category_classification/datasets/download_common.py

from pathlib import Path

from kaggle import api as kapi
import pandas as pd
from sklearn.model_selection import train_test_split as sk_train_test_split


def download_dataset(dest_dir, dataset, filename):
    if (Path(dest_dir) / filename).exists():
        print('Dataset already exists, do not download')
        return
    print('Downloading dataset...')
    kapi.dataset_download_file(dataset=dataset, file_name=filename, path=dest_dir, quiet=False)


# Takes a lot of RAM
def read_dataset(dest_dir, filename) -> pd.DataFrame:
    print('Reading dataset...')
    json_file_path = Path(dest_dir) / filename
    df = pd.read_json(json_file_path, lines=True)
    print('Dataset read')
    return df


def download_and_read_dataset(dest_dir, dataset, filename):
    download_dataset(dest_dir=dest_dir, dataset=dataset, filename=filename)
    return read_dataset(dest_dir=dest_dir, filename=filename)


def filter_columns(df: pd.DataFrame, columns) -> pd.DataFrame:
    print("Removing unwanted columns...")
    df = df[columns]
    print("Columns removed...")
    return df


def create_features_labels(df: pd.DataFrame, old_label, new_label):
    def transform_categories(categories):
        categories = categories.split()
        category = categories[0]
        if '.' in category:
            return category[: category.index(".")]
        return category
    labels = df[old_label].apply(transform_categories)
    labels = labels.rename(new_label)
    features = df.drop(old_label, axis=1)
    return features, labels


def train_test_split(X, y, test_size=0.25):
    return sk_train_test_split(X, y, test_size=test_size, stratify=y)


def write_dataset(dest_dir, X, y, filename):
    dest_dir = Path(dest_dir)
    df = pd.concat((X, y), axis=1)
    df.to_json(filename, orient="records", lines=True)