def objective_mean(dset):
sample = util.sample_if_large(dset, self.dset_samples)
vals = []
for ex in util.verboserate(sample):
vals.append(experiment.model.objective(ex.sentences,
ex.mask, ex.question, ex.answer[0], ex.hints))
return np.mean(vals)
def accuracy_mean(dset):
sample = util.sample_if_large(dset, self.dset_samples)
vals = []
for ex in util.verboserate(sample):
correct = ex.answer == experiment.model.predict(ex.sentences, ex.mask, ex.question)
vals.append(correct)
return np.mean(vals)
def summarize_neighborhood(graph,
seed=None,
max_depth=2,
nbr_samples=20,
save_path=None):
if seed is None:
seed = random.choice(graph.neighbors.keys())
print 'seed:', seed
triples = set()
explored = set()
queue = deque()
queue.append((seed, 0))
while len(queue) != 0:
entity, depth = queue.popleft()
if depth >= max_depth:
continue
# loop through each available relation
for r in graph.neighbors[entity]:
# sample neighbors
nbrs = graph.neighbors[entity][r]
sampled_nbrs = util.sample_if_large(nbrs,
nbr_samples,
replace=False)
num_missed = len(nbrs) - len(sampled_nbrs)
edge_crossed = lambda target: (entity, r, target) if not inverted(
r) else (target, invert(r), entity)
# document edges crossed, and add nbrs to queue
for nbr in sampled_nbrs:
triples.add(edge_crossed(nbr))
if nbr not in explored:
queue.append((nbr, depth + 1))
# add "summary entity" for all entities we missed
if num_missed > 0:
triples.add(
edge_crossed('{}_{}_{}'.format(entity, r, num_missed)))
if save_path is not None:
with open(save_path, 'w') as f:
for tr in triples:
f.write('\t'.join(tr) + '\n')
return list(triples)
def observe(self, maximizer, thresholds=None):
if (maximizer.steps + 1) % self.report_wait != 0:
return None
samples = util.sample_if_large(self.examples, self.eval_samples, replace=True)
# score
samples = copy.deepcopy(samples)
for ex in samples:
try:
ex.score = maximizer.objective.predict(maximizer.params, ex).ravel()[0]
except KeyError:
print 'out of vocab'
ex.score = float('inf')
if thresholds is None:
thresholds = compute_best_thresholds(samples)
acc = accuracy(thresholds, samples)
return {('accuracy', 'test'): acc}
def summarize_neighborhood(graph, seed=None, max_depth=2, nbr_samples=20, save_path=None):
if seed is None:
seed = random.choice(graph.neighbors.keys())
print 'seed:', seed
triples = set()
explored = set()
queue = deque()
queue.append((seed, 0))
while len(queue) != 0:
entity, depth = queue.popleft()
if depth >= max_depth:
continue
# loop through each available relation
for r in graph.neighbors[entity]:
# sample neighbors
nbrs = graph.neighbors[entity][r]
sampled_nbrs = util.sample_if_large(nbrs, nbr_samples, replace=False)
num_missed = len(nbrs) - len(sampled_nbrs)
edge_crossed = lambda target: (entity, r, target) if not inverted(r) else (target, invert(r), entity)
# document edges crossed, and add nbrs to queue
for nbr in sampled_nbrs:
triples.add(edge_crossed(nbr))
if nbr not in explored:
queue.append((nbr, depth + 1))
# add "summary entity" for all entities we missed
if num_missed > 0:
triples.add(edge_crossed('{}_{}_{}'.format(entity, r, num_missed)))
if save_path is not None:
with open(save_path, 'w') as f:
for tr in triples:
f.write('\t'.join(tr) + '\n')
return list(triples)
def observe(self, maximizer, thresholds=None):
if (maximizer.steps + 1) % self.report_wait != 0:
return None
samples = util.sample_if_large(self.examples,
self.eval_samples,
replace=True)
# score
samples = copy.deepcopy(samples)
for ex in samples:
try:
ex.score = maximizer.objective.predict(maximizer.params,
ex).ravel()[0]
except KeyError:
print 'out of vocab'
ex.score = float('inf')
if thresholds is None:
thresholds = compute_best_thresholds(samples)
acc = accuracy(thresholds, samples)
return {('accuracy', 'test'): acc}
def mean_rank(self, maximizer, dset):
sample = util.sample_if_large(dset, self.eval_samples)
ranks = [self.rank(maximizer, ex) for ex in util.verboserate(sample)]
return np.nanmean(ranks)
def final_evaluation(dataset_path,
model_name,
params_path,
eval_type,
eval_samples=float('inf'),
max_negative_samples=float('inf'),
type_matching_negs=True):
dset = parse_dataset(dataset_path)
model = CompositionalModel(None, path_model=model_name, objective='margin')
params = load_params(params_path, model_name)
neg_gen = NegativeGenerator(dset.full_graph,
max_negative_samples,
type_matching_negs=type_matching_negs)
queries = util.sample_if_large(dset.test, eval_samples, replace=False)
# Define different evaluation functions
# ----- ----- ----- ----- -----
scores = lambda query: model.predict(params, query).ravel()
def performance(query):
s, r, t = query.s, query.r, query.t
negatives = neg_gen(query, 't')
pos_query = PathQuery(s, r, t)
neg_query = PathQuery(s, r, negatives)
# don't score queries with no negatives
if len(negatives) == 0:
query.quantile = np.nan
else:
query.quantile = util.average_quantile(scores(pos_query),
scores(neg_query))
query.num_candidates = len(negatives) + 1
attributes = query.s, ','.join(query.r), query.t, str(
query.quantile), str(query.num_candidates)
return '\t'.join(attributes)
def report(queries):
# filter out NaNs
queries = [q for q in queries if not np.isnan(q.quantile)]
util.metadata('mean_quantile', np.mean([q.quantile for q in queries]))
util.metadata(
'h10',
np.mean([
1.0
if util.rank_from_quantile(q.quantile, q.num_candidates) <= 10
else 0.0 for q in queries
]))
def average_quantile(s, p):
negatives, positives = neg_gen(PathQuery(s, p, ''),
't',
return_positives=True)
pos_query = PathQuery(s, p, positives)
neg_query = PathQuery(s, p, negatives)
return util.average_quantile(scores(pos_query), scores(neg_query))
def intermediate_aqs(query):
s, path = query.s, query.r
aqs = []
for length in 1 + np.arange(len(path)):
p = path[:length]
aq = average_quantile(s, p)
aqs.append(aq)
attributes = query.s, ','.join(query.r), query.t, ','.join(
str(aq) for aq in aqs)
return '\t'.join(attributes)
# ----- ----- ----- ----- -----
if eval_type == 'mean_quantile':
eval_fxn = performance
eval_report = report
elif eval_type == 'intermediate_aqs':
eval_fxn = intermediate_aqs
eval_report = lambda qs: None
else:
raise ValueError(eval_type)
with open('results.tsv', 'w') as f:
def progress(steps, elapsed):
print '{} of {} processed ({} s)'.format(steps, len(queries),
elapsed)
util.metadata('steps', steps)
util.metadata('gb_used', util.gb_used())
sys.stdout.flush()
f.flush()
for query in util.verboserate(queries, report=progress):
s = eval_fxn(query)
f.write(s)
f.write('\n')
eval_report(queries)
with open('queries.cpkl', 'w') as f:
pickle.dump(queries, f)
def avg_steps(self, experiment, dset):
sample = util.sample_if_large(dset, self.eval_samples)
steps = [self.get_steps(experiment, goal) for goal in sample]
return np.mean(steps)
def mean_rank(self, maximizer, dset):
sample = util.sample_if_large(dset, self.eval_samples)
ranks = [self.rank(maximizer, ex) for ex in util.verboserate(sample)]
return np.nanmean(ranks)
def final_evaluation(dataset_path, model_name, params_path, eval_type, eval_samples=float('inf'),
max_negative_samples=float('inf'), type_matching_negs=True):
dset = parse_dataset(dataset_path)
model = CompositionalModel(None, path_model=model_name, objective='margin')
params = load_params(params_path, model_name)
neg_gen = NegativeGenerator(dset.full_graph, max_negative_samples, type_matching_negs=type_matching_negs)
queries = util.sample_if_large(dset.test, eval_samples, replace=False)
# Define different evaluation functions
# ----- ----- ----- ----- -----
scores = lambda query: model.predict(params, query).ravel()
def performance(query):
s, r, t = query.s, query.r, query.t
negatives = neg_gen(query, 't')
pos_query = PathQuery(s, r, t)
neg_query = PathQuery(s, r, negatives)
# don't score queries with no negatives
if len(negatives) == 0:
query.quantile = np.nan
else:
query.quantile = util.average_quantile(scores(pos_query), scores(neg_query))
query.num_candidates = len(negatives) + 1
attributes = query.s, ','.join(query.r), query.t, str(query.quantile), str(query.num_candidates)
return '\t'.join(attributes)
def report(queries):
# filter out NaNs
queries = [q for q in queries if not np.isnan(q.quantile)]
util.metadata('mean_quantile', np.mean([q.quantile for q in queries]))
util.metadata('h10', np.mean([1.0 if util.rank_from_quantile(q.quantile, q.num_candidates) <= 10 else 0.0 for q in queries]))
def average_quantile(s, p):
negatives, positives = neg_gen(PathQuery(s, p, ''), 't', return_positives=True)
pos_query = PathQuery(s, p, positives)
neg_query = PathQuery(s, p, negatives)
return util.average_quantile(scores(pos_query), scores(neg_query))
def intermediate_aqs(query):
s, path = query.s, query.r
aqs = []
for length in 1 + np.arange(len(path)):
p = path[:length]
aq = average_quantile(s, p)
aqs.append(aq)
attributes = query.s, ','.join(query.r), query.t, ','.join(str(aq) for aq in aqs)
return '\t'.join(attributes)
# ----- ----- ----- ----- -----
if eval_type == 'mean_quantile':
eval_fxn = performance
eval_report = report
elif eval_type == 'intermediate_aqs':
eval_fxn = intermediate_aqs
eval_report = lambda qs: None
else:
raise ValueError(eval_type)
with open('results.tsv', 'w') as f:
def progress(steps, elapsed):
print '{} of {} processed ({} s)'.format(steps, len(queries), elapsed)
util.metadata('steps', steps)
util.metadata('gb_used', util.gb_used())
sys.stdout.flush()
f.flush()
for query in util.verboserate(queries, report=progress):
s = eval_fxn(query)
f.write(s)
f.write('\n')
eval_report(queries)
with open('queries.cpkl', 'w') as f:
pickle.dump(queries, f)
def objective_mean(dset):
sample = util.sample_if_large(dset, self.dset_samples)
vals = [maximizer.objective.value(maximizer.params, ex) for ex in util.verboserate(sample)]
return np.mean(vals)
def objective_mean(dset):
sample = util.sample_if_large(dset, self.dset_samples)
vals = [maximizer.objective.value(maximizer.params, ex) for ex in util.verboserate(sample)]
return np.mean(vals)
