Add ctu-toxicology
Browse files- ctu-toxicology/README.md +34 -0
- ctu-toxicology/db/atom.parquet +3 -0
- ctu-toxicology/db/bond.parquet +3 -0
- ctu-toxicology/db/connected.parquet +3 -0
- ctu-toxicology/db/molecule.parquet +3 -0
- ctu-toxicology/manifest.yaml +27 -0
- ctu-toxicology/schema.svg +103 -0
- ctu-toxicology/tasks/toxicology-original/manifest.yaml +9 -0
- ctu-toxicology/tasks/toxicology-original/test.parquet +3 -0
- ctu-toxicology/tasks/toxicology-original/train.parquet +3 -0
- ctu-toxicology/tasks/toxicology-original/val.parquet +3 -0
ctu-toxicology/README.md
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---
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tags:
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- relbench
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- relational-deep-learning
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pretty_name: ctu-toxicology
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---
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# ctu-toxicology
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Predictive Toxicology Challenge (2000) consists of more than three hundreds of organic molecules marked according to their carcinogenicity on male and female mice and rats. Sourced from the CTU Prague Relational Learning Repository (https://relational.fel.cvut.cz/).
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## Schema
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Open [`schema.svg`](schema.svg) for a zoomable view: each table lists all of its columns and types, with primary keys, foreign keys, and the foreign-key edges between tables.
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Splits: validation `2262-04-11`, test `2262-04-11` (rows up to a split's timestamp are the inputs for that split).
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## Tasks
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| task | kind | type | description |
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|---|---|---|---|
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| `toxicology-original` | external | binary_classification | Predict the binary `label` label of each `molecule`. (random split) |
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## Loading
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```python
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import relbench
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ds = relbench.load_dataset("ctu-toxicology")
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task = relbench.load_task("ctu-toxicology", "<task>")
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```
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Manifest layout (`manifest.yaml` + plain parquet); see the RelBench [CONTRIBUTING guide](https://github.com/snap-stanford/relbench/blob/main/CONTRIBUTING.md).
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ctu-toxicology/db/atom.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:d2fed265381d8b33bedcd67dace7165b75365ed6507b1134410d1199e89fef7e
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size 85595
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ctu-toxicology/db/bond.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:cc742c08f2e3d5f453768d90a2c9bca214f8c6f142e3780b853c3db1568e397a
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size 82908
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ctu-toxicology/db/connected.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:89aef4b61a281a82795ba831833655f9d8112deaf3a521bc951a660c48e294d4
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size 714258
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ctu-toxicology/db/molecule.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:00995b6492971e4b992ce2357a0d8b0c2a889994bbea761d622b3ca4d84cc646
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size 3519
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ctu-toxicology/manifest.yaml
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name: ctu-toxicology
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manifest_version: 1
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description: Predictive Toxicology Challenge (2000) consists of more than three hundreds of organic molecules marked according to their carcinogenicity on male and female mice and rats. Sourced from the CTU Prague Relational Learning Repository (https://relational.fel.cvut.cz/).
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val_timestamp: '2262-04-11'
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test_timestamp: '2262-04-11'
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tables:
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bond:
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pkey: __PK__
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time_col: null
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fkeys:
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FK_molecule_molecule_id: molecule
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connected:
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pkey: __PK__
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time_col: null
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fkeys:
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FK_atom_atom_id: atom
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FK_atom_atom_id2: atom
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FK_bond_bond_id: bond
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molecule:
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pkey: __PK__
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time_col: null
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fkeys: {}
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atom:
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pkey: __PK__
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time_col: null
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fkeys:
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FK_molecule_molecule_id: molecule
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ctu-toxicology/schema.svg
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ctu-toxicology/tasks/toxicology-original/manifest.yaml
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name: toxicology-original
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kind: external
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task_type: binary_classification
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description: Predict the binary `label` label of each `molecule`. (random split)
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entity_table: molecule
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entity_col: __PK__
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target_col: label
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timedelta: 0 days
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manifest_version: 1
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ctu-toxicology/tasks/toxicology-original/test.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:b8a502477bd354172b5ec9703f2bc15fc3186bc8fb39131d32281815be8f3bf9
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size 1945
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ctu-toxicology/tasks/toxicology-original/train.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:b9dd5f91f4b3b6bd5d42417457f15f4a1f8a0af84d1d0844c2144001bbba17a9
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size 3227
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ctu-toxicology/tasks/toxicology-original/val.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:5c63bd6ba2d0311af4bf8eae43997ab5f3d62ff413d96b98dbc72371b4190aa3
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size 1943
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