def test_multilabel(self):
x = Classifier(CONFIG)
tdata = np.array([
[0, 1, 0],
[0, 0, 1],
[1, 0, 0],
], dtype='f8')
ttargets = np.array([1, 2, 0])
x.partial_fit(tdata, ttargets)
y = x.predict(np.array([
[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
], dtype='f8'))
self.assertTrue(isinstance(y, np.ndarray))
self.assertEqual(y.dtype, np.int32)
self.assertTrue(np.equal(y, np.array([0, 1, 2])).all())
y = x.decision_function(
np.array([
[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
], dtype='f8'))
self.assertTrue(isinstance(y, np.ndarray))
self.assertEqual((3, 3), y.shape)
self.assertTrue(y[0][0] > y[0][1] and y[0][0] > y[0][2])
self.assertTrue(y[1][1] > y[1][0] and y[1][1] > y[1][2])
self.assertTrue(y[2][2] > y[2][0] and y[2][2] > y[2][1])
def test_sparse(self):
from scipy.sparse import csr_matrix
x = Classifier(CONFIG)
tdata = csr_matrix(np.array([
[1, 0, 1],
[0, 1, 1],
], dtype='f8'))
ttargets = np.array([1, 0])
x.partial_fit(tdata, ttargets)
y = x.predict(
csr_matrix(np.array([
[1, 0, 0],
[0, 1, 0],
], dtype='f8')))
self.assertEqual(1, y[0])
self.assertEqual(0, y[1])
y = x.decision_function(
csr_matrix(np.array([
[1, 0, 0],
[0, 1, 0],
], dtype='f8')))
self.assertEqual(2, len(y))
self.assertTrue(not isinstance(y[0], (list, tuple, np.ndarray)))
self.assertTrue(y[0] > 0)
self.assertTrue(y[1] < 0)
def test_numpy(self):
x = Classifier(CONFIG)
tdata = np.array([
[1, 0, 1],
[0, 1, 1],
], dtype='f8')
ttargets = np.array([1, 0])
x.partial_fit(tdata, ttargets)
y = x.predict(np.array([
[1, 0, 0],
[0, 1, 0],
], dtype='f8'))
self.assertEqual(1, y[0])
self.assertEqual(0, y[1])
y = x.decision_function(np.array([
[1, 0, 0],
[0, 1, 0],
], dtype='f8'))
self.assertEqual(2, len(y))
self.assertTrue(not isinstance(y[0], (list, tuple, np.ndarray)))
self.assertTrue(y[0] > 0)
self.assertTrue(y[1] < 0)
self.assertEqual([0, 1], sorted(x.classes_))
model = x.save_bytes()
x = Classifier(CONFIG)
x.load_bytes(model)
self.assertEqual([0, 1], sorted(x.classes_))
y = x.predict(np.array([
[1, 0, 0],
[0, 1, 0],
], dtype='f8'))
self.assertEqual(1, y[0])
self.assertEqual(0, y[1])
def test_multilabel(self):
x = Classifier(CONFIG)
tdata = np.array([
[0, 1, 0],
[0, 0, 1],
[1, 0, 0],
], dtype='f8')
ttargets = np.array([1, 2, 0])
x.partial_fit(tdata, ttargets)
y = x.predict(np.array([
[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
], dtype='f8'))
self.assertTrue(isinstance(y, np.ndarray))
self.assertEqual(y.dtype, np.int32)
self.assertTrue(np.equal(y, np.array([0, 1, 2])).all())
y = x.decision_function(np.array([
[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
], dtype='f8'))
self.assertTrue(isinstance(y, np.ndarray))
self.assertEqual((3, 3), y.shape)
self.assertTrue(y[0][0] > y[0][1] and y[0][0] > y[0][2])
self.assertTrue(y[1][1] > y[1][0] and y[1][1] > y[1][2])
self.assertTrue(y[2][2] > y[2][0] and y[2][2] > y[2][1])
def test_numpy(self):
x = Classifier(CONFIG)
tdata = np.array([
[1, 0, 1],
[0, 1, 1],
], dtype='f8')
ttargets = np.array([1, 0])
x.partial_fit(tdata, ttargets)
y = x.predict(np.array([
[1, 0, 0],
[0, 1, 0],
], dtype='f8'))
self.assertEqual(1, y[0])
self.assertEqual(0, y[1])
y = x.decision_function(np.array([
[1, 0, 0],
[0, 1, 0],
], dtype='f8'))
self.assertEqual(2, len(y))
self.assertTrue(not isinstance(y[0], (list, tuple, np.ndarray)))
self.assertTrue(y[0] > 0)
self.assertTrue(y[1] < 0)
def test_sparse(self):
from scipy.sparse import csr_matrix
x = Classifier(CONFIG)
tdata = csr_matrix(np.array([
[1, 0, 1],
[0, 1, 1],
], dtype='f8'))
ttargets = np.array([1, 0])
x.partial_fit(tdata, ttargets)
y = x.predict(csr_matrix(np.array([
[1, 0, 0],
[0, 1, 0],
], dtype='f8')))
self.assertEqual(1, y[0])
self.assertEqual(0, y[1])
y = x.decision_function(csr_matrix(np.array([
[1, 0, 0],
[0, 1, 0],
], dtype='f8')))
self.assertEqual(2, len(y))
self.assertTrue(not isinstance(y[0], (list, tuple, np.ndarray)))
self.assertTrue(y[0] > 0)
self.assertTrue(y[1] < 0)
