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main.py
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from allennlp.models import model
from allennlp.models.archival import load_archive
from allennlp.predictors import Predictor
from allennlp.common import Params
from allennlp.data import Vocabulary
from allennlp.data import DataIterator#
from allennlp.data.dataset_readers import DatasetReader
from allennlp.models import Model
from allennlp.training import Trainer
from allennlp.training.util import evaluate
from allennlp.common.util import prepare_global_logging, cleanup_global_logging, prepare_environment
from allennlp.data.token_indexers import TokenIndexer, SingleIdTokenIndexer, PretrainedBertIndexer
from allennlp.data import vocabulary
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from models import NSDSlotTaggingModel
from predictors import SlotFillingPredictor
from dataset_readers import MultiFileDatasetReader
from metrics import NSDSpanBasedF1Measure
from utils import *
from typing import Any, Union, Dict, Iterable, List, Optional, Tuple
from time import *
import numpy as np
import pandas as pd
import argparse
import os
import logging
vocabulary.DEFAULT_OOV_TOKEN = "[UNK]" # set for bert
def parse_args():
arg_parser = argparse.ArgumentParser()
arg_parser.add_argument("--mode",type=str,choices=["train", "test", "both"], default="test",
help="Specify running mode: only train, only test or both.")
arg_parser.add_argument("--dataset",type=str,choices=["SnipsNSD5%", "SnipsNSD15%", "SnipsNSD30%"], default=None,
help="The dataset to use.")
arg_parser.add_argument("--output_dir",type=str, default="./output",
help="The path of trained model.")
arg_parser.add_argument("--cuda",type=int, default=1,
help="cuda device.")
arg_parser.add_argument("--threshold", type=float,default=None,
help="The specified threshold value.")
arg_parser.add_argument("--batch_size",type=int, default=200,
help="Batch size.")
args = arg_parser.parse_args()
return args
def SlotTrain(config_path,output_dir):
if not os.path.exists(output_dir):
os.makedirs(output_dir)
params = Params.from_file(config_path)
stdout_handler = prepare_global_logging(output_dir, False)
prepare_environment(params)
reader = DatasetReader.from_params(params["dataset_reader"])
train_dataset = reader.read(file_path=params.pop("train_data_path", None))
valid_dataset = reader.read(params.pop("validation_data_path", None))
test_data_path = params.pop("test_data_path", None)
if test_data_path:
test_dataset = reader.read(test_data_path)
vocab = Vocabulary.from_instances(train_dataset + valid_dataset + test_dataset)
else:
test_dataset = None
vocab = Vocabulary.from_instances(train_dataset + valid_dataset)
model_params = params.pop("model", None)
model = Model.from_params(model_params.duplicate(), vocab=vocab)
vocab.save_to_files(os.path.join(output_dir, "vocabulary"))
# copy config file
with open(config_path, "r", encoding="utf-8") as f_in:
with open(os.path.join(output_dir, "config.json"), "w", encoding="utf-8") as f_out:
f_out.write(f_in.read())
iterator = DataIterator.from_params(params.pop("iterator", None))
iterator.index_with(vocab)
trainer_params = params.pop("trainer", None)
trainer = Trainer.from_params(model=model,
serialization_dir=output_dir,
iterator=iterator,
train_data=train_dataset,
validation_data=valid_dataset,
params=trainer_params.duplicate())
trainer.train()
# evaluate on the test set
if test_dataset:
logging.info("Evaluating on the test set")
import torch # import here to ensure the republication of the experiment
model.load_state_dict(torch.load(os.path.join(output_dir, "best.th")))
test_metrics = evaluate(model, test_dataset, iterator,
cuda_device=trainer_params.pop("cuda_device", 1),
batch_weight_key=None)
logging.info(f"Metrics on the test set: {test_metrics}")
with open(os.path.join(output_dir, "test_metrics.txt"), "w", encoding="utf-8") as f_out:
f_out.write(f"Metrics on the test set: {test_metrics}")
cleanup_global_logging(stdout_handler)
args = parse_args()
# Train
if args.mode in ["train","both"]:
output_dir = os.path.join(args.output_dir,args.dataset)
config_path = "./config/"+args.dataset+".json"
if not os.path.exists(output_dir):
os.makedirs(output_dir)
params = Params.from_file(config_path)
stdout_handler = prepare_global_logging(output_dir, False)
prepare_environment(params)
reader = DatasetReader.from_params(params["dataset_reader"])
train_dataset = reader.read(file_path=params.pop("train_data_path", None))
valid_dataset = reader.read(params.pop("validation_data_path", None))
test_data_path = params.pop("test_data_path", None)
if test_data_path:
test_dataset = reader.read(test_data_path)
vocab = Vocabulary.from_instances(train_dataset + valid_dataset + test_dataset)
else:
test_dataset = None
vocab = Vocabulary.from_instances(train_dataset + valid_dataset)
model_params = params.pop("model", None)
model = Model.from_params(model_params.duplicate(), vocab=vocab)
vocab.save_to_files(os.path.join(output_dir, "vocabulary"))
# copy config file
with open(config_path, "r", encoding="utf-8") as f_in:
with open(os.path.join(output_dir, "config.json"), "w", encoding="utf-8") as f_out:
f_out.write(f_in.read())
iterator = DataIterator.from_params(params.pop("iterator", None))
iterator.index_with(vocab)
trainer_params = params.pop("trainer", None)
trainer = Trainer.from_params(model=model,
serialization_dir=output_dir,
iterator=iterator,
train_data=train_dataset,
validation_data=valid_dataset,
params=trainer_params.duplicate())
trainer.train()
# evaluate on the test set
if test_dataset:
logging.info("Evaluating on the test set")
import torch # import here to ensure the republication of the experiment
model.load_state_dict(torch.load(os.path.join(output_dir, "best.th")))
test_metrics = evaluate(model, test_dataset, iterator,
cuda_device=trainer_params.pop("cuda_device", 1),
batch_weight_key=None)
logging.info(f"Metrics on the test set: {test_metrics}")
with open(os.path.join(output_dir, "test_metrics.txt"), "w", encoding="utf-8") as f_out:
f_out.write(f"Metrics on the test set: {test_metrics}")
cleanup_global_logging(stdout_handler)
# Test
if args.mode in ["test","both"]:
if args.mode == "both":
model_dir = output_dir
else:
model_dir = os.path.join(args.output_dir,args.dataset)
# predict
archive = load_archive(model_dir,cuda_device=args.cuda)
predictor = Predictor.from_archive(archive=archive, predictor_name="slot_filling_predictor")
train_outputs = predictor.predict_multi(file_path = os.path.join("data",args.dataset,"train") ,batch_size = args.batch_size)
test_outputs = predictor.predict_multi(file_path = os.path.join("data",args.dataset,"test") ,batch_size = args.batch_size)
ns_labels = ["ns","B-ns","I-ns"]
# GDA
gda = LinearDiscriminantAnalysis(solver="lsqr", shrinkage=None, store_covariance=True)
gda.fit(np.array(train_outputs["encoder_outs"]), train_outputs["true_labels"])
gda_means = gda.means_
test_gda_result = confidence(np.array(test_outputs["encoder_outs"]), gda.means_, "euclidean", gda.covariance_)
test_score = pd.Series(test_gda_result.min(axis=1))
test_ns_idx = [idx_vo for idx_vo , _vo in enumerate(test_outputs["true_labels"]) if _vo in ns_labels]
test_ind_idx = [idx_vi for idx_vi , _vi in enumerate(test_outputs["true_labels"]) if _vi not in ns_labels]
test_ns_score = test_score[test_ns_idx]
test_ind_score = test_score[test_ind_idx]
# threshold
threshold = args.threshold
# override
test_y_ns = pd.Series(test_outputs["predict_labels"])
test_y_ns[test_score[test_score> threshold].index] = "ns"
test_y_ns = list(test_y_ns)
# Metrics —— ROSE
start_idx = 0
end_idx = 0
test_pred_lines = []
test_true_lines = []
seq_lines = pd.DataFrame(test_outputs["tokens"])
for i,seq in enumerate(seq_lines["tokens"]):
start_idx = end_idx
end_idx = start_idx + len(seq)
adju_pred_line = parse_line(test_y_ns[start_idx:end_idx])
test_true_line = test_outputs["true_labels"][start_idx:end_idx]
test_pred_lines.append(adju_pred_line)
test_true_lines.append(test_true_line)
rose_metric(test_true_lines,test_pred_lines)
# Metrics —— Token
test_pred_tokens = parse_token(test_y_ns)
test_true_tokens = parse_token(test_outputs["true_labels"])
token_metric(test_true_tokens,test_pred_tokens)