def test_3():
# test using a callable metric. should get same results
model1 = LandmarkAgglomerative(n_clusters=10, n_landmarks=20, metric='euclidean')
model2 = LandmarkAgglomerative(n_clusters=10, n_landmarks=20, metric=lambda target, ref, i: np.sqrt(np.sum((target-ref[i])**2, axis=1)))
data = np.random.RandomState(0).randn(100, 2)
eq(model1.fit_predict([data])[0], model2.fit_predict([data])[0])
def test_callable_metric():
def my_euc(target, ref, i):
return np.sqrt(np.sum((target - ref[i]) ** 2, axis=1))
model1 = LandmarkAgglomerative(n_clusters=10, n_landmarks=20,
metric='euclidean')
model2 = LandmarkAgglomerative(n_clusters=10, n_landmarks=20, metric=my_euc)
data = np.random.RandomState(0).randn(100, 2)
eq(model1.fit_predict([data])[0], model2.fit_predict([data])[0])
def test_2():
# this should be a really easy clustering problem
x = [random.randn(20,2)+10, random.randn(20,2)]
n_clusters = 2
model1 = LandmarkAgglomerative(n_clusters=n_clusters)
model2 = LandmarkAgglomerative(n_clusters=n_clusters,
landmark_strategy='random', random_state=random, n_landmarks=20)
labels1 = model1.fit_predict(x)
labels2 = model2.fit_predict(x)
assert adjusted_rand_score(np.concatenate(labels1), np.concatenate(labels2)) == 1.0
def test_3():
# test using a callable metric. should get same results
model1 = LandmarkAgglomerative(n_clusters=10,
n_landmarks=20,
metric='euclidean')
model2 = LandmarkAgglomerative(n_clusters=10,
n_landmarks=20,
metric=lambda target, ref, i: np.sqrt(
np.sum((target - ref[i])**2, axis=1)))
data = np.random.RandomState(0).randn(100, 2)
eq(model1.fit_predict([data])[0], model2.fit_predict([data])[0])
def test_2():
# this should be a really easy clustering problem
x = [random.randn(20, 2) + 10, random.randn(20, 2)]
n_clusters = 2
model1 = LandmarkAgglomerative(n_clusters=n_clusters)
model2 = LandmarkAgglomerative(n_clusters=n_clusters,
landmark_strategy='random',
random_state=random, n_landmarks=20)
labels1 = model1.fit_predict(x)
labels2 = model2.fit_predict(x)
assert adjusted_rand_score(np.concatenate(labels1),
np.concatenate(labels2)) == 1.0
def test_agglom_with_metric_msm():
my_list = [_get_random_prob_dist(4) for i in range(100)]
my_flat = np.array([x.flatten() for x in my_list])
model = LandmarkAgglomerative(n_clusters=2,
metric=sym_kl_divergence_msm,
linkage='complete')
assert model.fit_predict([my_flat])[0].shape == (100, )
def test_cluster_centers():
x = [random.randn(20, 2) + 10, random.randn(20, 2)]
n_clusters = np.random.randint(2, 7)
model = LandmarkAgglomerative(n_clusters=n_clusters,
linkage='ward')
labels = model.fit_predict(x)
print(model.cluster_centers_)
assert model.cluster_centers_.shape == (n_clusters, 2)
def test_alanine_dipeptide():
# test for rmsd metric compatibility with ward clustering
# keep n_landmarks small or this will get really slow
trajectories = AlanineDipeptide().get_cached().trajectories
n_clusters = 4
model = LandmarkAgglomerative(n_clusters=n_clusters, n_landmarks=20,
linkage='ward', metric='rmsd')
labels = model.fit_predict(trajectories[0][0:100])
assert len(np.unique(np.concatenate(labels))) <= n_clusters
def test_1():
x = [random.randn(10,2), random.randn(10,2)]
n_clusters = 2
model1 = LandmarkAgglomerative(n_clusters=n_clusters)
model2 = LandmarkAgglomerative(n_clusters=n_clusters,
n_landmarks=sum(len(s) for s in x))
labels0 = clone(model1).fit(x).predict(x)
labels1 = model1.fit_predict(x)
labels2 = model2.fit_predict(x)
assert len(labels0) == 2
assert len(labels1) == 2
assert len(labels2) == 2
eq(labels0[0], labels1[0])
eq(labels0[1], labels1[1])
eq(labels0[0], labels2[0])
eq(labels0[1], labels2[1])
assert len(np.unique(np.concatenate(labels0))) == n_clusters
def test_1():
x = [random.randn(10, 2), random.randn(10, 2)]
n_clusters = 2
model1 = LandmarkAgglomerative(n_clusters=n_clusters)
model2 = LandmarkAgglomerative(n_clusters=n_clusters,
n_landmarks=sum(len(s) for s in x))
labels0 = clone(model1).fit(x).predict(x)
labels1 = model1.fit_predict(x)
labels2 = model2.fit_predict(x)
assert len(labels0) == 2
assert len(labels1) == 2
assert len(labels2) == 2
eq(labels0[0], labels1[0])
eq(labels0[1], labels1[1])
eq(labels0[0], labels2[0])
eq(labels0[1], labels2[1])
assert len(np.unique(np.concatenate(labels0))) == n_clusters
