def test_iris_embedding():
iris = datasets.load_iris()
x = iris.data
y = iris.target
ivis_iris = Ivis(n_epochs_without_progress=5)
ivis_iris.k = 15
ivis_iris.batch_size = 16
y_pred_iris = ivis_iris.fit_transform(x)
def test_1d_supervied_iris_embedding():
iris = datasets.load_iris()
x = iris.data
y = iris.target
ivis_iris = Ivis(epochs=2, embedding_dims=1)
ivis_iris.k = 15
ivis_iris.batch_size = 16
y_pred_iris = ivis_iris.fit_transform(x, y)
def test_iris_embedding():
iris = datasets.load_iris()
x = iris.data
y = iris.target
mask = np.random.choice(range(len(y)), size=len(y) // 2, replace=False)
y[mask] = -1
ivis_iris = Ivis(epochs=5)
ivis_iris.k = 15
ivis_iris.batch_size = 16
y_pred_iris = ivis_iris.fit_transform(x, y)
def test_custom_ndarray_neighbour_matrix():
iris = datasets.load_iris()
x = iris.data
y = iris.target
class_indicies = {label: np.argwhere(y == label).ravel() for label in np.unique(y)}
neighbour_matrix = np.array([class_indicies[label] for label in y])
ivis_iris = Ivis(epochs=5, neighbour_matrix=neighbour_matrix)
ivis_iris.k = 15
ivis_iris.batch_size = 16
y_pred_iris = ivis_iris.fit_transform(x)