Adding XGWT configs generation
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araison
parent
27e8a8a4d8
commit
408bab4bc4
129
config_gen.py
129
config_gen.py
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@ -10,6 +10,13 @@ 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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def chunks(lst, n):
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"""Yield successive n-sized chunks from lst."""
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for i in range(0, len(lst), n):
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yield lst[i : i + n]
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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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@ -68,6 +75,7 @@ def explainer_conf(explainer: str, **kwargs):
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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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@ -77,6 +85,23 @@ def explainer_conf(explainer: str, **kwargs):
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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["shapley_value_approx"] = kwargs.get("shapley_value_approx")
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elif explainer == "XGWT":
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explaining_cfg["wav_approx"] = kwargs.get("wav_approx")
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explaining_cfg["wav_passband"] = kwargs.get("wav_passband")
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explaining_cfg["wav_norm"] = kwargs.get("wav_norm")
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explaining_cfg["candidates"] = kwargs.get("candidates")
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explaining_cfg["samples"] = kwargs.get("samples")
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explaining_cfg["c_proc"] = kwargs.get("c_proc")
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explaining_cfg["pred_thres_strat"] = kwargs.get("pred_thres_strat")
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explaining_cfg["CI_thres"] = kwargs.get("CI_thres")
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explaining_cfg["mix"] = kwargs.get("mix")
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explaining_cfg["scales"] = kwargs.get("scales")
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explaining_cfg["pred_thres"] = kwargs.get("pred_thres")
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explaining_cfg["incl_prob"] = kwargs.get("incl_prob")
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explaining_cfg["top_k"] = kwargs.get("top_k")
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explaining_cfg["get_DAG"] = kwargs.get("get_DAG")
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return explaining_cfg
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@ -161,6 +186,7 @@ if "__main__" == __name__:
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"GNNExplainer",
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"EIXGNN",
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"SCGNN",
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"XGWT",
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]
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for dataset_name in DATASET:
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@ -193,6 +219,8 @@ if "__main__" == __name__:
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+ ".yaml",
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)
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explainer_path.append(path_explainer)
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if os.path.exists(path_explainer):
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continue
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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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@ -215,7 +243,89 @@ if "__main__" == __name__:
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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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if os.path.exists(path_explainer):
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continue
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write_yaml(config, path_explainer)
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if explainer_name == "XGWT":
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explainer_config = []
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explainer_path = []
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for wav_approx in [False]:
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for wav_passband in ["heat"]:
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for wav_norm in [True]:
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for candidates in [10, 15, 30, 50]:
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for samples in [10, 25, 50]:
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for c_proc in ["auto"]:
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for pred_thres_strat in ["regular"]:
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for CI_thres in [0.05]:
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for mix in ["uniform"]:
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for scales in [
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[2],
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[3],
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[5],
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[9],
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[2, 3, 5],
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[2, 3, 5, 9],
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[5, 9],
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[2, 3],
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]:
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for pred_thres in [
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0.1,
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0.25,
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0.5,
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]:
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for incl_prob in [
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0.2,
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0.4,
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0.6,
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]:
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for top_k in [
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2,
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5,
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10,
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]:
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for get_DAG in [
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False
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]:
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config = explainer_conf(
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"XGWT",
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wav_approx=wav_approx,
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wav_passband=wav_passband,
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wav_norm=wav_norm,
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candidates=candidates,
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samples=samples,
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c_proc=c_proc,
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pred_thres_strat=pred_thres_strat,
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CI_thres=CI_thres,
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mix=mix,
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scales=scales,
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pred_thres=pred_thres,
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incl_prob=incl_prob,
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top_k=top_k,
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get_DAG=get_DAG,
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)
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path_explainer = os.path.join(
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explainer_folder,
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"XGWT_"
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+ obj_config_to_str(
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config
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)
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+ ".yaml",
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)
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explainer_path.append(
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path_explainer
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)
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explainer_config.append(
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config
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)
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if os.path.exists(
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path_explainer
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):
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continue
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write_yaml(
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config,
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path_explainer,
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)
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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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@ -236,16 +346,15 @@ if "__main__" == __name__:
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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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os.makedirs(explaining_folder + "/0", exist_ok=True)
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os.makedirs(explaining_folder + "/1", exist_ok=True)
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a = glob.glob(explaining_folder + "/*.yaml")
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a = sorted(glob.glob(explaining_folder + "/*.yaml"))
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for path in a[:8050]:
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num_GPU = 4
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for i in range(num_GPU):
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os.makedirs(explaining_folder + f"/{i}", exist_ok=True)
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split_size = int(len(a) / num_GPU)
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data = chunks(a, split_size)
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for i, d in enumerate(data):
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for path in d:
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basename = os.path.basename(path)
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dirname = os.path.dirname(path)
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os.rename(path, dirname + "/0/" + basename)
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for path in a[8050:]:
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basename = os.path.basename(path)
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dirname = os.path.dirname(path)
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os.rename(path, dirname + "/1/" + basename)
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os.rename(path, dirname + f"/{i}/" + basename)
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@ -38,17 +38,18 @@ def set_xgwt_cfg(xgwt_cfg):
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xgwt_cfg.wav_approx = False
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xgwt_cfg.wav_passband = "heat"
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xgwt_cfg.wav_normalization = True
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xgwt_cfg.num_candidates = 30
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xgwt_cfg.num_samples = 10
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xgwt_cfg.c_procedure = "auto"
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xgwt_cfg.wav_norm = True
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xgwt_cfg.candidates = 30
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xgwt_cfg.samples = 10
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xgwt_cfg.c_proc = "auto"
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xgwt_cfg.pred_thres_strat = "regular"
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xgwt_cfg.CI_threshold = 0.05
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xgwt_cfg.mixing = "uniform"
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xgwt_cfg.CI_thres = 0.05
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xgwt_cfg.mix = "uniform"
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xgwt_cfg.scales = [3]
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xgwt_cfg.pred_thres = 0.1
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xgwt_cfg.incl_prob = 0.4
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xgwt_cfg.top_k = 5
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xgwt_cfg.get_DAG = False
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def assert_cfg(xgwt_cfg):
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@ -300,16 +300,17 @@ class ExplainingOutline(object):
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explaining_algorithm = XGWT(
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wav_approx=self.explainer_cfg.wav_approx,
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wav_passband=self.explainer_cfg.wav_passband,
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wav_normalization=self.explainer_cfg.wav_normalization,
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num_candidates=self.explainer_cfg.num_candidates,
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num_samples=self.explainer_cfg.num_samples,
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c_procedure=self.explainer_cfg.c_procedure,
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wav_norm=self.explainer_cfg.wav_norm,
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candidates=self.explainer_cfg.candidates,
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samples=self.explainer_cfg.samples,
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c_proc=self.explainer_cfg.c_proc,
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pred_thres_strat=self.explainer_cfg.pred_thres_strat,
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CI_threshold=self.explainer_cfg.CI_threshold,
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mixing=self.explainer_cfg.mixing,
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CI_thres=self.explainer_cfg.CI_thres,
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mix=self.explainer_cfg.mix,
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pred_thres=self.explainer_cfg.pred_thres,
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incl_prob=self.explainer_cfg.incl_prob,
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top_k=self.explainer_cfg.top_k,
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get_DAG=self.explainer_cfg.get_DAG,
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scales=self.explainer_cfg.scales,
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)
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elif name == "SCGNN":
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