Question

我目前正在使用Scikit-Learn版本0.19.2和Python 3.6.3

由于某些原因，我无法从我的cv_results_访问GridSearchCV属性。

这是我正在使用的代码：

df = pd.read_csv(input_file, sep = ";", header=None)

numpy_array = df.as_matrix()
y=numpy_array[:,1]
y[y=='RR']=1
y[y=='AIRR']=0
print(y)
y=y.astype('int')

vectorizer = TfidfVectorizer(sublinear_tf=True, max_df=0.5, stop_words=stopwords)

X=numpy_array[:,0]
X=vectorizer.fit_transform(X)

param_grid = {"base_estimator__criterion" : ["gini", "entropy"],
              "base_estimator__splitter" :   ["best", "random"],
              "n_estimators": [1, 2]
             }

DTC = DecisionTreeClassifier(random_state = 11, max_features = "auto", class_weight = "balanced",max_depth = None)


# Create and fit an AdaBoosted decision tree
bdt = AdaBoostClassifier(base_estimator = DTC)

grid_search_ABC = GridSearchCV(bdt, param_grid=param_grid, scoring = 'roc_auc', cv=5, refit=True)

pred = grid_search_ABC.fit(X,y)

print(metrics.confusion_matrix(y, pred))

mean=grid_search_ABC.cv_results_['mean_test_score']
std=grid_search_ABC.cv_results_['std_test_score']

我了解到，这主要与GridSearchCV可能不适合有关，但我可以完全使用它来预测新实例等。

请问有指针吗？

Answer 1

问题可能出在您的数据集上。因此，本网站鼓励您发布可验证的示例。

我刚刚尝试在虹膜数据集上运行您的代码，效果很好：

from sklearn import datasets
from sklearn.model_selection import GridSearchCV
iris = datasets.load_iris()
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import AdaBoostClassifier

param_grid = {"base_estimator__criterion" : ["gini", "entropy"],
              "base_estimator__splitter" :   ["best", "random"],
              "n_estimators": [1, 2]
             }

DTC = DecisionTreeClassifier(random_state = 11, max_features = "auto", class_weight = "balanced",max_depth = None)
bdt = AdaBoostClassifier(base_estimator = DTC)
grid_search_ABC = GridSearchCV(bdt, param_grid=param_grid, scoring = 'roc_auc', cv=5, refit=True)

pred = grid_search_ABC.fit(iris.data, iris.target>0)
print(grid_search_ABC.cv_results_['mean_test_score'])

效果很好

AttributeError：scikit-learn 0.19.2上的“ GridSearchCV”对象没有属性“ cv_results_”

1 个答案: