xai_tabular.py

import argparse
import tensorflow as tf
from pathlib import Path
from sklearn.utils import resample
import json

from config import XAI, DATASETS

parser = argparse.ArgumentParser()
parser.add_argument("-d", help=f"Dataset name {list(DATASETS.keys())}", default="IOT_DNL")
parser.add_argument("-x", help="XAI method to use", default="PI")
parser.add_argument("-p", help="Percentage of the test dataset to use", default=10, type=float)
args = parser.parse_args()

if args.x not in XAI:
    raise ValueError(f"XAI method {args.x} not found")

# all .keras and .h5 files in models folder
models = Path(f"{args.d}/models").glob("*.keras")
models = list(models) + list(Path(f"{args.d}/models").glob("*.h5"))
models = [str(model) for model in models]
print("\n\n\n")
print(f"Found {len(models)} models")
print(f"Models: {models}")

folder = Path(f"{args.d}/xai/{args.x}")
folder.mkdir(parents=True, exist_ok=True)
# get all files in the folder
files = Path(folder).glob("*")
# remove the extension and get only the name
files = [str(file).split("/")[-1].split(".")[0] for file in files]
# remove from models the ones that already have results
models = [model for model in models if model.split("/")[-1].split(".")[0]]
print(f"Models to process: {len(models)}")
print(f"Models: {files}")

dataset_util = DATASETS[args.d]
x_train, y_train = dataset_util.load_training_data()
x_test, y_test = dataset_util.load_validation_data()
n_samples = int(x_test.shape[0] * args.p / 100)

x_test, y_test = resample(x_test, y_test, n_samples=n_samples, random_state=42, stratify=y_test)
print(f"X_test shape: {x_test.shape}")

for model_path in models:
    print(f"Model: {model_path.split('/')[-1]}")
    model = tf.keras.models.load_model(model_path)
    model_name = model_path.split("/")[-1].split(".")[0]

    feature_importance = XAI[args.x](x_train, y_train, x_test, y_test, model)

    with open(folder / f"{model_name}.json", "w") as f: 
        json.dump(feature_importance, f, indent=4)
print("\n\n\n")