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