def Main():
os.environ['CUDA_VISIBLE_DEVICES'] = '1'
all_ckpts = glob.glob('./models/{}'.format(args.glob))
all_ckpts = glob.glob('./models/' + str(args.dataset) + '*.pt')
print(args.blacklist)
if args.blacklist:
all_ckpts = [ckpt for ckpt in all_ckpts if args.blacklist not in ckpt]
print(all_ckpts)
selected_ckpts = all_ckpts
oracle_cards = LoadOracleCardinalities()
print('ckpts', selected_ckpts)
os.environ['CUDA_VISIBLE_DEVICES'] = '1'
if not args.run_bn:
# OK to load tables now
table, train_data, oracle_est = MakeTable()
cols_to_train = table.columns
Ckpt = collections.namedtuple(
'Ckpt', 'epoch model_bits bits_gap path loaded_model seed')
parsed_ckpts = []
for s in selected_ckpts:
if args.order is None:
z = re.match('.+model([\d\.]+)-data([\d\.]+).+seed([\d\.]+).*.pt',
s)
else:
z = re.match(
'.+model([\d\.]+)-data([\d\.]+).+seed([\d\.]+)-order.*.pt', s)
assert z
model_bits = float(z.group(1))
data_bits = float(z.group(2))
seed = int(z.group(3))
print('model_bits:', model_bits)
bits_gap = model_bits - data_bits
order = None
if args.order is not None:
order = list(args.order)
if args.heads > 0:
model = MakeTransformer(cols_to_train=table.columns,
fixed_ordering=order,
seed=seed)
else:
if args.dataset in ['dmv-tiny', 'dmv', 'forest', 'power']:
model = MakeMade(
scale=args.fc_hiddens,
cols_to_train=table.columns,
seed=seed,
fixed_ordering=order,
)
else:
assert False, args.dataset
assert order is None or len(order) == model.nin, order
ReportModel(model)
print('Loading ckpt:', s)
model.load_state_dict(torch.load(s))
model.eval()
print(s, bits_gap, seed)
parsed_ckpts.append(
Ckpt(path=s,
epoch=None,
model_bits=model_bits,
bits_gap=bits_gap,
loaded_model=model,
seed=seed))
# Estimators to run.
if args.run_bn:
estimators = RunNParallel(estimator_factory=MakeBnEstimators,
parallelism=50,
rng=np.random.RandomState(1234),
num=args.num_queries,
num_filters=None,
oracle_cards=oracle_cards)
else:
print(parsed_ckpts)
for c in parsed_ckpts:
print(c.loaded_model, table, args.psample, args.column_masking)
estimators = [
estimators_lib.ProgressiveSampling(
c.loaded_model,
table,
args.psample,
device=DEVICE,
shortcircuit=args.column_masking) for c in parsed_ckpts
]
print(estimators)
for est, ckpt in zip(estimators, parsed_ckpts):
est.name = str(est) + '_{}_{:.3f}'.format(ckpt.seed, ckpt.bits_gap)
if args.inference_opts:
print('Tracing forward_with_encoded_input()...')
for est in estimators:
encoded_input = est.model.EncodeInput(
torch.zeros(args.psample, est.model.nin, device=DEVICE))
# NOTE: this line works with torch 1.0.1.post2 (but not 1.2).
# The 1.2 version changes the API to
# torch.jit.script(est.model) and requires an annotation --
# which was found to be slower.
est.traced_fwd = torch.jit.trace(
est.model.forward_with_encoded_input, encoded_input)
if args.run_sampling:
SAMPLE_RATIO = {'dmv': [0.0013]} # ~1.3MB.
for p in SAMPLE_RATIO.get(args.dataset, [0.01]):
estimators.append(estimators_lib.Sampling(table, p=p))
if args.run_maxdiff:
estimators.append(
estimators_lib.MaxDiffHistogram(table, args.maxdiff_limit))
# Other estimators can be appended as well.
if len(estimators):
RunN(table,
cols_to_train,
estimators,
rng=np.random.RandomState(1234),
num=args.num_queries,
log_every=1,
num_filters=None,
oracle_cards=oracle_cards,
oracle_est=oracle_est)
print(len(estimators))
SaveEstimators(args.err_csv, estimators)
print('...Done, result:', args.err_csv)
def Main():
all_ckpts = glob.glob('./models/{}'.format(args.glob))
if args.blacklist:
all_ckpts = [ckpt for ckpt in all_ckpts if args.blacklist not in ckpt]
selected_ckpts = all_ckpts
oracle_cards = LoadOracleCardinalities()
print('ckpts', selected_ckpts)
if not args.run_bn:
# OK to load tables now
table, train_data, oracle_est = MakeTable()
cols_to_train = table.columns
Ckpt = collections.namedtuple(
'Ckpt', 'epoch model_bits bits_gap path loaded_model seed')
parsed_ckpts = []
for s in selected_ckpts:
if args.order is None:
z = re.match('.+model([\d\.]+)-data([\d\.]+).+seed([\d\.]+).*.pt',
s)
else:
z = re.match(
'.+model([\d\.]+)-data([\d\.]+).+seed([\d\.]+)-order.*.pt', s)
assert z
model_bits = float(z.group(1))
data_bits = float(z.group(2))
seed = int(z.group(3))
bits_gap = model_bits - data_bits
order = None
if args.order is not None:
order = list(args.order)
if args.heads > 0:
model = MakeTransformer(cols_to_train=table.columns,
fixed_ordering=order,
seed=seed)
else:
if args.dataset in ['dmv-tiny', 'dmv']:
model = MakeMade(
scale=args.fc_hiddens,
cols_to_train=table.columns,
seed=seed,
fixed_ordering=order,
)
else:
assert False, args.dataset
assert order is None or len(order) == model.nin, order
ReportModel(model)
print('Loading ckpt:', s)
model.load_state_dict(torch.load(s))
model.eval()
print(s, bits_gap, seed)
parsed_ckpts.append(
Ckpt(path=s,
epoch=None,
model_bits=model_bits,
bits_gap=bits_gap,
loaded_model=model,
seed=seed))
# Estimators to run.
if args.run_bn:
ests, truths, training_time, test_time, estimators = RunNParallelNew(estimator_factory=MakeBnEstimatorsNew,
parallelism=50,
rng=np.random.RandomState(1234),
num=args.num_queries,
num_filters=None,
oracle_cards=oracle_cards,
test_file_path=args.test_file_path,
single_data_path=args.single_data_path,
join_data_path=args.join_data_path,
join_num_path=args.join_num_path,
join_sample_size=args.join_sample_size)
# with open(args.test_file_path+'.bayesian.model', 'wb') as f:
# dill.dump(models, f) #pickle.dump(models)
ests_new = []
truths_new = []
for e, t in zip(ests, truths):
if e >= 0:
ests_new.append(e)
truths_new.append(t)
print('Training Time {}s'.format(training_time))
print('Testing Time Per Query {}ms'.format(test_time))
print_qerror(np.array(ests_new), np.array(truths_new))
print_mse(np.array(ests_new), np.array(truths_new))
print_mape(np.array(ests_new), np.array(truths_new))
print_pearson_correlation(np.array(ests_new), np.array(truths_new))
else:
estimators = [
estimators_lib.ProgressiveSampling(c.loaded_model,
table,
args.psample,
device=DEVICE,
shortcircuit=args.column_masking)
for c in parsed_ckpts
]
for est, ckpt in zip(estimators, parsed_ckpts):
est.name = str(est) + '_{}_{:.3f}'.format(ckpt.seed, ckpt.bits_gap)
if args.inference_opts:
print('Tracing forward_with_encoded_input()...')
for est in estimators:
encoded_input = est.model.EncodeInput(
torch.zeros(args.psample, est.model.nin, device=DEVICE))
# NOTE: this line works with torch 1.0.1.post2 (but not 1.2).
# The 1.2 version changes the API to
# torch.jit.script(est.model) and requires an annotation --
# which was found to be slower.
est.traced_fwd = torch.jit.trace(
est.model.forward_with_encoded_input, encoded_input)
if args.run_sampling:
SAMPLE_RATIO = {'dmv': [0.0013]} # ~1.3MB.
for p in SAMPLE_RATIO.get(args.dataset, [0.01]):
estimators.append(estimators_lib.Sampling(table, p=p))
if args.run_maxdiff:
estimators.append(
estimators_lib.MaxDiffHistogram(table, args.maxdiff_limit))
# Other estimators can be appended as well.
if len(estimators):
RunN(table,
cols_to_train,
estimators,
rng=np.random.RandomState(1234),
num=args.num_queries,
log_every=1,
num_filters=None,
oracle_cards=oracle_cards,
oracle_est=oracle_est)