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run_model.py
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import time
import itertools
from models import fusion_model
from models.pre_processing.post_processing_utils import z_score_normalization
import numpy as np
from itertools import product
from typing import List, Callable
from nltk.stem import PorterStemmer
from models.pre_processing.dataset_embeddings_memory import EmbeddingsMemory
from models.pre_processing.language_mapping import choose_tagger, choose_lemmatizer
from models.pre_processing.pos_tagging import POS_tagger_spacy
from models.fusion_model import FusionModel
from models.embedrank.embedrank_model import EmbedRank
from models.maskrank.maskrank_model import MaskRank
from models.graphrank.graphrank_model import GraphRank
from models.mdkperank.mdkperank_model import MDKPERank
from models.candidate_extract.candidate_extract_model import CandidateExtract
from datasets.process_datasets import *
from evaluation.config import POS_TAG_DIR, EMBEDS_DIR
from evaluation.evaluation_tools import evaluate_kp_extraction, extract_dataset_labels, extract_res_labels, extract_res_labels_x, output_top_cands
from tqdm import tqdm
import argparse
def str2bool(input_str : str):
if str(input_str).lower() in ["yes", "y", "true", "t"]:
return True
elif str(input_str).lower() in ["no", "n", "false", "f"]:
return False
return None
def save_model_pos_tags(dataset_obj : Callable, pos_tagger_model : str,
model : Callable ) -> None:
for dataset in dataset_obj.dataset_content:
model.update_tagger(dataset)
if not os.path.isdir(f'{POS_TAG_DIR}{dataset}/'):
os.mkdir(f'{POS_TAG_DIR}{dataset}/')
model.tagger.pos_tag_to_file(dataset_obj.dataset_content[dataset], f'{POS_TAG_DIR}{dataset}/{pos_tagger_model}/', 0)
def save_model_embeds(dataset_obj : Callable, embed_model : str,
pos_tagger_model : str, model : Callable) -> None:
for dataset in dataset_obj.dataset_content:
model.update_tagger(dataset)
if not os.path.isdir(f'{EMBEDS_DIR}{dataset}/'):
os.mkdir(f'{EMBEDS_DIR}{dataset}/')
mem = EmbeddingsMemory(dataset_obj)
mem.save_embeddings(dataset_obj, model.model, f'{embed_model}', EMBEDS_DIR, POS_tagger_spacy(f'{pos_tagger_model}'), False, 0)
def run_single_model(datasets : List[str],
embed_model : str,
pos_tagger_model : str, model_class : Callable,
save_pos_tags : bool, save_embeds : bool,
use_memory : bool,
stemming : bool, lemmatize : bool,
doc_cand_modes : List[List[str]],
**kwargs) -> None:
dataset_obj = DataSet(datasets)
model = model_class(f'{embed_model}', f'{pos_tagger_model}')
if save_pos_tags:
save_model_pos_tags(dataset_obj, pos_tagger_model, model)
if save_embeds:
save_model_embeds(dataset_obj, embed_model, pos_tagger_model, model)
for d_mode, c_mode in doc_cand_modes:
res = {}
for dataset in dataset_obj.dataset_content:
if use_memory == True:
pos_tag_memory_dir = f'{POS_TAG_DIR}{dataset}/{pos_tagger_model}/'
embed_memory_dir = f'{EMBEDS_DIR}{dataset}/{embed_model}/'
res[dataset] = model.extract_kp_from_corpus(dataset_obj.dataset_content[dataset], dataset, 15, 5, stemming, lemmatize,\
doc_mode = d_mode, cand_mode = c_mode, pos_tag_memory = pos_tag_memory_dir, embed_memory = embed_memory_dir, **kwargs)
else:
res[dataset] = model.extract_kp_from_corpus(dataset_obj.dataset_content[dataset], dataset, 15, 5, stemming, lemmatize, \
doc_mode = d_mode, cand_mode = c_mode, **kwargs)
stemmer = PorterStemmer()
lemmer = choose_lemmatizer(dataset) if lemmatize else None
evaluate_kp_extraction(extract_res_labels(res, stemmer, lemmer), extract_dataset_labels(dataset_obj.dataset_content, stemmer, lemmer), \
model.name, True, True, k_set = [5, 10, 15])
return
def run_fusion_model(datasets : List[str],
embed_model : str,
pos_tagger_model : str, models : List[Callable],
save_pos_tags : bool, save_embeds : bool,
use_memory : bool, stemming : bool , lemmatize : bool,
doc_cand_modes : List[List[str]], weights : List[float], **kwargs) -> None:
dataset_obj = DataSet(datasets)
model_list = [model(f'{embed_model}', f'{pos_tagger_model}') for model in models]
fusion_model = FusionModel(model_list, weights)
if save_pos_tags:
for model in models:
save_model_pos_tags(dataset_obj, pos_tagger_model, model)
if save_embeds:
for model in models:
save_model_embeds(dataset_obj, embed_model, pos_tagger_model, model)
for d_mode, c_mode in doc_cand_modes:
res = {}
for dataset in dataset_obj.dataset_content:
if use_memory == True:
pos_tag_memory_dir = f'{POS_TAG_DIR}{dataset}/{pos_tagger_model}/'
embed_memory_dir = f'{EMBEDS_DIR}{dataset}/{embed_model}/'
print(embed_memory_dir)
res[dataset] = fusion_model.extract_kp_from_corpus(dataset_obj.dataset_content[dataset], dataset, 15, 5, stemming, lemmatize,\
doc_mode = d_mode, cand_mode = c_mode, pos_tag_memory = pos_tag_memory_dir, embed_memory = embed_memory_dir, **kwargs)
else:
res[dataset] = fusion_model.extract_kp_from_corpus(dataset_obj.dataset_content[dataset], dataset, 15, 5, stemming, doc_mode = d_mode, cand_mode = c_mode, **kwargs)
evaluate_kp_extraction(extract_res_labels(res, PorterStemmer()), extract_dataset_labels(dataset_obj.dataset_content, PorterStemmer(), None), \
fusion_model.name, True, True)
return
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--datasets','--list', nargs='+', help='Choose which datasets to run from the list of supported ones', default="DUC")
parser.add_argument('--embed_model', type=str, help='Defines the embedding model to use',
default = "longformer-paraphrase-multilingual-mpnet-base-v2")
parser.add_argument('--rank_method','--list', nargs='+', help='Choose which rank method to use from [EmbedRank, MaskRank and FusionRank]',
default="EmbedRank")
parser.add_argument('--weights','--list', nargs='+', help='Weight list for Fusion Rank, in .2f',
default="0.50 0.50")
parser.add_argument('--save_pos_tags', type=str, help='bool flag to save POS tags', default = "False")
parser.add_argument('--save_embeds', type=str, help='bool flag to save generated embeds', default = "False")
parser.add_argument('--use_memory', type=str, help='bool flag to use pos tags and embeds from memory', default = "False")
parser.add_argument('--stemming', type=str, help='bool flag to use stemming', default = "False")
parser.add_argument('--lemmatization', type=str, help='bool flag to use lemmatization', default = "False")
args = parser.parse_args()
torch.cuda.is_available = lambda : False
doc_cand_modes = itertools.product([""], [""])
if len(args.rank_method) == 1:
run_single_model(args.datasets, args.embed_model, choose_tagger(args.datasets[0]), args.rank_method[0], doc_cand_modes,
str2bool(args.save_pos_tags), str2bool(args.save_embeds), str2bool(args.use_memory), str2bool(args.stemming), str2bool(args.lemmatization))
else:
run_fusion_model(args.datasets, args.embed_model, choose_tagger(args.datasets[0]), args.rank_method, doc_cand_modes, [0.50, 0.50],
str2bool(args.save_pos_tags), str2bool(args.save_embeds), str2bool(args.use_memory), str2bool(args.stemming), str2bool(args.lemmatization))