Adding config_gen.py
This commit is contained in:
araison
parent
1c62a5a561
commit
45ea7e4255
238
config_gen.py
Normal file
238
config_gen.py
Normal file
@ -0,0 +1,238 @@
|
||||
import glob
|
||||
import os
|
||||
import shutil
|
||||
import sys
|
||||
|
||||
from torch_geometric.data.makedirs import makedirs
|
||||
from torch_geometric.graphgym.loader import create_dataset
|
||||
from torch_geometric.graphgym.utils.io import string_to_python
|
||||
|
||||
from explaining_framework.utils.io import (obj_config_to_str, read_yaml,
|
||||
write_yaml)
|
||||
|
||||
# class BaseConfigGenerator(object):
|
||||
# def __init__(self,dataset_name:str,explainer_name:str, explainer_config:str, model_folder:str):
|
||||
# self.dataset_name=dataset_name
|
||||
# self.explainer_name=explainer_name
|
||||
# self.explainer_config = explainer_config
|
||||
# self.model_folder = model_folder
|
||||
|
||||
|
||||
# class ExplainingConfigGenerator(object):
|
||||
# def __init__(
|
||||
# self,
|
||||
# dataset_name: str,
|
||||
# explainer_name: str,
|
||||
# model_folder: str,
|
||||
# explainer_config: str = "default",
|
||||
# item: list = None,
|
||||
# ckpt: str = "best",
|
||||
# ):
|
||||
# self.dataset_name = dataset_name
|
||||
# self.explainer_name = explainer_name
|
||||
# self.explainer_config = explainer_config
|
||||
# self.item = item
|
||||
# self.ckpt = ckpt
|
||||
# self.model_folder = model_folder
|
||||
|
||||
|
||||
def explaining_conf(
|
||||
dataset: str, model_kind: str, explainer: str, explainer_config: str = "default"
|
||||
):
|
||||
explaining_cfg = {}
|
||||
explaining_cfg[
|
||||
"cfg_dest"
|
||||
] = f"dataset={dataset_name}-model={model_kind}-explainer={explainer_name}.yaml"
|
||||
explaining_cfg["dataset"] = {}
|
||||
explaining_cfg["dataset"]["name"] = dataset
|
||||
explaining_cfg["explainer"] = {}
|
||||
explaining_cfg["explainer"]["cfg"] = explainer_config
|
||||
explaining_cfg["explainer"]["name"] = explainer
|
||||
explaining_cfg["explainer"]["force"] = True
|
||||
explaining_cfg["explanation_type"] = "phenomenon"
|
||||
explaining_cfg["model"] = {}
|
||||
explaining_cfg["model"]["ckpt"] = model_kind
|
||||
explaining_cfg["model"]["path"] = sys.argv[1]
|
||||
# explaining_cfg['out_dir']='./explanation'
|
||||
explaining_cfg["threshold"] = {}
|
||||
explaining_cfg["threshold"]["value"] = {}
|
||||
explaining_cfg["threshold"]["value"]["hard"] = [0, 0.1, 0.3, 0.5, 0.7, 0.9]
|
||||
explaining_cfg["threshold"]["value"]["topk"] = [2, 5, 10, 20, 50]
|
||||
return explaining_cfg
|
||||
|
||||
|
||||
def explainer_conf(explainer: str, **kwargs):
|
||||
explaining_cfg = {}
|
||||
if explainer == "SCGNN":
|
||||
explaining_cfg["target_baseline"] = kwargs.get("target_baseline")
|
||||
explaining_cfg["depth"] = "all"
|
||||
explaining_cfg["score_map_norm"] = kwargs.get("score_map_norm")
|
||||
explaining_cfg["interest_map_norm"] = kwargs.get("interest_map_norm")
|
||||
elif explainer == "EIXGNN":
|
||||
explaining_cfg["L"] = kwargs.get("L")
|
||||
explaining_cfg["p"] = kwargs.get("p")
|
||||
explaining_cfg["importance_sampling_strategy"] = kwargs.get(
|
||||
"importance_sampling_strategy"
|
||||
)
|
||||
explaining_cfg["domain_similarity"] = kwargs.get("domain_similarity")
|
||||
explaining_cfg["signal_similarity"] = kwargs.get("signal_similarity")
|
||||
explaining_cfg["shap_val_approx"] = kwargs.get("shap_val_approx")
|
||||
return explaining_cfg
|
||||
|
||||
|
||||
if "__main__" == __name__:
|
||||
config_folder = os.path.abspath(
|
||||
os.path.join(os.path.abspath(os.path.dirname(__name__)), "configs")
|
||||
)
|
||||
makedirs(config_folder)
|
||||
explaining_folder = os.path.join(config_folder, "explaining")
|
||||
makedirs(explaining_folder)
|
||||
explainer_folder = os.path.join(config_folder, "explainer")
|
||||
makedirs(explainer_folder)
|
||||
|
||||
# TODO Make a single list for all dataset name or explaining method name, etc
|
||||
|
||||
DATASET = [
|
||||
"CIFAR10",
|
||||
"TRIANGLES",
|
||||
"COLORS-3",
|
||||
"REDDIT-BINARY",
|
||||
"REDDIT-MULTI-5K",
|
||||
"REDDIT-MULTI-12K",
|
||||
"COLLAB",
|
||||
"DBLP_v1",
|
||||
"COIL-DEL",
|
||||
"COIL-RAG",
|
||||
"Fingerprint",
|
||||
"Letter-high",
|
||||
"Letter-low",
|
||||
"Letter-med",
|
||||
"MSRC_9",
|
||||
"MSRC_21",
|
||||
"MSRC_21C",
|
||||
"DD",
|
||||
"ENZYMES",
|
||||
"PROTEINS",
|
||||
"QM9",
|
||||
"MUTAG",
|
||||
"Mutagenicity",
|
||||
"AIDS",
|
||||
"PATTERN",
|
||||
"CLUSTER",
|
||||
"MNIST",
|
||||
"CIFAR10",
|
||||
"TSP",
|
||||
"CSL",
|
||||
"KarateClub",
|
||||
"CS",
|
||||
"Physics",
|
||||
"BBBP",
|
||||
"Tox21",
|
||||
"HIV",
|
||||
"PCBA",
|
||||
"MUV",
|
||||
"BACE",
|
||||
"SIDER",
|
||||
"ClinTox",
|
||||
"AIFB",
|
||||
"AM",
|
||||
"MUTAG",
|
||||
"BGS",
|
||||
"FAUST",
|
||||
"DynamicFAUST",
|
||||
"ShapeNet",
|
||||
"ModelNet10",
|
||||
"ModelNet40",
|
||||
"PascalVOC-SP",
|
||||
"COCO-SP",
|
||||
]
|
||||
EXPLAINER = [
|
||||
"CAM",
|
||||
"GradCAM",
|
||||
# "GNN_LRP",
|
||||
"GradExplainer",
|
||||
"GuidedBackPropagation",
|
||||
"IntegratedGradients",
|
||||
# "PGExplainer",
|
||||
"PGMExplainer",
|
||||
"RandomExplainer",
|
||||
# "SubgraphX",
|
||||
"GraphMASK",
|
||||
"GNNExplainer",
|
||||
"EIXGNN",
|
||||
"SCGNN",
|
||||
]
|
||||
|
||||
for dataset_name in DATASET:
|
||||
for model_kind in ["best", "worst"]:
|
||||
for explainer_name in EXPLAINER:
|
||||
explainer_path = ["default"]
|
||||
explainer_config = [None]
|
||||
if explainer_name == "EIXGNN":
|
||||
explainer_config = []
|
||||
explainer_path = []
|
||||
for imp_str in ["node", "neighborhood", "no_prior"]:
|
||||
for dom_sim in ["relative_edge_density"]:
|
||||
for sig_sim in ["KL", "KL_sym"]:
|
||||
for sh_val in [1000]:
|
||||
for L in [5, 10, 15, 20, 30, 50]:
|
||||
for p in [0.2, 0.3, 0.5, 0.7]:
|
||||
config = explainer_conf(
|
||||
"EIXGNN",
|
||||
importance_sampling_strategy=imp_str,
|
||||
domain_similarity=dom_sim,
|
||||
signal_similarity=sig_sim,
|
||||
shap_val_approx=sh_val,
|
||||
L=L,
|
||||
p=p,
|
||||
)
|
||||
path_explainer = os.path.join(
|
||||
explainer_folder,
|
||||
"EIXGNN_"
|
||||
+ obj_config_to_str(config)
|
||||
+ ".yaml",
|
||||
)
|
||||
explainer_path.append(path_explainer)
|
||||
explainer_config.append(config)
|
||||
write_yaml(config, path_explainer)
|
||||
if explainer_name == "SCGNN":
|
||||
explainer_config = []
|
||||
explainer_path = []
|
||||
for target_baseline in [None, "inference"]:
|
||||
for depth in ["all"]:
|
||||
for sc_map in [True, False]:
|
||||
for in_map in [True, False]:
|
||||
config = explainer_conf(
|
||||
"SCGNN",
|
||||
target_baseline=target_baseline,
|
||||
depth=depth,
|
||||
score_map_norm=sc_map,
|
||||
interest_map_norm=in_map,
|
||||
)
|
||||
path_explainer = os.path.join(
|
||||
explainer_folder,
|
||||
"SCGNN_" + obj_config_to_str(config) + ".yaml",
|
||||
)
|
||||
explainer_path.append(path_explainer)
|
||||
explainer_config.append(config)
|
||||
write_yaml(config, path_explainer)
|
||||
for explainer_p, explainer_c in zip(explainer_path, explainer_config):
|
||||
explaining_cfg = explaining_conf(
|
||||
dataset=dataset_name,
|
||||
model_kind=model_kind,
|
||||
explainer=explainer_name,
|
||||
explainer_config=explainer_p,
|
||||
)
|
||||
if explainer_c is None:
|
||||
PATH = os.path.join(
|
||||
explaining_folder
|
||||
+ "/"
|
||||
+ f"dataset={dataset_name}-model={model_kind}-explainer={explainer_name}.yaml"
|
||||
)
|
||||
else:
|
||||
PATH = os.path.join(
|
||||
explaining_folder
|
||||
+ "/"
|
||||
+ f"dataset={dataset_name}-model={model_kind}-explainer={explainer_name}_{obj_config_to_str(explainer_c)}.yaml"
|
||||
)
|
||||
write_yaml(explaining_cfg, PATH)
|
||||
Loading…
Reference in New Issue
Block a user