def test_peristence_perm_cv_parmethods_pipe_vs_sklearn(self):
key_y_pred = 'y' + conf.SEP + conf.PREDICTION
X, y = datasets.make_classification(n_samples=12, n_features=10,
n_informative=2)
n_folds_nested = 2
#random_state = 0
C_values = [.1, 0.5, 1, 2, 5]
kernels = ["linear", "rbf"]
# With EPAC
methods = Methods(*[SVC(C=C, kernel=kernel)
for C in C_values for kernel in kernels])
wf = CVBestSearchRefit(methods, n_folds=n_folds_nested)
# Save workflow
# -------------
import tempfile
#store = StoreFs("/tmp/toto", clear=True)
store = StoreFs(tempfile.mktemp())
wf.save_tree(store=store)
wf = store.load()
wf.run(X=X, y=y)
## Save results
wf.save_tree(store=store)
wf = store.load()
r_epac = wf.reduce().values()[0]
# - Without EPAC
r_sklearn = dict()
clf = SVC(kernel="linear")
parameters = {'C': C_values, 'kernel': kernels}
cv_nested = StratifiedKFold(y=y, n_folds=n_folds_nested)
gscv = grid_search.GridSearchCV(clf, parameters, cv=cv_nested)
gscv.fit(X, y)
r_sklearn[key_y_pred] = gscv.predict(X)
r_sklearn[conf.BEST_PARAMS] = gscv.best_params_
r_sklearn[conf.BEST_PARAMS]['name'] = 'SVC'
# - Comparisons
comp = np.all(r_epac[key_y_pred] == r_sklearn[key_y_pred])
self.assertTrue(comp, u'Diff CVBestSearchRefit: prediction')
comp = np.all([r_epac[conf.BEST_PARAMS][0][p] == r_sklearn[conf.BEST_PARAMS][p]
for p in r_sklearn[conf.BEST_PARAMS]])
self.assertTrue(comp, u'Diff CVBestSearchRefit: best parameters')
