_test,y_test)
if score > best_score:
best_score = score
best_parameters = {'gamma':gamma,'C':C}
print("best_score:{:.2f}".format(best_score))
print("best_parameters:{}".format(best_parameters))
輸出:
訓練集個數:112 驗證集個數:38
best_score:0.97
best_parameters:{'gamma': 0.001, 'C': 100}
存在的問題:
原來的數據集分割爲訓練集和測試集之後,其中測試集起到的作用有兩個,一個是用來調整參數,一個是用來評價模型的好壞,這樣會導致評分值會比實際效果要好。(因爲我們將測試集送到了模型裏面去測試模型的好壞,而我們目的是要將訓練模型應用在沒使用過的數據上。)
解決方法:
我們可以通過把數據集劃分三份,一份是訓練集(訓練數據),一份是驗證集(調整參數),一份是測試集(測試模型)。
具體代碼如下:
X_trainval,X_test,y_trainval,y_test = train_test_split(iris.data,iris.target)
X_train,X_val,y_train,y_val = train_test_split(X_trainval,y_trainval)
print("訓練集個數:%d 驗證集個數:%d 測試集個數:%d "%((len(X_train)),len(X_val),len(X_test)))
best_scroe = 0
for gamma in [0.001,0.01,0.1,1,10,100]:
for C in [0.001,0.01,0.1,1,10,100]:
svm = SVC(gamma=gamma,C=C)
svm.fit(X_train,y_train)
score = svm.score(X_val,y_val)
if score > best_score:
best_score = score
best_parameters = {'gamma':gamma,'C':C}
svm = SVC(**best_parameters)
svm.fit(X_trainval,y_trainval)
test_score = svm.score(X_test,y_test)
print("best_score:{:.2f}".format(best_score))
print("best_parameters:{}".format(best_parameters))
print("best_score:{:.2f}".format(test_score))
輸出:
訓練集個數:84 驗證集個數:28 測試集個數:38
best_score:1.00
best_parameters:{'gamma': 0.001, 'C': 100}
best_score:0.95
進一步改進:
爲了防止模型過擬合,我們使用交叉驗證的方法
Grid Search with Cross Validation(GridSearchCV)
from sklearn.model_selection import cross_val_score
best_score = 0.0
for gamma in [0.001,0.01,0.1,1,10,100]:
for C in [0.001,0.01,0.1,1,10,100]:
svm = SVC(gamma=gamma,C=C)
scores = cross_val_score(svm,X_trainval,y_trainval,cv=5)
score = scores.mean()
if score > best_score:
best_score = score
best_parameters = {'gamma':gamma,'C':C}
svm = SVC(**best_parameters)
svm.fit(X_trainval,y_trainval)
test_score = svm.score(X_test,y_test)
print("best_score:{:.2f}".format(best_score))
print("best_parameters:{}".format(best_parameters))
print("best_score:{:.2f}".format(test_score))
輸出:
best_score:0.97
best_parameters:{'gamma': 0.1, 'C': 1}
best_score:0.95
爲了方便調參,sklearn 設置了一個類 GridSearchCV ,用來實現上面的fit,score等功能。
from sklearn.model_selection import GridSearchCV
#需要求的參數的範圍(列表的形式)
param_grid = {"gamma":[0.001,0.01,0.1,1,10,100],
"C":[0.001,0.01,0.1,1,10,100]}
#estimator模型 (將所求參數之外的確定的參數給出 )
estimator = SVC()
grid_search = GridSearchCV(estimator,param_grid,cv = 5)
X_train,X_test,y_train,y_test = train_test_split(iris.data,iris.target,random_state=10)
grid_search.fit(X_train,y_train)
print("Best set score:{:.2f}".format(grid_search.best_score_))
print("Best parameters:{}".format(grid_search.best_params_))
print("Test set score:{:.2f}".format(grid_search.score(X_test,y_test)))
輸出
Best set score:0.98
Best parameters:{'gamma': 0.1, 'C': 10}
Test set score:0.97
總結
GridSearchCV能夠使我們找到範圍內最優的參數,param_grid參數越多,組合越多,計算的時間也需要越多,GridSearchCV使用於小數據集。