shopping-queries-image-dataset.py

# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# TODO: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""


import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{SIGIR-eCom 2024,
title = {Shopping Queries Image Dataset (SQID): An Image-Enriched ESCI Dataset for Exploring Multimodal Learning in Product Search},
author={Marie Al Ghossein, Ching-Wei Chen, Jason Tang},
year={2024}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
The Shopping Queries Image Dataset (SQID) is an extension of the Amazon Shopping Queries Dataset which has been enriched with image information associated with 190,000 products.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = "MIT"

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_BASE_URL = "https://huggingface.co/datasets/crossingminds/shopping-queries-image-dataset/data"
_URLS = {
    "product_image_urls": "https://huggingface.co/datasets/crossingminds/shopping-queries-image-dataset/data/product_image_urls.parquet",
    "product_features": "https://huggingface.co/datasets/crossingminds/shopping-queries-image-dataset/data/product_features.parquet",
    "query_features": "https://huggingface.co/datasets/crossingminds/shopping-queries-image-dataset/data/query_features.parquet",
    "supp_product_image_urls": "https://huggingface.co/datasets/crossingminds/shopping-queries-image-dataset/data/supp_product_image_urls.parquet",
}


# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class ShoppingQueriesImageDataset(datasets.GeneratorBasedBuilder):
    """Shopping Queries Image Dataset"""

    VERSION = datasets.Version("1.0.0")

    # This is an example of a dataset with multiple configurations.
    # If you don't want/need to define several sub-sets in your dataset,
    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

    # If you need to make complex sub-parts in the datasets with configurable options
    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
    # BUILDER_CONFIG_CLASS = MyBuilderConfig

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('my_dataset', 'first_domain')
    # data = datasets.load_dataset('my_dataset', 'second_domain')
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="product_image_urls", version=VERSION, description="Image URLs for products"),
        datasets.BuilderConfig(name="product_features", version=VERSION, description="CLIP embeddings for products"),
        datasets.BuilderConfig(name="query_features", version=VERSION, description="CLIP embeddings for queries"),
        datasets.BuilderConfig(name="supp_product_image_urls", version=VERSION, description="Image URLs for supplemental set of products"),
    ]

    DEFAULT_CONFIG_NAME = "product_image_urls"

    def _info(self):
        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
        if self.config.name == "product_image_urls":  
            features = datasets.Features(
                {
                    "product_id": datasets.Value("string"),
                    "image_url": datasets.Value("string")
                }
            )
        elif self.config.name == "product_features":
            features = datasets.Features(
                {
                    "product_id": datasets.Value("string"),
                    "clip_text_features": datasets.Sequence(datasets.Value("float32")),
                    "clip_image_features": datasets.Sequence(datasets.Value("float32"))
                }
            )
        elif self.config.name == "query_features":
            features = datasets.Features(
                {
                    "query_id": datasets.Value("string"),
                    "clip_text_features": datasets.Sequence(datasets.Value("float32"))
                }
            )
        elif self.config.name == "product_features":
            features = datasets.Features(
                {
                    "product_id": datasets.Value("string"),
                    "image_url": datasets.Value("string")
                }
            )
        else:
            raise ValueError(f"Invalid configuration name: {self.config.name}")
        
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=features,  # Here we define them above because they are different between the two configurations
            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
            # supervised_keys=("sentence", "label"),
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
        urls = _URLS[self.config.name]
        data_path = dl_manager.download_and_extract(urls)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_path,
                    "split": "train",
                },
            ),
            #datasets.SplitGenerator(
            #    name=datasets.Split.VALIDATION,
            #    # These kwargs will be passed to _generate_examples
            #    gen_kwargs={
            #        "filepath": os.path.join(data_dir, "dev.jsonl"),
            #        "split": "dev",
            #    },
            #),
            #datasets.SplitGenerator(
            #    name=datasets.Split.TEST,
            #    # These kwargs will be passed to _generate_examples
            #    gen_kwargs={
            #        "filepath": os.path.join(data_dir, "test.jsonl"),
            #        "split": "test"
            #    },
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath, split):
        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
        with open(filepath, encoding="utf-8") as f:
            for key, row in enumerate(f):
                data = json.loads(row)
                if self.config.name == "product_image_urls":
                    # Yields examples as (key, example) tuples
                    yield key, {
                        "product_id": data["product_id"],
                        "image_url": data["image_url"]
                    }
                elif self.config_name == "product_features":
                    yield key, {
                        "product_id": data["product_id"],
                        "clip_text_features": data["clip_text_features"],
                        "clip_image_features": data["clip_image_features"],
                    }
                elif self.config_name == "query_features":
                    yield key, {
                        "query_id": data["query_id"],
                        "clip_text_features": data["clip_text_features"],
                    }
                elif self.config_name == "supp_product_image_urls":
                    yield key, {
                        "product_id": data["product_id"],
                        "image_url": data["image_url"]
                    }
                else:
                    raise ValueError(f"Unknown config name: {self.config_name}")