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import ast
import logging
import os
import sys
from dataclasses import dataclass, field
import pandas as pd
from sklearn.model_selection import train_test_split
from tqdm import tqdm
from typing import Dict, List, Optional, Tuple
from datasets import load_dataset
from transformers import (
HfArgumentParser,
)
from data_utils import (
filter_by_lang_regex,
filter_by_steps,
filter_by_length,
filter_by_item,
filter_by_num_sents,
filter_by_num_tokens,
normalizer
)
logger = logging.getLogger(__name__)
@dataclass
class DataArguments:
"""
Arguments to which dataset we are going to set up.
"""
output_dir: str = field(
default=".",
metadata={"help": "The output directory where the config will be written."},
)
dataset_name: str = field(
default=None,
metadata={"help": "The name of the dataset to use (via the datasets library)."}
)
dataset_data_dir: Optional[str] = field(
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
)
cache_dir: Optional[str] = field(
default=None,
metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
)
def main():
parser = HfArgumentParser([DataArguments])
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
data_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))[0]
else:
data_args = parser.parse_args_into_dataclasses()[0]
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout)],
)
logger.setLevel(logging.INFO)
logger.info(f"Preparing the dataset")
if data_args.dataset_name is not None:
dataset = load_dataset(
data_args.dataset_name,
data_dir=data_args.dataset_data_dir,
cache_dir=data_args.cache_dir
)
else:
dataset = load_dataset(
data_args.dataset_name,
cache_dir=data_args.cache_dir
)
def cleaning(text, item_type="ner"):
# NOTE: DO THE CLEANING LATER
text = normalizer(text, do_lowercase=True)
return text
def recipe_preparation(item_dict):
ner = item_dict["ner"]
title = item_dict["title"]
ingredients = item_dict["ingredients"]
steps = item_dict["directions"]
conditions = []
conditions += [filter_by_item(ner, 2)]
conditions += [filter_by_length(title, 4)]
conditions += [filter_by_item(ingredients, 2)]
conditions += [filter_by_item(steps, 2)]
# conditions += filter_by_steps(" ".join(steps))
if not all(conditions):
return None
ner = ", ".join(ner)
ingredients = " <sep> ".join(ingredients)
steps = " <sep> ".join(steps)
# Cleaning
ner = cleaning(ner, "ner")
title = cleaning(title, "title")
ingredients = cleaning(ingredients, "ingredients")
steps = cleaning(steps, "steps")
return {
"inputs": ner,
# "targets": f"title: {title} <section> ingredients: {ingredients} <section> directions: {steps}"
"targets": f"title: {title} <section> ingredients: {ingredients} <section> directions: {steps}"
}
if len(dataset.keys()) > 1:
for subset in dataset.keys():
data_dict = []
for item in tqdm(dataset[subset], position=0, total=len(dataset[subset])):
item = recipe_preparation(item)
if item:
data_dict.append(item)
data_df = pd.DataFrame(data_dict)
logger.info(f"Preparation of [{subset}] set consists of {len(data_df)} records!")
output_path = os.path.join(data_args.output_dir, f"{subset}.csv")
os.makedirs(os.path.dirname(output_path), exist_ok=True)
data_df.to_csv(output_path, sep="\t", encoding="utf-8", index=False)
logger.info(f"Data saved here {output_path}")
else:
data_dict = []
subset = list(dataset.keys())[0]
for item in tqdm(dataset[subset], position=0, total=len(dataset[subset])):
item = recipe_preparation(item)
if item:
data_dict.append(item)
data_df = pd.DataFrame(data_dict)
logger.info(f"Preparation - [before] consists of {len(dataset[subset])} records!")
logger.info(f"Preparation - [after] consists of {len(data_df)} records!")
train, test = train_test_split(data_df, test_size=0.05, random_state=101)
train = train.reset_index(drop=True)
test = test.reset_index(drop=True)
logger.info(f"Preparation of [train] set consists of {len(train)} records!")
logger.info(f"Preparation of [test] set consists of {len(test)} records!")
os.makedirs(data_args.output_dir, exist_ok=True)
train.to_csv(os.path.join(data_args.output_dir, "train.csv"), sep="\t", encoding="utf-8", index=False)
test.to_csv(os.path.join(data_args.output_dir, "test.csv"), sep="\t", encoding="utf-8", index=False)
logger.info(f"Data saved here {data_args.output_dir}")
if __name__ == '__main__':
main()
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