8000 [MRG] Fix aesthetic example roc crossval by glemaitre · Pull Request #8232 · scikit-learn/scikit-learn · GitHub
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[MRG] Fix aesthetic example roc crossval #8232

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Merged
merged 4 commits into from
Jan 25, 2017
Merged

[MRG] Fix aesthetic example roc crossval #8232

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411b5c3
Fix esthetic example roc crossval
Jan 25, 2017
d71804e
different colors for the roc
Jan 25, 2017
be461a8
update for the sigma
Jan 25, 2017
a8ef87e
modify std rendering
Jan 25, 2017
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35 changes: 21 additions & 14 deletions examples/model_selection/plot_roc_crossval.py
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Original file line number Diff line number Diff line change
Expand Up @@ -62,32 +62,39 @@
classifier = svm.SVC(kernel='linear', probability=True,
random_state=random_state)

mean_tpr = 0.0
tprs = []
aucs = []
mean_fpr = np.linspace(0, 1, 100)

colors = cycle(['cyan', 'indigo', 'seagreen', 'yellow', 'blue', 'darkorange'])
lw = 2

i = 0
for (train, test), color in zip(cv.split(X, y), colors):
for train, test in cv.split(X, y):
probas_ = classifier.fit(X[train], y[train]).predict_proba(X[test])
# Compute ROC curve and area the curve
fpr, tpr, thresholds = roc_curve(y[test], probas_[:, 1])
mean_tpr += interp(mean_fpr, fpr, tpr)
mean_tpr[0] = 0.0
tprs.append(interp(mean_fpr, fpr, tpr))
tprs[-1][0] = 0.0
roc_auc = auc(fpr, tpr)
plt.plot(fpr, tpr, lw=lw, color=color,
label='ROC fold %d (area = %0.2f)' % (i, roc_auc))
aucs.append(roc_auc)
plt.plot(fpr, tpr, lw=1, alpha=0.3,
label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))

i += 1
plt.plot([0, 1], [0, 1], linestyle='--', lw=lw, color='k',
label='Luck')
plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',
label='Luck', alpha=.8)

mean_tpr /= cv.get_n_splits(X, y)
mean_tpr = np.mean(tprs, axis=0)
mean_tpr[-1] = 1.0
mean_auc = auc(mean_fpr, mean_tpr)
plt.plot(mean_fpr, mean_tpr, color='g', linestyle='--',
label='Mean ROC (area = %0.2f)' % mean_auc, lw=lw)
std_auc = np.std(aucs)
plt.plot(mean_fpr, mean_tpr, color='b',
label=r'Mean ROC (AUC = %0.2f $\pm$ %0.2f)' % (mean_auc, std_auc),
lw=2, alpha=.8)

std_tpr = np.std(tprs, axis=0)
tprs_upper = np.minimum(mean_tpr + std_tpr, 1)
tprs_lower = np.maximum(mean_tpr - std_tpr, 0)
plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,
label=r'$\pm$ 1 std. dev.')

plt.xlim([-0.05, 1.05])
plt.ylim([-0.05, 1.05])
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