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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import gzip
import json
from functools import partial
from pathlib import Path
import datasets
from datasets import DatasetDict, load_dataset
AVAILABLE_DATASETS = {
'main': 'https://amazon-berkeley-objects.s3.amazonaws.com/archives/abo-listings.tar'
}
VERSION = datasets.Version("0.0.1")
_FIELDS = [
"item_id",
"brand",
"bullet_point",
"color",
"item_name",
"model_name",
"model_number",
"model_year",
"product_type",
"style",
"main_image_id",
"other_image_id",
"item_keywords",
"country",
"marketplace",
"domain_name",
"node",
]
class AbolistingsDataset(datasets.GeneratorBasedBuilder):
"""AbolistingsDataset dataset."""
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name=data_name, version=VERSION, description=f"{data_name} abolistings dataset"
)
for data_name in AVAILABLE_DATASETS
]
@staticmethod
def load(data_name_config: str = "main") -> DatasetDict:
ds = load_dataset(__file__, data_name_config)
return ds
def _info(self):
return datasets.DatasetInfo(
description="",
features=datasets.Features(
{
"item_id": datasets.Value("string"),
"brand": datasets.Sequence(datasets.Value("string")),
"bullet_point": datasets.Sequence(datasets.Value("string")),
"color": datasets.Sequence(datasets.Value("string")),
"item_name": datasets.Sequence(datasets.Value("string")),
"model_name": datasets.Sequence(datasets.Value("string")),
"model_number": datasets.Sequence(datasets.Value("string")),
"model_year": datasets.Sequence(datasets.Value("string")),
"product_type": datasets.Sequence(datasets.Value("string")),
"style": datasets.Sequence(datasets.Value("string")),
"main_image_id": datasets.Value("string"),
"other_image_id": datasets.Sequence(datasets.Value("string")),
"item_keywords": datasets.Sequence(datasets.Value("string")),
"country": datasets.Value("string"),
"marketplace": datasets.Value("string"),
"domain_name": datasets.Value("string"),
"node": datasets.Sequence(datasets.Value("string")),
}
),
supervised_keys=None,
homepage="https://amazon-berkeley-objects.s3.amazonaws.com/index.html#download",
citation="",
)
def _split_generators(self, dl_manager):
downloader = partial(
lambda: dl_manager.download_and_extract(AVAILABLE_DATASETS[self.config.name])
)
# There is no predefined train/val/test split for this dataset.
root_path = Path(downloader()) / 'listings' / 'metadata'
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"root_path": root_path}
),
]
def _generate_examples(self, root_path):
root_path = Path(root_path)
files = list(root_path.glob("*.json.gz"))
idx = 0
for file in files:
with gzip.GzipFile(file) as f_in:
for l in f_in:
l = l.decode("utf-8")
sample = json.loads(l)
yield idx, {k: sample.get(k) for k in _FIELDS}
idx += 1
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