def test_sag():
for clf in (
SAGClassifier(eta=1e-3, max_iter=20, verbose=0, random_state=0),
SAGAClassifier(eta=1e-3, max_iter=20, verbose=0, random_state=0),
PySAGClassifier(eta=1e-3, max_iter=20, random_state=0)
):
clf.fit(X_bin, y_bin)
assert not hasattr(clf, 'predict_proba')
assert_equal(clf.score(X_bin, y_bin), 1.0)
assert_equal(list(clf.classes_), [-1, 1])
def saga_cv(which, alphas, l1_ratio):
if which == 'cdcp':
n_folds = 3
path = os.path.join("data", "process", "erule", "folds", "{}", "{}")
elif which == 'ukp':
n_folds = 5
path = os.path.join("data", "process", "ukp-essays", "folds", "{}",
"{}")
else:
raise ValueError
clf_link = SAGAClassifier(loss='smooth_hinge',
penalty='l1',
tol=1e-4,
max_iter=100,
random_state=0,
verbose=0)
clf_prop = clone(clf_link)
link_scores = np.zeros((n_folds, len(alphas)))
prop_scores = np.zeros_like(link_scores)
for k in range(n_folds):
X_tr_link, y_tr_link = load_csr(path.format(k, 'train.npz'),
return_y=True)
X_te_link, y_te_link = load_csr(path.format(k, 'val.npz'),
return_y=True)
X_tr_prop, y_tr_prop = load_csr(path.format(k, 'prop-train.npz'),
return_y=True)
X_te_prop, y_te_prop = load_csr(path.format(k, 'prop-val.npz'),
return_y=True)
le = LabelEncoder()
y_tr_prop_enc = le.fit_transform(y_tr_prop)
y_te_prop_enc = le.transform(y_te_prop)
link_sw = compute_sample_weight('balanced', y_tr_link)
for j, alpha in enumerate(alphas):
beta = alpha * l1_ratio
alpha *= 1 - l1_ratio
clf_link.set_params(alpha=alpha, beta=beta)
clf_prop.set_params(alpha=alpha, beta=beta)
clf_link.fit(X_tr_link, y_tr_link, sample_weight=link_sw)
y_pred_link = clf_link.predict(X_te_link)
clf_prop.fit(X_tr_prop, y_tr_prop_enc)
y_pred_prop = clf_prop.predict(X_te_prop)
with warnings.catch_warnings() as w:
warnings.simplefilter('ignore')
link_f = f1_score(y_te_link, y_pred_link, average='binary')
prop_f = f1_score(y_te_prop_enc, y_pred_prop, average='macro')
link_scores[k, j] = link_f
prop_scores[k, j] = prop_f
return link_scores, prop_scores
