How to graph grid scores from GridSearchCV?

Question

I am looking for a way to graph grid_scores_ from GridSearchCV in sklearn. In this example I am trying to grid search for best gamma and C parameters for an SVR algorithm. My c

Answer 1

from sklearn.svm import SVC
from sklearn.grid_search import GridSearchCV
from sklearn import datasets
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

digits = datasets.load_digits()
X = digits.data
y = digits.target

clf_ = SVC(kernel='rbf')
Cs = [1, 10, 100, 1000]
Gammas = [1e-3, 1e-4]
clf = GridSearchCV(clf_,
            dict(C=Cs,
                 gamma=Gammas),
                 cv=2,
                 pre_dispatch='1*n_jobs',
                 n_jobs=1)

clf.fit(X, y)

scores = [x[1] for x in clf.grid_scores_]
scores = np.array(scores).reshape(len(Cs), len(Gammas))

for ind, i in enumerate(Cs):
    plt.plot(Gammas, scores[ind], label='C: ' + str(i))
plt.legend()
plt.xlabel('Gamma')
plt.ylabel('Mean score')
plt.show()

• Code is based on this.
• Only puzzling part: will sklearn always respect the order of C & Gamma -> official example uses this "ordering"

Output: