clueweb_reco.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



import csv
import json
import os
from decimal import Decimal

import datasets

# TODO: citation 
# # Find for instance the citation on arxiv or on the dataset repo/website
# _CITATION = """\
# @InProceedings{huggingface:dataset,
# title = {A great new dataset},
# author={huggingface, Inc.
# },
# year={2020}
# }
# """
_CITATION = ""

# 
_DESCRIPTION = """\
ClueWeb-Reco is a novel zero-shot test set derived from real, \
    consented user browsing sequences, 
    aligning with modern recommendation scenarios while ensuring privacy.
"""

_HOMEPAGE = "https://huggingface.co/datasets/cx-cmu/ClueWeb-Reco"

_LICENSE = "mit"

# 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)
_URLS = {
    "input": "https://huggingface.co/datasets/cx-cmu/ClueWeb-Reco/tree/main/interaction_splits",
    "target": "https://huggingface.co/datasets/cx-cmu/ClueWeb-Reco/tree/main/interaction_splits",
    "mapping": "https://huggingface.co/datasets/cx-cmu/ClueWeb-Reco/tree/main",
}


class ClueWebRecoDataset(datasets.GeneratorBasedBuilder):
    """Process the ClueWeb-Reco zero-shot dataset"""

    VERSION = datasets.Version("1.1.0")

    # 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="input", version=VERSION, description="This is the input parts of the dataset"),
        datasets.BuilderConfig(name="target", version=VERSION, description="This is the target parts of the dataset"),
        datasets.BuilderConfig(name="mapping", version=VERSION, description="This is the mapping between official ClueWeb ids and our internal ClueWeb ids"),
    ]

    DEFAULT_CONFIG_NAME = "input"  

    def _info(self):
        if self.config.name == "input":  # This is the name of the configuration selected in BUILDER_CONFIGS above
            features = datasets.Features(
                {
                    "session_id": datasets.Value("string"),
                    "cw_internal_id": datasets.Value("int32"),
                    "timestamp": datasets.Value("string")
                }
            )
        elif self.config.name == "target":
            features = datasets.Features(
                {
                    "session_id": datasets.Value("string"),
                    "target_cw_internal_id": datasets.Value("int32"),
                    "timestamp": datasets.Value("string")
                }
            )
        elif self.config.name == "mapping": 
            features = datasets.Features(
                {
                    "cwid": datasets.Value("string"),
                    "cw_internal_id": datasets.Value("int32"),
                }
            )
        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_dir = dl_manager.download_and_extract(urls)
        data_dir = self.config.data_dir 

        if self.config.name == "input": 
            return [
                datasets.SplitGenerator(
                    name=datasets.Split.VALIDATION,
                    gen_kwargs={
                        "input_path": os.path.join(data_dir, "interaction_splits/valid_inter_input.tsv"),
                    }
                ),
                datasets.SplitGenerator(
                    name=datasets.Split.TEST,
                    gen_kwargs={
                        "input_path": os.path.join(data_dir, "interaction_splits/test_inter_input.tsv"),
                    },
                ),
            ]
        elif self.config.name == "target": 
            return [
                datasets.SplitGenerator(
                    name=datasets.Split.VALIDATION,
                    gen_kwargs={
                        "target_path": os.path.join(data_dir, "interaction_splits/valid_inter_target.tsv"),
                    },
                ),
            ]

        elif self.config.name == "mapping": 
            return [
                datasets.SplitGenerator(
                    name=datasets.Split.TRAIN,
                    gen_kwargs={
                        "mapping_path": os.path.join(data_dir, "cwid_to_id.tsv"),
                    },
                ),
            ]
    

    def _generate_examples(self, input_path=None, target_path=None, mapping_path=None):
        """
        Generates examples based on the input and (optionally) target files.
        If the configuration is `input`, `target`, or `mapping`, this handles each separately.
        """

        if self.config.name == "input":
            if input_path is None:
                raise ValueError("Input configuration requires an input_path.")
            
            # Process the `input` configuration
            with open(input_path, encoding="utf-8") as f:
                input_lines = f.readlines()[1:]
            for idx, line in enumerate(input_lines):
                session_id, cw_internal_id, timestamp = line.strip().split("\t")
                yield idx, {
                    "session_id": session_id.strip(),
                    "cw_internal_id": int(cw_internal_id.strip()),
                    "timestamp": str(Decimal(timestamp)),
                }
        
        elif self.config.name == "target":
            # if target_path is None:
            #     # Test target is hidden; yield nothing
            #     return
            
            if target_path is None:
                raise ValueError("Target configuration requires an target_path.")
            
            with open(target_path, encoding="utf-8") as f:
                target_lines = f.readlines()[1:]
                for idx, line in enumerate(target_lines):
                    session_id, target_cw_internal_id, timestamp = line.strip().split("\t")
                    yield idx, {
                        "session_id": session_id.strip(),
                        "target_cw_internal_id": int(target_cw_internal_id.strip()),
                        "timestamp": str(Decimal(timestamp)),
                    }

        elif self.config.name == "mapping":

            if mapping_path is None:
                raise ValueError("Mapping configuration requires an mapping_path.")
    
            # Process the `mapping` configuration
            with open(mapping_path, encoding="utf-8") as f:
                for idx, line in enumerate(f):
                    cwid, cw_internal_id = line.strip().split("\t")
                    yield idx, {
                        "cwid": cwid.strip(),
                        "cw_internal_id": int(cw_internal_id.strip()),
                    }