def test_fit_nonlinear_auto(self):
""" GENSVM: Fit and predict with nonlinear kernel """
data = load_iris()
X = data.data
y = data.target_names[data.target]
X_train, X_test, y_train, y_test = train_test_split(X,
y,
random_state=123)
clf = GenSVM(kernel="rbf", max_iter=1000, random_state=123)
clf.fit(X_train, y_train)
pred = clf.predict(X_test, trainX=X_train)
self.assertTrue(
set(pred).issubset(set(["versicolor", "virginica", "setosa"])))
def test_fit_predict_strings(self):
""" GENSVM: Test fit and predict with string targets """
digits = load_digits(4)
n_samples = len(digits.images)
X = digits.images.reshape(n_samples, -1)
y = digits.target
labels = np.array(["zero", "one", "two", "three"])
yy = labels[y]
X_train, X_test, y_train, y_test = train_test_split(X, yy)
clf = GenSVM(epsilon=1e-3) # faster testing
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
pred_set = set(y_pred)
label_set = set(labels)
self.assertTrue(pred_set.issubset(label_set))