Add loading script

cora.py

@@ -0,0 +1,183 @@
+# 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("1.0.0")
+
+    # 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="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.Value("string")
+            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}