def test_ground_atoms():
pk = 973
questions = geoserver_interface.download_questions(pk)
question = questions.values()[0]
label_data = geoserver_interface.download_labels(pk)[pk]
diagram = open_image(question.diagram_path)
graph_parse = diagram_to_graph_parse(diagram)
match_parse = parse_match_from_known_labels(graph_parse, label_data)
AB = v('AB', 'line')
AC = v('AC', 'line')
BC = v('BC', 'line')
ED = v('ED', 'line')
AE = v('AE', 'line')
E = v('E', 'point')
D = v('D', 'point')
x = v('x', 'number')
p1 = f('LengthOf', AB) == f('LengthOf', AC)
p2 = f('IsMidpointOf', E, AB)
p3 = f('IsMidpointOf', D, AC)
p4 = f('LengthOf', AE) == x
p5 = f('LengthOf', ED) == 4
qn = f('LengthOf', BC)
grounded_atoms = ground_formula_nodes(match_parse,
[p1, p2, p3, p4, p5, qn])
for grounded_atom in grounded_atoms:
print grounded_atom
graph_parse.core_parse.display_points()
def test_solving():
pk = 973
questions = geoserver_interface.download_questions(pk)
question = questions.values()[0]
label_data = geoserver_interface.download_labels(pk)[pk]
diagram = open_image(question.diagram_path)
graph_parse = diagram_to_graph_parse(diagram)
match_parse = parse_match_from_known_labels(graph_parse, label_data)
AB = v('AB', 'line')
AC = v('AC', 'line')
BC = v('BC', 'line')
ED = v('ED', 'line')
AE = v('AE', 'line')
E = v('E', 'point')
D = v('D', 'point')
x = v('x', 'number')
p1 = f('LengthOf', AB) == f('LengthOf', AC)
p2 = f('IsMidpointOf', E, AB)
p3 = f('IsMidpointOf', D, AC)
p4 = f('LengthOf', AE) == x
p5 = f('LengthOf', ED) == 4
qn = f('LengthOf', BC)
confident_atoms = parse_confident_formulas(graph_parse)
text_atoms = ground_formula_nodes(match_parse, [p1, p2, p3, p4, p5])
atoms = confident_atoms + text_atoms
grounded_qn = ground_formula_nodes(match_parse, [qn])[0]
ns = NumericSolver(atoms)
print ns.evaluate(grounded_qn)
def test_solving():
pk = 973
questions = geoserver_interface.download_questions(pk)
question = questions.values()[0]
label_data = geoserver_interface.download_labels(pk)[pk]
diagram = open_image(question.diagram_path)
graph_parse = diagram_to_graph_parse(diagram)
match_parse = parse_match_from_known_labels(graph_parse, label_data)
AB = v('AB', 'line')
AC = v('AC', 'line')
BC = v('BC', 'line')
ED = v('ED', 'line')
AE = v('AE', 'line')
E = v('E', 'point')
D = v('D', 'point')
x = v('x', 'number')
p1 = f('LengthOf', AB) == f('LengthOf', AC)
p2 = f('IsMidpointOf', E, AB)
p3 = f('IsMidpointOf', D, AC)
p4 = f('LengthOf', AE) == x
p5 = f('LengthOf', ED) == 4
qn = f('LengthOf', BC)
confident_atoms = parse_confident_formulas(graph_parse)
text_atoms = ground_formula_nodes(match_parse, [p1, p2, p3, p4, p5])
atoms = confident_atoms + text_atoms
grounded_qn = ground_formula_nodes(match_parse, [qn])[0]
ns = NumericSolver(atoms)
print ns.evaluate(grounded_qn)
def test_ground_atoms():
pk = 973
questions = geoserver_interface.download_questions(pk)
question = questions.values()[0]
label_data = geoserver_interface.download_labels(pk)[pk]
diagram = open_image(question.diagram_path)
graph_parse = diagram_to_graph_parse(diagram)
match_parse = parse_match_from_known_labels(graph_parse, label_data)
AB = v('AB', 'line')
AC = v('AC', 'line')
BC = v('BC', 'line')
ED = v('ED', 'line')
AE = v('AE', 'line')
E = v('E', 'point')
D = v('D', 'point')
x = v('x', 'number')
p1 = f('LengthOf', AB) == f('LengthOf', AC)
p2 = f('IsMidpointOf', E, AB)
p3 = f('IsMidpointOf', D, AC)
p4 = f('LengthOf', AE) == x
p5 = f('LengthOf', ED) == 4
qn = f('LengthOf', BC)
grounded_atoms = ground_formula_nodes(match_parse, [p1, p2, p3, p4, p5, qn])
for grounded_atom in grounded_atoms:
print grounded_atom
graph_parse.core_parse.display_points()
def _annotated_unit_test(query):
questions = geoserver_interface.download_questions(query)
all_annotations = geoserver_interface.download_semantics(query)
pk, question = questions.items()[0]
choice_formulas = get_choice_formulas(question)
label_data = geoserver_interface.download_labels(pk)[pk]
diagram = open_image(question.diagram_path)
graph_parse = diagram_to_graph_parse(diagram)
core_parse = graph_parse.core_parse
# core_parse.display_points()
# core_parse.primitive_parse.display_primitives()
match_parse = parse_match_from_known_labels(graph_parse, label_data)
match_formulas = parse_match_formulas(match_parse)
diagram_formulas = parse_confident_formulas(graph_parse)
all_formulas = match_formulas + diagram_formulas
for number, sentence_words in question.sentence_words.iteritems():
syntax_parse = stanford_parser.get_best_syntax_parse(sentence_words)
annotation_nodes = [annotation_to_semantic_tree(syntax_parse, annotation)
for annotation in all_annotations[pk][number].values()]
expr_formulas = {key: prefix_to_formula(expression_parser.parse_prefix(expression))
for key, expression in question.sentence_expressions[number].iteritems()}
truth_expr_formulas, value_expr_formulas = _separate_expr_formulas(expr_formulas)
text_formula_parse = semantic_trees_to_text_formula_parse(annotation_nodes)
completed_formulas = complete_formulas(text_formula_parse)
grounded_formulas = [ground_formula(match_parse, formula, value_expr_formulas)
for formula in completed_formulas+truth_expr_formulas]
text_formulas = filter_formulas(flatten_formulas(grounded_formulas))
all_formulas.extend(text_formulas)
reduced_formulas = reduce_formulas(all_formulas)
for reduced_formula in reduced_formulas:
score = evaluate(reduced_formula, core_parse.variable_assignment)
scores = [evaluate(child, core_parse.variable_assignment) for child in reduced_formula.children]
print reduced_formula, score, scores
# core_parse.display_points()
ans = solve(reduced_formulas, choice_formulas, assignment=core_parse.variable_assignment)
print "ans:", ans
if choice_formulas is None:
attempted = True
if abs(ans - float(question.answer)) < 0.01:
correct = True
else:
correct = False
else:
attempted = True
c = max(ans.iteritems(), key=lambda pair: pair[1].conf)[0]
if c == int(question.answer):
correct = True
else:
correct = False
result = SimpleResult(query, False, attempted, correct)
return result
def _annotated_unit_test(query):
questions = geoserver_interface.download_questions(query)
all_annotations = geoserver_interface.download_semantics(query)
pk, question = questions.items()[0]
choice_formulas = get_choice_formulas(question)
label_data = geoserver_interface.download_labels(pk)[pk]
diagram = open_image(question.diagram_path)
graph_parse = diagram_to_graph_parse(diagram)
core_parse = graph_parse.core_parse
# core_parse.display_points()
# core_parse.primitive_parse.display_primitives()
match_parse = parse_match_from_known_labels(graph_parse, label_data)
match_formulas = parse_match_formulas(match_parse)
diagram_formulas = parse_confident_formulas(graph_parse)
all_formulas = match_formulas + diagram_formulas
for number, sentence_words in question.sentence_words.iteritems():
syntax_parse = stanford_parser.get_best_syntax_parse(sentence_words)
annotation_nodes = [annotation_to_semantic_tree(syntax_parse, annotation)
for annotation in all_annotations[pk][number].values()]
expr_formulas = {key: prefix_to_formula(expression_parser.parse_prefix(expression))
for key, expression in question.sentence_expressions[number].iteritems()}
truth_expr_formulas, value_expr_formulas = _separate_expr_formulas(expr_formulas)
text_formula_parse = semantic_trees_to_text_formula_parse(annotation_nodes)
completed_formulas = complete_formulas(text_formula_parse)
grounded_formulas = [ground_formula(match_parse, formula, value_expr_formulas)
for formula in completed_formulas+truth_expr_formulas]
text_formulas = filter_formulas(flatten_formulas(grounded_formulas))
all_formulas.extend(text_formulas)
reduced_formulas = reduce_formulas(all_formulas)
for reduced_formula in reduced_formulas:
score = evaluate(reduced_formula, core_parse.variable_assignment)
scores = [evaluate(child, core_parse.variable_assignment) for child in reduced_formula.children]
print reduced_formula, score, scores
# core_parse.display_points()
ans = solve(reduced_formulas, choice_formulas, assignment=core_parse.variable_assignment)
print "ans:", ans
if choice_formulas is None:
attempted = True
if abs(ans - float(question.answer)) < 0.01:
correct = True
else:
correct = False
else:
attempted = True
c = max(ans.iteritems(), key=lambda pair: pair[1].conf)[0]
if c == int(question.answer):
correct = True
else:
correct = False
result = SimpleResult(query, False, attempted, correct)
return result
def test_parse_match_from_known_labels():
questions = geoserver_interface.download_questions(977)
for pk, question in questions.iteritems():
label_data = geoserver_interface.download_labels(pk)[pk]
diagram = open_image(question.diagram_path)
graph_parse = diagram_to_graph_parse(diagram)
match_parse = parse_match_from_known_labels(graph_parse, label_data)
for key, value in match_parse.match_dict.iteritems():
print key, value
graph_parse.core_parse.display_points()
def test_parse_match_from_known_labels():
questions = geoserver_interface.download_questions(977)
for pk, question in questions.iteritems():
label_data = geoserver_interface.download_labels(pk)[pk]
diagram = open_image(question.diagram_path)
graph_parse = diagram_to_graph_parse(diagram)
match_parse = parse_match_from_known_labels(graph_parse, label_data)
for key, value in match_parse.match_dict.iteritems():
print key, value
graph_parse.core_parse.display_points()
def test_parse_match_atoms():
questions = geoserver_interface.download_questions(977)
for pk, question in questions.iteritems():
label_data = geoserver_interface.download_labels(pk)[pk]
diagram = open_image(question.diagram_path)
graph_parse = diagram_to_graph_parse(diagram)
match_parse = parse_match_from_known_labels(graph_parse, label_data)
match_atoms = parse_match_formulas(match_parse)
for match_atom in match_atoms:
print match_atom
graph_parse.core_parse.display_points()
def test_parse_match_atoms():
questions = geoserver_interface.download_questions(977)
for pk, question in questions.iteritems():
label_data = geoserver_interface.download_labels(pk)[pk]
diagram = open_image(question.diagram_path)
graph_parse = diagram_to_graph_parse(diagram)
match_parse = parse_match_from_known_labels(graph_parse, label_data)
match_atoms = parse_match_formulas(match_parse)
for match_atom in match_atoms:
print match_atom
graph_parse.core_parse.display_points()
def question_to_match_parse(question, label_data):
graph_parse = question_to_graph_parse(question)
match_parse = parse_match_from_known_labels(graph_parse, label_data)
return match_parse
def question_to_match_parse(question, label_data):
graph_parse = question_to_graph_parse(question)
match_parse = parse_match_from_known_labels(graph_parse, label_data)
return match_parse
