def test_context_composition(self):
G = InferenceGraph()
a = Alist(
**{
tt.ID: '1',
tt.SUBJECT: 'Ghana',
tt.PROPERTY: 'P1082',
tt.TIME: '2023',
tt.OBJECT: '',
tt.OPVAR: '?x',
tt.COST: 1
})
ctx1 = [{
ctx.nationality: 'United Kingdom'
}, {
ctx.place: 'United Kingdom',
ctx.device: 'computer',
ctx.datetime: '2010-07-27 11:00:00'
}, {}]
a.set(tt.CONTEXT, ctx1)
G.add_alist(a)
query_ctx = frank.context.inject_query_context
# query context should infer the ctx.accuracy from ctx.device
op_alist = Temporal().decompose(query_ctx(a), G)
self.assertEqual(
(op_alist.get(tt.OP), len(G.child_alists(op_alist.id))),
('gpregress', 19))
def test_normalize_filter_with_location(self):
alist = Alist(
**{
tt.ID: '1',
tt.SUBJECT: '$y',
tt.PROPERTY: 'P1082',
tt.OBJECT: '?x',
tt.TIME: '2010',
tt.OPVAR: '?x',
tt.COST: 1,
'$y': {
"$filter": [{
"p": "type",
"o": "country"
}, {
"p": "location",
"o": "Africa"
}]
}
})
G = InferenceGraph()
G.add_alist(alist)
normalize = Normalize()
results = normalize.decompose(alist, G)
self.assertTrue(len(G.child_alists(alist.id)) > 0)
def descendant_explanation(self, G: InferenceGraph, alist: Alist, summary,
max_length, length):
if length <= max_length:
# for child in alist.children:
for child in G.child_alists(alist.id):
summary = f"{summary}{' ' + child.get('how') if 'how' in child.attributes else ''}" + \
f"{' ' + child.get('what') if 'what' in child.attributes else ''}".strip()
summary = self.descendant_explanation(G, child, summary,
max_length, length + 1)
return summary
def why(self, G: InferenceGraph, alist: Alist, decomp_op, in_place=True):
''' Explain a decomposition of this alist.
Assumes a failed instantiation of this alist following KB searches'''
expl = ""
time = ""
children = G.child_alists(alist.id)
if alist.get(tt.TIME):
time = f" in {alist.get(tt.TIME)}"
if decomp_op == 'temporal':
expl = f"Could not find the {alist.get(tt.PROPERTY)} of {alist.instantiation_value(tt.SUBJECT)}{time}. "
decomp_items = []
# for c in alist.children[0].children:
for c in children:
decomp_items.append(c.get(tt.TIME))
if len(decomp_items) >= 2:
expl += f"Attempted to infer the required value{time} by finding the {alist.get(tt.PROPERTY)} of {alist.instantiation_value(tt.SUBJECT)} " + \
f"at other times between {min(decomp_items)} and {max(decomp_items)}."
elif decomp_op == 'geospatial':
expl = f"Could not find the {alist.get(tt.PROPERTY)} of {alist.instantiation_value(tt.SUBJECT)}{time}. "
decomp_items = []
# for c in alist.children[0].children:
for c in G.child_alists(children[0].id):
decomp_items.append(c.instantiation_value(tt.SUBJECT))
entities = ''
if len(decomp_items) > 8:
entities = f"{', '.join(decomp_items[0:8])} etc"
else:
entities = f"{', '.join(decomp_items[0:len(decomp_items)-1])} and {decomp_items[-1]}"
if decomp_items:
expl += f"Finding the {alist.get(tt.PROPERTY)}{time} for the constituent parts of " + \
f" {alist.instantiation_value(tt.SUBJECT)}: {entities}."
elif decomp_op == 'normalize':
expl = f"Need to solve the sub-queries before determining the {alist.get(tt.PROPERTY)}{time}."
elif decomp_op == 'comparison':
expl = f"Need to solve the sub-queries to determine the items to compare."
if in_place:
alist.set("why", expl)
G.add_alist(alist)
def test_comp(self):
# root = Alist(**{tt.ID: '1', tt.SUBJECT: '$y', tt.PROPERTY: 'P1082',
# tt.OBJECT: '?x', tt.TIME: '2016', tt.OPVAR: '?x', tt.COST: 1})
# node101 = Alist(**{tt.OP:'comp', tt.ID: '1', tt.SUBJECT: '$y', tt.PROPERTY: 'P1082',
# tt.OBJECT: '?x', tt.TIME: '2016', tt.OPVAR: '?x', tt.COST: 1})
a = Alist(
**{
tt.ID: '1',
tt.SUBJECT: '$y',
tt.PROPERTY: 'P1082',
tt.OBJECT: '?x',
tt.TIME: '2010',
tt.OPVAR: '?x',
tt.COST: 1,
'$y': {
"$is": "Ghana"
}
})
G = InferenceGraph()
G.add_alist(a)
normalize.Normalize().decompose(a, G)
child1 = G.child_alists(a.id)[0]
result = frank.reduce.comp.reduce(child1, G.child_alists(child1.id), G)
self.assertTrue(result != None)
def test_geospatial(self):
alist = Alist(
**{
tt.ID: '1',
tt.SUBJECT: 'Africa',
tt.PROPERTY: 'P1082',
tt.OBJECT: '',
tt.TIME: '2010',
tt.OPVAR: '?x',
tt.COST: 1
})
G = InferenceGraph()
G.add_alist(alist)
geospatial = Geospatial()
results = geospatial.decompose(alist, G)
self.assertTrue(
len(G.child_alists(alist.id)) > 0,
"geospatial decomp should return more than one child")
def test_temporal(self):
alist = Alist(
**{
tt.ID: '0',
tt.SUBJECT: 'Ghana',
tt.PROPERTY: 'P1082',
tt.OBJECT: '',
tt.TIME: '2010',
tt.OPVAR: '?x',
tt.COST: 1
})
G = InferenceGraph()
G.add_alist(alist)
temporal = Temporal()
results = temporal.decompose(alist, G)
self.assertTrue(
len(G.child_alists(alist.id)) > 0,
"should have more than one element")
def test_gpregress_2(self):
alist = Alist(
**{
tt.ID: '101',
tt.SUBJECT: 'Ghana',
tt.PROPERTY: 'P1082',
tt.OBJECT: '?x',
tt.TIME: '2020',
tt.OPVAR: '?x',
tt.COST: 1
})
c1 = Alist(
**{
tt.ID: '21011',
tt.SUBJECT: 'Ghana',
tt.PROPERTY: 'P1082',
tt.OBJECT: '?x',
tt.TIME: '2019.0',
tt.OPVAR: '?x',
tt.COST: 1,
'?x': 1839758040765.62
})
c2 = Alist(
**{
tt.ID: '21012',
tt.SUBJECT: 'Ghana',
tt.PROPERTY: 'P1082',
tt.OBJECT: '?x',
tt.TIME: '2018.0',
tt.OPVAR: '?x',
tt.COST: 1,
'?x': 1885482534238.33
})
G = InferenceGraph()
G.add_alist(alist)
G.link(alist, c1)
G.link(alist, c2)
a = frank.reduce.gpregress.reduce(alist, G.child_alists(alist.id), G)
print(a)
self.assertAlmostEqual(a.instantiation_value(tt.OPVAR),
1792866444829.7,
places=1)
def test_normalize_is(self):
alist = Alist(
**{
tt.ID: '1',
tt.SUBJECT: '$y',
tt.PROPERTY: 'P1082',
tt.OBJECT: '?x',
tt.TIME: '2010',
tt.OPVAR: '?x',
tt.COST: 1,
'$y': {
"$is": "Ghana"
}
})
G = InferenceGraph()
G.add_alist(alist)
normalize = Normalize()
results = normalize.decompose(alist, G)
self.assertTrue(len(G.child_alists(alist.id)) > 0)
class TestReduce(unittest.TestCase):
def setUp(self):
self.G = InferenceGraph()
self.alist = Alist(
**{
tt.ID: '1',
tt.SUBJECT: 'Ghana',
tt.PROPERTY: 'P1082',
tt.OBJECT: '?x',
tt.TIME: '2020',
tt.OPVAR: '?x',
tt.COST: 1
})
self.c1 = Alist(
**{
tt.ID: '2',
tt.SUBJECT: 'Ghana',
tt.PROPERTY: 'P1082',
tt.OBJECT: '?x',
tt.TIME: '2010',
tt.OPVAR: '?x',
tt.COST: 1,
'?x': ''
})
self.c1.instantiate_variable('?x', '120')
self.c2 = Alist(
**{
tt.ID: '3',
tt.SUBJECT: 'Ghana',
tt.PROPERTY: 'P1082',
tt.OBJECT: '?x',
tt.TIME: '2011',
tt.OPVAR: '?x',
tt.COST: 1,
'?x': ''
})
self.c2.instantiate_variable('?x', '122')
self.c3 = Alist(
**{
tt.ID: '4',
tt.SUBJECT: 'Ghana',
tt.PROPERTY: 'P1082',
tt.OBJECT: '?x',
tt.TIME: '2012',
tt.OPVAR: '?x',
tt.COST: 1,
'?x': ''
})
self.c3.instantiate_variable('?x', '126')
self.c4 = Alist(
**{
tt.ID: '5',
tt.SUBJECT: 'Ghana',
tt.PROPERTY: 'P1082',
tt.OBJECT: '?x',
tt.TIME: '2013',
tt.OPVAR: '?x',
tt.COST: 1,
'?x': ''
})
self.c4.instantiate_variable('?x', '125')
self.c5 = Alist(
**{
tt.ID: '5',
tt.SUBJECT: 'Ghana',
tt.PROPERTY: 'P1082',
tt.OBJECT: '?x',
tt.TIME: '2014',
tt.OPVAR: '?x',
tt.COST: 1,
'?x': ''
})
self.c5.instantiate_variable('?x', '126')
self.c6 = Alist(
**{
tt.ID: '6',
tt.SUBJECT: 'Ghana',
tt.PROPERTY: 'P1082',
tt.OBJECT: '?x',
tt.TIME: '2015',
tt.OPVAR: '?x',
tt.COST: 1,
'?x': ''
})
self.c6.instantiate_variable('?x', '128')
self.c7 = Alist(
**{
tt.ID: '7',
tt.SUBJECT: 'Ghana',
tt.PROPERTY: 'P1082',
tt.OBJECT: '?x',
tt.TIME: '2016',
tt.OPVAR: '?x',
tt.COST: 1,
'?x': ''
})
self.c7.instantiate_variable('?x', '129')
self.G.add_alist(self.alist)
self.G.link(self.alist, self.c1)
self.G.link(self.alist, self.c2)
self.G.link(self.alist, self.c3)
self.G.link(self.alist, self.c4)
self.G.link(self.alist, self.c5)
self.G.link(self.alist, self.c6)
self.G.link(self.alist, self.c7)
self.G2 = InferenceGraph()
self.alist2 = Alist(
**{
tt.ID: '1',
tt.SUBJECT: 'Ghana',
tt.PROPERTY: 'P1082',
tt.OBJECT: '?x',
tt.TIME: '2020',
tt.OPVAR: '?x',
tt.COST: 1
})
self.c21 = Alist(
**{
tt.ID: '2',
tt.SUBJECT: 'Ghana',
tt.PROPERTY: 'P1082',
tt.OBJECT: '?x',
tt.TIME: '2010',
tt.OPVAR: '?x',
tt.COST: 1,
'?x': ''
})
self.c21.instantiate_variable('?x', 'a')
self.c22 = Alist(
**{
tt.ID: '3',
tt.SUBJECT: 'Ghana',
tt.PROPERTY: 'P1082',
tt.OBJECT: '?x',
tt.TIME: '2011',
tt.OPVAR: '?x',
tt.COST: 1,
'?x': ''
})
self.c22.instantiate_variable('?x', 'b')
self.c23 = Alist(
**{
tt.ID: '4',
tt.SUBJECT: 'Ghana',
tt.PROPERTY: 'P1082',
tt.OBJECT: '?x',
tt.TIME: '2012',
tt.OPVAR: '?x',
tt.COST: 1,
'?x': ''
})
self.c23.instantiate_variable('?x', 'c')
self.c24 = Alist(
**{
tt.ID: '5',
tt.SUBJECT: 'Ghana',
tt.PROPERTY: 'P1082',
tt.OBJECT: '?x',
tt.TIME: '2013',
tt.OPVAR: '?x',
tt.COST: 1,
'?x': ''
})
self.c24.instantiate_variable('?x', 'd')
self.c25 = Alist(
**{
tt.ID: '5',
tt.SUBJECT: 'Ghana',
tt.PROPERTY: 'P1082',
tt.OBJECT: '?x',
tt.TIME: '2014',
tt.OPVAR: '?x',
tt.COST: 1,
'?x': ''
})
self.c25.instantiate_variable('?x', 'a')
self.G2.add_alist(self.alist2)
self.G2.link(self.alist2, self.c21)
self.G2.link(self.alist2, self.c22)
self.G2.link(self.alist2, self.c23)
self.G2.link(self.alist2, self.c24)
self.G2.link(self.alist2, self.c25)
def test_value(self):
a = frank.reduce.value.reduce(self.alist,
self.G.child_alists(self.alist.id),
self.G)
self.assertTrue(a.instantiation_value(tt.OBJECT), '124')
def test_value2(self):
a = frank.reduce.value.reduce(self.alist2,
self.G2.child_alists(self.alist2.id),
self.G2)
self.assertTrue(a.instantiation_value(tt.OBJECT), 'a')
def test_values(self):
a = frank.reduce.values.reduce(self.alist,
self.G.child_alists(self.alist.id),
self.G)
self.assertEqual(a.instantiation_value(tt.OBJECT),
'120,122,126,125,126,128,129')
def test_sum(self):
a = frank.reduce.sum.reduce(self.alist,
self.G.child_alists(self.alist.id), self.G)
self.assertEqual(float(a.instantiation_value(tt.OPVAR)), 876.0)
def test_max(self):
a = frank.reduce.max.reduce(self.alist,
self.G.child_alists(self.alist.id), self.G)
self.assertEqual(int(a.instantiation_value(tt.OPVAR)), 129)
def test_min(self):
a = frank.reduce.min.reduce(self.alist,
self.G.child_alists(self.alist.id), self.G)
self.assertEqual(a.instantiation_value(tt.OPVAR), '120')
def test_count(self):
a = frank.reduce.count.reduce(self.alist,
self.G.child_alists(self.alist.id),
self.G)
self.assertEqual(a.instantiation_value(tt.OPVAR), 7)
def test_product(self):
a = frank.reduce.product.reduce(self.alist,
self.G.child_alists(self.alist.id),
self.G)
self.assertEqual(a.instantiation_value(tt.OPVAR), 479724456960000.0)
def test_regress(self):
a = frank.reduce.regress.reduce(self.alist,
self.G.child_alists(self.alist.id),
self.G)
print(a)
self.assertAlmostEqual(a.instantiation_value(tt.OPVAR),
134.89,
places=2)
def test_gpregress(self):
a = frank.reduce.gpregress.reduce(self.alist,
self.G.child_alists(self.alist.id),
self.G)
print(a)
self.assertAlmostEqual(a.instantiation_value(tt.OPVAR), 134, places=0)
def test_gpregress_2(self):
alist = Alist(
**{
tt.ID: '101',
tt.SUBJECT: 'Ghana',
tt.PROPERTY: 'P1082',
tt.OBJECT: '?x',
tt.TIME: '2020',
tt.OPVAR: '?x',
tt.COST: 1
})
c1 = Alist(
**{
tt.ID: '21011',
tt.SUBJECT: 'Ghana',
tt.PROPERTY: 'P1082',
tt.OBJECT: '?x',
tt.TIME: '2019.0',
tt.OPVAR: '?x',
tt.COST: 1,
'?x': 1839758040765.62
})
c2 = Alist(
**{
tt.ID: '21012',
tt.SUBJECT: 'Ghana',
tt.PROPERTY: 'P1082',
tt.OBJECT: '?x',
tt.TIME: '2018.0',
tt.OPVAR: '?x',
tt.COST: 1,
'?x': 1885482534238.33
})
G = InferenceGraph()
G.add_alist(alist)
G.link(alist, c1)
G.link(alist, c2)
a = frank.reduce.gpregress.reduce(alist, G.child_alists(alist.id), G)
print(a)
self.assertAlmostEqual(a.instantiation_value(tt.OPVAR),
1792866444829.7,
places=1)
def test_do_gpregress(self):
data = [[2019.0, 1839758040765.62], [2018.0, 1885482534238.33],
[2017.0, 2055505502224.73], [2016.0, 1793989048409.29],
[2015.0, 1802214373741.32], [2014.0, 2455993625159.37],
[2013.0, 2472806919901.67], [2012.0, 2465188674415.03],
[2011.0, 2616201578192.25], [2010.0, 2208871646202.82],
[2009.0, 1667019780934.28], [2008.0, 1695824571927.15],
[2007.0, 1397084345950.39], [2006.0, 1107640297889.95]]
X, y = [], []
for d in data:
X.append([d[0]])
y.append(d[1])
X = np.array(X)
y = np.array(y)
predict = frank.reduce.gpregress.do_gpregress(X, y, np.array(
[2022.]), (np.max(y) - np.min(y))**2, 1)
y_predict = predict[0]['y']
self.assertAlmostEqual(y_predict, 1324535292167, places=0)
@unittest.skip
def test_nnpredict(self):
a = frank.reduce.nnpredict.reduce(self.alist,
self.G.child_alists(self.alist.id),
self.G)
self.assertAlmostEqual(a.instantiation_value(tt.OPVAR),
158.97,
places=2)
def test_comp(self):
# root = Alist(**{tt.ID: '1', tt.SUBJECT: '$y', tt.PROPERTY: 'P1082',
# tt.OBJECT: '?x', tt.TIME: '2016', tt.OPVAR: '?x', tt.COST: 1})
# node101 = Alist(**{tt.OP:'comp', tt.ID: '1', tt.SUBJECT: '$y', tt.PROPERTY: 'P1082',
# tt.OBJECT: '?x', tt.TIME: '2016', tt.OPVAR: '?x', tt.COST: 1})
a = Alist(
**{
tt.ID: '1',
tt.SUBJECT: '$y',
tt.PROPERTY: 'P1082',
tt.OBJECT: '?x',
tt.TIME: '2010',
tt.OPVAR: '?x',
tt.COST: 1,
'$y': {
"$is": "Ghana"
}
})
G = InferenceGraph()
G.add_alist(a)
normalize.Normalize().decompose(a, G)
child1 = G.child_alists(a.id)[0]
result = frank.reduce.comp.reduce(child1, G.child_alists(child1.id), G)
self.assertTrue(result != None)
def test_eq(self):
a = Alist(**{
tt.ID: '1',
tt.OPVAR: '$x $y',
'$x': '?x1',
'$y': '?y1',
'?_eq_': ''
})
b = Alist(**{tt.ID: '2', tt.OPVAR: '?x1', '?x1': 20})
c = Alist(**{tt.ID: '3', tt.OPVAR: '?y1', '?y1': 20})
G = InferenceGraph()
G.add_alist(a)
G.link(a, b)
G.link(a, c)
result = frank.reduce.eq.reduce(a, [b, c], G)
self.assertTrue(True if result.instantiation_value('?_eq_') ==
'true' else False)
def test_gt(self):
a = Alist(**{
tt.ID: '1',
tt.OPVAR: '$x $y',
'$x': '?x1',
'$y': '?y1',
'?_gt_': ''
})
b = Alist(**{tt.ID: '2', tt.OPVAR: '?x1', '?x1': 36})
c = Alist(**{tt.ID: '3', tt.OPVAR: '?y1', '?y1': 33})
G = InferenceGraph()
G.add_alist(a)
G.link(a, b)
G.link(a, c)
result = frank.reduce.gt.reduce(a, [b, c], G)
self.assertTrue(True if result.instantiation_value('?_gt_') ==
'true' else False)
def test_gte(self):
a = Alist(**{
tt.ID: '1',
tt.OPVAR: '$x $y',
'$x': '?x1',
'$y': '?y1',
'?_gte_': ''
})
b = Alist(**{tt.ID: '2', tt.OPVAR: '?x1', '?x1': 33})
c = Alist(**{tt.ID: '3', tt.OPVAR: '?y1', '?y1': 33})
G = InferenceGraph()
G.add_alist(a)
G.link(a, b)
G.link(a, c)
result = frank.reduce.gte.reduce(a, [b, c], G)
self.assertTrue(True if result.instantiation_value('?_gte_') ==
'true' else False)
def test_lt(self):
a = Alist(**{
tt.ID: '1',
tt.OPVAR: '$x $y',
'$x': '?x1',
'$y': '?y1',
'?_lt_': ''
})
b = Alist(**{tt.ID: '2', tt.OPVAR: '?x1', '?x1': 20})
c = Alist(**{tt.ID: '3', tt.OPVAR: '?y1', '?y1': 30})
G = InferenceGraph()
G.add_alist(a)
G.link(a, b)
G.link(a, c)
result = frank.reduce.lt.reduce(a, [b, c], G)
self.assertTrue(True if result.instantiation_value('?_lt_') ==
'true' else False)
def test_lte(self):
a = Alist(**{
tt.ID: '1',
tt.OPVAR: '$x $y',
'$x': '?x1',
'$y': '?y1',
'?_lte_': ''
})
b = Alist(**{tt.ID: '2', tt.OPVAR: '?x1', '?x1': 30})
c = Alist(**{tt.ID: '3', tt.OPVAR: '?y1', '?y1': 30})
G = InferenceGraph()
G.add_alist(a)
G.link(a, b)
G.link(a, c)
result = frank.reduce.lte.reduce(a, [b, c], G)
self.assertTrue(True if result.instantiation_value('?_lte_') ==
'true' else False)
def reduce(alist: Alist, children: List[Alist], G: InferenceGraph):
if not children:
return None
nodes_enqueue = []
nodes_enqueue_process = []
# get intersection of child values
common_items = set()
head, *tail = children
has_head_children = False
has_tail_children = False
for c in G.child_alists(head.id):
has_head_children = True
if c.get(tt.OP) != 'comp':
if c.get(tt.OPVAR).startswith(vx.NESTING):
common_items.add(str(c.instantiation_value(c.get(tt.OPVAR))))
else:
projVars = c.projection_variables()
if projVars != None:
for pvkey, pvval in projVars.items():
common_items.add(c.instantiation_value(pvkey))
for t in tail:
c_items = set()
for tc in G.child_alists(t.id):
has_tail_children = True
if tc.get(tt.OPVAR).startswith(vx.NESTING):
c_items.add(str(c.instantiation_value(tc.get(tt.OPVAR))))
projVars = tc.projection_variables()
if projVars != None:
for pvkey, pvval in projVars.items():
c_items.add(tc.instantiation_value(pvkey))
common_items = common_items.intersection(c_items)
if not common_items and not has_head_children and not has_tail_children:
for c in children:
if c.get(tt.OP) != 'comp':
if c.get(tt.OPVAR).startswith(vx.NESTING):
common_items.add(
str(c.instantiation_value(c.get(tt.OPVAR))))
else:
projVars = c.projection_variables()
if projVars != None:
for pvkey, pvval in projVars.items():
common_items.add(c.instantiation_value(pvkey))
if not common_items:
return None
else:
# if common items not empty, ignore existing siblings before creating new siblings
sibs = G.child_alists(G.parent_alists(alist.id)[0].id)
for x in sibs:
if x.id != alist.id:
# x.prune()
G.prune(x.id)
print(
f'{pcol.RED}sibling pruned {x.id}{pcol.RESET} {x}{pcol.RESETALL}'
)
# setup new sibling branch(s)
parent = G.parent_alists(alist.id)[0]
op_alist = parent.copy()
op_alist.set(alist.get(tt.OPVAR), '')
op_alist.set(tt.OP, parent.get(tt.OP))
op_alist.set(tt.OPVAR, parent.get(tt.OPVAR))
op_alist.set(op_alist.get(tt.OPVAR), '')
op_alist.state = states.EXPLORED
# set as an aggregation node to help with display rendering
op_alist.node_type = nt.HNODE
G.link(parent, op_alist, 'comp')
G.link(alist, op_alist, 'set-comp', create_new_id=False)
nodes_enqueue.append((op_alist, parent, False, 'comp'))
print(
f'{pcol.BLUE}set-comp >> {op_alist.id}{pcol.RESET} {op_alist}{pcol.RESETALL}'
)
if alist.children:
nodes_enqueue.append((op_alist, alist, False, 'setcomp'))
# create children of the new branch
# copy to avoid using different version from another thread in loop
op_alist_copy = op_alist.copy()
for ff in common_items:
new_sibling: Alist = op_alist_copy.copy()
new_sibling.set(tt.OP, 'value')
new_sibling.set(tt.OPVAR, op_alist_copy.get(tt.OPVAR))
new_sibling.set(alist.get(tt.OPVAR), ff)
new_sibling.instantiate_variable(alist.get(tt.OPVAR), ff)
for ref in new_sibling.variable_references(alist.get(tt.OPVAR)):
if ref not in [tt.OPVAR]:
new_sibling.set(ref, ff)
new_sibling.node_type = nt.ZNODE
G.link(op_alist, new_sibling, 'comp_lookup')
nodes_enqueue_process.append(
(new_sibling, op_alist, True, 'comp_lookup'))
print(
f'{pcol.BLUE} set-comp-child >>> {new_sibling.id}{pcol.RESET} {new_sibling}{pcol.RESETALL}'
)
alist.state = states.IGNORE
alist.nodes_to_enqueue_only = nodes_enqueue
alist.nodes_to_enqueue_and_process = nodes_enqueue_process
return alist
def what(self,
G: InferenceGraph,
alist: Alist,
is_reduced: bool,
in_place=True):
''' Explain a reduction of this alist.
'''
what = ''
how = ''
time = ""
if alist.get(tt.TIME):
time = f" in {alist.get(tt.TIME)}"
if not is_reduced:
if alist.get(tt.OP) in ['eq', 'gt', 'gte', 'lt', 'lte']:
what = f"Failed to compare the values since the values of all items being compare are not known. "
elif alist.get(tt.OP) in ['comp']:
what = f"Failed to solve the sub-problem. "
elif alist.get(tt.OP) in ['value', 'values']:
what = f"Failed to determine the {self.ops_text[alist.get(tt.OP)]} of {alist.get(tt.PROPERTY)}{time}."
else:
what = f"Failed to calculate the {self.ops_text[alist.get(tt.OP)]} of {alist.get(tt.PROPERTY)}{time}."
else:
if alist.get(tt.OP) in ['eq', 'gt', 'gte', 'lt', 'lte']:
vars_compared = alist.get(tt.OPVAR).split(' ')
if len(vars_compared) > 1:
what = f"Inferred value is '{alist.instantiation_value('?'+ alist.get(tt.OP))}'."
how = f"Did a comparison to determine if {alist.instantiation_value(vars_compared[0])} is " + \
f"{self.ops_text[alist.get(tt.OP)]} {alist.instantiation_value(vars_compared[1])}."
elif alist.get(tt.OP) in ['comp']:
listed_str = ''
listed = alist.instantiation_value(alist.get(tt.OPVAR))
if listed:
listed = listed.split(',')
if len(listed) > 8:
listed_str += f"{', '.join(listed[0:8])}, etc"
else:
listed_str += ', '.join(listed)
if listed_str:
what = f"Solved the sub-query and found the following values: {listed_str}."
else:
inferred_value = ''
projected = alist.projection_variables()
if projected:
inferred_value = list(projected.values())[0]
if not inferred_value:
inferred_value = alist.instantiation_value(
alist.get(tt.OPVAR))
if inferred_value:
if ':' in alist.get(tt.PROPERTY):
listed_str = ''
listed = alist.instantiation_value(alist.get(
tt.OPVAR)).split(',')
if len(listed) > 8:
listed_str += f"{', '.join(listed[0:8])}, etc"
else:
listed_str += ', '.join(listed)
what = f"The {alist.get(tt.PROPERTY).split(':')[1]} values found for the sub-query include: {listed_str}."
elif (projected
or inferred_value) and not alist.get(tt.PROPERTY):
# for alists with just a projected value but no property
what = f"An input value for operation is {inferred_value}."
elif projected and alist.get(
tt.OPVAR) not in projected and alist.get(
tt.OP) in ['max', 'min']:
what = f"The entity whose {alist.get(tt.PROPERTY)}{time} has the {self.ops_text[alist.get(tt.OP)]} of {alist.instantiation_value(alist.get(tt.OPVAR))} is {inferred_value}."
elif projected and alist.get(
tt.OPVAR) not in projected and alist.get(
tt.OP) not in ['max', 'min']:
what = f"The {self.ops_text[alist.get(tt.OP)]} of the {alist.get(tt.PROPERTY)}{time} of {inferred_value} is {alist.instantiation_value(alist.get(tt.OPVAR))}."
else:
what = f"The {self.ops_text[alist.get(tt.OP)]} of the {alist.get(tt.PROPERTY)} of {alist.instantiation_value(tt.SUBJECT)}{time} is {inferred_value}."
if alist.get(tt.OP) in [
'regress', 'nnpredict', 'linregress', 'gpregress',
'nnregress'
]:
decomp_items = []
children = G.child_alists(alist.id)
# for c in alist.children[0].children:
for c in G.child_alists(children[0].id):
decomp_items.append(c.get(tt.TIME))
if len(decomp_items) > 0:
how = f"Generated a regression function from times between {min(decomp_items)} and {max(decomp_items)}."
if in_place:
alist.set("what", what)
alist.set("how", how)
G.add_alist(alist)