# 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} | |