def execute_using_cv(X, y, use_ovr=False):
'''
Stand-alone function to executes and plot evaluation metrics using explicit (averaged) 10-Fold CV and SVM.
'use_ovr' for multiclass data.
Ref: http://scikit-learn.org/stable/auto_examples/exercises/plot_cv_digits.html#sphx-glr-auto-examples-exercises-plot-cv-digits-py
'''
print(__doc__)
import numpy as np
from sklearn.model_selection import cross_val_score
from sklearn import datasets, svm
#digits = datasets.load_digits()
#X = digits.data
#y = digits.target
svc = svm.SVC(kernel='linear')
if use_ovr:
from sklearn.multiclass import OneVsRestClassifier
svc = OneVsRestClassifier(svc)
C_s = np.logspace(-2, 1, 10)
scores = list()
scores_std = list()
print("Running 10-Fold CV on Linear SVM, with varying C..")
for C in C_s:
svc.C = C
this_scores = cross_val_score(svc, X, y, cv=10, n_jobs=4)
mean_score = np.mean(this_scores)
scores.append(mean_score)
scores_std.append(np.std(this_scores))
print("Score with C={} is {}".format(C, mean_score))
# Do the plotting
import matplotlib.pyplot as plt
plt.figure(1, figsize=(4, 3))
plt.clf()
plt.semilogx(C_s, scores)
plt.semilogx(C_s, np.array(scores) + np.array(scores_std), 'b--')
plt.semilogx(C_s, np.array(scores) - np.array(scores_std), 'b--')
locs, labels = plt.yticks()
plt.yticks(locs, list(map(lambda x: "%g" % x, locs)))
plt.ylabel('CV score')
plt.xlabel('Parameter C')
plt.ylim(0, 1.1)
plt.show()
