def test_kmeans_constraint_weights(self):
mat = numpy.array([[0, 0], [0.2, 0.2], [-0.1, -0.1], [1, 1]])
km = ConstraintKMeans(n_clusters=2,
verbose=10,
kmeans0=False,
random_state=1,
strategy='weights')
buf = BufferedPrint()
km.fit(mat, fLOG=buf.fprint)
km = ConstraintKMeans(n_clusters=2,
verbose=5,
kmeans0=False,
random_state=1,
strategy='weights')
km.fit(mat, fLOG=buf.fprint)
self.assertEqual(km.cluster_centers_.shape, (2, 2))
self.assertLesser(km.inertia_, 4.55)
self.assertEqual(km.cluster_centers_,
numpy.array([[0.6, 0.6], [-0.05, -0.05]]))
self.assertEqual(km.labels_, numpy.array([1, 0, 1, 0]))
pred = km.predict(mat)
self.assertEqual(pred, numpy.array([1, 1, 1, 0]))
dist = km.transform(mat)
self.assertEqual(dist.shape, (4, 2))
score = km.score(mat)
self.assertEqual(score.shape, (4, ))
self.assertIn("CKMeans", str(buf))
def test_kmeans_constraint_pickle(self):
df = pandas.DataFrame(dict(y=[0, 1, 0, 1, 0, 1, 0, 1],
X1=[0.5, 0.6, 0.52, 0.62,
0.5, 0.6, 0.51, 0.61],
X2=[0.5, 0.6, 0.7, 0.5, 1.5, 1.6, 1.7, 1.8]))
X = df.drop('y', axis=1)
y = df['y']
model = ConstraintKMeans(n_clusters=2, strategy='distance')
model.fit(X, y)
pred = model.transform(X)
st = BytesIO()
pickle.dump(model, st)
st = BytesIO(st.getvalue())
rec = pickle.load(st)
pred2 = rec.transform(X)
self.assertEqualArray(pred, pred2)
