def even_numbers_test():
B = [Atom([Term(False, '0')], 'zero')] + \
[Atom([Term(False, str(i)), Term(False, str(i + 1))], 'succ')
for i in range(0, 20)]
P = [Atom([Term(False, str(i))], 'target') for i in range(0, 21, 2)]
N = [Atom([Term(False, str(i))], 'target') for i in range(1, 21, 2)]
print(P)
term_x_0 = Term(True, 'X_0')
term_x_1 = Term(True, 'X_1')
p_e = [Atom([term_x_0], 'zero'), Atom([term_x_0, term_x_1], 'succ')]
p_a = [Atom([term_x_0, term_x_1], 'pred')]
target = Atom([term_x_0], 'target')
constants = [str(i) for i in range(0, 21)]
# Define rules for intensional predicates
p_a_rule = (Rule_Template(1, False), None)
target_rule = (Rule_Template(0, False), Rule_Template(1, True))
rules = {p_a[0]: p_a_rule, target: target_rule}
langage_frame = Language_Frame(target, p_e, constants)
program_template = Program_Template(p_a, rules, 10)
#program_template = Program_Template(p_a, rules, 300)
dilp = DILP(langage_frame, B, P, N, program_template)
def generate_ground_atoms_test():
term1 = Term(True, 'X_0')
term2 = Term(True, 'X_1')
target = Atom([term1, term2], 'r')
p_e = [Atom([term1, term2], 'p')]
p_a = [Atom([term1, term2], 'q')]
rule_template_1 = Rule_Template(0, False)
rule_template_2 = Rule_Template(1, True)
language_frame = Language_Frame(target, p_e, set(['a', 'b']))
program_template = Program_Template(p_a,
(rule_template_1, rule_template_2),
300)
term_a = Term(False, 'a')
term_b = Term(False, 'b')
# Background facts: p(a,a) p(a,b)
background = [Atom([term_a, term_a], 'p'), Atom([term_a, term_b], 'p')]
# Positive: p(b,a)
positive = [Atom([term_b, term_a], 'p')]
# Negative: p(b,b)
negative = [Atom([term_b, term_b], 'p')]
ilp = ILP(language_frame, background, positive, negative, program_template)
initial_valuation = ilp.convert()
for valuation in initial_valuation:
if valuation[0] in background:
assert valuation[1] == 1
else:
assert valuation[1] == 0
def less_than():
B = [Atom([Term(False, '0')], 'zero')] + \
[Atom([Term(False, str(i)), Term(False, str(i + 1))], 'succ')
for i in range(0, 9)]
P = []
N = []
for i in range(0, 10):
for j in range(0, 10):
if j >= i:
N.append(
Atom([Term(False, str(j)), Term(False, str(i))], 'target'))
else:
P.append(
Atom([Term(False, str(j)), Term(False, str(i))], 'target'))
term_x_0 = Term(True, 'X_0')
term_x_1 = Term(True, 'X_1')
p_e = [Atom([term_x_0], 'zero'), Atom([term_x_0, term_x_1], 'succ')]
p_a = []
target = Atom([term_x_0, term_x_1], 'target')
# target_rule = (Rule_Template(0, False), Rule_Template(1, True))
target_rule = (Rule_Template(0, False), Rule_Template(1, True))
rules = {target: target_rule}
constants = [str(i) for i in range(0, 10)]
langage_frame = Language_Frame(target, p_e, constants)
program_template = Program_Template(p_a, rules, 10)
dilp = DILP(langage_frame, B, P, N, program_template)
dilp.train()
def optimized_rule_test():
rule_template_1 = Rule_Template(0, False)
rule_template_2 = Rule_Template(1, True)
target = Atom([Term(True, 'X_0'), Term(True, 'X_1')], 'q')
p_e = [Atom([Term(True, 'X_0'), Term(True, 'X_1')], 'p')]
rule_manager = Optimized_Combinatorial_Generator(
[target], (rule_template_1, rule_template_2), target, p_e)
rule_matrix = rule_manager.generate_clauses()
assert len(rule_matrix[0]) == 8
assert len(rule_matrix[1]) == 58
def f_c_test2():
'''Testing f_c function
'''
term1 = Term(True, 'X_0')
term2 = Term(True, 'X_1')
target = Atom([term1], 'r')
p_e = [Atom([term1, term2], 'p')]
p_a = [Atom([term1, term2], 'q')]
rule_template_1 = Rule_Template(0, False)
rule_template_2 = Rule_Template(1, True)
language_frame = Language_Frame(target, p_e, set(['a', 'b']))
program_template = Program_Template(p_a,
(rule_template_1, rule_template_2),
300)
term_a = Term(False, 'a')
term_b = Term(False, 'b')
# Background facts: p(a,a) p(a,b)
background = [Atom([term_a, term_a], 'p'), Atom([term_a, term_b], 'p')]
# Positive: p(b,a)
positive = [Atom([term_b, term_a], 'p')]
# Negative: p(b,b)
negative = [Atom([term_b, term_b], 'p')]
ilp = ILP(language_frame, background, positive, negative, program_template)
initial_valuation, valuation_mapping = ilp.convert()
term_x = Term(True, 'X_0')
term_y = Term(True, 'X_1')
term_z = Term(True, 'X_2')
r = Atom([term_x], 'r')
p = Atom([term_x, term_z], 'p')
q = Atom([term_z, term_y], 'q')
clause = Clause(r, [p, q])
# Example from the book
example_val = {
Atom([term_a, term_a], 'p'): 1.0,
Atom([term_a, term_b], 'p'): 0.9,
Atom([term_a, term_a], 'q'): 0.1,
Atom([term_b, term_a], 'q'): 0.2,
Atom([term_b, term_b], 'q'): 0.8,
}
for key in example_val:
initial_valuation[valuation_mapping[key]] = example_val[key]
x_c = Inference.x_c(clause, valuation_mapping, ['a', 'b'])
print(valuation_mapping)
print(x_c)
assert False
def action_up_2():
B_0, P_up, P_sw, P_stay, N_up, N_sw, N_stay = get_B_N_P()
B = [Atom([Term(False, str(x[1]))], x[0]) for x in B_0]
N = [Atom([Term(False, str(id))], 'up') for id in P_up]
P = [Atom([Term(False, str(id))], 'up') for id in N_up]
term_x_0 = Term(True, 'X')
term_x_1 = Term(True, 'X1')
p_e = [
Atom([term_x_0], 'busy'),
Atom([term_x_0], 'NObusy'),
Atom([term_x_0], 'side'),
Atom([term_x_0], 'front'),
Atom([term_x_0], 'back')
]
p_a = []
target = Atom([term_x_0], 'up')
# target_rule = (Rule_Template(0, False), Rule_Template(1, True))
target_rule = (Rule_Template(0, False), None)
rules = {target: target_rule}
constants = [str(i) for i in range(0, 400)]
langage_frame = Language_Frame(target, p_e, constants)
program_template = Program_Template(p_a, rules, 10)
dilp = DILP(langage_frame, B, P, N, program_template)
dilp.train(steps=250)
def action_switch_2():
num = 30
inx = random.sample(range(0, 100), num)
neg_inx = list(set(range(100)) - set(inx))
B = [Atom([Term(False, str(id))], 'busy') for id in inx
] + [Atom([Term(False, str(id))], 'NObusy') for id in neg_inx] + [
Atom([Term(False, str(id))], 'front')
for id in neg_inx if id % 2 == 0
] + [
Atom([Term(False, str(id))], 'back')
for id in neg_inx if id % 2 != 0
]
P = [Atom([Term(False, str(id))], 'switch') for id in neg_inx]
N = [Atom([Term(False, str(id))], 'switch') for id in inx]
term_x_0 = Term(True, 'X')
term_x_1 = Term(True, 'X1')
p_e = [
Atom([term_x_0], 'busy'),
Atom([term_x_0], 'NObusy'),
Atom([term_x_0], 'side'),
Atom([term_x_0], 'front'),
Atom([term_x_0], 'back')
]
p_a = []
target = Atom([term_x_0], 'switch')
# target_rule = (Rule_Template(0, False), Rule_Template(1, True))
target_rule = (Rule_Template(0, False), None)
rules = {target: target_rule}
constants = [str(i) for i in range(0, 100)]
langage_frame = Language_Frame(target, p_e, constants)
program_template = Program_Template(p_a, rules, 10)
dilp = DILP(langage_frame, B, P, N, program_template)
dilp.train(steps=252)
def action_sw(bg_all):
#num = 70
#inx=random.sample(range(0,100),num)
#neg_inx = list(set(range(100))-set(inx))
B_0, P_up, P_sw, P_stay, N_up, N_sw, N_stay = get_B_N_P(bg_all)
B = [Atom([Term(False, str(x[1]))], x[0]) for x in B_0]
P = [Atom([Term(False, str(id))], 'sw') for id in P_sw]
N = [Atom([Term(False, str(id))], 'sw') for id in N_sw]
term_x_0 = Term(True, 'X')
term_x_1 = Term(True, 'X1')
p_e = [
Atom([term_x_0], 'busy'),
Atom([term_x_0], 'NObusy'),
Atom([term_x_0], 'side'),
Atom([term_x_0], 'front'),
Atom([term_x_0], 'back')
]
p_a = []
target = Atom([term_x_0], 'sw')
# target_rule = (Rule_Template(0, False), Rule_Template(1, True))
target_rule = (Rule_Template(0, False), None)
rules = {target: target_rule}
constants = [str(i) for i in range(0, 400)]
langage_frame = Language_Frame(target, p_e, constants)
program_template = Program_Template(p_a, rules, 10)
dilp = DILP(langage_frame, B, P, N, program_template)
loss, cl_set = dilp.train(steps=51)
return loss, cl_set
def program_template_test():
rule_template = Rule_Template(1, True)
try:
program_template = Program_Template([atom1],
(rule_template, rule_template),
300)
assert False
except ValueError as err:
assert True
program_template = Program_Template([atom3],
(rule_template, rule_template), 300)
def action_switch():
#num = 70
#inx=random.sample(range(0,100),num)
#neg_inx = list(set(range(100))-set(inx))
B_0, P_up, P_sw, P_stay, N_up, N_sw, N_stay = get_B_N_P()
B = [Atom([Term(False, str(x[1]))], x[0]) for x in B_0]
# B = [Atom([Term(False, str(id))], 'busy')
# for id in inx]+ [Atom([Term(False, str(id))], 'NObusy')
# for id in neg_inx]
# print("B..........")
# print(B)
P = [Atom([Term(False, str(id))], 'sw') for id in P_sw]
N = [] #[Atom([Term(False, str(id))], 'sw')
#for id in N_sw]
# print("P..........")
# print(P)
# print("N..........")
# print(N)
term_x_0 = Term(True, 'X')
term_x_1 = Term(True, 'X1')
p_e = [
Atom([term_x_0], 'busy'),
Atom([term_x_0], 'NObusy'),
Atom([term_x_0], 'side'),
Atom([term_x_0], 'front'),
Atom([term_x_0], 'back')
]
#p_a = []
p_a = [Atom([term_x_0], 'pred')]
target = Atom([term_x_0], 'sw')
# target_rule = (Rule_Template(0, False), Rule_Template(1, True))
target_rule = (Rule_Template(0, True), Rule_Template(0, False))
p_a_rule = (Rule_Template(0, False), None)
rules = {p_a[0]: p_a_rule, target: target_rule}
constants = [str(i) for i in range(0, 400)]
langage_frame = Language_Frame(target, p_e, constants)
program_template = Program_Template(p_a, rules, 10)
dilp = DILP(langage_frame, B, P, N, program_template)
dilp.train(steps=252)
B, pred_f, constants_f = process_file('%s/facts.dilp' % sys.argv[1])
P, target_p, constants_p = process_file('%s/positive.dilp' % sys.argv[1])
N, target_n, constants_n = process_file('%s/negative.dilp' % sys.argv[1])
if not (target_p == target_n):
raise Exception('Positive and Negative files have different targets')
elif not len(target_p) == 1:
raise Exception('Can learn only one predicate at a time')
elif not constants_n.issubset(constants_f) or not constants_p.issubset(
constants_f):
raise Exception(
'Constants not in fact file exists in positive/negative file')
term_x_0 = Term(True, 'X_0')
term_x_1 = Term(True, 'X_1')
p_e = list(pred_f)
p_a = [Atom([term_x_0, term_x_1], 'pred')]
target = list(target_p)[0]
constants = constants_f
p_a_rule = (Rule_Template(1, False), None)
target_rule = (Rule_Template(0, False), Rule_Template(1, True))
rules = {p_a[0]: p_a_rule, target: target_rule}
langage_frame = Language_Frame(target, p_e, constants)
program_template = Program_Template(p_a, rules, 10)
dilp = DILP(langage_frame, B, P, N, program_template)
dilp.train()
'''File for testing inference functions (Inference.py)
'''
from src.core import Term, Atom, Clause
from src.ilp import Language_Frame, Program_Template, Rule_Template, Inference, ILP
epsilon = 0.001
term1 = Term(True, 'X_0')
term2 = Term(True, 'X_1')
target = Atom([term1, term2], 'r')
p_e = [Atom([term1, term2], 'p')]
p_a = [Atom([term1, term2], 'q')]
rule_template_1 = Rule_Template(0, False)
rule_template_2 = Rule_Template(1, True)
language_frame = Language_Frame(target, p_e, set(['a', 'b']))
program_template = Program_Template(p_a, (rule_template_1, rule_template_2),
300)
term_a = Term(False, 'a')
term_b = Term(False, 'b')
# Background facts: p(a,a) p(a,b)
background = [Atom([term_a, term_a], 'p'), Atom([term_a, term_b], 'p')]
# Positive: p(b,a)
positive = [Atom([term_b, term_a], 'p')]
# Negative: p(b,b)
negative = [Atom([term_b, term_b], 'p')]
ilp = ILP(language_frame, background, positive, negative, program_template)
(initial_valuation, valuation_mapping) = ilp.convert()
def rule_template_test():
rule_template = Rule_Template(1, True)
assert rule_template.v == 1
assert rule_template.allow_intensional == True
