def test_classification_kmeans_intercept_weights(self):
iris = datasets.load_iris()
X, y = iris.data, iris.target
clr = ClassifierAfterKMeans()
clr.fit(X, y, sample_weight=numpy.ones((X.shape[0], )))
acc = clr.score(X, y)
self.assertGreater(acc, 0)
def test_classification_kmeans(self):
iris = datasets.load_iris()
X, y = iris.data, iris.target
clr = ClassifierAfterKMeans()
clr.fit(X, y)
acc = clr.score(X, y)
self.assertGreater(acc, 0)
prob = clr.predict_proba(X)
self.assertEqual(prob.shape[1], 3)
dec = clr.decision_function(X)
self.assertEqual(prob.shape, dec.shape)
def test_classification_kmeans_intercept_weights(self):
iris = datasets.load_iris()
X, y = iris.data, iris.target
clr = ClassifierAfterKMeans()
try:
clr.fit(X, y, sample_weight=numpy.ones((X.shape[0], )))
except AttributeError as e:
if compare_module_version(sklver, "0.24") < 0:
return
raise e
acc = clr.score(X, y)
self.assertGreater(acc, 0)
def test_classification_kmeans(self):
iris = datasets.load_iris()
X, y = iris.data, iris.target
clr = ClassifierAfterKMeans()
try:
clr.fit(X, y)
except AttributeError as e:
if compare_module_version(sklver, "0.24") < 0:
return
raise e
acc = clr.score(X, y)
self.assertGreater(acc, 0)
prob = clr.predict_proba(X)
self.assertEqual(prob.shape[1], 3)
dec = clr.decision_function(X)
self.assertEqual(prob.shape, dec.shape)
def test_classification_kmeans_relevance(self):
state = RandomState(seed=0)
Xs = []
Ys = []
n = 20
for i in range(0, 5):
for j in range(0, 4):
x1 = state.rand(n) + i * 1.1
x2 = state.rand(n) + j * 1.1
Xs.append(numpy.vstack([x1, x2]).T)
cl = state.randint(0, 4)
Ys.extend([cl for i in range(n)])
X = numpy.vstack(Xs)
Y = numpy.array(Ys)
clk = ClassifierAfterKMeans(c_n_clusters=6, c_random_state=state)
clk.fit(X, Y)
score = clk.score(X, Y)
self.assertGreater(score, 0.95)
def test_classification_kmeans_relevance(self):
state = RandomState(seed=0)
Xs = []
Ys = []
n = 20
for i in range(0, 5):
for j in range(0, 4):
x1 = state.rand(n) + i * 1.1
x2 = state.rand(n) + j * 1.1
Xs.append(numpy.vstack([x1, x2]).T)
cl = state.randint(0, 4)
Ys.extend([cl for i in range(n)])
X = numpy.vstack(Xs)
Y = numpy.array(Ys)
clk = ClassifierAfterKMeans(c_n_clusters=6, c_random_state=state)
try:
clk.fit(X, Y)
except AttributeError as e:
if compare_module_version(sklver, "0.24") < 0:
return
raise e
score = clk.score(X, Y)
self.assertGreater(score, 0.95)