def setUp(self):
add_histogram_inference_support()
add_piecewise_inference_support()
from sklearn.model_selection import train_test_split
from sklearn.datasets import fetch_mldata
from sklearn.preprocessing import StandardScaler
from sklearn.utils import check_random_state
def create_leaf(data, ds_context, scope):
return create_piecewise_leaf(data,
ds_context,
scope,
isotonic=False,
prior_weight=None)
#return create_histogram_leaf(data, ds_context, scope, alpha=0.1)
add_piecewise_inference_support()
add_histogram_inference_support()
add_parametric_inference_support()
memory = Memory(cachedir="cache", verbose=0, compress=9)
data = []
for x in range(10):
for y in range(10):
for z in range(10):
data.append([x, y, z, int(((x + y + z) / 5))])
data = np.array(data).astype(np.float)
types = [
MetaType.DISCRETE, MetaType.DISCRETE, MetaType.DISCRETE, MetaType.DISCRETE
]
ds_context = Context(meta_types=types)
def test_Piecewise_full(self):
from spn.structure.leaves.piecewise.PiecewiseLinear import PiecewiseLinear
from spn.structure.leaves.piecewise.Inference import add_piecewise_inference_support
add_piecewise_inference_support()
#In order to create piecewise nodes
def create_piecewise_node(x_range, y_range, scope):
x_range, y_range = np.array(x_range), np.array(y_range)
auc = np.trapz(y_range, x_range)
y_range = y_range / auc
return PiecewiseLinear(x_range=x_range,
y_range=y_range,
bin_repr_points=x_range[1:-1],
scope=scope)
#Create node
node1 = create_piecewise_node([0., 1., 2., 3., 4.],
[0., 10., 20., 30., 0.], [0])
node2 = create_piecewise_node([0., 1., 2., 3., 4.],
[0., 30., 20., 10., 0.], [0])
#Test expectation node1
true_value = 2.33333333
expectation = Expectation(node1, set([0]), None, None)
self.assertAlmostEqual(true_value, expectation[0, 0], 5)
#Test expectation node2
true_value = 1.66666666
expectation = Expectation(node2, set([0]), None, None)
self.assertAlmostEqual(true_value, expectation[0, 0], 5)
#Test expectation with evidence
true_value = 1.
evidence = np.zeros((1, 1))
evidence[0, 0] = 1.
with self.assertRaises(AssertionError):
expectation = Expectation(node2, set([0]), set([0]), evidence)
#self.assertAlmostEqual(true_value, expectation[0, 0], 5)
'''
Above fails because the evidence is ignored on features for which the expectation
is computed. This is even more important if we evaluate ranges.
'''
#Test expectation of SPN with node1 and node2
spn1 = 0.5 * node1 + 0.5 * node2
true_value = 2.
expectation = Expectation(spn1, set([0]), None, None)
self.assertAlmostEqual(true_value, expectation[0, 0], 5)
#Create more nodes
node3 = create_piecewise_node([0., 1., 2., 3., 4., 5.],
[0., 10., 10., 10., 0., 0.], [1])
node4 = create_piecewise_node([0., 1., 2., 3., 4., 5.],
[0., 0., 10., 10., 10., 0.], [1])
#Test expectation node3
true_value = 2.
expectation = Expectation(node3, set([1]), None, None)
self.assertAlmostEqual(true_value, expectation[0, 0], 5)
#Test expectation node4
true_value = 3.
expectation = Expectation(node4, set([1]), None, None)
self.assertAlmostEqual(true_value, expectation[0, 0], 5)
#Test expectation of SPN with node1, node2, node3 and node4
spn2 = 0.5 * (node1 * node3) + 0.5 * (node2 * node4)
true_value = 2.5
expectation = Expectation(spn2, set([1]), None, None)
self.assertAlmostEqual(true_value, expectation[0, 0], 5)
# Probability of both subtrees is the same due to the evidence
# since the expectation of node3 and node3 have the same weight
# resulting in expectation of 2.5
true_value = 2.5
evidence = np.zeros((1, 2))
evidence[
0,
0] = 2. #Since node1 and node2 return 33% the true value will be the same as without evidence
evidence[0, 1] = np.nan
expectation = Expectation(spn2, set([1]), set([0]), evidence)
self.assertAlmostEqual(true_value, expectation[0, 0], 5)
# Probability of right subtree will be higher due to the evidence
# since node2 has a higher probability for 1. than node1
# Hence the expectation of node4 has a higher impact
evidence = np.zeros((1, 2))
evidence[0, 0] = 1.
evidence[0, 1] = np.nan
expectation = Expectation(spn2, set([1]), set([0]), evidence)
self.assertTrue(2.5 < expectation[0, 0], 5)
# Probability of left subtree will be higher due to the evidence
# since node1 has a higher probability for 3. than node2
# Hence the expectation of node3 has a higher impact
evidence = np.zeros((1, 2))
evidence[0, 0] = 3.
evidence[0, 1] = np.nan
expectation = Expectation(spn2, set([1]), set([0]), evidence)
self.assertTrue(2.5 > expectation[0, 0], 5)
with self.assertRaises(AssertionError):
#Test with evidence is None
expectation = Expectation(spn2, set([0]), set([1]), None)
'''
