def test_fit_predict_strings(self):
""" GENSVM_GRID: Test fit and predict with string targets """
iris = load_iris()
X = iris.data
y = iris.target
labels = iris.target_names
yy = labels[y]
X_train, X_test, y_train, y_test = train_test_split(X, yy)
pg = {
"p": [1, 1.5, 2.0],
"kappa": [-0.9, 1.0],
"lmd": [0.1, 1.0],
"epsilon": [0.01, 0.002],
"gamma": [1.0, 2.0],
"weights": ["unit", "group"],
}
clf = GenSVMGridSearchCV(pg)
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))
def test_params_rbf_kernel(self):
""" GENSVM_GRID: Test best params with RBF kernel """
X, y = load_iris(return_X_y=True)
X = maxabs_scale(X)
X_train, X_test, y_train, y_test = train_test_split(X, y)
pg = {"lmd": [1e-4, 100, 10000], "kernel": ["rbf"]}
clf = GenSVMGridSearchCV(pg)
clf.fit(X_train, y_train)
self.assertTrue(hasattr(clf, "best_params_"))
y_pred = clf.predict(X_test, trainX=X_train)
del y_pred