train_ds.read()
dev_ds.read()
test_ds = None
if args.test is not None:
test_ds = DataSet(file=args.test, reader=jlr, formatter=formatter)
test_ds.read()
train_feats, dev_feats, test_feats = f.load(train_ds, dev_ds, test_ds)
f.save_vocab(mname)
input_shape = train_feats[0].shape[1]
model = SimpleMLP(input_shape,100,3)
if gpu():
model.cuda()
if model_exists(mname) and os.getenv("TRAIN").lower() not in ["y","1","t","yes"]:
model.load_state_dict(torch.load("models/{0}.model".format(mname)))
else:
train(model, train_feats, 500, 1e-2, 90,dev_feats,early_stopping=EarlyStopping(mname))
torch.save(model.state_dict(), "models/{0}.model".format(mname))
print_evaluation(model, dev_feats, FEVERLabelSchema())
if args.test is not None:
print_evaluation(model, test_feats, FEVERLabelSchema())
print("Number of features aux (=): {0}".format(aux_train_fs[0].shape[1]))
model = MTMLP(primary_train_fs[0].shape[1], get_model_shape(), 3, 3)
if gpu():
model.cuda()
if model_exists(mname) and os.getenv("TRAIN").lower() not in [
"y", "1", "t", "yes"
]:
model.load_state_dict(torch.load("models/{0}.model".format(mname)))
else:
train_mt(model, (primary_train_fs, aux_train_fs),
50,
1e-3,
45,
dev=dev_fs,
lr_schedule=lambda a, b: exp_lr_scheduler(a, b, 0.5, 5))
torch.save(model.state_dict(), "models/{0}.model".format(mname))
create_log_dir(mname)
print_evaluation(model,
test_fs_primary,
WaseemLabelSchema(),
log="logs/{0}/primary.jsonl".format(mname),
predict_method=lambda a, b, c: predict_mt(a, b, c, 0))
print_evaluation(model,
test_fs_aux,
WaseemLabelSchema(),
log="logs/{0}/aux.jsonl".format(mname),
predict_method=lambda a, b, c: predict_mt(a, b, c, 1))
print("Number of features: {0}".format(train_fs[0].shape[1]))
model = MLP(train_fs[0].shape[1], get_model_shape(), 3)
if gpu():
model.cuda()
if model_exists(mname) and os.getenv("TRAIN").lower() not in [
"y", "1", "t", "yes"
]:
model.load_state_dict(torch.load("models/{0}.model".format(mname)))
else:
train(model,
train_fs,
50,
1e-3,
60,
dev=dev_fs,
early_stopping=EarlyStopping(mname),
lr_schedule=lambda a, b: exp_lr_scheduler(a, b, 0.5, 5))
torch.save(model.state_dict(), "models/{0}.model".format(mname))
create_log_dir(mname)
print_evaluation(model,
dev_fs,
DavidsonLabelSchema(),
log="logs/{0}/dev.jsonl".format(mname))
print_evaluation(model,
test_fs,
DavidsonLabelSchema(),
log="logs/{0}/test.jsonl".format(mname))
waseem_te_composite = CompositeDataset(name="waseem_composite_test")
for dataset in datasets_te:
dataset.read()
waseem_te_composite.add(dataset)
davidson_te = DataSet(os.path.join("data","davidson.te.csv"),reader=csvreader,formatter=df,name="davidson_test")
davidson_te.read()
features = Features(get_feature_functions(mname))
train_fs, dev_fs, test_fs_primary, test_fs_aux = features.load(waseem_tr_composite, waseem_de_composite, waseem_te_composite, davidson_te)
print("Number of features in primary: {0}".format(train_fs[0].shape[1]))
model = MLP(train_fs[0].shape[1],get_model_shape(),3)
if gpu():
model.cuda()
if model_exists(mname) and os.getenv("TRAIN").lower() not in ["y","1","t","yes"]:
model.load_state_dict(torch.load("models/{0}.model".format(mname)))
else:
train(model, train_fs, 50, 1e-3, 45, dev=dev_fs,
lr_schedule=lambda a, b: exp_lr_scheduler(a, b, 0.5, 5))
torch.save(model.state_dict(), "models/{0}.model".format(mname))
create_log_dir(mname)
print_evaluation(model,test_fs_primary, WaseemLabelSchema(),log="logs/{0}/primary.jsonl".format(mname))
print_evaluation(model,test_fs_aux, WaseemLabelSchema(),log="logs/{0}/aux.jsonl".format(mname))
logger.info("Model name is {0}".format(mname))
ffns = []
if args.sentence:
logger.info("Model is Sentence level")
ffns.append(SentenceLevelTermFrequencyFeatureFunction(db,
naming=mname))
else:
logger.info("Model is Document level")
ffns.append(TermFrequencyFeatureFunction(db, naming=mname))
f = Features(mname, ffns)
f.load_vocab(mname)
jlr = JSONLineReader()
formatter = FEVERGoldFormatter(None, FEVERLabelSchema())
test_ds = DataSet(file=args.test, reader=jlr, formatter=formatter)
test_ds.read()
feats = f.lookup(test_ds)
input_shape = feats[0].shape[1]
model = SimpleMLP(input_shape, 100, 3)
if gpu():
model.cuda()
model.load_state_dict(torch.load("models/{0}.model".format(mname)))
print_evaluation(model, feats, FEVERLabelSchema(), args.log)
