def test_label_class_to_softmax_output(self):
y_label = numpy.array([0, 1, 0, 0])
self.assertRaise(lambda: label_class_to_softmax_output(y_label.reshape((-1, 1))),
ValueError)
soft_y = label_class_to_softmax_output(y_label)
self.assertEqual(soft_y.shape, (4, 2))
self.assertEqual(soft_y[:, 1], y_label)
self.assertEqual(soft_y[:, 0], 1 - y_label)
def test_neural_net_gradient(self):
X = numpy.arange(8).astype(numpy.float64).reshape((-1, 2))
y = ((X[:, 0] + X[:, 1] * 2) > 10).astype(numpy.int64)
ny = label_class_to_softmax_output(y)
tree = DecisionTreeClassifier(max_depth=2)
tree.fit(X, y)
root = NeuralTreeNet.create_from_tree(tree, 10)
_, out, err = self.capture(lambda: root.fit(X, ny, verbose=True))
self.assertIn("loss:", out)
self.assertEmpty(err)
def test_neural_net_gradient_fit(self):
X = numpy.arange(16).astype(numpy.float64).reshape((-1, 2))
y = ((X[:, 0] + X[:, 1] * 2) > 15).astype(numpy.int64)
ny = label_class_to_softmax_output(y)
tree = DecisionTreeClassifier(max_depth=2)
tree.fit(X, y)
root = NeuralTreeNet.create_from_tree(tree, 10)
loss1 = root.loss(X, ny).sum()
self.assertGreater(loss1, -1e-5)
self.assertLess(loss1, 1.)
_, out, err = self.capture(
lambda: root.fit(X, ny, verbose=True, max_iter=20))
self.assertEmpty(err)
self.assertNotEmpty(out)
loss2 = root.loss(X, ny).sum()
self.assertLess(loss2, loss1 + 1)
def test_convert_compact_fit(self):
X = numpy.arange(8).astype(numpy.float64).reshape((-1, 2))
y = ((X[:, 0] + X[:, 1] * 2) > 10).astype(numpy.int64)
y2 = y.copy()
y2[0] = 2
tree = DecisionTreeClassifier(max_depth=2)
tree.fit(X, y)
root = NeuralTreeNet.create_from_tree(tree, 10, arch='compact')
self.assertNotEmpty(root)
exp = tree.predict_proba(X)
got = root.predict(X)
self.assertEqual(exp.shape[0], got.shape[0])
self.assertEqualArray(exp + 1e-8, got[:, -2:] + 1e-8)
ny = label_class_to_softmax_output(y)
loss1 = root.loss(X, ny).sum()
_, out, err = self.capture(
lambda: root.fit(X, ny, verbose=True, max_iter=20))
self.assertEmpty(err)
self.assertNotEmpty(out)
loss2 = root.loss(X, ny).sum()
self.assertLess(loss2, loss1 + 1)