def evaluate(
self, examples: Task, clause: Clause,
hypothesis_space: HypothesisSpace) -> typing.Union[int, float]:
"""
Evaluates a clause by calling the Learner's eval_fn.
Returns a number (the higher the better)
"""
# add_to_cache(node,key,val)
# retrieve_from_cache(node,key) -> val or None
# remove_from_cache(node,key) -> None
# Cache holds sets of examples that were covered before
covered = hypothesis_space.retrieve_from_cache(clause, "covered")
# We have executed this clause before
if covered is not None:
# Note that _eval.fn.evaluate() will ignore clauses in `covered`
# that are not in the current Task
result = self._eval_fn.evaluate(clause, examples, covered)
# print("No query here.")
return result
else:
covered = self._execute_program(clause)
# if 'None', i.e. trivial hypothesis, all clauses are covered
if covered is None:
pos, neg = examples.get_examples()
covered = pos.union(neg)
result = self._eval_fn.evaluate(clause, examples, covered)
hypothesis_space.add_to_cache(clause, "covered", covered)
return result
def get_initial_weights(self, examples: Task) -> dict:
example_weights = {}
for examples in examples.get_examples():
for example in examples:
example_weights[example] = 1
return example_weights
def learn(self, examples: Task, background_location: string, hypothesis_space: TopDownHypothesisSpace):
"""
General learning loop
"""
t1 = time.time()
self._solver.consult(background_location)
self._solver.asserta(c_pred("test_task", 1)(Structure(c_functor("s", 2), [List([]), List([])])))
final_program = []
examples_to_use = examples
pos, _ = examples_to_use.get_examples()
print(pos)
while len(final_program) == 0 or len(pos) > 0:
# learn a single clause
cl = self._learn_one_clause(examples_to_use, hypothesis_space)
if self.stopped_early:
break
final_program.append(cl)
self.rules += 1
# update covered positive examples
covered = self._execute_program(examples, cl)
pos, neg = examples_to_use.get_examples()
pos = pos.difference(covered)
examples_to_use = Task(pos, neg)
# Reset example weights
self.example_weights = {}
print("\n FOUND A RULE, CURRENT PROGRAM AND COVERED: ")
print("\t", final_program)
print("\t", covered)
self.ex_time = time.time() - t1
for rule in final_program:
self._solver.retract(rule)
if self.stopped_early:
print("STOPPED EARLY...")
print("\n EXECUTION TIME:")
print("\t", self.ex_time, " seconds")
print("\n EXTENSION LENGTH:")
print("\t", self.exp_len)
print("\n EXTENSION AMOUNT:")
print("\t", self.exp_count)
print("\n MAX POOL SIZE:")
print("\t", self.max_queue_len)
print("\n LEARNED RULES:")
print("\t", self.rules)
ss = SearchStats(self.stopped_early, self.ex_time, self.exp_len, self.exp_count, self.max_queue_len, self.rules)
return final_program, ss
def _execute_program(self, examples: Task, clause: Clause) -> typing.Sequence[Atom]:
"""
Evaluates a clause using the Prolog engine and background knowledge
Returns a set of atoms that the clause covers
"""
if len(clause.get_body().get_literals()) == 0:
return []
else:
self._solver.asserta(clause)
covered_examples = []
pos, neg = examples.get_examples()
total_examples = pos.union(neg)
for example in total_examples:
if self._solver.has_solution(example):
covered_examples.append(example)
self._solver.retract(clause)
# head_predicate = clause.get_head().get_predicate()
# head_variables = clause.get_head_variables()
#
# sols = self._solver.query(*clause.get_body().get_literals())
#
# sols = [head_predicate(*[s[v] for v in head_variables]) for s in sols]
return covered_examples
def learn(self, examples: Task, knowledge: Knowledge,
hypothesis_space: TopDownHypothesisSpace):
"""
General learning loop
"""
self._assert_knowledge(knowledge)
final_program = []
examples_to_use = examples
pos, _ = examples_to_use.get_examples()
self._result = []
self._expansion = 0
while len(final_program) == 0 or (len(pos) > 0):
# learn na single clause
cl = self._learn_one_clause(examples_to_use, hypothesis_space)
final_program.append(cl)
# update covered positive examples
covered = self._execute_program(examples_to_use, cl)
print('covered', covered)
pos, neg = examples_to_use.get_examples()
pos = pos.difference(covered)
examples_to_use = Task(pos, neg)
with open('Search3.csv', 'w') as f:
writer = csv.writer(f, delimiter=';', lineterminator='\n')
writer.writerows(self._result)
return final_program
def learn(self, examples: Task, knowledge: Knowledge,
hypothesis_space: TopDownHypothesisSpace):
"""
General learning loop
"""
# self._assert_knowledge(knowledge)
self._solver.consult(
"../inputfiles/StringTransformations_BackgroundKnowledge.pl"
) # TODO Look if this is good enough for our background knowledge
final_program = []
examples_to_use = examples
pos, _ = examples_to_use.get_examples()
while len(final_program) == 0 or len(pos) > 0:
# learn na single clause
cl = self._learn_one_clause(examples_to_use, hypothesis_space)
final_program.append(cl)
# update covered positive examples
covered = self._execute_program(examples, cl)
pos, neg = examples_to_use.get_examples()
pos = pos.difference(covered)
examples_to_use = Task(pos, neg)
return final_program
def learn(self, examples: Task, knowledge: Knowledge,
hypothesis_space: TopDownHypothesisSpace):
"""
General learning loop
"""
self._assert_knowledge(knowledge)
final_program = []
examples_to_use = examples
pos, _ = examples_to_use.get_examples()
while len(final_program) == 0 or len(pos) > 0:
# learn na single clause
cl = self._learn_one_clause(examples_to_use, hypothesis_space)
final_program.append(cl)
# update covered positive examples
covered = self._execute_program(cl)
pos, neg = examples_to_use.get_examples()
pos = pos.difference(covered)
examples_to_use = Task(pos, neg)
return final_program
def learn_with_constants():
"""
Consider a row of blocks [ block1 block2 block3 block4 block5 block6 ]
The order of this row is expressed using follows(X,Y)
The color of a block is expressed using color(X,Color)
Goal: learn a function f that says: a block is positive when it is followed by a red block
pos(X) :- next(X,Y), color(Y,red)
"""
block = c_type("block")
col = c_type("col")
block1 = c_const("block1", domain=block) # blue -> positive
block2 = c_const("block2", domain=block) # red
block3 = c_const("block3", domain=block) # green -> positive
block4 = c_const("block4", domain=block) # red -> positive
block5 = c_const("block5", domain=block) # red
block6 = c_const("block6", domain=block) # green
block7 = c_const("block7", domain=block) # blue
block8 = c_const("block8", domain=block) # blue
red = c_const("red", domain="col")
green = c_const("green", domain="col")
blue = c_const("blue", domain="col")
follows = c_pred("follows", 2, domains=[block, block])
color = c_pred("color", 2, domains=[block, col])
# Predicate to learn:
f = c_pred("f", 1, domains=[block])
bk = Knowledge(follows(block1, block2), follows(block2, block3),
follows(block3, block4), follows(block4, block5),
follows(block5, block6), follows(block6, block7),
follows(block7, block8), color(block1, blue),
color(block2, red), color(block3,
green), color(block4, red),
color(block5, red), color(block6, green),
color(block7, blue), color(block8, blue))
pos = {f(x) for x in [block1, block3, block4]}
neg = {f(x) for x in [block2, block5, block6, block7, block8]}
task = Task(positive_examples=pos, negative_examples=neg)
solver = SWIProlog()
# EvalFn must return an upper bound on quality to prune search space.
eval_fn1 = Coverage(return_upperbound=True)
eval_fn2 = Compression(return_upperbound=True)
eval_fn3 = Accuracy(return_upperbound=True)
learners = [
Aleph(solver, eval_fn, max_body_literals=4, do_print=False)
for eval_fn in [eval_fn1, eval_fn3]
]
for learner in learners:
res = learner.learn(task, bk, None, minimum_freq=1)
print(res)
def learn(self, examples: Task, knowledge: Knowledge,
hypothesis_space: TopDownHypothesisSpace):
"""
General learning loop
"""
self._assert_knowledge(knowledge)
final_program = []
examples_to_use = examples
pos, _ = examples_to_use.get_examples()
i = 0
start = datetime.datetime.now()
while len(final_program) == 0 or len(pos) > 0:
# learn na single clause
if self._print:
print(f"Iteration {i}")
print("- Current program:")
for program_clause in final_program:
print("\t" + str(program_clause))
cl = self._learn_one_clause(examples_to_use, hypothesis_space)
final_program.append(cl)
# update covered positive examples
covered = self._execute_program(cl)
# Find intermediate quality of program at this point, add to learnresult (don't cound these as Prolog queries)
c = set()
for cl in final_program:
c = c.union(self._execute_program(cl, count_as_query=False))
pos_covered = len(c.intersection(examples._positive_examples))
neg_covered = len(c.intersection(examples._negative_examples))
self.__intermediate_coverage.append((pos_covered, neg_covered))
# Remove covered examples and start next iteration
pos, neg = examples_to_use.get_examples()
pos = pos.difference(covered)
examples_to_use = Task(pos, neg)
i += 1
total_time = (datetime.datetime.now() - start).total_seconds()
if self._print:
print("Done! Search took {:.5f} seconds.".format(total_time))
# Wrap results into learnresult and return
self._learnresult["final_program"] = final_program
self._learnresult["total_time"] = total_time
self._learnresult["num_iterations"] = i
self._learnresult[
"evalfn_evaluations"] = self._eval_fn._clauses_evaluated
self._learnresult["prolog_queries"] = self._prolog_queries
self._learnresult[
"intermediate_coverage"] = self._intermediate_coverage
return self._learnresult
def learn_simpsons():
# define the predicates
father = c_pred("father", 2)
mother = c_pred("mother", 2)
grandparent = c_pred("grandparent", 2)
# specify the background knowledge
background = Knowledge(father("homer", "bart"), father("homer", "lisa"),
father("homer", "maggie"), mother("marge", "bart"),
mother("marge", "lisa"), mother("marge", "maggie"),
mother("mona", "homer"), father("abe", "homer"),
mother("jacqueline", "marge"),
father("clancy", "marge"))
# positive examples
pos = {
grandparent("abe", "bart"),
grandparent("abe", "lisa"),
grandparent("abe", "maggie"),
grandparent("mona", "bart"),
grandparent("abe", "lisa"),
grandparent("abe", "maggie"),
grandparent("jacqueline", "bart"),
grandparent("jacqueline", "lisa"),
grandparent("jacqueline", "maggie"),
grandparent("clancy", "bart"),
grandparent("clancy", "lisa"),
grandparent("clancy", "maggie"),
}
# negative examples
neg = {
grandparent("abe", "marge"),
grandparent("abe", "homer"),
grandparent("abe", "clancy"),
grandparent("abe", "jacqueline"),
grandparent("homer", "marge"),
grandparent("homer", "jacqueline"),
grandparent("jacqueline", "marge"),
grandparent("clancy", "homer"),
grandparent("clancy", "abe")
}
task = Task(positive_examples=pos, negative_examples=neg)
solver = SWIProlog()
# EvalFn must return an upper bound on quality to prune search space.
eval_fn = Coverage(return_upperbound=True)
eval_fn2 = Compression(return_upperbound=True)
eval_fn3 = Compression(return_upperbound=True)
learner = Aleph(solver, eval_fn, max_body_literals=4, do_print=False)
learner2 = Aleph(solver, eval_fn2, max_body_literals=4, do_print=False)
learner3 = Aleph(solver, eval_fn3, max_body_literals=4, do_print=False)
result = learner.learn(task, background, None)
print(result)
def evaluate_distinct(self, examples: Task,
clause: Clause) -> typing.Tuple[int, int]:
covered = self._execute_program(examples, clause)
pos, neg = examples.get_examples()
covered_pos = pos.intersection(covered)
covered_neg = neg.intersection(covered)
return len(covered_pos), len(covered_neg)
def evaluate(self, clause: Clause, examples: Task,
covered: Sequence[Atom]):
self._clauses_evaluated += 1
pos, neg = examples.get_examples()
covered_pos = len(pos.intersection(covered))
covered_neg = len(neg.intersection(covered))
if self._return_upperbound:
return (covered_pos - covered_neg), covered_pos
return covered_pos - covered_neg
def evaluate(self, examples: Task,
clause: Clause) -> typing.Union[int, float]:
covered = self._execute_program(clause)
pos, neg = examples.get_examples()
covered_pos = pos.intersection(covered)
covered_neg = neg.intersection(covered)
if len(covered_neg) > 0:
return 0
else:
return len(covered_pos)
def _execute_program(self, examples: Task,
clause: Clause) -> typing.Sequence[Atom]:
"""
Evaluates a clause using the Prolog engine and background knowledge
Returns a set of atoms that the clause covers
"""
pos, neg = examples.get_examples()
self._solver.assertz(clause)
coverage = []
for example in pos:
if self._solver.has_solution(example):
coverage.append(example)
self._solver.retract(clause)
return coverage
def evaluate(self, clause: Clause, examples: Task,
covered: Sequence[Atom]):
self._clauses_evaluated += 1
pos, neg = examples.get_examples()
covered_pos = len(pos.intersection(covered))
covered_neg = len(neg.intersection(covered))
if covered_pos + covered_neg == 0:
return 0 if not self._return_upperbound else 0, 0
return (
covered_pos / (covered_pos + covered_neg)
if not self._return_upperbound else covered_pos /
(covered_pos + covered_neg),
1,
)
def evaluate(self, clause: Clause, examples: Task,
covered: Sequence[Atom]):
self._clauses_evaluated += 1
pos, neg = examples.get_examples()
covered_pos = len(pos.intersection(covered))
covered_neg = len(neg.intersection(covered))
if covered_pos + covered_neg == 0:
return 0
p = covered_pos / (covered_pos + covered_neg)
# Perfect split, no entropy
if p == 1 or p == 0:
return 0
return -(p * math.log10(p) + (1 - p) * math.log10(1 - p))
def get_best_primitives(
self, examples: Task, current_cand: typing.Union[Clause, Recursion,
Body]
) -> typing.Sequence[typing.Union[Clause, Body, Procedure]]:
scores = [0] * 22
# Filter examples (e.g. only use positive/negative examples)
# examples = self.filter_examples(examples)
pos, neg = examples.get_examples()
# Calculate nn output for each example
for example in pos:
# Update output
nn_output = self.model.predict(
get_nn_input_data(current_cand, example,
self.current_primitives.tolist()))[0]
nn_output = self.process_output(nn_output)
# Update score vector
scores = self.update_score(current_cand, example, scores,
nn_output)
for example in neg:
# Update output
nn_output = self.model.predict(
get_nn_input_data(current_cand, example,
self.current_primitives.tolist()))[0]
nn_output = self.process_output(nn_output)
# Update score vector
scores = self.update_score(current_cand, example, scores,
-0.2 * nn_output)
# Return x best primitives
indices = numpy.argpartition(
scores, -self.amount_chosen_from_nn)[-self.amount_chosen_from_nn:]
# TODO now a random order but could be replaced to the ordering via the score
print("PRIMITIVES CHOSEN BY NN: ")
print(self.current_primitives[indices], "\n")
return self.current_primitives[indices]
def evaluate(self, examples: Task,
clause: Clause) -> typing.Union[int, float]:
covered = self._execute_program(examples, clause)
pos, neg = examples.get_examples()
covered_pos = pos.intersection(covered)
covered_neg = neg.intersection(covered)
print("\t covered neg: ", len(covered_neg))
print("\t covered pos: ", len(covered_pos))
if (len(covered_pos) + len(covered_neg)) == 0:
print("\t score: ", 0)
else:
print("\t score: ",
len(covered_pos) / (len(covered_pos) + len(covered_neg)))
if len(covered_neg) > 0:
return 0
else:
return len(covered_pos)
def evaluate(self, examples: Task, clause: Clause):
pos, neg = examples.get_examples()
numberofpositivecoverance = 0
self._solver.assertz(clause)
for example in pos:
if self._solver.has_solution(example):
numberofpositivecoverance += 1
numberofnegativecoverance = 0
for example in neg:
if self._solver.has_solution(example):
numberofnegativecoverance += 1
# print(example)
self._solver.retract(clause)
if numberofnegativecoverance + numberofpositivecoverance == 0:
return [0, 0]
else:
return [
numberofpositivecoverance /
(numberofpositivecoverance + numberofnegativecoverance) *
(numberofpositivecoverance) / len(pos),
numberofnegativecoverance
]
def train_task(task_id: string,
pos_multiplier: int,
neg_example_offset: int,
nn_amount: int,
pos=None,
neg=None):
# Load needed files
bk, predicates = createKnowledge(
"../inputfiles/StringTransformations_BackgroundKnowledge.pl", task_id)
if pos is None and neg is None:
pos, neg = generate_examples(task_id, pos_multiplier,
neg_example_offset)
task = Task(positive_examples=pos, negative_examples=neg)
# Calculate predicates
total_predicates = []
filtered_predicates = []
for predicate in predicates:
if predicate.name not in ["s", task_id
] and predicate not in filtered_predicates:
total_predicates.append(lambda x, pred=predicate: plain_extension(
x, pred, connected_clauses=True))
filtered_predicates.append(predicate)
# create the hypothesis space
hs = TopDownHypothesisSpace(
primitives=total_predicates,
head_constructor=c_pred("test_task", 1),
recursive_procedures=True,
expansion_hooks_keep=[
lambda x, y: connected_clause(x, y),
lambda x, y: only_1_pred_for_1_var(x, y),
lambda x, y: head_first(x, y)
],
expansion_hooks_reject=[ # lambda x, y: has_singleton_vars(x, y),
# Singleton-vars constraint is reduced to this constraint
lambda x, y: has_not_previous_output_as_input(x, y), # Strict
# lambda x, y: has_new_input(x, y), # Not as strict
# lambda x, y: has_unexplained_last_var(x, y), # For the 'write' predicate
lambda x, y: has_unexplained_last_var_strict(
x, y), # Strict version of above
lambda x, y: has_duplicated_literal(x, y),
lambda x, y: has_g1_same_vars_in_literal(x, y),
lambda x, y: has_duplicated_var_set(x, y),
lambda x, y: has_double_recursion(x, y),
lambda x, y: has_endless_recursion(x, y)
])
learner = NeuralSearcher1(solver_instance=prolog,
primitives=filtered_predicates,
model_location="../utility/Saved_model_covered",
max_body_literals=10,
amount_chosen_from_nn=nn_amount,
filter_amount=30,
threshold=0.1)
# Try to learn the program
program, ss = learner.learn(
task, "../inputfiles/StringTransformations_BackgroundKnowledge.pl", hs)
print(program)
return ss
def learn_text():
"""
We describe piece of text spanning multiple lines:
"node A red <newline> node B green <newline> node C blue <newline>"
using the next\2, linestart\2, lineend\2, tokenlength\2 predicates
"""
token = c_type("token")
num = c_type("num")
next = c_pred("next", 2, ("token", "token"))
linestart = c_pred("linestart", 2, ("token", "token"))
lineend = c_pred("lineend", 2, ("token", "token"))
tokenlength = c_pred("tokenlength", 2, ("token", "num"))
n1 = c_const("n1", num)
n3 = c_const("n3", num)
n4 = c_const("n4", num)
n5 = c_const("n5", num)
node1 = c_const("node1", token)
node2 = c_const("node2", token)
node3 = c_const("node3", token)
red = c_const("red", token)
green = c_const("green", token)
blue = c_const("blue", token)
a_c = c_const("a_c", token)
b_c = c_const("b_c", token)
c_c = c_const("c_c", token)
start = c_const("c_START", token)
end = c_const("c_END", token)
bk = Knowledge(next(start, node1), next(node1, a_c), next(a_c, red),
next(red, node2), next(node2, green), next(green, b_c),
next(b_c, node3), next(node3, c_c), next(c_c, blue),
next(blue, end), tokenlength(node1, n4),
tokenlength(node2, n4), tokenlength(node3, n4),
tokenlength(a_c, n1), tokenlength(b_c, n1),
tokenlength(c_c, n1), tokenlength(red, n3),
tokenlength(green, n5), tokenlength(blue, n4),
linestart(node1, node1), linestart(a_c, node1),
linestart(red, node1), linestart(node2, node2),
linestart(b_c, node2), linestart(green, node2),
linestart(node3, node3), linestart(c_c, node3),
linestart(blue, node3), lineend(node1, a_c),
lineend(a_c, red), lineend(node2, red), lineend(b_c, green),
lineend(node3, blue), lineend(c_c, blue), lineend(red, red),
lineend(green, green), lineend(blue, blue))
solver = SWIProlog()
eval_fn1 = Coverage(return_upperbound=True)
learner = Aleph(solver, eval_fn1, max_body_literals=3, do_print=False)
# 1. Consider the hypothesis: f1(word) :- word is the second word on a line
if True:
f1 = c_pred("f1", 1, [token])
neg = {f1(x) for x in [node1, node2, node3, blue, green, red]}
pos = {f1(x) for x in [a_c, b_c, c_c]}
task = Task(positive_examples=pos, negative_examples=neg)
res = learner.learn(task, bk, None)
print(res)
# 2. Consider the hypothesis: f2(word) :- word is the first word on a line
if True:
f2 = c_pred("f2", 1, [token])
neg = {f1(x) for x in [a_c, b_c, c_c, blue, green, red]}
pos = {f1(x) for x in [node1, node2, node3]}
task2 = Task(positive_examples=pos, negative_examples=neg)
res = learner.learn(task2, bk, None)
print(res)
# 3. Assume we have learned the predicate node(X) before (A, B and C and nodes).
# We want to learn f3(Node,X) :- X is the next token after Node
if True:
node = c_pred("node", 1, [token])
color = c_pred("color", 1, [token])
nodecolor = c_pred("nodecolor", 2, [token, token])
a = c_var("A", token)
b = c_var("B", token)
bk_old = bk.get_all()
bk = Knowledge(*bk_old, node(a_c), node(b_c), node(c_c), node(a_c),
node(b_c), node(c_c), color(red), color(green),
color(blue))
pos = {
nodecolor(a_c, red),
nodecolor(b_c, green),
nodecolor(c_c, blue)
}
neg = set()
neg = {
nodecolor(node1, red),
nodecolor(node2, red),
nodecolor(node3, red),
nodecolor(node1, blue),
nodecolor(node2, blue),
nodecolor(node2, blue),
nodecolor(node1, green),
nodecolor(node2, green),
nodecolor(node3, green),
nodecolor(a_c, green),
nodecolor(a_c, blue),
nodecolor(b_c, blue),
nodecolor(b_c, red),
nodecolor(c_c, red),
nodecolor(c_c, green)
}
task3 = Task(positive_examples=pos, negative_examples=neg)
# prog = learner.learn(task3,bk,None,initial_clause=Body(node(a),color(b)))
result = learner.learn(task3,
bk,
None,
initial_clause=Body(node(a), color(b)),
minimum_freq=3)
print(result)
def filter_examples(self, examples: Task) -> Task:
pos, _ = examples.get_examples()
return pos
mother = c_pred("mother", 2)
grandparent = c_pred("grandparent", 2)
# specify the background knowledge
background = Knowledge(father("a", "b"), mother("a",
"b"), mother("b", "c"),
father("e", "f"), father("f", "g"),
mother("h", "i"), mother("i", "j"))
# positive examples
pos = {grandparent("a", "c"), grandparent("e", "g"), grandparent("h", "j")}
# negative examples
neg = {grandparent("a", "b"), grandparent("a", "g"), grandparent("i", "j")}
task = Task(positive_examples=pos, negative_examples=neg)
# create Prolog instance
prolog = SWIProlog()
learner = SimpleBreadthFirstLearner(prolog, max_body_literals=3)
# create the hypothesis space
hs = TopDownHypothesisSpace(primitives=[
lambda x: plain_extension(x, father, connected_clauses=True),
lambda x: plain_extension(x, mother, connected_clauses=True)
],
head_constructor=grandparent,
expansion_hooks_reject=[
lambda x, y: has_singleton_vars(x, y),
lambda x, y: has_duplicated_literal(x, y)
