My apologies, there was a typo in the code below, second example, should
read:
train_scores1, test_scores1 = validation_curve(SVC_classifier_LOWO_VC1, X,
y, "C", parm_range1, cv =logo.split(X, y, groups=groups), scoring =
'accuracy')
Everything else is correct.
On Fri, December 2, 2016 10:28 pm, Matteo Niccoli wrote:
HI all,
I want to plot learning curves on a trained SVM classifier, using a
custom scorer, and using Leave One Group Out as the method of
crossvalidation. I thought I had it figured out, but two different scorers
- 'f1_micro' and
'accuracy' - will yield identical values. I am confused, is that supposed
to be the case?
Here's my code (unfortunately I cannot share the data as it is not open):
from sklearn import svm SVC_classifier_LOWO_VC0 = svm.SVC(cache_size=800,
class_weight=None, coef0=0.0, decision_function_shape=None, degree=3,
gamma=0.01, kernel='rbf', max_iter=-1, probability=False, random_state=1,
shrinking=True, tol=0.001, verbose=False) training_data =
pd.read_csv('training_data.csv') scaler =
preprocessing.StandardScaler().fit(X) X = scaler.transform(X)
y = training_data['Targets'].values groups = training_data["Groups"].values
Fscorer = make_scorer(f1_score, average = 'micro')
logo = LeaveOneGroupOut() parm_range0 = np.logspace(-2, 6, 9)
train_scores0,
test_scores0 = validation_curve(SVC_classifier_LOWO_VC0, X, y, "C",
parm_range0, cv =logo.split(X, y, groups=groups), scoring = Fscorer)
Now, from:
train_scores_mean0 = np.mean(train_scores0, axis=1) train_scores_std0 =
np.std(train_scores0, axis=1) test_scores_mean0 = np.mean(test_scores0,
axis=1) test_scores_std0 = np.std(test_scores0, axis=1) print
test_scores_mean0 print np.amax(test_scores_mean0) print np.logspace(-2,
6, 9)[test_scores_mean0.argmax(axis=0)]
I get:
[ 0.20257407 0.35551122 0.40791047 0.49887676 0.5021742 0.50030438
0.49426622 0.48066419 0.4868987 ]
0.502174200206
100.0
If I create a new classifier, but with the same parameters, and run
everything exactly as before, except for the scoring, e.g.:
parm_range1 = np.logspace(-2, 6, 9) train_scores1, test_scores1 =
validation_curve(SVC_classifier_LOWO_VC1, X, y, "C", parm_range1, cv
=logo.split(X, y, groups=wells), scoring =
'accuracy')
train_scores_mean1 = np.mean(train_scores1, axis=1) train_scores_std1=
np.std(train_scores1, axis=1) test_scores_mean1 = np.mean(test_scores1,
axis=1) test_scores_std1 = np.std(test_scores1, axis=1) print
test_scores_mean1 print np.amax(test_scores_mean1) print np.logspace(-2,
6, 9)[test_scores_mean1.argmax(axis=0)]
I get exactly the same answer:
[ 0.20257407 0.35551122 0.40791047 0.49887676 0.5021742 0.50030438
0.49426622 0.48066419 0.4868987 ]
0.502174200206
100.0
How is that possible, am I doing something wrong, or missing something?
Thanks
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