Reformating, fixing
This commit is contained in:
araison
parent
d9628ff947
commit
7e32d6fd3a
@ -57,7 +57,7 @@ def set_cfg(explaining_cfg):
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explaining_cfg.dataset.name = "Cora"
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explaining_cfg.dataset.specific_items = None
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explaining_cfg.dataset.items = None
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explaining_cfg.run_topological_stat = True
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@ -110,26 +110,33 @@ def set_cfg(explaining_cfg):
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# Thresholding options
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# ----------------------------------------------------------------------- #
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explaining_cfg.threshold_config = CN()
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explaining_cfg.threshold = CN()
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explaining_cfg.threshold_config.threshold_type = None
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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_config.value = [i * 0.05 for i in range(21)]
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explaining_cfg.threshold_config.relu_and_normalize = True
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# Select device: 'cpu', 'cuda', 'auto'
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explaining_cfg.threshold.value = CN()
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explaining_cfg.threshold.value.hard = [i * 0.05 for i in range(21)]
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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.name = "all"
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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.metrics.force = False
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explaining_cfg.adjust = CN()
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explaining_cfg.adjust.strategy = "rpn"
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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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@ -3,17 +3,6 @@ import itertools
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from typing import Any
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from eixgnn.eixgnn import EiXGNN
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from scgnn.scgnn import SCGNN
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from torch_geometric import seed_everything
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from torch_geometric.data import Batch, Data
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from torch_geometric.explain import Explainer
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from torch_geometric.explain.config import ThresholdConfig
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from torch_geometric.graphgym.config import cfg
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from torch_geometric.graphgym.loader import create_dataset
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from torch_geometric.graphgym.model_builder import cfg, create_model
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from torch_geometric.graphgym.utils.device import auto_select_device
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from torch_geometric.loader.dataloader import DataLoader
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from explaining_framework.config.explainer_config.eixgnn_config import \
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eixgnn_cfg
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from explaining_framework.config.explainer_config.scgnn_config import scgnn_cfg
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@ -22,6 +11,7 @@ from explaining_framework.explainers.wrappers.from_captum import CaptumWrapper
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from explaining_framework.explainers.wrappers.from_graphxai import \
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GraphXAIWrapper
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from explaining_framework.metric.accuracy import Accuracy
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from explaining_framework.metric.base import Metric
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from explaining_framework.metric.fidelity import Fidelity
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from explaining_framework.metric.robust import Attack
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from explaining_framework.metric.sparsity import Sparsity
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@ -34,6 +24,17 @@ from explaining_framework.utils.explanation.io import (
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explanation_verification, get_pred)
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from explaining_framework.utils.io import (is_exists, obj_config_to_str,
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read_json, write_json, write_yaml)
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from scgnn.scgnn import SCGNN
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from torch_geometric import seed_everything
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from torch_geometric.data import Batch, Data
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from torch_geometric.explain import Explainer
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from torch_geometric.explain.config import ThresholdConfig
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from torch_geometric.explain.explanation import Explanation
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from torch_geometric.graphgym.config import cfg
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from torch_geometric.graphgym.loader import create_dataset
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from torch_geometric.graphgym.model_builder import cfg, create_model
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from torch_geometric.graphgym.utils.device import auto_select_device
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from torch_geometric.loader.dataloader import DataLoader
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all__captum = [
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"LRP",
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@ -120,6 +121,9 @@ class ExplainingOutline(object):
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self.attacks = None
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self.model_signature = None
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self.indexes = None
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self.sparsities = None
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self.fidelities = None
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self.accuracies = None
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self.explaining_algorithm = None
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self.explainer = None
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self.adjusts = None
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@ -248,8 +252,10 @@ class ExplainingOutline(object):
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ind = self.explaining_cfg.dataset.specific_items
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self.dataset = self.dataset[ind]
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def load_dataset_to_dataloader(self):
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def load_dataset_to_dataloader(self, to_iter=True):
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self.dataset = DataLoader(dataset=self.dataset, shuffle=False, batch_size=1)
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if to_iter:
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self.dataset = iter(self.dataset)
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def load_explaining_algorithm(self):
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self.load_explainer_cfg()
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@ -381,25 +387,27 @@ class ExplainingOutline(object):
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]
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elif name is None:
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all_metrics = []
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self.accuraties = all_metrics
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else:
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raise ValueError(
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f"Provided dataset needs explanation groundtruths for using Accuracies metric, e.g BASHAPES dataset"
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)
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else:
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raise ValueError(
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f"Provided dataset needs explanation groundtruths for using Accuracies metric, e.g BASHAPES dataset"
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)
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all_metrics = []
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self.accuracies = all_metrics
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def load_metric(self):
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if self.cfg is None:
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self.load_cfg()
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if self.explaining_cfg is None:
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self.load_explaining_cfg()
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if self.accuraties is None:
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if self.accuracies is None:
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self.load_accuracy()
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if self.sparsities is None:
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self.load_sparsity()
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if self.fidelities is None:
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self.load_fidelity()
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self.metrics = self.fidelities + self.accuraties + self.sparsities
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self.metrics = self.fidelities + self.accuracies + self.sparsities
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def load_attack(self):
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if self.cfg is None:
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@ -432,16 +440,18 @@ class ExplainingOutline(object):
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strategy = self.explaining_cfg.adjust.strategy
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if strategy == "all":
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self.adjusts = [Adjust(strategy=strat) for strat in all_adjusts_filters]
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elif isinstance(name, str):
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if name in all_adjusts_filters:
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all_metrics = [Adjust(strategy=name)]
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elif isinstance(strategy, str):
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if strategy in all_adjusts_filters:
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all_metrics = [Adjust(strategy=strategy)]
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else:
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raise ValueError(
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f"This Adjust metric {name} is not supported yet. Supported are {all_adjusts_filters}"
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)
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elif isinstance(name, list):
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elif isinstance(strategy, list):
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all_metrics = [
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Adjust(strategy=name_) for name_ in name if name_ in all_robust
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Adjust(strategy=name_)
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for name_ in strategy
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if name_ in all_adjusts_filters
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]
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elif name is None:
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all_metrics = []
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@ -450,7 +460,7 @@ class ExplainingOutline(object):
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def load_threshold(self):
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if self.explaining_cfg is None:
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self.load_explaining_cfg()
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threshold_type = self.explaining_cfg.threshold_config.type
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threshold_type = self.explaining_cfg.threshold.config.type
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if threshold_type == "all":
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th_hard = [
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{"threshold_type": "hard", "value": th_value}
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@ -523,11 +533,50 @@ class ExplainingOutline(object):
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explanation = _load_explanation(path)
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else:
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explanation = _get_explanation(self.explainer, item)
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get_pred(self.explainer, explanation)
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_save_explanation(explanation, path)
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explanation = explanation.to(cfg.accelerator)
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return explanation
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def get_adjust(self, adjust: Adjust, item: Explanation, path: str):
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if is_exists(path):
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if self.explaining_cfg.explainer.force:
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exp_adjust = adjust.forward(item)
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else:
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exp_adjust = _load_explanation(path)
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else:
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exp_adjust = adjust.forward(item)
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get_pred(self.explainer, exp_adjust)
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_save_explanation(exp_adjust, path)
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exp_adjust = exp_adjust.to(cfg.accelerator)
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return exp_adjust
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def get_threshold(self, item: Explanation, path: str):
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if is_exists(path):
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if self.explaining_cfg.explainer.force:
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exp_threshold = self.explainer._post_process(item)
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else:
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exp_threshold = _load_explanation(path)
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else:
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exp_threshold = self.explainer._post_process(item)
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get_pred(self.explainer, exp_threshold)
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_save_explanation(exp_threshold, path)
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exp_threshold = exp_threshold.to(cfg.accelerator)
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return exp_threshold
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def get_metric(self, metric: Metric, item: Explanation, path: str):
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if is_exists(path):
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if self.explaining_cfg.explainer.force:
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out_metric = metric.forward(item)
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else:
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out_metric = read_json(path)
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else:
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out_metric = metric.forward(item)
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data = {f"{metric.name}": out_metric}
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write_json(data, path)
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return out_metric
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def get_stat(self, item: Data, path: str):
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if self.graphstat is None:
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self.load_graphstat()
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131
main.py
131
main.py
@ -18,9 +18,6 @@ from explaining_framework.config.explaining_config import explaining_cfg
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from explaining_framework.utils.explaining.cmd_args import parse_args
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from explaining_framework.utils.explaining.outline import ExplainingOutline
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from explaining_framework.utils.explanation.adjust import Adjust
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from explaining_framework.utils.explanation.io import (
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explanation_verification, get_explanation, get_pred, load_explanation,
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save_explanation)
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from explaining_framework.utils.io import (is_exists, obj_config_to_str,
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read_json, write_json, write_yaml)
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@ -31,81 +28,63 @@ if __name__ == "__main__":
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args = parse_args()
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outline = ExplainingOutline(args.explaining_cfg_file)
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# Load components
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# RAJOUTER INDEXES
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out_dir = os.path.join(outline.explaining_cfg.out_dir, outline.model_signature)
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makedirs(out_dir)
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# Global path
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global_path = os.path.join(outline.explaining_cfg.out_dir, outline.model_signature, outline.explaining_cfg.explainer.name + "_" + obj_config_to_str(outline.explaining_algorithm))
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makedirs(global_path)
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write_yaml(cfg, os.path.join(global_path, "config.yaml"))
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write_json(model_info, os.path.join(global_path, "info.json"))
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write_yaml(outline.cfg, os.path.join(out_dir, "config.yaml"))
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write_json(outline.model_info, os.path.join(out_dir, "info.json"))
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makedirs(global_path)
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write_yaml(outline.explaining_cfg, os.path.join(global_path, explaining_cfg.cfg_dest))
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write_yaml(outline.explainer_cfg, os.path.join(global_path, "explainer_cfg.yaml"))
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explainer_path = os.path.join(
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out_dir,
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outline.explaining_cfg.explainer.name
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+ "_"
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+ obj_config_to_str(outline.explaining_algorithm),
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)
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global_path = os.path.join(global_path, obj_config_to_str(outline.explaining_algorithm))
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makedirs(global_path)
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# SET UP EXPLAINER
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# Save explaining configuration
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item,index = outline.get_item()
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while not(item is None or index is None):
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raw_path = os.path.join(global_path, "raw")
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makedirs(raw_path)
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explanation_path = os.path.join(save_raw_path, f"{index}.json")
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makedirs(explainer_path)
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write_yaml(
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outline.explaining_cfg, os.path.join(explainer_path, explaining_cfg.cfg_dest)
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)
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write_yaml(
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outline.explainer_cfg, os.path.join(explainer_path, "explainer_cfg.yaml")
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)
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for apply_relu in [True, False]:
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for apply_absolute in [True, False]:
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adjust = Adjust(apply_relu=apply_relu, apply_absolute=apply_absolute)
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save_raw_path_ = os.path.join(
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global_path, f"adjust-{obj_config_to_str(adjust)}"
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specific_explainer_path = os.path.join(
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explainer_path, obj_config_to_str(outline.explaining_algorithm)
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)
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makedirs(specific_explainer_path)
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raw_path = os.path.join(specific_explainer_path, "raw")
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makedirs(raw_path)
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item, index = outline.get_item()
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while not (item is None or index is None):
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explanation_path = os.path.join(raw_path, f"{index}.json")
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raw_exp = outline.get_explanation(item=item, path=explanation_path)
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for adjust in outline.adjusts:
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adjust_path = os.path.join(raw_path, f"adjust-{obj_config_to_str(adjust)}")
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makedirs(adjust_path)
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exp_adjust_path = os.path.join(exp_adjust_path, f"{index}.json")
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exp_adjust = outline.get_adjust(
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adjust=adjust, item=raw_exp, path=exp_adjust_path
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)
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for threshold_conf in outline.thresholds_configs:
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outline.set_explainer_threshold_config(threshold_conf)
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masking_path = os.path.join(
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save_raw_path_,
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"-".join([f"{k}={v}" for k, v in threshold_conf.items()]),
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)
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explanation__ = copy.copy(explanation).to(cfg.accelerator)
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makedirs(save_raw_path_)
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explanation = adjust.forward(explanation__)
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explanation_path = os.path.join(save_raw_path_, f"{index}.json")
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get_pred(explainer, explanation__)
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save_explanation(explanation__, explanation_path)
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for threshold_approach in ["hard", "topk", "topk_hard"]:
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if threshold_approach == "hard":
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threshold_values = explaining_cfg.threshold_config.value
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elif "topk" in threshold_approach:
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threshold_values = [3, 5, 10, 20]
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for threshold_value in threshold_values:
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masking_path = os.path.join(
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save_raw_path_,
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f"threshold={threshold_approach}-value={threshold_value}",
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)
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makedirs(masking_path)
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exp_threshold_path = os.path.join(masking_path, f"{index}.json")
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if is_exists(exp_threshold_path):
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exp_threshold = load_explanation(exp_threshold_path)
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else:
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threshold_conf = {
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"threshold_type": threshold_approach,
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"value": threshold_value,
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}
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explainer.threshold_config = ThresholdConfig.cast(
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threshold_conf
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)
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expl = copy.copy(explanation__).to(cfg.accelerator)
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exp_threshold = explainer._post_process(expl)
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exp_threshold = exp_threshold.to(cfg.accelerator)
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get_pred(explainer, exp_threshold)
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save_explanation(exp_threshold, exp_threshold_path)
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for metric in metrics:
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metric_path = os.path.join(
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masking_path, f"{obj_config_to_str(metric)}"
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)
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makedirs(metric_path)
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if is_exists(os.path.join(metric_path, f"{index}.json")):
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continue
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else:
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out = metric.forward(exp_threshold)
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write_json(
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{f"{metric.name}": out},
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os.path.join(metric_path, f"{index}.json"),
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)
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makedirs(masking_path)
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exp_masked_path = os.path.join(masking_path, f"{index}.json")
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exp_masked = outline.get_threshold(
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item=exp_adjust, path=exp_masked_path
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)
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for metric in outline.metrics:
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metric_path = os.path.join(
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masking_path, f"{obj_config_to_str(metric)}"
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)
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makedirs(metric_path)
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metric_path = os.path.join(metric_path, f"{index}.json")
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out_metric = outline.get_metric(
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metric=metric, item=exp_masked, path=metric_path
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)
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