def test_levenshtein_normalize():
links1 = [
"https://news.ycombinator.com/from?site=eff.org",
"https://news.ycombinator.com/from?site=martinfowler.com",
"https://news.ycombinator.com/news?p=2",
"https://news.ycombinator.com/news?p=3"
]
links2 = ["/fromsite=eff.org",
"/fromsite=martinfowler.com",
"/newsp=2",
"/newsp=3"]
actual_links1 = distance_matrix(links1, measure=levenshtein_distance,
normalize=True)
actual_links2 = distance_matrix(links2, measure=levenshtein_distance,
normalize=True)
expected_links1 = np.array([[0., 0.25454545, 0.32608696, 0.32608696],
[0.25454545, 0., 0.43636364, 0.43636364],
[0.32608696, 0.43636364, 0., 0.02702703],
[0.32608696, 0.43636364, 0.02702703, 0.]])
expected_links2 = np.array([[0., 0.53846154, 0.82352941, 0.82352941],
[0.53846154, 0., 0.88461538, 0.88461538],
[0.82352941, 0.88461538, 0., 0.125],
[0.82352941, 0.88461538, 0.125, 0.]])
assert_almost_equal(actual_links1, expected_links1, decimal=7)
assert_almost_equal(actual_links2, expected_links2, decimal=7)
def test_label_spreading_algorithms():
"""
Compare scikit's algorithm and our algorithm
"""
x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])
# scikit takes different input that our algorithm
y_sklearn = np.array([1, 2, -1, -1])
y_custom = np.array([[1, 0], [0, 1], [0, 0], [0, 0]])
# scikit's algorithm
alpha = 0.2
max_iter = 30
tol = 1e-3
label_spreading = LabelSpreadingSKLearn(kernel="rbf",
max_iter=max_iter,
alpha=alpha, tol=tol)
model = label_spreading.fit(x, y_sklearn)
expected = model.predict(x)
# our algorithm
w = distance_matrix(x, measure=rbf_distance)
ls = LabelSpreadingCustom(alpha=alpha, max_iter=max_iter, tol=tol)
ls = ls.fit(w, y_custom)
actual = ls.predict(y_custom)
actual = np.array(actual) + 1 # add plus 1 to every prediction
assert_array_equal(actual, expected)
def test_rbf_distance():
"""
Compare radial basis function with scikit and our version
"""
x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])
expected = sklearn_rbf_kernel(x, gamma=0.5)
actual = distance_matrix(x, measure=rbf_distance)
n = len(actual)
# scikits rbf returns 1 on diagonal as exp(0) = 1
# so we change to 1 to assure equality
actual[range(n), range(n)] = 1
assert_almost_equal(actual, expected, decimal=20)
def test_levenshtein_distance():
"""
compare the score of levenshtein distance
"""
links = [
"https://news.ycombinator.com/from?site=eff.org",
"https://news.ycombinator.com/from?site=martinfowler.com",
"https://news.ycombinator.com/news?p=2",
"https://news.ycombinator.com/news?p=3"
]
actual = distance_matrix(links, measure=levenshtein_distance,
normalize=False)
expected = np.array([[0, 14, 15, 15],
[14, 0, 24, 24],
[15, 24, 0, 1],
[15, 24, 1, 0]])
assert_array_equal(actual, expected)