def main_build(args):
# load language model
ft_model_filename = args.path_model
print(f"Loading language model from: {ft_model_filename}")
model = ck.load_FT_model(ft_model_filename)
print("Loaded embeddings!")
# build new embedder
es_embedder = ck.SIFEmbedder(model)
print("Built embedder!")
# get keywords
keyword_filename = args.path_keywords
print(f"Loading keywords from: {keyword_filename}")
keywords = ck.load_csv_column(
keyword_filename,
args.keywords_column,
delimiter = args.keywords_delimiter)
print(f'Loaded {len(keywords)} keywords.')
# run embedder
es_embedder.fit(keywords, sample_size=args.sample)
# store parameters
embedder_params_filename = args.path_embedder_parameters
print(f"Dumping embedder parameters to: {embedder_params_filename}")
with open(embedder_params_filename, "w") as outfile:
outfile.write(es_embedder.serialize())
def main_build(args):
# load language model
ft_model_filename = args.path_model
print(f"Loading language model from: {ft_model_filename}")
model = ck.load_FT_model(ft_model_filename)
print("Loaded embeddings!")
# build new embedder
es_embedder = ck.SIFEmbedder(model)
print("Built embedder!")
# get keywords
keyword_filename = args.path_keywords
print(f"Loading keywords from: {keyword_filename}")
keywords = ck.load_csv_column(keyword_filename,
args.keywords_column,
delimiter=args.keywords_delimiter)
print(f'Loaded {len(keywords)} keywords.')
# run embedder
embeddings = es_embedder.fit_embed(keywords)
# store parameters
embedder_params_filename = args.path_embedder_parameters
print(f"Dumping embedder parameters to: {embedder_params_filename}")
with open(embedder_params_filename, "w") as outfile:
outfile.write(es_embedder.serialize())
# if specified, store embeddings
if args.path_embeddings is not None:
embeddings_path = args.path_embeddings
print(f"Dumping embeddings to: {embeddings_path}")
np.save(open(embeddings_path, 'wb'), embeddings)
def main_categorise(args):
# load language model
ft_model_filename = args.path_model
print(f"Loading language model from: {ft_model_filename}")
model = ck.load_FT_model(ft_model_filename)
print("Loaded embeddings!")
# build embedder
embedder_parameters_filename = args.path_embedder_parameters
print(f"Loading embedder parameters from: {embedder_parameters_filename}")
de_embedder_parameters_json = open(embedder_parameters_filename).read()
de_embedder = ck.SIFEmbedder(model)
de_embedder.load(de_embedder_parameters_json)
print("Built embedder!")
# get categories
categories_filename = args.path_categories
print(f"Loading categories from: {categories_filename}")
categories = ck.load_csv_column(categories_filename,
args.categories_column,
delimiter=',')
category_ids = ck.load_csv_column(categories_filename,
args.categories_id_column,
delimiter=',')
print(f'Loaded {len(categories)} categories.')
# build categorizer
categorizer = ck.Categorizer(de_embedder)
categorizer.fit(categories, category_ids=category_ids)
print("Categorizer built!")
# get keywords
keyword_filename = args.path_keywords
print(f"Loading keywords from: {keyword_filename}")
keywords = ck.load_csv_column(keyword_filename,
args.keywords_column,
delimiter=args.keywords_delimiter)
print(f'Loaded {len(keywords)} keywords.')
# run categorizer
n_categories = args.n_categories
keyword_categories = categorizer.categorize(keywords,
n_categories=n_categories)
output_filename = args.path_output
print(f"Writing categories to: {output_filename}")
with open(output_filename, "w", encoding="utf8") as outfile:
outwriter = csv.writer(outfile, delimiter=",", quotechar='"')
# write header
out_header = ["keyword"]
for cat_i in range(1, n_categories + 1):
out_header.extend(
[f"category{cat_i}", f"category{cat_i}_distance"])
outwriter.writerow(out_header)
# write results row by row
for keyword, categories in zip(keywords, keyword_categories):
row = [f"{keyword}"]
for category, distance in categories:
row.extend([f"{category}", f"{distance}"])
outwriter.writerow(row)
print("DONE!")
def main_categorise(args):
# load language model
ft_model_filename = args.path_model
print(f"Loading language model from: {ft_model_filename}")
model = ck.load_FT_model(ft_model_filename)
print("Loaded embeddings!")
# build embedder
embedder_parameters_filename = args.path_embedder_parameters
print(f"Loading embedder parameters from: {embedder_parameters_filename}")
de_embedder_parameters_json = open(embedder_parameters_filename).read()
de_embedder = ck.SIFEmbedder(model)
de_embedder.load(de_embedder_parameters_json)
print("Built embedder!")
# get categories
categories_filename = args.path_categories
print(f"Loading categories from: {categories_filename}")
categories = ck.load_csv_column(categories_filename, args.categories_column, delimiter=',')
category_ids = ck.load_csv_column(categories_filename, args.categories_id_column, delimiter=',')
print(f'Loaded {len(categories)} categories.')
# build categorizer
categorizer = ck.Categorizer(de_embedder)
categorizer.fit(categories, category_ids=category_ids)
print("Categorizer built!")
# get keywords
keyword_filename = args.path_keywords
print(f"Loading keywords from: {keyword_filename}")
keywords = ck.load_csv_column(
keyword_filename,
args.keywords_column,
delimiter = args.keywords_delimiter)
print(f'Loaded {len(keywords)} keywords.')
# assigning closest 'n_categories' to each keyword
if args.mode == "categorise_keywords":
n_categories = args.n_categories
keyword_categories = categorizer.categorize(keywords, n_categories=n_categories)
output_filename = args.path_output
print(f"Writing categories to: {output_filename}")
with open(output_filename, "w", encoding="utf8") as outfile:
outwriter = csv.writer(outfile, delimiter=",", quotechar='"')
# write header
out_header = ["keyword"]
for cat_i in range(1, n_categories + 1):
out_header.extend([f"category{cat_i}_id", f"category{cat_i}", f"category{cat_i}_distance"])
outwriter.writerow(out_header)
# write results row by row
for keyword, categories in zip(keywords, keyword_categories):
row = [f"{keyword}"]
for category, id, distance in categories:
row.extend([f"{id}", f"{category}", f"{distance}"])
outwriter.writerow(row)
# assigning closest 'n_keywords' to each category
elif args.mode == "relevance_to_category":
n_keywords = args.n_keywords
keyword_categories = categorizer.closest_keywords(keywords, n_keywords=n_keywords)
output_filename = args.path_output
print(f"Writing categories to: {output_filename}")
with open(output_filename, "w", encoding="utf8") as outfile:
outwriter = csv.writer(outfile, delimiter=",", quotechar='"')
# write header
out_header = ["keyword", "categories"]
outwriter.writerow(out_header)
# write results row by row
for keyword, categories in tqdm(zip(keywords, keyword_categories), desc='Writting to file'):
row = [f"{keyword}"]
if len(categories) == 0:
row += ["none"]
else:
#row += ["#".join([f"{category}({id})" for category, id, distance in categories])]
for category, id, distance in categories:
tmp_row = row + [f"{category}({id})"]
outwriter.writerow(tmp_row)
print("DONE!")
print(f"Loading language model from: {ft_model_filename}")
model = ck.load_FT_model(ft_model_filename)
print("Loaded embeddings!")
# build embedder
embedder_parameters_filename = args.path_embedder_parameters
print(f"Loading embedder parameters from: {embedder_parameters_filename}")
de_embedder_parameters_json = open(embedder_parameters_filename).read()
de_embedder = ck.SIFEmbedder(model)
de_embedder.load(de_embedder_parameters_json)
print("Built embedder!")
# get categories
categories_filename = args.path_categories
print(f"Loading categories from: {categories_filename}")
categories = ck.load_csv_column(categories_filename,
args.categories_column,
delimiter=',')
category_ids = ck.load_csv_column(categories_filename,
args.categories_id_column,
delimiter=',')
print(f'Loaded {len(categories)} categories.')
# build categorizer
categorizer = ck.Categorizer(de_embedder)
categorizer.fit(categories, category_ids=category_ids)
print("Categorizer built!")
# run server
app.run(host='0.0.0.0', port=args.port)