New module for parsing explaining stats

stat_parser.py

@@ -0,0 +1,84 @@
+import argparse
+import glob
+import multiprocessing as mp
+import os
+from collections import defaultdict
+
+import pandas as pd
+
+from explaining_framework.utils.io import read_json, read_yaml
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+    "--exp_dir",
+    help="Parent directory of all explanations",
+    default="./explanations",
+    dest="ed",
+)
+parser.add_argument(
+    "--explainer_name",
+    help="Name of the explaining methods you want to parse from",
+    dest="en",
+)
+parser.add_argument("--dataset_name", help="Name of the explained dataset", dest="dn")
+parser.add_argument("--metric_name", help="Name of the objective metric", dest="mn")
+parser.add_argument("--outfile", help="Path for output CSV", dest="op")
+args = parser.parse_args()
+
+PARENT_PATH = os.path.abspath(args.ed)
+DATA = []
+DATA_DICT = defaultdict(list)
+pd1 = pd.DataFrame()
+
+for p1 in glob.glob(os.path.join(PARENT_PATH, args.dn, "**")):
+    CONFIG_path = os.path.join(p1, "config.yaml")
+    INFO_path = os.path.join(p1, "info.json")
+
+    CONFIG_DICT = read_yaml(CONFIG_path)
+    INFO_DICT = read_yaml(INFO_path)
+    INFO_DICT_KEYS = [k for k in INFO_DICT.keys() if "path" in k]
+
+    for k in INFO_DICT_KEYS:
+        INFO_DICT.pop(k)
+    for p2 in glob.glob(os.path.join(p1, args.en, "**")):
+        EXPLAINER_CONFIG_path = os.path.join(p2, "explainer_cfg.yaml")
+        EXPLAINER_CONFIG_DICT = read_yaml(EXPLAINER_CONFIG_path)
+
+        for p3 in glob.glob(os.path.join(p2, "Attack", "**")):
+            attack_type = os.path.basename(p3)[5:]
+
+            for p4 in glob.glob(os.path.join(p3, "Adjust", "**")):
+                adjust_type = os.path.basename(p4)[9:]
+
+                for p5 in glob.glob(os.path.join(p4, "**", "**")):
+                    thres_type = os.path.basename(p5)[15:]
+
+                    for p6 in glob.glob(os.path.join(p5, args.mn, "**")):
+                        metric_type = os.path.basename(p6)[5:]
+
+                        all_file = sorted(
+                            glob.glob(os.path.join(p6, "**", "*.json"), recursive=True)
+                        )
+                        with mp.Pool(mp.cpu_count()) as pool:
+                            data = pool.map(read_json, all_file)
+
+                        d1 = [
+                            pd.json_normalize(INFO_DICT),
+                            pd.json_normalize(CONFIG_DICT),
+                            pd.json_normalize(EXPLAINER_CONFIG_DICT),
+                            pd.DataFrame(
+                                {
+                                    "Adjust": [adjust_type],
+                                    "Threshold": [thres_type],
+                                    "Metric": [metric_type],
+                                }
+                            ),
+                        ]
+
+                        for da in data:
+                            v = pd.json_normalize({"val": list(da.values())[0]})
+                            d_ = pd.concat((*d1, v), axis=1)
+                            pd1 = pd.concat((pd1, d_))
+
+
+pd1.to_csv(args.op)