aiops/ContraLSP/switchstate/ab_both.py [39:225]:
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):
    # If deterministic, seed everything
    if deterministic:
        seed_everything(seed=seed, workers=True)

    # Get accelerator and device
    accelerator = device.split(":")[0]
    print(accelerator)
    device_id = 1
    if len(device.split(":")) > 1:
        device_id = [int(device.split(":")[1])]

    # Create lock
    lock = mp.Lock()

    # Load data
    switch = Switch(n_folds=5, fold=fold, seed=seed)

    # Create classifier
    classifier = SpikeClassifierNet(
        feature_size=3,
        n_state=2,
        hidden_size=200,
        regres=True,
        loss="cross_entropy",
        lr=0.0001,
        l2=1e-3,
    )

    # Train classifier
    trainer = Trainer(
        max_epochs=50,
        accelerator=accelerator,
        devices=device_id,
        deterministic=deterministic,
        logger=TensorBoardLogger(
            save_dir=".",
            version=random.getrandbits(128),
        ),
    )
    if is_train:
        trainer.fit(classifier, datamodule=switch)
        if not os.path.exists("./model/"):
            os.makedirs("./model/")
        th.save(classifier.state_dict(), "./model/classifier_{}_{}".format(fold, seed))
    else:
        classifier.load_state_dict(th.load("./model/classifier_{}_{}".format(fold, seed)))

    # Get data for explainers
    with lock:
        x_train = switch.preprocess(split="train")["x"].to(device)
        x_test = switch.preprocess(split="test")["x"].to(device)
        y_test = switch.preprocess(split="test")["y"].to(device)
        true_saliency = switch.true_saliency(split="test").to(device)

    print("==============The sum of true_saliency is", true_saliency.sum(), "==============\n" + 70 * "=")

    # # Switch to eval
    classifier.eval()
    classifier.zero_grad()

    # Set model to device
    classifier.to(device)

    # Disable cudnn if using cuda accelerator.
    # Please see https://captum.ai/docs/faq#how-can-i-resolve-cudnn-rnn-backward-error-for-rnn-or-lstm-network
    # for more information.
    if accelerator == "cuda":
        th.backends.cudnn.enabled = False

    # Create dict of attributions
    attr = dict()

    if "gate_mask" in explainers:
        trainer = Trainer(
            max_epochs=500,
            accelerator=accelerator,
            devices=device_id,
            log_every_n_steps=2,
            deterministic=deterministic,
            logger=TensorBoardLogger(
                save_dir=".",
                version=random.getrandbits(128),
            ),
        )
        mask = GateMaskNet(
            forward_func=classifier,
            model=nn.Sequential(
                RNN(
                    input_size=x_test.shape[-1],
                    rnn="gru",
                    hidden_size=x_test.shape[-1],
                    bidirectional=True,
                ),
                MLP([2 * x_test.shape[-1], x_test.shape[-1]]),
            ),
            lambda_1=lambda_1,
            lambda_2=lambda_2,
            optim="adam",
            lr=0.01,
        )
        explainer = GateMask(classifier)
        _attr = explainer.attribute(
            x_test,
            additional_forward_args=(True,),
            trainer=trainer,
            mask_net=mask,
            batch_size=x_test.shape[0],
            sigma=0.8,
        )
        attr["gate_mask"] = _attr.to(device)
        print_results(attr["gate_mask"], true_saliency)

    with open(output_file, "a") as fp, lock:
        for k, v in attr.items():
            fp.write(str(seed) + ",")
            fp.write(str(fold) + ",")
            fp.write(k + ",")
            fp.write(str(lambda_1) + ",")
            fp.write(str(lambda_2) + ",")
            fp.write(f"{aup(v, true_saliency):.4},")
            fp.write(f"{aur(v, true_saliency):.4},")
            fp.write(f"{information(v, true_saliency):.4},")
            fp.write(f"{entropy(v, true_saliency):.4},")
            fp.write(f"{roc_auc(v, true_saliency):.4},")
            fp.write(f"{auprc(v, true_saliency):.4}")
            fp.write("\n")


def parse_args():
    parser = ArgumentParser()
    parser.add_argument(
        "--explainers",
        type=str,
        default=[
            "gate_mask",# tensor(14289.1562) tensor(0.4865, grad_fn=<MeanBackward0>) tensor(0.0310, gra>) 1.1 1 tensor(0.1030, grad_fn=<MseLossBackward0>)
        ],
        nargs="+",
        metavar="N",
        help="List of explainer to use.",
    )
    parser.add_argument(
        "--device",
        type=str,
        default="cpu",
        help="Which device to use.",
    )
    parser.add_argument(
        "--fold",
        type=int,
        default=1,
        help="Fold of the cross-validation.",
    )
    parser.add_argument(
        "--seed",
        type=int,
        default=42,
        help="Random seed for data generation.",
    )
    parser.add_argument(
        "--train",
        type=bool,
        default=False,
        help="Train the rnn classifier.",
    )
    parser.add_argument(
        "--deterministic",
        action="store_true",
        help="Whether to make training deterministic or not.",
    )
    parser.add_argument(
        "--lambda-1",
        type=float,
        default=1,
        help="Lambda 1 hyperparameter.",
    )
    parser.add_argument(
        "--lambda-2",
        type=float,
        default=2,
        help="Lambda 2 hyperparameter.",
    )
    return parser.parse_args()


if __name__ == "__main__":
    args = parse_args()
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aiops/ContraLSP/switchstate/ab_smooth.py [39:225]:
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):
    # If deterministic, seed everything
    if deterministic:
        seed_everything(seed=seed, workers=True)

    # Get accelerator and device
    accelerator = device.split(":")[0]
    print(accelerator)
    device_id = 1
    if len(device.split(":")) > 1:
        device_id = [int(device.split(":")[1])]

    # Create lock
    lock = mp.Lock()

    # Load data
    switch = Switch(n_folds=5, fold=fold, seed=seed)

    # Create classifier
    classifier = SpikeClassifierNet(
        feature_size=3,
        n_state=2,
        hidden_size=200,
        regres=True,
        loss="cross_entropy",
        lr=0.0001,
        l2=1e-3,
    )

    # Train classifier
    trainer = Trainer(
        max_epochs=50,
        accelerator=accelerator,
        devices=device_id,
        deterministic=deterministic,
        logger=TensorBoardLogger(
            save_dir=".",
            version=random.getrandbits(128),
        ),
    )
    if is_train:
        trainer.fit(classifier, datamodule=switch)
        if not os.path.exists("./model/"):
            os.makedirs("./model/")
        th.save(classifier.state_dict(), "./model/classifier_{}_{}".format(fold, seed))
    else:
        classifier.load_state_dict(th.load("./model/classifier_{}_{}".format(fold, seed)))

    # Get data for explainers
    with lock:
        x_train = switch.preprocess(split="train")["x"].to(device)
        x_test = switch.preprocess(split="test")["x"].to(device)
        y_test = switch.preprocess(split="test")["y"].to(device)
        true_saliency = switch.true_saliency(split="test").to(device)

    print("==============The sum of true_saliency is", true_saliency.sum(), "==============\n" + 70 * "=")

    # # Switch to eval
    classifier.eval()
    classifier.zero_grad()

    # Set model to device
    classifier.to(device)

    # Disable cudnn if using cuda accelerator.
    # Please see https://captum.ai/docs/faq#how-can-i-resolve-cudnn-rnn-backward-error-for-rnn-or-lstm-network
    # for more information.
    if accelerator == "cuda":
        th.backends.cudnn.enabled = False

    # Create dict of attributions
    attr = dict()

    if "gate_mask" in explainers:
        trainer = Trainer(
            max_epochs=500,
            accelerator=accelerator,
            devices=device_id,
            log_every_n_steps=2,
            deterministic=deterministic,
            logger=TensorBoardLogger(
                save_dir=".",
                version=random.getrandbits(128),
            ),
        )
        mask = GateMaskNet(
            forward_func=classifier,
            model=nn.Sequential(
                RNN(
                    input_size=x_test.shape[-1],
                    rnn="gru",
                    hidden_size=x_test.shape[-1],
                    bidirectional=True,
                ),
                MLP([2 * x_test.shape[-1], x_test.shape[-1]]),
            ),
            lambda_1=lambda_1,
            lambda_2=lambda_2,
            optim="adam",
            lr=0.01,
        )
        explainer = GateMask(classifier)
        _attr = explainer.attribute(
            x_test,
            additional_forward_args=(True,),
            trainer=trainer,
            mask_net=mask,
            batch_size=x_test.shape[0],
            sigma=0.8,
        )
        attr["gate_mask"] = _attr.to(device)
        print_results(attr["gate_mask"], true_saliency)

    with open(output_file, "a") as fp, lock:
        for k, v in attr.items():
            fp.write(str(seed) + ",")
            fp.write(str(fold) + ",")
            fp.write(k + ",")
            fp.write(str(lambda_1) + ",")
            fp.write(str(lambda_2) + ",")
            fp.write(f"{aup(v, true_saliency):.4},")
            fp.write(f"{aur(v, true_saliency):.4},")
            fp.write(f"{information(v, true_saliency):.4},")
            fp.write(f"{entropy(v, true_saliency):.4},")
            fp.write(f"{roc_auc(v, true_saliency):.4},")
            fp.write(f"{auprc(v, true_saliency):.4}")
            fp.write("\n")


def parse_args():
    parser = ArgumentParser()
    parser.add_argument(
        "--explainers",
        type=str,
        default=[
            "gate_mask",# tensor(14289.1562) tensor(0.4865, grad_fn=<MeanBackward0>) tensor(0.0310, gra>) 1.1 1 tensor(0.1030, grad_fn=<MseLossBackward0>)
        ],
        nargs="+",
        metavar="N",
        help="List of explainer to use.",
    )
    parser.add_argument(
        "--device",
        type=str,
        default="cpu",
        help="Which device to use.",
    )
    parser.add_argument(
        "--fold",
        type=int,
        default=1,
        help="Fold of the cross-validation.",
    )
    parser.add_argument(
        "--seed",
        type=int,
        default=42,
        help="Random seed for data generation.",
    )
    parser.add_argument(
        "--train",
        type=bool,
        default=False,
        help="Train the rnn classifier.",
    )
    parser.add_argument(
        "--deterministic",
        action="store_true",
        help="Whether to make training deterministic or not.",
    )
    parser.add_argument(
        "--lambda-1",
        type=float,
        default=1,
        help="Lambda 1 hyperparameter.",
    )
    parser.add_argument(
        "--lambda-2",
        type=float,
        default=2,
        help="Lambda 2 hyperparameter.",
    )
    return parser.parse_args()


if __name__ == "__main__":
    args = parse_args()
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