def test_singleton_class():
X = iris_data
y = iris_target
# one singleton class
singleton_class = 1
ind_singleton, = np.where(y == singleton_class)
y[ind_singleton] = 2
y[ind_singleton[0]] = singleton_class
nca = NeighborhoodComponentsAnalysis(max_iter=30)
nca.fit(X, y)
# One non-singleton class
ind_1, = np.where(y == 1)
ind_2, = np.where(y == 2)
y[ind_1] = 0
y[ind_1[0]] = 1
y[ind_2] = 0
y[ind_2[0]] = 2
nca = NeighborhoodComponentsAnalysis(max_iter=30)
nca.fit(X, y)
# Only singleton classes
ind_0, = np.where(y == 0)
ind_1, = np.where(y == 1)
ind_2, = np.where(y == 2)
X = X[[ind_0[0], ind_1[0], ind_2[0]]]
y = y[[ind_0[0], ind_1[0], ind_2[0]]]
nca = NeighborhoodComponentsAnalysis(init='identity', max_iter=30)
nca.fit(X, y)
assert_array_equal(X, nca.transform(X))
def test_one_class():
X = iris_data[iris_target == 0]
y = iris_target[iris_target == 0]
nca = NeighborhoodComponentsAnalysis(max_iter=30,
n_components=X.shape[1],
init='identity')
nca.fit(X, y)
assert_array_equal(X, nca.transform(X))
def test_simple_example():
"""Test on a simple example.
Puts four points in the input space where the opposite labels points are
next to each other. After transform the samples from the same class
should be next to each other.
"""
X = np.array([[0, 0], [0, 1], [2, 0], [2, 1]])
y = np.array([1, 0, 1, 0])
nca = NeighborhoodComponentsAnalysis(n_components=2, init='identity',
random_state=42)
nca.fit(X, y)
X_t = nca.transform(X)
assert_array_equal(pairwise_distances(X_t).argsort()[:, 1],
np.array([2, 3, 0, 1]))