New features

explaining_framework/config/explaining_config.py

@@ -57,6 +57,8 @@ def set_cfg(explaining_cfg):
 
     explaining_cfg.dataset.name = "Cora"
 
+    explaining_cfg.dataset.specific_items = None
+
     explaining_cfg.run_topological_stat = True
 
     # ----------------------------------------------------------------------- #
@@ -80,6 +82,9 @@ def set_cfg(explaining_cfg):
     # Whether or not to provide specific explaining methods configuration or default configuration
     explaining_cfg.explainer.cfg = "default"
 
+    # Whether or not recomputing explanation if they already exist
+    explaining_cfg.explainer.force = False
+
     # ----------------------------------------------------------------------- #
     # Explaining options
     # ----------------------------------------------------------------------- #
@@ -98,27 +103,26 @@ def set_cfg(explaining_cfg):
     # Do not modify it, we always assume here that model output are 'raw'
     explaining_cfg.model_config.return_type = "raw"
 
+    # ----------------------------------------------------------------------- #
+    # Thresholding options
+    # ----------------------------------------------------------------------- #
+
     explaining_cfg.threshold_config = CN()
 
     explaining_cfg.threshold_config.threshold_type = None
 
-    explaining_cfg.threshold_config.value = 0.5
+    explaining_cfg.threshold_config.value = [0.3, 0.5, 0.7]
 
-    # Set print destination: stdout / file / both
-    explaining_cfg.print = "both"
+    explaining_cfg.threshold_config.relu_and_normalize = True
 
     # Select device: 'cpu', 'cuda', 'auto'
     explaining_cfg.accelerator = "auto"
 
-    # Config name (in out_dir)
-    explaining_cfg.explaining_cfg_dest = "config.yaml"
-
-    explaining_cfg.seed = 0
-
-    explaining_cfg.dataset.dir = "./datasets"
-
-    explaining_cfg.relu_and_normalize = True
+    # which objectives metrics to computes, either all or one in particular if implemented
+    explaining_cfg.metrics = "all"
 
+    # Whether or not recomputing metrics if they already exist
+    explaining_cfg.metrics.force = False
 
 
 def assert_cfg(explaining_cfg):
@@ -185,8 +189,6 @@ def set_out_dir(out_dir, fname):
     # Make output directory
     if explaining_cfg.train.auto_resume:
         os.makedirs(explaining_cfg.out_dir, exist_ok=True)
-    else:
-        makedirs_rm_exist(explaining_cfg.out_dir)
 
 
 def set_run_dir(out_dir):
@@ -200,8 +202,6 @@ def set_run_dir(out_dir):
     # Make output directory
     if explaining_cfg.train.auto_resume:
         os.makedirs(explaining_cfg.run_dir, exist_ok=True)
-    else:
-        makedirs_rm_exist(explaining_cfg.run_dir)
 
 
 set_cfg(explaining_cfg)

explaining_framework/utils/explaining/cmd_args.py

@@ -0,0 +1,27 @@
+import argparse
+
+
+def parse_args() -> argparse.Namespace:
+    r"""Parses the command line arguments."""
+    parser = argparse.ArgumentParser(description="GraphGym")
+
+    parser.add_argument(
+        "--cfg",
+        dest="cfg_file",
+        type=str,
+        required=True,
+        help="The configuration file path.",
+    )
+    parser.add_argument(
+        "--explaining_cfg",
+        dest="explaining_cfg_file",
+        type=str,
+        required=True,
+        help="The explaining configuration file path.",
+    )
+    parser.add_argument(
+        "--mark_done",
+        action="store_true",
+        help="Mark yaml as done after a job has finished.",
+    )
+    return parser.parse_args()

explaining_framework/utils/explaining/explaining_exp.py

@@ -0,0 +1,5 @@
+class Explaining(object):
+    def __init__(self, cfg: dict, explaining_cfg: dict, explainer_cfg: dict = None):
+        self.cfg = cfg
+        self.explaining_cfg = explaining_cfg
+        self.explainer_cfg = explainer_cfg

explaining_framework/utils/explaining/load_model.py

@@ -1,62 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-
-import glob
-import json
-import logging
-import os
-
-import torch
-from explaining_framework.utils.io import read_yaml
-from torch_geometric.graphgym.model_builder import create_model
-from torch_geometric.graphgym.train import GraphGymDataModule
-from torch_geometric.graphgym.utils.io import json_to_dict_list
-
-MODEL_STATE = "model_state"
-OPTIMIZER_STATE = "optimizer_state"
-SCHEDULER_STATE = "scheduler_state"
-
-
-def load_ckpt(
-    model: torch.nn.Module,
-    ckpt_path: str,
-) -> torch.nn.Module:
-    r"""Loads the model at given checkpoint."""
-
-    if not osp.exists(path):
-        return None
-
-    ckpt = torch.load(ckpt_path)
-    model.load_state_dict(ckpt[MODEL_STATE])
-
-    return model
-
-
-PATH = "/home/SIC/araison/test_ggym/pytorch_geometric/graphgym/results/test_cifar/"
-
-FOLDER = "graph_classif_base-dataset=PCBA-l_mp=2-l_post_mp=3-dim_inner=64-layer_type=gatconv-graph_pooling=mean"
-
-
-def xp_accuracies(path_to_xp: str, wrt_metric: str = "val") -> str:
-    acc = []
-    for path in glob.glob(os.path.join(path_to_xp, "[0-9]", wrt_metric, "stats.json")):
-        stats = json_to_dict_list(path)
-        for stat in stats:
-            acc.append(
-                {"path": path, "epoch": stat["epoch"], "accuracy": stat["accuracy"]}
-            )
-    return acc
-    # return sorted(acc, key=lambda item: item["accuracy"])
-
-
-def best_ckpt_path(dataset_name: str, models_dir_path) -> str:
-    paths = []
-    for path in glob.glob(os.path.join(models_dir_path, "**", "config.yaml")):
-        file = read_yaml(path)
-        dataset_name_ = file["dataset"]["name"]
-        if dataset_name == dataset_name_:
-            paths.append(os.path.dirname(path))
-    return paths
-
-
-print(load_best_given_exp(PATH))

explaining_framework/utils/io.py

@@ -0,0 +1,25 @@
+import json
+
+import yaml
+
+
+def read_json(path: str) -> dict:
+    with open(path, "r") as f:
+        data = json.load(f)
+    return data
+
+
+def write_json(data: dict, path: str) -> None:
+    with open(path, "w") as f:
+        data = json.dump(data, f)
+
+
+def read_yaml(path: str) -> dict:
+    with open(path, "r") as f:
+        data = yaml.safe_load(f)
+    return data
+
+
+def write_yaml(data: dict, path: str) -> None:
+    with open(path, "w") as f:
+        data = yaml.dump(data, f)