# 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()), }