| # 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 CiteSeer dataset consists of 3312 scientific publications classified into one of six classes. The citation network consists of 4732 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 3703 unique words. The README file in the dataset provides more details. | |
| """ | |
| # TODO: Add a link to an official homepage for the dataset here | |
| _HOMEPAGE = "https://linqs.soe.ucsc.edu/data" | |
| # 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/citeseer.tgz", | |
| "edges": "https://linqs-data.soe.ucsc.edu/public/lbc/citeseer.tgz" | |
| } | |
| _CLASS_LABELS = [ | |
| "Agents", | |
| "AI", | |
| "DB", | |
| "IR", | |
| "ML", | |
| "HCI" | |
| ] | |
| # TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case | |
| class CiteseerDataset(datasets.GeneratorBasedBuilder): | |
| 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 CiteSeer dataset"), | |
| datasets.BuilderConfig(name="edges", version=VERSION, | |
| description="The CiteSeer network") | |
| ] | |
| # It's not mandatory to have a default configuration. Just use one if it make sense. | |
| DEFAULT_CONFIG_NAME = "nodes" | |
| def _info(self): | |
| if self.config.name == "nodes": | |
| word_features = [f"word{i}" for i in range(3703)] | |
| features_dict = { | |
| w: datasets.Value("bool") | |
| for w in word_features | |
| } | |
| features_dict["node"] = datasets.Value("string") | |
| features_dict["label"] = datasets.ClassLabel(names=_CLASS_LABELS) | |
| 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) | |
| data_dir = os.path.join(data_dir, "citeseer") | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "edges_path": os.path.join(data_dir, "citeseer.cites"), | |
| "nodes_path": os.path.join(data_dir, "citeseer.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(3703)] + ["label"] | |
| dtypes = [str] + [bool] * 3703 + [str] | |
| nodes = pandas.read_csv( | |
| nodes_path, | |
| sep="\t", | |
| header=None, | |
| names=colnames, | |
| dtype=dict(zip(colnames, dtypes)) | |
| ) | |
| # Some nodes appears in the edgelist but not in the dataset, so we rm them | |
| all_nodes = set(neighbors.keys()) | |
| for vv in neighbors.values(): | |
| all_nodes.update(set(vv)) | |
| nodes_to_rm = all_nodes.difference(nodes["node"]) | |
| for n in neighbors.keys(): | |
| neighbors[n] = list(set(neighbors[n]).difference(nodes_to_rm)) | |
| 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(3703): | |
| 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} | |