def test_categorical_to_indicator_input_layer():
#
# creating all the data slices
# the slicing is a fake stub
# rows = 5
# cols = 5
var_1 = 0
values_1 = 2
var_2 = 1
values_2 = 3
var_3 = 2
values_3 = 4
node_1 = SumNode()
node_1.id = 1
node_2 = ProductNode()
node_2.id = 2
node_3 = SumNode()
node_3.id = 3
# adding first level
weight_12 = 0.4
weight_13 = 0.6
node_1.add_child(node_2, weight_12)
node_1.add_child(node_3, weight_13)
node_4 = ProductNode()
node_4.id = 4
leaf_5 = CategoricalSmoothedNode(var_1, values_1)
leaf_5.id = 5
# not adding the slice to the stack
node_2.add_child(node_4)
node_2.add_child(leaf_5)
node_6 = SumNode()
node_6.id = 6
node_7 = SumNode()
node_7.id = 7
weight_36 = 0.1
weight_37 = 0.9
node_3.add_child(node_6, weight_36)
node_3.add_child(node_7, weight_37)
node_8 = ProductNode()
node_8.id = 8
leaf_15 = CategoricalSmoothedNode(var_2, values_2)
leaf_15.id = 15
node_4.add_child(node_8)
node_4.add_child(leaf_15)
leaf_13 = CategoricalSmoothedNode(var_3, values_3)
leaf_13.id = 13
leaf_14 = CategoricalSmoothedNode(var_1, values_1)
leaf_14.id = 14
node_8.add_child(leaf_13)
node_8.add_child(leaf_14)
node_9 = ProductNode()
node_9.id = 9
leaf_16 = CategoricalSmoothedNode(var_2, values_2)
leaf_16.id = 16
leaf_17 = CategoricalSmoothedNode(var_3, values_3)
leaf_17.id = 17
node_9.add_child(leaf_16)
node_9.add_child(leaf_17)
node_10 = ProductNode()
node_10.id = 10
leaf_18 = CategoricalSmoothedNode(var_2, values_2)
leaf_18.id = 18
leaf_19 = CategoricalSmoothedNode(var_2, values_2)
leaf_19.id = 19
node_10.add_child(leaf_18)
node_10.add_child(leaf_19)
weight_69 = 0.3
weight_610 = 0.7
node_6.add_child(node_9, weight_69)
node_6.add_child(node_10, weight_610)
node_11 = ProductNode()
node_11.id = 11
leaf_20 = CategoricalSmoothedNode(var_1, values_1)
leaf_20.id = 20
leaf_21 = CategoricalSmoothedNode(var_3, values_3)
leaf_21.id = 21
node_11.add_child(leaf_20)
node_11.add_child(leaf_21)
node_12 = ProductNode()
node_12.id = 12
leaf_22 = CategoricalSmoothedNode(var_1, values_1)
leaf_22.id = 22
leaf_23 = CategoricalSmoothedNode(var_3, values_3)
leaf_23.id = 23
node_12.add_child(leaf_22)
node_12.add_child(leaf_23)
weight_711 = 0.5
weight_712 = 0.5
node_7.add_child(node_11, weight_711)
node_7.add_child(node_12, weight_712)
root_node = SpnFactory.layered_pruned_linked_spn(node_1)
print('ROOT nODE', root_node)
spn = SpnFactory.layered_linked_spn(root_node)
print('SPN', spn)
assert spn.n_layers() == 3
for i, layer in enumerate(spn.top_down_layers()):
if i == 0:
assert layer.n_nodes() == 1
elif i == 1:
assert layer.n_nodes() == 5
elif i == 2:
assert layer.n_nodes() == 12
#
# changing input layer
spn = linked_categorical_input_to_indicators(spn)
print('Changed input layer to indicator variables')
print(spn)
def test_layered_pruned_linked_spn_cltree():
#
# creating all the data slices
# the slicing is a fake stub
rows = 5
cols = 5
var = 1
values = 2
vars = [2, 3]
var_values = [2, 2]
s_data = numpy.array([[0, 1], [1, 1], [1, 0], [0, 0]])
node_1 = SumNode()
node_1.id = 1
node_2 = ProductNode()
node_2.id = 2
node_3 = SumNode()
node_3.id = 3
# adding first level
weight_12 = 0.4
weight_13 = 0.6
node_1.add_child(node_2, weight_12)
node_1.add_child(node_3, weight_13)
node_4 = ProductNode()
node_4.id = 4
leaf_5 = CategoricalSmoothedNode(var,
values)
leaf_5.id = 5
# not adding the slice to the stack
node_2.add_child(node_4)
node_2.add_child(leaf_5)
node_6 = SumNode()
node_6.id = 6
node_7 = SumNode()
node_7.id = 7
weight_36 = 0.1
weight_37 = 0.9
node_3.add_child(node_6, weight_36)
node_3.add_child(node_7, weight_37)
node_8 = ProductNode()
node_8.id = 8
#
# this is a cltree
leaf_15 = CLTreeNode(vars=vars,
var_values=var_values,
data=s_data)
leaf_15.id = 15
node_4.add_child(node_8)
node_4.add_child(leaf_15)
leaf_13 = CategoricalSmoothedNode(var,
values)
leaf_13.id = 13
leaf_14 = CLTreeNode(vars=vars,
var_values=var_values,
data=s_data)
leaf_14.id = 14
node_8.add_child(leaf_13)
node_8.add_child(leaf_14)
leaf_9 = CLTreeNode(vars=vars,
var_values=var_values,
data=s_data)
leaf_9.id = 9
node_10 = ProductNode()
node_10.id = 10
leaf_18 = CategoricalSmoothedNode(var,
values)
leaf_18.id = 18
leaf_19 = CategoricalSmoothedNode(var,
values)
leaf_19.id = 19
node_10.add_child(leaf_18)
node_10.add_child(leaf_19)
weight_69 = 0.3
weight_610 = 0.7
node_6.add_child(leaf_9, weight_69)
node_6.add_child(node_10, weight_610)
node_11 = ProductNode()
node_11.id = 11
leaf_20 = CategoricalSmoothedNode(var,
values)
leaf_20.id = 20
leaf_21 = CategoricalSmoothedNode(var,
values)
leaf_21.id = 21
node_11.add_child(leaf_20)
node_11.add_child(leaf_21)
node_12 = ProductNode()
node_12.id = 12
leaf_22 = CLTreeNode(vars=vars,
var_values=var_values,
data=s_data)
leaf_22.id = 22
leaf_23 = CategoricalSmoothedNode(var,
values)
leaf_23.id = 23
node_12.add_child(leaf_22)
node_12.add_child(leaf_23)
weight_711 = 0.5
weight_712 = 0.5
node_7.add_child(node_11, weight_711)
node_7.add_child(node_12, weight_712)
print('Added nodes')
root_node = SpnFactory.layered_pruned_linked_spn(node_1)
print('ROOT nODE', root_node)
spn = SpnFactory.layered_linked_spn(root_node)
print('SPN', spn)
assert spn.n_layers() == 3
for i, layer in enumerate(spn.top_down_layers()):
if i == 0:
assert layer.n_nodes() == 1
elif i == 1:
assert layer.n_nodes() == 4
elif i == 2:
assert layer.n_nodes() == 10
def test_layered_pruned_linked_spn_cltree():
#
# creating all the data slices
# the slicing is a fake stub
rows = 5
cols = 5
var = 1
values = 2
vars = [2, 3]
var_values = [2, 2]
s_data = numpy.array([[0, 1], [1, 1], [1, 0], [0, 0]])
node_1 = SumNode()
node_1.id = 1
node_2 = ProductNode()
node_2.id = 2
node_3 = SumNode()
node_3.id = 3
# adding first level
weight_12 = 0.4
weight_13 = 0.6
node_1.add_child(node_2, weight_12)
node_1.add_child(node_3, weight_13)
node_4 = ProductNode()
node_4.id = 4
leaf_5 = CategoricalSmoothedNode(var, values)
leaf_5.id = 5
# not adding the slice to the stack
node_2.add_child(node_4)
node_2.add_child(leaf_5)
node_6 = SumNode()
node_6.id = 6
node_7 = SumNode()
node_7.id = 7
weight_36 = 0.1
weight_37 = 0.9
node_3.add_child(node_6, weight_36)
node_3.add_child(node_7, weight_37)
node_8 = ProductNode()
node_8.id = 8
#
# this is a cltree
leaf_15 = CLTreeNode(vars=vars, var_values=var_values, data=s_data)
leaf_15.id = 15
node_4.add_child(node_8)
node_4.add_child(leaf_15)
leaf_13 = CategoricalSmoothedNode(var, values)
leaf_13.id = 13
leaf_14 = CLTreeNode(vars=vars, var_values=var_values, data=s_data)
leaf_14.id = 14
node_8.add_child(leaf_13)
node_8.add_child(leaf_14)
leaf_9 = CLTreeNode(vars=vars, var_values=var_values, data=s_data)
leaf_9.id = 9
node_10 = ProductNode()
node_10.id = 10
leaf_18 = CategoricalSmoothedNode(var, values)
leaf_18.id = 18
leaf_19 = CategoricalSmoothedNode(var, values)
leaf_19.id = 19
node_10.add_child(leaf_18)
node_10.add_child(leaf_19)
weight_69 = 0.3
weight_610 = 0.7
node_6.add_child(leaf_9, weight_69)
node_6.add_child(node_10, weight_610)
node_11 = ProductNode()
node_11.id = 11
leaf_20 = CategoricalSmoothedNode(var, values)
leaf_20.id = 20
leaf_21 = CategoricalSmoothedNode(var, values)
leaf_21.id = 21
node_11.add_child(leaf_20)
node_11.add_child(leaf_21)
node_12 = ProductNode()
node_12.id = 12
leaf_22 = CLTreeNode(vars=vars, var_values=var_values, data=s_data)
leaf_22.id = 22
leaf_23 = CategoricalSmoothedNode(var, values)
leaf_23.id = 23
node_12.add_child(leaf_22)
node_12.add_child(leaf_23)
weight_711 = 0.5
weight_712 = 0.5
node_7.add_child(node_11, weight_711)
node_7.add_child(node_12, weight_712)
print('Added nodes')
root_node = SpnFactory.layered_pruned_linked_spn(node_1)
print('ROOT nODE', root_node)
spn = SpnFactory.layered_linked_spn(root_node)
print('SPN', spn)
assert spn.n_layers() == 3
for i, layer in enumerate(spn.top_down_layers()):
if i == 0:
assert layer.n_nodes() == 1
elif i == 1:
assert layer.n_nodes() == 4
elif i == 2:
assert layer.n_nodes() == 10
