121 lines
4.2 KiB
Markdown
121 lines
4.2 KiB
Markdown
# Explaining framework
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PyTorch-Geometric add-on for **heavy and parallel** experiments running to explain Graph Neural Networks models. Based on a ```config.yaml``` file that you can set up as you wish, the framework do preprocess, handles simultaneous experiments as well as postprocess operations on his own.
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## How to
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1. Set up your experiment details (dataset, GNN architecture, explaining method, metrics, GPU workload limit, etc.).
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```python
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# ----------------------------------------------------------------------- #
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# Basic options
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# ----------------------------------------------------------------------- #
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# Set print destination: stdout / file / both
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explaining_cfg.print = "both"
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explaining_cfg.out_dir = "./explanations"
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explaining_cfg.cfg_dest = "explaining_config.yaml"
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explaining_cfg.seed = 0
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# ----------------------------------------------------------------------- #
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# Dataset options
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# ----------------------------------------------------------------------- #
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explaining_cfg.dataset = CN()
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explaining_cfg.dataset.name = "Cora"
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explaining_cfg.dataset.item = []
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# ----------------------------------------------------------------------- #
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# Model options
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# ----------------------------------------------------------------------- #
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explaining_cfg.model = CN()
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# Set wether or not load the best model for given dataset or a path
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explaining_cfg.model.ckpt = "best"
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# Setting the path of models folder
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explaining_cfg.model.path = "path"
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# ----------------------------------------------------------------------- #
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# Explainer options
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# ----------------------------------------------------------------------- #
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explaining_cfg.explainer = CN()
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# Name of the explaining method
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explaining_cfg.explainer.name = "EiXGNN"
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# Whether or not to provide specific explaining methods configuration or default configuration
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explaining_cfg.explainer.cfg = "default"
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# Whether or not recomputing explanation if they already exist
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explaining_cfg.explainer.force = False
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# ----------------------------------------------------------------------- #
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# Explaining options
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# ----------------------------------------------------------------------- #
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# 'ExplanationType : 'model' or 'phenomenon'
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explaining_cfg.explanation_type = "model"
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explaining_cfg.model_config = CN()
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# Do not modify it, will be handled by dataset , assuming one dataset = one learning task
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explaining_cfg.model_config.mode = "regression"
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# Do not modify it, will be handled by dataset , assuming one dataset = one learning task
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explaining_cfg.model_config.task_level = None
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# Do not modify it, we always assume here that model output are 'raw'
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explaining_cfg.model_config.return_type = "raw"
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# ----------------------------------------------------------------------- #
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# Thresholding options
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# ----------------------------------------------------------------------- #
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explaining_cfg.threshold = CN()
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explaining_cfg.threshold.config = CN()
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explaining_cfg.threshold.config.type = "all"
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explaining_cfg.threshold.value = CN()
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explaining_cfg.threshold.value.hard = [(i * 10) / 100 for i in range(10)]
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explaining_cfg.threshold.value.topk = [2, 3, 5, 10, 20, 30, 50]
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# which objectives metrics to computes, either all or one in particular if implemented
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explaining_cfg.metrics = CN()
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explaining_cfg.metrics.sparsity = CN()
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explaining_cfg.metrics.sparsity.name = "all"
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explaining_cfg.metrics.fidelity = CN()
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explaining_cfg.metrics.fidelity.name = "all"
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explaining_cfg.metrics.accuracy = CN()
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explaining_cfg.metrics.accuracy.name = "all"
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# Whether or not recomputing metrics if they already exist
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explaining_cfg.adjust = CN()
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explaining_cfg.adjust.strategy = "rpns"
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explaining_cfg.attack = CN()
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explaining_cfg.attack.name = "all"
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# Select device: 'cpu', 'cuda', 'auto'
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explaining_cfg.accelerator = "auto"
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```
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2. Provide the generated ```.yaml``` file to ```main.py``` or a folder path to a configs file stack for parallel running.
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3. Run
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4. Check already post-processed (statistics, plots) results.
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