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Merge pull request #20 from ChEB-AI/fix/inference-dependencies
move networkx call to read_data / reformatting with ruff
2 parents 21e4378 + cd2655d commit 8c2fd41

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‎.github/workflows/black.yml‎

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This file was deleted.

‎.github/workflows/lint.yml‎

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name: Lint
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on: [push, pull_request]
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jobs:
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lint:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v2
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- name: Set up Python
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uses: actions/setup-python@v4
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with:
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python-version: '3.10' # or any version your project uses
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install black==25.1.0 ruff==0.12.2
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- name: Run Black
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run: black --check .
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- name: Run Ruff (no formatting)
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run: ruff check . --no-fix

‎.pre-commit-config.yaml‎

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repos:
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#- repo: https://github.com/PyCQA/isort
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# rev: "5.12.0"
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# hooks:
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# - id: isort
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- repo: https://github.com/psf/black
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rev: "22.10.0"
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rev: "25.1.0"
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hooks:
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- id: black
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- id: black
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- id: black-jupyter # for formatting jupyter-notebook
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- repo: https://github.com/pycqa/isort
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rev: 5.13.2
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hooks:
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- id: isort
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name: isort (python)
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args: ["--profile=black"]
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- repo: https://github.com/asottile/seed-isort-config
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rev: v2.2.0
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hooks:
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- id: seed-isort-config
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- repo: https://github.com/pre-commit/pre-commit-hooks
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rev: v4.6.0
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hooks:
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- id: check-yaml
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- id: end-of-file-fixer
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- id: trailing-whitespace
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- repo: https://github.com/astral-sh/ruff-pre-commit
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rev: v0.12.2
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hooks:
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- id: ruff
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args: [--fix]

‎README.md‎

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## Installation
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To install this repository, download it and run
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To install this repository, download it and run
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```bash
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pip install .
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- `${TORCH}` with a PyTorch version (e.g., `2.6.0`; for later versions, check first if they are compatible with torch_scatter and torch_geometric)
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- `${CUDA}` with e.g. `cpu`, `cu118`, or `cu121` depending on your system and CUDA version
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If you already have `torch` installed, make sure that `torch_scatter` and `torch_geometric` are compatible with your
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If you already have `torch` installed, make sure that `torch_scatter` and `torch_geometric` are compatible with your
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PyTorch version and are installed with the same CUDA version.
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For a full list of currently available PyTorch versions and CUDA compatibility, please refer to libraries' official documentation:
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### Ontology Prediction
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This example command trains a Residual Gated Graph Convolutional Network on the ChEBI50 dataset (see [wiki](https://github.com/ChEB-AI/python-chebai/wiki/Data-Management)).
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The dataset has a customizable list of properties for atoms, bonds and molecules that are added to the graph.
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This example command trains a Residual Gated Graph Convolutional Network on the ChEBI50 dataset (see [wiki](https://github.com/ChEB-AI/python-chebai/wiki/Data-Management)).
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The dataset has a customizable list of properties for atoms, bonds and molecules that are added to the graph.
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The list can be found in the `configs/data/chebi50_graph_properties.yml` file.
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```bash
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python -m chebai fit --trainer=configs/training/default_trainer.yml --trainer.logger=configs/training/csv_logger.yml --model=../python-chebai-graph/configs/model/gnn_res_gated.yml --model.train_metrics=configs/metrics/micro-macro-f1.yml --model.test_metrics=configs/metrics/micro-macro-f1.yml --model.val_metrics=configs/metrics/micro-macro-f1.yml --data=../python-chebai-graph/configs/data/chebi50_graph_properties.yml --data.init_args.batch_size=128 --trainer.accumulate_grad_batches=4 --data.init_args.num_workers=10 --model.pass_loss_kwargs=false --data.init_args.chebi_version=241 --trainer.min_epochs=200 --trainer.max_epochs=200 --model.criterion=configs/loss/bce.yml
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```
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‎chebai_graph/models/gin_net.py‎

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import typing
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import torch
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import torch.nn.functional as F
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import torch_geometric
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from torch_scatter import scatter_add
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from chebai_graph.models.graph import GraphBaseNet
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import torch_geometric
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import torch.nn.functional as F
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import torch
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import typing
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class AggregateMLP(torch.nn.Module):

‎chebai_graph/preprocessing/__init__.py‎

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from chebai_graph.preprocessing.properties import *
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from chebai_graph.preprocessing.properties import (
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AtomAromaticity,
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AtomCharge,
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AtomChirality,
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AtomHybridization,
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AtomNumHs,
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AtomProperty,
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AtomType,
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BondAromaticity,
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BondInRing,
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BondProperty,
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BondType,
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MolecularProperty,
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MoleculeNumRings,
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MoleculeProperty,
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NumAtomBonds,
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RDKit2DNormalized,
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)
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__all__ = [
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"AtomAromaticity",
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"AtomCharge",
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"AtomChirality",
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"AtomHybridization",
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"AtomNumHs",
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"AtomProperty",
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"AtomType",
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"BondAromaticity",
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"BondInRing",
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"BondProperty",
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"BondType",
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"MolecularProperty",
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"MoleculeNumRings",
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"MoleculeProperty",
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"NumAtomBonds",
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"RDKit2DNormalized",
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]

‎chebai_graph/preprocessing/collate.py‎

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from typing import Dict
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import torch
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from chebai.preprocessing.collate import RaggedCollator
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from torch_geometric.data import Data as GeomData
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from torch_geometric.data.collate import collate as graph_collate
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from chebai_graph.preprocessing.structures import XYGraphData
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from chebai.preprocessing.collate import RaggedCollator
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from chebai_graph.preprocessing.structures import XYGraphData
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class GraphCollator(RaggedCollator):

‎chebai_graph/preprocessing/datasets/chebi.py‎

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features = [row["features"] for row in raw_data]
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# use vectorized version of encode function, apply only if value is present
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enc_if_not_none = lambda encode, value: (
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[encode(atom_v) for atom_v in value]
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if value is not None and len(value) > 0
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else None
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)
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def enc_if_not_none(encode, value):
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return (
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[encode(v) for v in value]
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if value is not None and len(value) > 0
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else None
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)
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for property in self.properties:
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if not os.path.isfile(self.get_property_path(property)):

‎chebai_graph/preprocessing/datasets/pubchem.py‎

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from chebai_graph.preprocessing.datasets.chebi import GraphPropertiesMixIn
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from chebai.preprocessing.datasets.pubchem import PubchemChem
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from chebai_graph.preprocessing.datasets.chebi import GraphPropertiesMixIn
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class PubChemGraphProperties(GraphPropertiesMixIn, PubchemChem):
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pass

‎chebai_graph/preprocessing/properties.py‎

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from descriptastorus.descriptors import rdNormalizedDescriptors
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from chebai_graph.preprocessing.property_encoder import (
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PropertyEncoder,
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IndexEncoder,
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OneHotEncoder,
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AsIsEncoder,
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BoolEncoder,
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IndexEncoder,
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OneHotEncoder,
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PropertyEncoder,
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)
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