# coding=utf-8 # 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: Add a description here.""" from datasets import features import pandas import os import datasets # TODO: Add BibTeX citation # Find for instance the citation on arxiv or on the dataset repo/website _CITATION = "" # TODO: Add description of the dataset here # You can copy an official description _DESCRIPTION = """\ The Cora dataset consists of 2708 scientific publications classified into one of seven classes. The citation network consists of 5429 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 1433 unique words. """ # TODO: Add a link to an official homepage for the dataset here _HOMEPAGE = "https://graphsandnetworks.com/the-cora-dataset/" # TODO: Add the licence for the dataset here if you can find it _LICENSE = "" # TODO: Add link to the official dataset URLs here # The HuggingFace dataset library don't host the datasets but only point to the original files # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) _URLs = { "nodes": "https://linqs-data.soe.ucsc.edu/public/lbc/cora.tgz", "edges": "https://linqs-data.soe.ucsc.edu/public/lbc/cora.tgz" } # TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case class CoraDataset(datasets.GeneratorBasedBuilder): """ This dataset is the MNIST equivalent in graph learning and we explore it somewhat explicitly here in function of other articles using again and again this dataset as a testbed.""" VERSION = datasets.Version("1.0.1") # 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="nodes", version=VERSION, description="The Cora dataset"), datasets.BuilderConfig(name="edges", version=VERSION, description="The Cora network") ] # It's not mandatory to have a default configuration. Just use one if it make sense. DEFAULT_CONFIG_NAME = "nodes" def _info(self): # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset # This is the name of the configuration selected in BUILDER_CONFIGS above if self.config.name == "nodes": features_dict = { f"word{i}": datasets.Value("bool") for i in range(1433) } features_dict["node"] = datasets.Value("string") features_dict["label"] = datasets.ClassLabel(names=[ "Case_Based", "Genetic_Algorithms", "Neural_Networks", "Probabilistic_Methods", "Reinforcement_Learning", "Rule_Learning", "Theory" ]) features_dict["neighbors"] = datasets.Sequence( datasets.Value("string") ) features = datasets.Features(features_dict) elif self.config.name == "edges": # This is an example to show how to have different features for "first_domain" and "second_domain" features = datasets.Features( { "source": datasets.Value("string"), "target": datasets.Value("string") } ) 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 # Here we define them above because they are different between the two configurations features=features, # If there's a common (input, target) tuple from the features, # specify them here. They'll be used if as_supervised=True in # builder.as_dataset. supervised_keys=None, # 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): """Returns SplitGenerators.""" # 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 my_urls = _URLs[self.config.name] data_dir = dl_manager.download_and_extract(my_urls) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "edges_path": os.path.join(data_dir, "cora", "cora.cites"), "nodes_path": os.path.join(data_dir, "cora", "cora.content"), "split": "train" } ) ] def _generate_examples( # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` self, edges_path, nodes_path, split ): """ Yields examples as (key, example) tuples. """ # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. # The `key` is here for legacy reason (tfds) and is not important in itself. if self.config.name == "nodes": neighbors = {} with open(edges_path, "rt", encoding="UTF-8") as f: for line in f: target, src = line.strip().split() for n in (target, src): if n not in neighbors: neighbors[n] = [] neighbors[src].append(target) colnames = ["node"] + [f"word{i}" for i in range(1433)] + ["label"] dtypes = [str] + [bool] * 1433 + [str] nodes = pandas.read_csv( nodes_path, sep="\t", header=None, names=colnames, dtype=dict(zip(colnames, dtypes)) ) col2idx = {col: i for i, col in enumerate(list(nodes))} for id, row in enumerate(nodes.itertuples(index=False, name=None)): n = row[col2idx["node"]] features = { "node": n, "label": row[col2idx["label"]], "neighbors": neighbors[n] } for i in range(1433): feature_name = f"word{i}" features[feature_name] = row[col2idx[feature_name]] yield id, features elif self.config.name == "edges": with open(edges_path, "rt", encoding="UTF-8") as f: for id, line in enumerate(f): target, src = line.strip().split() yield id, {"source": src, "target": target}