def test_cv(self):
X, y = datasets.make_classification(n_samples=20, n_features=5, n_informative=2)
n_folds = 2
# = With EPAC
wf = CV(SVC(kernel="linear"), n_folds=n_folds, reducer=ClassificationReport(keep=True))
r_epac = wf.top_down(X=X, y=y)
# = With SKLEARN
clf = SVC(kernel="linear")
r_sklearn = list()
for idx_train, idx_test in StratifiedKFold(y=y, n_folds=n_folds):
# idx_train, idx_test = cv.__iter__().next()
X_train = X[idx_train, :]
X_test = X[idx_test, :]
y_train = y[idx_train, :]
clf.fit(X_train, y_train)
r_sklearn.append(clf.predict(X_test))
# = Comparison
key2cmp = "y" + conf.SEP + conf.TEST + conf.SEP + conf.PREDICTION
for icv in range(n_folds):
comp = np.all(np.asarray(r_epac[0][key2cmp]) == np.asarray(r_sklearn[0]))
self.assertTrue(comp, u"Diff CV: EPAC vs sklearn")
# test reduce
r_epac_reduce = wf.reduce().values()[0][key2cmp]
comp = np.all(np.asarray(r_epac_reduce) == np.asarray(r_sklearn))
self.assertTrue(comp, u"Diff CV: EPAC reduce")
def test_cv(self):
X, y = datasets.make_classification(n_samples=20, n_features=5,
n_informative=2)
n_folds = 2
# = With EPAC
wf = CV(SVC(kernel="linear"), n_folds=n_folds,
reducer=ClassificationReport(keep=True))
r_epac = wf.top_down(X=X, y=y)
# = With SKLEARN
clf = SVC(kernel="linear")
r_sklearn = list()
for idx_train, idx_test in StratifiedKFold(y=y, n_folds=n_folds):
#idx_train, idx_test = cv.__iter__().next()
X_train = X[idx_train, :]
X_test = X[idx_test, :]
y_train = y[idx_train, :]
clf.fit(X_train, y_train)
r_sklearn.append(clf.predict(X_test))
# = Comparison
key2cmp = 'y' + conf.SEP + conf.TEST + conf.SEP + conf.PREDICTION
for icv in range(n_folds):
comp = np.all(np.asarray(r_epac[0][key2cmp])
== np.asarray(r_sklearn[0]))
self.assertTrue(comp, u'Diff CV: EPAC vs sklearn')
# test reduce
r_epac_reduce = wf.reduce().values()[0][key2cmp]
comp = np.all(np.asarray(r_epac_reduce) == np.asarray(r_sklearn))
self.assertTrue(comp, u'Diff CV: EPAC reduce')
