GridSearchCV Initialisierung

Question

Ich möchte GridSearchCV über eine Reihe von Alphas (LaPlace Glättungsparameter) verwenden, um zu überprüfen, was mir die beste Genauigkeit mit einem Bernoulli Naive Bayes Modell gibt.

def binarize_pixels(data, threshold=0.784): 
    # Initialize a new feature array with the same shape as the original data. 
    binarized_data = np.zeros(data.shape) 

    # Apply a threshold to each feature. 
    for feature in range(data.shape[1]): 
     binarized_data[:,feature] = data[:,feature] > threshold 
    return binarized_data 

binarized_train_data = binarize_pixels(mini_train_data) 

def BNB(): 
    clf = BernoulliNB() 
    clf.fit(binarized_train_data, mini_train_labels) 
    scoring = clf.score(mini_train_data, mini_train_labels) 
    predsNB = clf.predict(dev_data) 
    print "Bernoulli binarized model accuracy: {:.4}".format(np.mean(predsNB == dev_labels)) 

Das Modell läuft gut, während meine Gridsearch Kreuzvalidierung nicht der Fall ist:

pipeline = Pipeline([('classifier', BNB())]) 
def P8(alphas): 
    gs_clf = GridSearchCV(pipeline, param_grid = alphas, refit=True) 
    y_predictions = gs_clf.best_estimator_.predict(dev_data) 
    print classification_report(dev_labels, y_predictions) 
alphas = {'alpha' : [0.0, 0.0001, 0.001, 0.01, 0.1, 0.5, 1.0, 2.0, 10.0]} 
P8(alphas) 

I Attribute erhalten: 'GridSearchCV' Objekt kein Attribut 'best_estimator_'

Answer 1

Das Problem in der ist folgende zwei Zeilen:

gs_clf = GridSearchCV(pipeline, param_grid = alphas, refit=True) 
y_predictions = gs_clf.best_estimator_.predict(dev_data) 

Beachten Sie, dass vor predict mit , müssen Sie zuerst das Modell anpassen. Das heißt, gs_clf.fit anrufen. Siehe das folgende Beispiel aus der documentation:

>>> from sklearn import svm, datasets 
>>> from sklearn.model_selection import GridSearchCV 
>>> iris = datasets.load_iris() 
>>> parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]} 
>>> svr = svm.SVC() 
>>> clf = GridSearchCV(svr, parameters) 
>>> clf.fit(iris.data, iris.target) 
...        
GridSearchCV(cv=None, error_score=..., 
     estimator=SVC(C=1.0, cache_size=..., class_weight=..., coef0=..., 
        decision_function_shape=None, degree=..., gamma=..., 
        kernel='rbf', max_iter=-1, probability=False, 
        random_state=None, shrinking=True, tol=..., 
        verbose=False), 
     fit_params={}, iid=..., n_jobs=1, 
     param_grid=..., pre_dispatch=..., refit=..., return_train_score=..., 
     scoring=..., verbose=...) 
>>> sorted(clf.cv_results_.keys()) 
...        
['mean_fit_time', 'mean_score_time', 'mean_test_score',... 
'mean_train_score', 'param_C', 'param_kernel', 'params',... 
'rank_test_score', 'split0_test_score',... 
'split0_train_score', 'split1_test_score', 'split1_train_score',... 
'split2_test_score', 'split2_train_score',... 
'std_fit_time', 'std_score_time', 'std_test_score', 'std_train_score'...]