239 lines
8.7 KiB
Python
239 lines
8.7 KiB
Python
import glob
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import os
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import shutil
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import sys
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from torch_geometric.data.makedirs import makedirs
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from torch_geometric.graphgym.loader import create_dataset
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from torch_geometric.graphgym.utils.io import string_to_python
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from explaining_framework.utils.io import (obj_config_to_str, read_yaml,
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write_yaml)
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# class BaseConfigGenerator(object):
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# def __init__(self,dataset_name:str,explainer_name:str, explainer_config:str, model_folder:str):
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# self.dataset_name=dataset_name
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# self.explainer_name=explainer_name
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# self.explainer_config = explainer_config
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# self.model_folder = model_folder
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# class ExplainingConfigGenerator(object):
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# def __init__(
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# self,
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# dataset_name: str,
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# explainer_name: str,
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# model_folder: str,
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# explainer_config: str = "default",
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# item: list = None,
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# ckpt: str = "best",
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# ):
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# self.dataset_name = dataset_name
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# self.explainer_name = explainer_name
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# self.explainer_config = explainer_config
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# self.item = item
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# self.ckpt = ckpt
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# self.model_folder = model_folder
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def explaining_conf(
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dataset: str, model_kind: str, explainer: str, explainer_config: str = "default"
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):
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explaining_cfg = {}
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explaining_cfg[
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"cfg_dest"
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] = f"dataset={dataset_name}-model={model_kind}-explainer={explainer_name}.yaml"
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explaining_cfg["dataset"] = {}
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explaining_cfg["dataset"]["name"] = dataset
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explaining_cfg["explainer"] = {}
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explaining_cfg["explainer"]["cfg"] = explainer_config
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explaining_cfg["explainer"]["name"] = explainer
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explaining_cfg["explainer"]["force"] = True
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explaining_cfg["explanation_type"] = "phenomenon"
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explaining_cfg["model"] = {}
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explaining_cfg["model"]["ckpt"] = model_kind
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explaining_cfg["model"]["path"] = sys.argv[1]
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# explaining_cfg['out_dir']='./explanation'
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explaining_cfg["threshold"] = {}
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explaining_cfg["threshold"]["value"] = {}
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explaining_cfg["threshold"]["value"]["hard"] = [0, 0.1, 0.3, 0.5, 0.7, 0.9]
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explaining_cfg["threshold"]["value"]["topk"] = [2, 5, 10, 20, 50]
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return explaining_cfg
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def explainer_conf(explainer: str, **kwargs):
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explaining_cfg = {}
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if explainer == "SCGNN":
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explaining_cfg["target_baseline"] = kwargs.get("target_baseline")
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explaining_cfg["depth"] = "all"
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explaining_cfg["score_map_norm"] = kwargs.get("score_map_norm")
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explaining_cfg["interest_map_norm"] = kwargs.get("interest_map_norm")
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elif explainer == "EIXGNN":
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explaining_cfg["L"] = kwargs.get("L")
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explaining_cfg["p"] = kwargs.get("p")
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explaining_cfg["importance_sampling_strategy"] = kwargs.get(
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"importance_sampling_strategy"
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)
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explaining_cfg["domain_similarity"] = kwargs.get("domain_similarity")
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explaining_cfg["signal_similarity"] = kwargs.get("signal_similarity")
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explaining_cfg["shap_val_approx"] = kwargs.get("shap_val_approx")
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return explaining_cfg
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if "__main__" == __name__:
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config_folder = os.path.abspath(
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os.path.join(os.path.abspath(os.path.dirname(__name__)), "configs")
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)
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makedirs(config_folder)
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explaining_folder = os.path.join(config_folder, "explaining")
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makedirs(explaining_folder)
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explainer_folder = os.path.join(config_folder, "explainer")
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makedirs(explainer_folder)
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# TODO Make a single list for all dataset name or explaining method name, etc
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DATASET = [
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"CIFAR10",
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"TRIANGLES",
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"COLORS-3",
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"REDDIT-BINARY",
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"REDDIT-MULTI-5K",
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"REDDIT-MULTI-12K",
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"COLLAB",
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"DBLP_v1",
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"COIL-DEL",
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"COIL-RAG",
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"Fingerprint",
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"Letter-high",
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"Letter-low",
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"Letter-med",
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"MSRC_9",
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"MSRC_21",
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"MSRC_21C",
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"DD",
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"ENZYMES",
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"PROTEINS",
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"QM9",
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"MUTAG",
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"Mutagenicity",
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"AIDS",
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"PATTERN",
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"CLUSTER",
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"MNIST",
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"CIFAR10",
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"TSP",
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"CSL",
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"KarateClub",
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"CS",
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"Physics",
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"BBBP",
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"Tox21",
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"HIV",
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"PCBA",
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"MUV",
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"BACE",
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"SIDER",
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"ClinTox",
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"AIFB",
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"AM",
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"MUTAG",
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"BGS",
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"FAUST",
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"DynamicFAUST",
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"ShapeNet",
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"ModelNet10",
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"ModelNet40",
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"PascalVOC-SP",
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"COCO-SP",
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]
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EXPLAINER = [
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"CAM",
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"GradCAM",
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# "GNN_LRP",
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"GradExplainer",
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"GuidedBackPropagation",
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"IntegratedGradients",
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# "PGExplainer",
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"PGMExplainer",
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"RandomExplainer",
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# "SubgraphX",
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"GraphMASK",
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"GNNExplainer",
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"EIXGNN",
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"SCGNN",
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]
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for dataset_name in DATASET:
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for model_kind in ["best", "worst"]:
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for explainer_name in EXPLAINER:
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explainer_path = ["default"]
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explainer_config = [None]
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if explainer_name == "EIXGNN":
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explainer_config = []
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explainer_path = []
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for imp_str in ["node", "neighborhood", "no_prior"]:
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for dom_sim in ["relative_edge_density"]:
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for sig_sim in ["KL", "KL_sym"]:
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for sh_val in [1000]:
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for L in [5, 10, 15, 20, 30, 50]:
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for p in [0.2, 0.3, 0.5, 0.7]:
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config = explainer_conf(
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"EIXGNN",
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importance_sampling_strategy=imp_str,
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domain_similarity=dom_sim,
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signal_similarity=sig_sim,
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shap_val_approx=sh_val,
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L=L,
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p=p,
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)
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path_explainer = os.path.join(
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explainer_folder,
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"EIXGNN_"
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+ obj_config_to_str(config)
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+ ".yaml",
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)
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explainer_path.append(path_explainer)
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explainer_config.append(config)
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write_yaml(config, path_explainer)
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if explainer_name == "SCGNN":
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explainer_config = []
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explainer_path = []
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for target_baseline in [None, "inference"]:
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for depth in ["all"]:
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for sc_map in [True, False]:
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for in_map in [True, False]:
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config = explainer_conf(
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"SCGNN",
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target_baseline=target_baseline,
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depth=depth,
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score_map_norm=sc_map,
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interest_map_norm=in_map,
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)
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path_explainer = os.path.join(
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explainer_folder,
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"SCGNN_" + obj_config_to_str(config) + ".yaml",
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)
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explainer_path.append(path_explainer)
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explainer_config.append(config)
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write_yaml(config, path_explainer)
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for explainer_p, explainer_c in zip(explainer_path, explainer_config):
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explaining_cfg = explaining_conf(
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dataset=dataset_name,
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model_kind=model_kind,
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explainer=explainer_name,
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explainer_config=explainer_p,
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)
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if explainer_c is None:
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PATH = os.path.join(
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explaining_folder
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+ "/"
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+ f"dataset={dataset_name}-model={model_kind}-explainer={explainer_name}.yaml"
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)
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else:
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PATH = os.path.join(
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explaining_folder
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+ "/"
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+ f"dataset={dataset_name}-model={model_kind}-explainer={explainer_name}_{obj_config_to_str(explainer_c)}.yaml"
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)
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write_yaml(explaining_cfg, PATH)
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