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#!/usr/bin/python3
# -*- coding: utf-8 -*-
import argparse
import os
from pathlib import Path
import sys
pwd = os.path.abspath(os.path.dirname(__file__))
sys.path.append(os.path.join(pwd, "../../"))
import pandas as pd
from toolbox.torch.utils.data.vocabulary import Vocabulary
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--vocabulary_dir", default="vocabulary", type=str)
parser.add_argument("--train_dataset", default="train.xlsx", type=str)
parser.add_argument("--valid_dataset", default="valid.xlsx", type=str)
args = parser.parse_args()
return args
def main():
args = get_args()
train_dataset = pd.read_excel(args.train_dataset)
valid_dataset = pd.read_excel(args.valid_dataset)
# non_padded_namespaces
category_set = set()
for i, row in train_dataset.iterrows():
category = row["category"]
category_set.add(category)
for i, row in valid_dataset.iterrows():
category = row["category"]
category_set.add(category)
vocabulary = Vocabulary(non_padded_namespaces=["global_labels", *list(category_set)])
# train
for i, row in train_dataset.iterrows():
global_labels = row["global_labels"]
country_labels = row["country_labels"]
category = row["category"]
vocabulary.add_token_to_namespace(global_labels, "global_labels")
vocabulary.add_token_to_namespace(country_labels, category)
# valid
for i, row in valid_dataset.iterrows():
global_labels = row["global_labels"]
country_labels = row["country_labels"]
category = row["category"]
vocabulary.add_token_to_namespace(global_labels, "global_labels")
vocabulary.add_token_to_namespace(country_labels, category)
vocabulary.save_to_files(args.vocabulary_dir)
return
if __name__ == "__main__":
main()
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