New features

explaining_framework/utils/explaining/load_model.py

@@ -1,14 +1,16 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
+import glob
+import json
 import logging
 import os
-import json
-import glob
 
 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"
@@ -30,12 +32,31 @@ def load_ckpt(
     return model
 
 
-def load_best_given_exp(path_to_xp:str, wrt_metric:str:'val')->str:
-    path = os.path.normpath(path)
-    path.split(os.sep)
-    for path in glob.glob(os.path.join(path_to_xp,'[0-9]'*10,wrt_metric,'stats.json')):
-        print(path)
+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))