Update parquet files

.gitattributes

@@ -1,51 +0,0 @@
-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.lz4 filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
-# Audio files - uncompressed
-*.pcm filter=lfs diff=lfs merge=lfs -text
-*.sam filter=lfs diff=lfs merge=lfs -text
-*.raw filter=lfs diff=lfs merge=lfs -text
-# Audio files - compressed
-*.aac filter=lfs diff=lfs merge=lfs -text
-*.flac filter=lfs diff=lfs merge=lfs -text
-*.mp3 filter=lfs diff=lfs merge=lfs -text
-*.ogg filter=lfs diff=lfs merge=lfs -text
-*.wav filter=lfs diff=lfs merge=lfs -text
-# Image files - uncompressed
-*.bmp filter=lfs diff=lfs merge=lfs -text
-*.gif filter=lfs diff=lfs merge=lfs -text
-*.png filter=lfs diff=lfs merge=lfs -text
-*.tiff filter=lfs diff=lfs merge=lfs -text
-# Image files - compressed
-*.jpg filter=lfs diff=lfs merge=lfs -text
-*.jpeg filter=lfs diff=lfs merge=lfs -text
-*.webp filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,115 +0,0 @@
----
-licence: unknown
----
-
-# Dataset Card for AIDS
-
-## Table of Contents
-- [Table of Contents](#table-of-contents)
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-- [External Use](#external-use)
-  - [PyGeometric](#pygeometric)
-- [Dataset Structure](#dataset-structure)
-  - [Data Properties](#data-properties)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Additional Information](#additional-information)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-
-## Dataset Description
-- **[Homepage](https://wiki.nci.nih.gov/display/NCIDTPdata/AIDS+Antiviral+Screen+Data)**
-- **Paper:**:  (see citation)
-- **Leaderboard:**: [Papers with code leaderboard](https://paperswithcode.com/sota/graph-classification-on-aids)
-
-### Dataset Summary
-The `AIDS` dataset is a dataset containing compounds checked for evidence of anti-HIV activity..
-
- ### Supported Tasks and Leaderboards
-`AIDS` should be used for molecular classification, a binary classification task. The score used is accuracy with cross validation.
-
-
-## External Use
-### PyGeometric
-To load in PyGeometric, do the following:
-
-```python
-from datasets import load_dataset
-
-from torch_geometric.data import Data
-from torch_geometric.loader import DataLoader
-
-dataset_hf = load_dataset("graphs-datasets/<mydataset>")
-# For the train set (replace by valid or test as needed)
-dataset_pg_list = [Data(graph) for graph in dataset_hf["train"]]
-dataset_pg = DataLoader(dataset_pg_list)
-```
-
-## Dataset Structure
-
-### Data Properties
-| property | value |
-|---|---|
-| scale | medium |
-| #graphs | 1999 |
-| average #nodes | 15.5875 |
-| average #edges | 32.39 |
-
-### Data Fields
-
-Each row of a given file is a graph, with: 
-- `node_feat` (list: #nodes x #node-features): nodes
-- `edge_index` (list: 2 x #edges): pairs of nodes constituting edges
-- `edge_attr` (list: #edges x #edge-features): for the aforementioned edges, contains their features
-- `y` (list: 1 x #labels): contains the number of labels available to predict (here 1, equal to zero or one)
-- `num_nodes` (int): number of nodes of the graph
-
-### Data Splits
-
-This data is not split, and should be used with cross validation. It comes from the PyGeometric version of the dataset.
-
-## Additional Information
-
-### Licensing Information
-The dataset has been released under license unknown.
-
-### Citation Information
-```
-@inproceedings{Morris+2020,
-    title={TUDataset: A collection of benchmark datasets for learning with graphs},
-    author={Christopher Morris and Nils M. Kriege and Franka Bause and Kristian Kersting and Petra Mutzel and Marion Neumann},
-    booktitle={ICML 2020 Workshop on Graph Representation Learning and Beyond (GRL+ 2020)},
-    archivePrefix={arXiv},
-    eprint={2007.08663},
-    url={www.graphlearning.io},
-    year={2020}
-}
-```
-
-```
-
-@InProceedings{10.1007/978-3-540-89689-0_33,
-    author="Riesen, Kaspar
-    and Bunke, Horst",
-    editor="da Vitoria Lobo, Niels
-    and Kasparis, Takis
-    and Roli, Fabio
-    and Kwok, James T.
-    and Georgiopoulos, Michael
-    and Anagnostopoulos, Georgios C.
-    and Loog, Marco",
-    title="IAM Graph Database Repository for Graph Based Pattern Recognition and Machine Learning",
-    booktitle="Structural, Syntactic, and Statistical Pattern Recognition",
-    year="2008",
-    publisher="Springer Berlin Heidelberg",
-    address="Berlin, Heidelberg",
-    pages="287--297",
-    abstract="In recent years the use of graph based representation has gained popularity in pattern recognition and machine learning. As a matter of fact, object representation by means of graphs has a number of advantages over feature vectors. Therefore, various algorithms for graph based machine learning have been proposed in the literature. However, in contrast with the emerging interest in graph based representation, a lack of standardized graph data sets for benchmarking can be observed. Common practice is that researchers use their own data sets, and this behavior cumbers the objective evaluation of the proposed methods. In order to make the different approaches in graph based machine learning better comparable, the present paper aims at introducing a repository of graph data sets and corresponding benchmarks, covering a wide spectrum of different applications.",
-    isbn="978-3-540-89689-0"
-}
-
-
-```

dataset_infos.json

@@ -1 +0,0 @@
-{"graphs-datasets--AIDS": {"description": "", "citation": "", "homepage": "", "license": "", "features": {"edge_index": {"feature": {"feature": {"dtype": "int64", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}, "node_feat": {"feature": {"feature": {"dtype": "float64", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}, "edge_attr": {"feature": {"feature": {"dtype": "float64", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}, "y": {"feature": {"dtype": "int64", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "num_nodes": {"dtype": "int64", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "json", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"full": {"name": "full", "num_bytes": 12596900, "num_examples": 2000, "dataset_name": "AIDS"}}, "download_checksums": null, "download_size": 166463, "post_processing_size": null, "dataset_size": 12596900, "size_in_bytes": 12763363}}

data/full-00000-of-00001-2a1e5fb28f54a227.parquet → graphs-datasets--AIDS/parquet-full.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:13e78678aadb2644ba9d6d3f99c2f16a5ac9d49a82cf8a772bdc06896cd0ad36
-size 166463
+oid sha256:f5559f1936cc71a37f96081090685bcaacce3a5d7b6bfd7cfd5724edc6a4b135
+size 169738