def build_sectlabel_bow_elmo_model(dirname: str):
exp_dirpath = pathlib.Path(dirname)
DATA_PATH = pathlib.Path(DATA_DIR)
train_file = DATA_PATH.joinpath("sectLabel.train")
dev_file = DATA_PATH.joinpath("sectLabel.dev")
test_file = DATA_PATH.joinpath("sectLabel.test")
data_manager = TextClassificationDatasetManager(
train_filename=str(train_file),
dev_filename=str(dev_file),
test_filename=str(test_file),
)
embedder = BowElmoEmbedder(layer_aggregation="last")
encoder = BOW_Encoder(aggregation_type="sum", embedder=embedder)
model = SimpleClassifier(
encoder=encoder,
encoding_dim=1024,
num_classes=data_manager.num_labels["label"],
classification_layer_bias=True,
datasets_manager=data_manager,
)
infer_client = ClassificationInference(
model=model,
model_filepath=str(exp_dirpath.joinpath("checkpoints",
"best_model.pt")),
datasets_manager=data_manager,
)
return infer_client
def _get_infer_client(self):
client = ClassificationInference(
model=self.model,
model_filepath=self.final_model_dir.joinpath("best_model.pt"),
datasets_manager=self.data_manager,
)
return client
def build_infer(self):
parsect_inference = ClassificationInference(
model=self.model,
model_filepath=self.hparams.get("model_filepath"),
datasets_manager=self.data_manager,
)
return parsect_inference
def build_sectlabel_elmobilstm_model(dirname: str):
exp_dirpath = pathlib.Path(dirname)
DATA_PATH = pathlib.Path(DATA_DIR)
train_file = DATA_PATH.joinpath("sectLabel.train")
dev_file = DATA_PATH.joinpath("sectLabel.dev")
test_file = DATA_PATH.joinpath("sectLabel.test")
data_manager = TextClassificationDatasetManager(
train_filename=str(train_file),
dev_filename=str(dev_file),
test_filename=str(test_file),
)
DEVICE = "cpu"
EMBEDDING_TYPE = "glove_6B_50"
HIDDEN_DIM = 512
BIDIRECTIONAL = True
COMBINE_STRATEGY = "concat"
elmo_embedder = BowElmoEmbedder(cuda_device_id=-1 if DEVICE ==
"cpu" else int(DEVICE.split("cuda:")[1]))
vanilla_embedder = WordEmbedder(embedding_type=EMBEDDING_TYPE)
embedders = ConcatEmbedders([vanilla_embedder, elmo_embedder])
encoder = LSTM2VecEncoder(
embedder=embedders,
hidden_dim=HIDDEN_DIM,
bidirectional=BIDIRECTIONAL,
combine_strategy=COMBINE_STRATEGY,
device=torch.device(DEVICE),
)
encoding_dim = (2 * HIDDEN_DIM if BIDIRECTIONAL
and COMBINE_STRATEGY == "concat" else HIDDEN_DIM)
model = SimpleClassifier(
encoder=encoder,
encoding_dim=encoding_dim,
num_classes=23,
classification_layer_bias=True,
datasets_manager=data_manager,
)
inference = ClassificationInference(
model=model,
model_filepath=str(exp_dirpath.joinpath("checkpoints",
"best_model.pt")),
datasets_manager=data_manager,
)
return inference
def get_bilstm_lc_infer_parsect(dirname: str):
exp_dirpath = pathlib.Path(dirname)
hyperparam_config_filepath = exp_dirpath.joinpath("config.json")
test_dataset_params = exp_dirpath.joinpath("test_dataset_params.json")
with open(hyperparam_config_filepath, "r") as fp:
config = json.load(fp)
with open(test_dataset_params, "r") as fp:
test_dataset_args = json.load(fp)
EMBEDDING_DIM = config["EMBEDDING_DIMENSION"]
HIDDEN_DIM = config["HIDDEN_DIMENSION"]
COMBINE_STRATEGY = config["COMBINE_STRATEGY"]
BIDIRECTIONAL = config["BIDIRECTIONAL"]
VOCAB_SIZE = config["VOCAB_SIZE"]
NUM_CLASSES = config["NUM_CLASSES"]
MODEL_SAVE_DIR = config["MODEL_SAVE_DIR"]
model_filepath = os.path.join(MODEL_SAVE_DIR, "best_model.pt")
classifier_encoding_dim = 2 * HIDDEN_DIM if BIDIRECTIONAL else HIDDEN_DIM
embedding = nn.Embedding(VOCAB_SIZE, EMBEDDING_DIM)
embedder = VanillaEmbedder(embedding_dim=EMBEDDING_DIM,
embedding=embedding)
encoder = LSTM2VecEncoder(
emb_dim=EMBEDDING_DIM,
embedder=embedder,
hidden_dim=HIDDEN_DIM,
combine_strategy=COMBINE_STRATEGY,
bidirectional=BIDIRECTIONAL,
)
model = SimpleClassifier(
encoder=encoder,
encoding_dim=classifier_encoding_dim,
num_classes=NUM_CLASSES,
classification_layer_bias=True,
)
dataset = SectLabelDataset(**test_dataset_args)
inference = ClassificationInference(model=model,
model_filepath=model_filepath,
dataset=dataset)
return inference
def setup_sectlabel_bow_glove_infer(request, clf_datasets_manager,
tmpdir_factory):
track_for_best = request.param
sample_proportion = 0.5
datasets_manager = clf_datasets_manager
word_embedder = WordEmbedder(embedding_type="glove_6B_50")
bow_encoder = BOW_Encoder(embedder=word_embedder)
classifier = SimpleClassifier(
encoder=bow_encoder,
encoding_dim=word_embedder.get_embedding_dimension(),
num_classes=2,
classification_layer_bias=True,
datasets_manager=datasets_manager,
)
train_metric = PrecisionRecallFMeasure(datasets_manager=datasets_manager)
validation_metric = PrecisionRecallFMeasure(
datasets_manager=datasets_manager)
test_metric = PrecisionRecallFMeasure(datasets_manager=datasets_manager)
optimizer = torch.optim.Adam(params=classifier.parameters())
batch_size = 1
save_dir = tmpdir_factory.mktemp("experiment_1")
num_epochs = 1
save_every = 1
log_train_metrics_every = 10
engine = Engine(
model=classifier,
datasets_manager=datasets_manager,
optimizer=optimizer,
batch_size=batch_size,
save_dir=save_dir,
num_epochs=num_epochs,
save_every=save_every,
log_train_metrics_every=log_train_metrics_every,
train_metric=train_metric,
validation_metric=validation_metric,
test_metric=test_metric,
track_for_best=track_for_best,
sample_proportion=sample_proportion,
)
engine.run()
model_filepath = pathlib.Path(save_dir).joinpath("best_model.pt")
infer = ClassificationInference(
model=classifier,
model_filepath=str(model_filepath),
datasets_manager=datasets_manager,
)
return infer
def get_bow_bert_emb_lc_gensect_infer(dirname: str):
exp_dirpath = pathlib.Path(dirname)
hyperparam_config_filepath = exp_dirpath.joinpath("config.json")
test_dataset_params = exp_dirpath.joinpath("test_dataset_params.json")
with open(hyperparam_config_filepath, "r") as fp:
config = json.load(fp)
with open(test_dataset_params, "r") as fp:
test_dataset_args = json.load(fp)
EMBEDDING_DIM = config["EMBEDDING_DIMENSION"]
NUM_CLASSES = config["NUM_CLASSES"]
BERT_TYPE = config["BERT_TYPE"]
DEVICE = config["DEVICE"]
MODEL_SAVE_DIR = config["MODEL_SAVE_DIR"]
model_filepath = os.path.join(MODEL_SAVE_DIR, "best_model.pt")
embedder = BertEmbedder(
emb_dim=EMBEDDING_DIM,
dropout_value=0.0,
aggregation_type="average",
bert_type=BERT_TYPE,
device=torch.device(DEVICE),
)
encoder = BOW_Encoder(
embedder=embedder, emb_dim=EMBEDDING_DIM, aggregation_type="average"
)
model = SimpleClassifier(
encoder=encoder,
encoding_dim=EMBEDDING_DIM,
num_classes=NUM_CLASSES,
classification_layer_bias=True,
)
dataset = GenericSectDataset(**test_dataset_args)
parsect_inference = ClassificationInference(
model=model, model_filepath=model_filepath, dataset=dataset
)
return parsect_inference
def get_bow_lc_parsect_infer(dirname: str):
exp_dirpath = pathlib.Path(dirname)
hyperparam_config_filepath = exp_dirpath.joinpath("config.json")
test_dataset_params = exp_dirpath.joinpath("test_dataset_params.json")
with open(hyperparam_config_filepath, "r") as fp:
config = json.load(fp)
with open(test_dataset_params, "r") as fp:
test_dataset_args = json.load(fp)
EMBEDDING_DIMENSION = config["EMBEDDING_DIMENSION"]
MODEL_SAVE_DIR = config["MODEL_SAVE_DIR"]
VOCAB_SIZE = config["VOCAB_SIZE"]
NUM_CLASSES = config["NUM_CLASSES"]
model_filepath = os.path.join(MODEL_SAVE_DIR, "best_model.pt")
embedding = nn.Embedding(VOCAB_SIZE, EMBEDDING_DIMENSION)
embedder = VanillaEmbedder(embedding_dim=EMBEDDING_DIMENSION,
embedding=embedding)
encoder = BOW_Encoder(
emb_dim=EMBEDDING_DIMENSION,
embedder=embedder,
dropout_value=0.0,
aggregation_type="sum",
)
model = SimpleClassifier(
encoder=encoder,
encoding_dim=EMBEDDING_DIMENSION,
num_classes=NUM_CLASSES,
classification_layer_bias=True,
)
dataset = SectLabelDataset(**test_dataset_args)
dataset.print_stats()
parsect_inference = ClassificationInference(model=model,
model_filepath=model_filepath,
dataset=dataset)
return parsect_inference
def build_sectlabel_bilstm_model(dirname: str):
exp_dirpath = pathlib.Path(dirname)
DATA_PATH = pathlib.Path(DATA_DIR)
train_file = DATA_PATH.joinpath("sectLabel.train")
dev_file = DATA_PATH.joinpath("sectLabel.dev")
test_file = DATA_PATH.joinpath("sectLabel.test")
data_manager = TextClassificationDatasetManager(
train_filename=str(train_file),
dev_filename=str(dev_file),
test_filename=str(test_file),
)
HIDDEN_DIM = 512
BIDIRECTIONAL = True
COMBINE_STRATEGY = "concat"
classifier_encoding_dim = 2 * HIDDEN_DIM if BIDIRECTIONAL else HIDDEN_DIM
embedder = WordEmbedder(embedding_type="glove_6B_50")
encoder = LSTM2VecEncoder(
embedder=embedder,
hidden_dim=HIDDEN_DIM,
combine_strategy=COMBINE_STRATEGY,
bidirectional=BIDIRECTIONAL,
)
model = SimpleClassifier(
encoder=encoder,
encoding_dim=classifier_encoding_dim,
num_classes=23,
classification_layer_bias=True,
datasets_manager=data_manager,
)
inference = ClassificationInference(
model=model,
model_filepath=str(exp_dirpath.joinpath("checkpoints", "best_model.pt")),
datasets_manager=data_manager,
)
return inference
def build_sectlabel_bow_model(dirname: str):
"""
Parameters
----------
dirname : The directory where sciwing stores your outputs for the model
Returns
-------
"""
exp_dirpath = pathlib.Path(dirname)
DATA_PATH = pathlib.Path(DATA_DIR)
train_file = DATA_PATH.joinpath("sectLabel.train")
dev_file = DATA_PATH.joinpath("sectLabel.dev")
test_file = DATA_PATH.joinpath("sectLabel.test")
data_manager = TextClassificationDatasetManager(
train_filename=str(train_file),
dev_filename=str(dev_file),
test_filename=str(test_file),
)
embedder = WordEmbedder(embedding_type="glove_6B_50")
encoder = BOW_Encoder(embedder=embedder)
model = SimpleClassifier(
encoder=encoder,
encoding_dim=embedder.get_embedding_dimension(),
num_classes=data_manager.num_labels["label"],
classification_layer_bias=True,
datasets_manager=data_manager,
)
infer = ClassificationInference(
model=model,
model_filepath=str(exp_dirpath.joinpath("checkpoints",
"best_model.pt")),
datasets_manager=data_manager,
)
return infer
def get_elmo_emb_lc_infer_gensect(dirname: str):
exp_dirpath = pathlib.Path(dirname)
hyperparam_config_filepath = exp_dirpath.joinpath("config.json")
test_dataset_params = exp_dirpath.joinpath("test_dataset_params.json")
with open(hyperparam_config_filepath, "r") as fp:
config = json.load(fp)
with open(test_dataset_params, "r") as fp:
test_dataset_args = json.load(fp)
EMBEDDING_DIM = config["EMBEDDING_DIMENSION"]
NUM_CLASSES = config["NUM_CLASSES"]
EMBEDDING_DIMENSION = config["EMBEDDING_DIMENSION"]
LAYER_AGGREGATION = config["LAYER_AGGREGATION"]
WORD_AGGREGATION = config["WORD_AGGREGATION"]
embedder = BowElmoEmbedder(emb_dim=EMBEDDING_DIMENSION,
layer_aggregation=LAYER_AGGREGATION)
encoder = BOW_Encoder(
emb_dim=EMBEDDING_DIMENSION,
embedder=embedder,
aggregation_type=WORD_AGGREGATION,
)
model = SimpleClassifier(
encoder=encoder,
encoding_dim=EMBEDDING_DIM,
num_classes=NUM_CLASSES,
classification_layer_bias=True,
)
MODEL_SAVE_DIR = config["MODEL_SAVE_DIR"]
model_filepath = os.path.join(MODEL_SAVE_DIR, "best_model.pt")
dataset = GenericSectDataset(**test_dataset_args)
parsect_inference = ClassificationInference(model=model,
model_filepath=model_filepath,
dataset=dataset)
return parsect_inference
def build_sectlabel_bow_bert(dirname: str):
exp_dirpath = pathlib.Path(dirname)
DATA_PATH = pathlib.Path(DATA_DIR)
train_file = DATA_PATH.joinpath("sectLabel.train")
dev_file = DATA_PATH.joinpath("sectLabel.dev")
test_file = DATA_PATH.joinpath("sectLabel.test")
data_manager = TextClassificationDatasetManager(
train_filename=str(train_file),
dev_filename=str(dev_file),
test_filename=str(test_file),
)
embedder = BertEmbedder(
dropout_value=0.0,
aggregation_type="average",
bert_type="bert-base-uncased",
device=torch.device("cpu"),
)
encoder = BOW_Encoder(embedder=embedder, aggregation_type="average")
model = SimpleClassifier(
encoder=encoder,
encoding_dim=768,
num_classes=23,
classification_layer_bias=True,
datasets_manager=data_manager,
)
parsect_inference = ClassificationInference(
model=model,
model_filepath=str(exp_dirpath.joinpath("checkpoints",
"best_model.pt")),
datasets_manager=data_manager,
)
return parsect_inference
def get_elmo_bilstm_lc_infer(dirname: str):
exp_dirpath = pathlib.Path(dirname)
hyperparam_config_filepath = exp_dirpath.joinpath("config.json")
test_dataset_params = exp_dirpath.joinpath("test_dataset_params.json")
with open(hyperparam_config_filepath, "r") as fp:
config = json.load(fp)
with open(test_dataset_params, "r") as fp:
test_dataset_args = json.load(fp)
DEVICE = config["DEVICE"]
EMBEDDING_DIM = config["EMBEDDING_DIMENSION"]
VOCAB_SIZE = config["VOCAB_SIZE"]
HIDDEN_DIM = config["HIDDEN_DIMENSION"]
BIDIRECTIONAL = config["BIDIRECTIONAL"]
COMBINE_STRATEGY = config["COMBINE_STRATEGY"]
NUM_CLASSES = config["NUM_CLASSES"]
MODEL_SAVE_DIR = config["MODEL_SAVE_DIR"]
model_filepath = os.path.join(MODEL_SAVE_DIR, "best_model.pt")
embedding = nn.Embedding(VOCAB_SIZE, EMBEDDING_DIM)
elmo_embedder = BowElmoEmbedder(
layer_aggregation="sum",
cuda_device_id=-1 if DEVICE == "cpu" else int(
DEVICE.split("cuda:")[1]),
)
vanilla_embedder = VanillaEmbedder(embedding=embedding,
embedding_dim=EMBEDDING_DIM)
embedders = ConcatEmbedders([vanilla_embedder, elmo_embedder])
encoder = LSTM2VecEncoder(
emb_dim=EMBEDDING_DIM + 1024,
embedder=embedders,
hidden_dim=HIDDEN_DIM,
bidirectional=BIDIRECTIONAL,
combine_strategy=COMBINE_STRATEGY,
device=torch.device(DEVICE),
)
encoding_dim = (2 * HIDDEN_DIM if BIDIRECTIONAL
and COMBINE_STRATEGY == "concat" else HIDDEN_DIM)
model = SimpleClassifier(
encoder=encoder,
encoding_dim=encoding_dim,
num_classes=NUM_CLASSES,
classification_layer_bias=True,
)
dataset = SectLabelDataset(**test_dataset_args)
inference = ClassificationInference(model=model,
model_filepath=model_filepath,
dataset=dataset)
return inference
