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Models API Reference

This page provides detailed API documentation for all model classes in Samay.

LPTMModel

Large Pre-trained Time Series Model for forecasting, classification, and anomaly detection.

LPTMModel(config=None)

Bases: Basemodel

Source code in Samay\src\samay\model.py 
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def __init__(self, config=None):
    super().__init__(config=config, repo=None)
    # config["patch_len"] = config["max_patch"]
    self.model = LPTMPipeline.from_pretrained(
        "kage08/lptm-large2", model_kwargs=self.config
    )

    self.model.init()

TimesfmModel

Google's Time Series Foundation Model for zero-shot forecasting.

TimesfmModel(config=None, repo=None, ckpt=None, **kwargs)

Bases: Basemodel

Parameters:

Name Type Description Default
config

dict, model configuration

 None
repo

str, Huggingface model repository id

 None
Source code in Samay\src\samay\model.py 
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def __init__(self, config=None, repo=None, ckpt=None, **kwargs):
    """
    Args:
        config: dict, model configuration
        repo: str, Huggingface model repository id
    """
    super().__init__(config=config, repo=repo)
    hparams = tfm.TimesFmHparams(**self.config)
    if repo:
        try:
            ckpt = tfm.TimesFmCheckpoint(huggingface_repo_id=repo)
        except:
            ckpt = None
            raise ValueError(f"Repository {repo} not found")

    self.model = tfm.TimesFm(hparams=hparams, checkpoint=ckpt)

evaluate(dataset, **kwargs)

Evaluate the model. Args: dataset: dataset for evaluation, call get_data_loader() to get the dataloader Returns: Dict[str, float]: evaluation metrics, including mse, mae, mase, mape, rmse, nrmse, smape, msis, nd, mwsq, crps

Source code in Samay\src\samay\model.py 
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def evaluate(self, dataset, **kwargs):
    """
    Evaluate the model.
    Args:
        dataset: dataset for evaluation, call get_data_loader() to get the dataloader
    Returns:
        Dict[str, float]: evaluation metrics, including mse, mae, mase, mape, rmse, nrmse, smape, msis, nd, mwsq, crps
    """
    dataloader = dataset.get_data_loader()
    trues, preds, histories, quantiles, losses = [], [], [], [], []

    with torch.no_grad():
        for i, (inputs) in enumerate(dataloader):
            inputs = dataset.preprocess(inputs)
            input_ts = inputs["input_ts"]
            input_ts = np.squeeze(input_ts, axis=0)
            actual_ts = inputs["actual_ts"].detach().cpu().numpy()
            actual_ts = np.squeeze(actual_ts, axis=0)

            output, quantile_output = self.model.forecast(input_ts)
            output = output[:, 0 : actual_ts.shape[1]]
            quantile_output = quantile_output[:, 0 : actual_ts.shape[1], 1:].transpose(2, 0, 1)  # q, b, h

            loss = np.mean((output - actual_ts) ** 2)
            losses.append(loss.item())
            trues.append(actual_ts)
            preds.append(output)
            histories.append(input_ts)
            quantiles.append(quantile_output)

    losses = np.array(losses)
    average_loss = np.average(losses)
    trues = np.concatenate(trues, axis=0).reshape(
        -1, dataset.num_ts, trues[-1].shape[-1]
    )
    preds = np.concatenate(preds, axis=0).reshape(
        -1, dataset.num_ts, preds[-1].shape[-1]
    )
    histories = np.concatenate(histories, axis=0).reshape(
        -1, dataset.num_ts, histories[-1].shape[-1]
    )
    quantiles = np.concatenate(quantiles, axis=1).reshape(
        quantiles[-1].shape[0], -1, dataset.num_ts, quantiles[-1].shape[-1]
    )

    # denormalize
    if dataset.normalize:
        trues = dataset._denormalize_data(trues)
        preds = dataset._denormalize_data(preds)
        histories = dataset._denormalize_data(histories)
        new_quantiles = []
        for i in range(quantiles.shape[0]):
            new_quantiles.append(dataset._denormalize_data(quantiles[i]))
        quantiles = np.array(new_quantiles)

    mse = MSE(trues, preds)
    mae = MAE(trues, preds)
    mase = MASE(trues, preds)
    mape = MAPE(trues, preds)
    rmse = RMSE(trues, preds)
    nrmse = NRMSE(trues, preds)
    smape = SMAPE(trues, preds)
    msis = MSIS(trues, preds)
    nd = ND(trues, preds)
    mwsq = MWSQ(trues, quantiles, self.config["quantiles"])
    crps = CRPS(trues, quantiles, self.config["quantiles"])

    return {
        "mse": mse,
        "mae": mae,
        "mase": mase,
        "mape": mape,
        "rmse": rmse,
        "nrmse": nrmse,
        "smape": smape,
        "msis": msis,
        "nd": nd,
        "mwsq": mwsq,
        "crps": crps,
    }

finetune(dataset, freeze_transformer=True, **kwargs)

Parameters:

Name Type Description Default
dataset

dataset for finetuning, call get_data_loader() to get the dataloader

 required
freeze_transformer

bool, whether to freeze the transformer layers

 True

Returns: FinetuneModel: ppd.PatchedDecoderFinetuneModel, finetuned model

Source code in Samay\src\samay\model.py 
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def finetune(self, dataset, freeze_transformer=True, **kwargs):
    """
    Args:
        dataset: dataset for finetuning, call get_data_loader() to get the dataloader
        freeze_transformer: bool, whether to freeze the transformer layers
    Returns:
        FinetuneModel: ppd.PatchedDecoderFinetuneModel, finetuned model
    """
    lr = 1e-4 if "lr" not in kwargs else kwargs["lr"]
    epoch = 5 if "epoch" not in kwargs else kwargs["epoch"]

    core_layer_tpl = self.model._model
    # Todo: whether add freq
    FinetunedModel = ppd.PatchedDecoderFinetuneModel(core_layer_tpl=core_layer_tpl)
    if freeze_transformer:
        for param in FinetunedModel.core_layer.stacked_transformer.parameters():
            param.requires_grad = False
    FinetunedModel.to(self.device)
    FinetunedModel.train()
    dataloader = dataset.get_data_loader()
    optimizer = torch.optim.Adam(FinetunedModel.parameters(), lr=lr)

    for epoch in range(epoch):
        avg_loss = 0
        for i, (inputs) in enumerate(dataloader):
            inputs = dataset.preprocess(inputs)
            inputs = {k: v.to(self.device) for k, v in inputs.items()}
            optimizer.zero_grad()
            outputs = FinetunedModel.compute_predictions(
                inputs, train_horizon_len=self.config["horizon_len"]
            )  # b, n, seq_len, 1+quantiles
            loss = FinetunedModel.compute_loss(outputs, inputs)
            loss.backward()
            optimizer.step()
            avg_loss += loss.item()
        avg_loss /= len(dataloader)
        print(f"Epoch {epoch}, Loss: {avg_loss}")

    self.model._model = FinetunedModel.core_layer
    return self.model

forecast(input, **kwargs)

Parameters:

Name Type Description Default
input

torch.Tensor, input data

 required

Returns: Tuple[torch.Tensor, torch.Tensor]: - the mean forecast of size (# inputs, # forecast horizon), - the full forecast (mean + quantiles) of size (# inputs, # forecast horizon, 1 + # quantiles).

Source code in Samay\src\samay\model.py 
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def forecast(self, input, **kwargs):
    """
    Args:
        input: torch.Tensor, input data
    Returns:
        Tuple[torch.Tensor, torch.Tensor]:
            - the mean forecast of size (# inputs, # forecast horizon),
            - the full forecast (mean + quantiles) of size
            (# inputs,  # forecast horizon, 1 + # quantiles).
    """
    return self.model.forecast(input)

plot(dataset, **kwargs)

Plot the forecast results. Args: dataset: dataset for plotting, call get_data_loader() to get the dataloader

Source code in Samay\src\samay\model.py 
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def plot(self, dataset, **kwargs):
    """
    Plot the forecast results.
    Args:
        dataset: dataset for plotting, call get_data_loader() to get the dataloader
    """
    dataloader = dataset.get_data_loader()
    trues, preds, histories, losses, quantiles = [], [], [], [], []
    with torch.no_grad():
        for i, (inputs) in enumerate(dataloader):
            inputs = dataset.preprocess(inputs)
            input_ts = inputs["input_ts"]
            input_ts = np.squeeze(input_ts, axis=0)
            actual_ts = inputs["actual_ts"].detach().cpu().numpy()
            actual_ts = np.squeeze(actual_ts, axis=0)

            output, quantile_output = self.model.forecast(input_ts)
            output = output[:, 0 : actual_ts.shape[1]]

            quantile_output = quantile_output.transpose(2, 0, 1)  # q, b, h
            quantile_output = quantile_output[..., 0 : actual_ts.shape[1]]

            loss = np.mean((output - actual_ts) ** 2)
            losses.append(loss.item())
            trues.append(actual_ts)
            preds.append(output)
            histories.append(input_ts)
            quantiles.append(quantile_output)

    losses = np.array(losses)
    average_loss = np.average(losses)
    trues = np.concatenate(trues, axis=0).reshape(
        -1, dataset.num_ts, trues[-1].shape[-1]
    )
    preds = np.concatenate(preds, axis=0).reshape(
        -1, dataset.num_ts, preds[-1].shape[-1]
    )
    histories = np.concatenate(histories, axis=0).reshape(
        -1, dataset.num_ts, histories[-1].shape[-1]
    )
    quantiles = np.concatenate(quantiles, axis=1).reshape(
        quantiles[-1].shape[0], -1, dataset.num_ts, quantiles[-1].shape[-1]
    )

    # denormalize
    if dataset.normalize:
        trues = dataset._denormalize_data(trues.reshape(-1, 1)).reshape(trues.shape)
        preds = dataset._denormalize_data(preds.reshape(-1, 1)).reshape(preds.shape)
        histories = dataset._denormalize_data(histories.reshape(-1, 1)).reshape(histories.shape)
        quantiles = dataset._denormalize_data(quantiles.reshape(-1, 1)).reshape(quantiles.shape)

    visualize(
        task_name="forecasting",
        trues=trues,
        preds=preds,
        history=histories,
        quantiles=quantiles,
        **kwargs,
    )

MomentModel

Multi-task time-series foundation model supporting forecasting, classification, detection, and imputation.

MomentModel(config=None, repo=None)

Bases: Basemodel

Parameters:

Name Type Description Default
config

dict, model configuration

 None
repo

str, Huggingface model repository id

 None
Source code in Samay\src\samay\model.py 
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def __init__(self, config=None, repo=None):
    """
    Args:
        config: dict, model configuration
        repo: str, Huggingface model repository id
    """
    super().__init__(config=config, repo=repo)
    if not repo:
        # raise ValueError("Moment model requires a repository")
        print("Initializing a new MOMENT model without pre-trained weights")
        base_config = json.load(
            open("/nethome/sli999/TSFMProject/config/moment_base.json", "r")
        )
        self.model = MOMENTPipeline(config=base_config, model_kwargs=self.config)
    else:
        print(f"Loading MOMENT model from {repo}")
        self.model = MOMENTPipeline.from_pretrained(repo, model_kwargs=self.config)
    self.model.init()

evaluate(dataset, task_name='forecasting')

Evaluate the model. Args: dataset: dataset for evaluation, call get_data_loader() to get the dataloader task_name: str, task name, forecasting, imputation, detection, classification Returns: Dict[str, float]: evaluation metrics, including mse, mae, mase, mape, rmse, nrmse, smape, msis, nd, mwsq, crps

Source code in Samay\src\samay\model.py 
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def evaluate(self, dataset, task_name="forecasting"):
    """
    Evaluate the model.
    Args:
        dataset: dataset for evaluation, call get_data_loader() to get the dataloader
        task_name: str, task name, forecasting, imputation, detection, classification
    Returns:
        Dict[str, float]: evaluation metrics, including mse, mae, mase, mape, rmse, nrmse, smape, msis, nd, mwsq, crps
    """
    dataloader = dataset.get_data_loader()
    self.model.to(self.device)
    self.model.eval()
    if task_name == "forecasting":
        criterion = torch.nn.MSELoss()
        trues, preds, histories, losses = [], [], [], []
        with torch.no_grad():
            for i, data in enumerate(dataloader):
                # unpack the data
                timeseries, input_mask, forecast = data
                # Move the data to the GPU
                timeseries = timeseries.float().to(self.device)
                input_mask = input_mask.to(self.device)
                forecast = forecast.float().to(self.device)

                output = self.model(x_enc=timeseries, input_mask=input_mask)
                loss = criterion(output.forecast, forecast)
                losses.append(loss.item())
                trues.append(forecast.detach().cpu().numpy())
                preds.append(output.forecast.detach().cpu().numpy())
                histories.append(timeseries.detach().cpu().numpy())

        losses = np.array(losses)
        trues = np.concatenate(trues, axis=0)
        preds = np.concatenate(preds, axis=0)
        histories = np.concatenate(histories, axis=0)

        # denormalize
        trues = dataset._denormalize_data(trues)
        preds = dataset._denormalize_data(preds)
        histories = dataset._denormalize_data(histories)
        mse = MSE(trues, preds)
        mae = MAE(trues, preds)
        mase = MASE(trues, preds)
        mape = MAPE(trues, preds)
        rmse = RMSE(trues, preds)
        nrmse = NRMSE(trues, preds)
        smape = SMAPE(trues, preds)
        msis = MSIS(trues, preds)
        nd = ND(trues, preds)

        return {
            "mse": mse,
            "mae": mae,
            "mase": mase,
            "mape": mape,
            "rmse": rmse,
            "nrmse": nrmse,
            "smape": smape,
            "msis": msis,
            "nd": nd,
        }

    elif task_name == "classification":
        accuracy = 0
        total = 0
        embeddings = []
        labels = []
        with torch.no_grad():
            for i, data in enumerate(dataloader):
                # unpack the data
                timeseries, input_mask, label = data
                timeseries = timeseries.to(self.device).float()
                label = label.to(self.device).long()
                labels.append(label.detach().cpu().numpy())
                input_mask = input_mask.to(self.device).long()
                output = self.model(x_enc=timeseries, input_mask=input_mask)
                embedding = output.embeddings.mean(dim=1)
                embeddings.append(embedding.detach().cpu().numpy())
                _, predicted = torch.max(output.logits, 1)
                total += label.size(0)
                accuracy += (predicted == label).sum().item()

        accuracy = accuracy / total
        embeddings = np.concatenate(embeddings)
        labels = np.concatenate(labels)
        return accuracy, embeddings, labels

finetune(dataset, task_name='forecasting', **kwargs)

Parameters:

Name Type Description Default
dataset

dataset for finetuning, call get_data_loader() to get the dataloader

 required
task_name

str, task name, forecasting, imputation, detection, classification

 'forecasting'

Returns: MOMENT model

Source code in Samay\src\samay\model.py 
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def finetune(self, dataset, task_name="forecasting", **kwargs):
    """
    Args:
        dataset: dataset for finetuning, call get_data_loader() to get the dataloader
        task_name: str, task name, forecasting, imputation, detection, classification
    Returns:
        MOMENT model
    """
    # arguments
    max_lr = 1e-4 if "lr" not in kwargs else kwargs["lr"]
    max_epoch = 5 if "epoch" not in kwargs else kwargs["epoch"]
    max_norm = 1.0 if "norm" not in kwargs else kwargs["norm"]
    mask_ratio = 0.25 if "mask_ratio" not in kwargs else kwargs["mask_ratio"]

    if task_name == "imputation" or task_name == "detection":
        mask_generator = Masking(mask_ratio=mask_ratio)

    dataloader = dataset.get_data_loader()
    criterion = torch.nn.MSELoss()
    if task_name == "classification":
        criterion = torch.nn.CrossEntropyLoss()
    optimizer = torch.optim.Adam(self.model.parameters(), lr=max_lr)
    criterion.to(self.device)
    scaler = torch.amp.GradScaler()

    total_steps = len(dataloader) * max_epoch
    scheduler = torch.optim.lr_scheduler.OneCycleLR(
        optimizer, max_lr=max_lr, total_steps=total_steps, pct_start=0.3
    )
    self.model.to(self.device)
    self.model.train()

    for epoch in range(max_epoch):
        losses = []
        for i, data in enumerate(dataloader):
            # unpack the data
            if task_name == "forecasting":
                timeseries, input_mask, forecast = data
                # Move the data to the GPU
                timeseries = timeseries.float().to(self.device)
                input_mask = input_mask.to(self.device)
                forecast = forecast.float().to(self.device)
                # with torch.amp.autocast(device_type='cuda'):
                output = self.model(x_enc=timeseries, input_mask=input_mask)
                loss = criterion(output.forecast, forecast)

            elif task_name == "imputation":
                timeseries, input_mask = data
                n_channels = timeseries.shape[1]
                # Move the data to the GPU
                timeseries = timeseries.float().to(self.device)
                timeseries = timeseries.reshape(-1, 1, timeseries.shape[-1])
                input_mask = input_mask.to(self.device).long()
                input_mask = input_mask.repeat_interleave(n_channels, axis=0)
                mask = (
                    mask_generator.generate_mask(
                        x=timeseries, input_mask=input_mask
                    )
                    .to(self.device)
                    .long()
                )
                output = self.model(
                    x_enc=timeseries, input_mask=input_mask, mask=mask
                )
                # with torch.amp.autocast(device_type='cuda'):
                recon_loss = criterion(output.reconstruction, timeseries)
                observed_mask = input_mask * (1 - mask)
                masked_loss = observed_mask * recon_loss
                loss = masked_loss.nansum() / (observed_mask.nansum() + 1e-7)

            elif task_name == "detection":
                timeseries, input_mask, label = data
                n_channels = timeseries.shape[1]
                seq_len = timeseries.shape[-1]
                timeseries = (
                    timeseries.reshape(-1, 1, seq_len).float().to(self.device)
                )
                input_mask = input_mask.to(self.device).long()
                input_mask = input_mask.repeat_interleave(n_channels, axis=0)
                mask = (
                    mask_generator.generate_mask(
                        x=timeseries, input_mask=input_mask
                    )
                    .to(self.device)
                    .long()
                )
                output = self.model(
                    x_enc=timeseries, input_mask=input_mask, mask=mask
                )
                # with torch.amp.autocast(device_type='cuda'):
                loss = criterion(output.reconstruction, timeseries)

            elif task_name == "classification":
                timeseries, input_mask, label = data
                timeseries = timeseries.to(self.device).float()
                label = label.to(self.device).long()
                output = self.model(x_enc=timeseries)
                # with torch.amp.autocast(device_type='cuda'):
                loss = criterion(output.logits, label)

            optimizer.zero_grad(set_to_none=True)
            # Scales the loss for mixed precision training
            scaler.scale(loss).backward()

            # Clip gradients
            scaler.unscale_(optimizer)
            torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm)

            scaler.step(optimizer)
            scaler.update()

            losses.append(loss.item())

        losses = np.array(losses)
        average_loss = np.average(losses)
        print(f"Epoch {epoch}: Train loss: {average_loss:.3f}")

        scheduler.step()

    return self.model

plot(dataset, task_name='forecasting')

Plot the forecast results. Args: dataset: dataset for plotting, call get_data_loader() to get the dataloader task_name: str, task name, forecasting, imputation, detection, classification

Source code in Samay\src\samay\model.py 
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def plot(self, dataset, task_name="forecasting"):
    """
    Plot the forecast results.
    Args:
        dataset: dataset for plotting, call get_data_loader() to get the dataloader
        task_name: str, task name, forecasting, imputation, detection, classification
    """
    dataloader = dataset.get_data_loader()
    criterion = torch.nn.MSELoss()
    self.model.to(self.device)
    self.model.eval()
    if task_name == "forecasting":
        trues, preds, histories, losses = [], [], [], []
        with torch.no_grad():
            for i, data in enumerate(dataloader):
                # unpack the data
                timeseries, input_mask, forecast = data
                # Move the data to the GPU
                timeseries = timeseries.float().to(self.device)
                input_mask = input_mask.to(self.device)
                forecast = forecast.float().to(self.device)

                output = self.model(x_enc=timeseries, input_mask=input_mask)
                loss = criterion(output.forecast, forecast)
                losses.append(loss.item())
                trues.append(forecast.detach().cpu().numpy())
                preds.append(output.forecast.detach().cpu().numpy())
                histories.append(timeseries.detach().cpu().numpy())

        losses = np.array(losses)
        average_loss = np.average(losses)
        trues = np.concatenate(trues, axis=0)
        preds = np.concatenate(preds, axis=0)
        histories = np.concatenate(histories, axis=0)

        visualize(
            task_name="forecasting", trues=trues, preds=preds, history=histories
        )

        # return average_loss, trues, preds, histories

    elif task_name == "imputation":
        trues, preds, masks = [], [], []
        mask_generator = Masking(mask_ratio=0.25)
        with torch.no_grad():
            for i, data in enumerate(dataloader):
                # unpack the data
                timeseries, input_mask = data
                trues.append(timeseries.numpy())
                n_channels = timeseries.shape[1]
                # Move the data to the GPU
                timeseries = timeseries.float().to(self.device)
                timeseries = timeseries.reshape(-1, 1, timeseries.shape[-1])
                # print(input_mask.shape)
                input_mask = input_mask.to(self.device).long()
                input_mask = input_mask.repeat_interleave(n_channels, axis=0)
                # print(timeseries.shape, input_mask.shape)
                mask = (
                    mask_generator.generate_mask(
                        x=timeseries, input_mask=input_mask
                    )
                    .to(self.device)
                    .long()
                )
                output = self.model(
                    x_enc=timeseries, input_mask=input_mask, mask=mask
                )
                reconstruction = output.reconstruction.reshape(
                    -1, n_channels, timeseries.shape[-1]
                )
                mask = mask.reshape(-1, n_channels, timeseries.shape[-1])
                preds.append(reconstruction.detach().cpu().numpy())
                masks.append(mask.detach().cpu().numpy())

        trues = np.concatenate(trues, axis=0)
        preds = np.concatenate(preds, axis=0)
        masks = np.concatenate(masks, axis=0)

        visualize(task_name="imputation", trues=trues, preds=preds, masks=masks)

        # return trues, preds, masks

    elif task_name == "detection":
        trues, preds, labels = [], [], []
        with torch.no_grad():
            for i, data in enumerate(dataloader):
                # unpack the data
                timeseries, input_mask, label = data
                timeseries = timeseries.to(self.device).float()
                input_mask = input_mask.to(self.device).long()
                label = label.to(self.device).long()
                output = self.model(x_enc=timeseries, input_mask=input_mask)

                trues.append(timeseries.detach().cpu().numpy())
                preds.append(output.reconstruction.detach().cpu().numpy())
                labels.append(label.detach().cpu().numpy())

        trues = np.concatenate(trues, axis=0).flatten()
        preds = np.concatenate(preds, axis=0).flatten()
        labels = np.concatenate(labels, axis=0).flatten()

        visualize(task_name="detection", trues=trues, preds=preds, labels=labels, pad_len=dataset.pad_len)

ChronosModel

Language model-based time-series forecasting with tokenization.

ChronosModel(config=None, repo=None)

Bases: Basemodel

Parameters:

Name Type Description Default
config

dict, model configuration

 None
repo

str, Huggingface model repository id

 None
Source code in Samay\src\samay\model.py 
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def __init__(self, config=None, repo=None):
    """
    Args:
        config: dict, model configuration
        repo: str, Huggingface model repository id
    """
    super().__init__(config=config, repo=repo)
    if repo:
        print("Loading Chronos model from Huggingface repository")
        try:
            self.pipeline = ChronosPipeline.from_pretrained(
                repo, device_map=self.device
            )
        except:
            raise ValueError(f"Repository {repo} not found")
    else:
        print("Initializing a new Chronos model without pre-trained weights")
        self.pipeline = ChronosPipeline(config=ChronosConfig(**config))

evaluate(dataset, horizon_len, quantile_levels, **kwargs)

Evaluate the model. Args: dataset: dataset for evaluation, call get_data_loader() to get the dataloader horizon_len: int, forecast horizon length quantile_levels: list, list of quantile levels Returns: Dict[str, float]: evaluation metrics, including mse, mae, mase, mape, rmse, nrmse, smape, msis, nd, mwsq, crps

Source code in Samay\src\samay\model.py 
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def evaluate(self, dataset, horizon_len, quantile_levels, **kwargs):
    """
    Evaluate the model.
    Args:
        dataset: dataset for evaluation, call get_data_loader() to get the dataloader
        horizon_len: int, forecast horizon length
        quantile_levels: list, list of quantile levels
    Returns:
        Dict[str, float]: evaluation metrics, including mse, mae, mase, mape, rmse, nrmse, smape, msis, nd, mwsq, crps
    """
    dataloader = dataset.get_data_loader()
    trues, preds, histories, quantile_forecasts = [], [], [], []
    for i, data in enumerate(dataloader):
        input_seq = data["input_seq"]
        forecast_seq = data["forecast_seq"]
        shape = input_seq.shape
        input_seq = input_seq.reshape(shape[0] * shape[1], shape[2])
        input_seq = torch.tensor(input_seq)
        quantiles, mean = self.pipeline.predict_quantiles(
            context=input_seq,
            prediction_length=horizon_len,
            quantile_levels=quantile_levels,
            limit_prediction_length=False,
        )
        trues.append(forecast_seq.detach().cpu().numpy())
        mean = mean.reshape(
            forecast_seq.shape[0], forecast_seq.shape[1], forecast_seq.shape[2]
        )
        preds.append(mean.detach().cpu().numpy())
        quantiles = quantiles.reshape(
            quantiles.shape[-1],
            forecast_seq.shape[0],
            forecast_seq.shape[1],
            forecast_seq.shape[2],
        )
        quantile_forecasts.append(quantiles.detach().cpu().numpy())
        input_seq = input_seq.reshape(shape[0], shape[1], shape[2])
        histories.append(input_seq.detach().cpu().numpy())

    trues = np.concatenate(trues, axis=0)
    preds = np.concatenate(preds, axis=0)
    histories = np.concatenate(histories, axis=0)
    quantile_forecasts = np.concatenate(quantile_forecasts, axis=1)

    mse = MSE(trues, preds)
    mae = MAE(trues, preds)
    mase = MASE(trues, preds)
    mape = MAPE(trues, preds)
    rmse = RMSE(trues, preds)
    nrmse = NRMSE(trues, preds)
    smape = SMAPE(trues, preds)
    msis = MSIS(trues, preds)
    nd = ND(trues, preds)
    mwsq = MWSQ(trues, quantile_forecasts, quantile_levels)
    crps = CRPS(trues, quantile_forecasts, quantile_levels)

    return {
        "mse": mse,
        "mae": mae,
        "mase": mase,
        "mape": mape,
        "rmse": rmse,
        "nrmse": nrmse,
        "smape": smape,
        "msis": msis,
        "nd": nd,
        "mwsq": mwsq,
        "crps": crps,
    }

finetune(dataset, **kwargs)

Parameters:

Name Type Description Default
dataset

dataset for finetuning, call get_data_loader() to get the dataloader

 required
Source code in Samay\src\samay\model.py 
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def finetune(self, dataset, **kwargs):
    """
    Args:
        dataset: dataset for finetuning, call get_data_loader() to get the dataloader
    """
    # Todo: finetune model
    finetune_model = self.pipeline.model.model
    dataloader = dataset.get_data_loader()
    finetune_model.to(self.device)
    finetune_model.train()
    optimizer = torch.optim.AdamW(finetune_model.parameters(), lr=1e-4)

    avg_loss = 0

    for epoch in range(5):
        for i, data in enumerate(dataloader):
            input_ids = data["input_ids"].to(self.device)
            ids_shape = input_ids.shape
            input_ids = input_ids.reshape(ids_shape[0] * ids_shape[1], ids_shape[2])
            attention_mask = data["attention_mask"].to(self.device)
            mask_shape = attention_mask.shape
            attention_mask = attention_mask.reshape(
                mask_shape[0] * mask_shape[1], mask_shape[2]
            )
            labels = data["labels"].to(self.device)
            label_shape = labels.shape
            labels = labels.reshape(label_shape[0] * label_shape[1], label_shape[2])
            optimizer.zero_grad()
            output = finetune_model(
                input_ids, attention_mask=attention_mask, labels=labels
            )
            loss = output.loss
            loss.backward()
            optimizer.step()
            avg_loss += loss.item()
        avg_loss /= len(dataloader)
        print(f"Epoch {epoch}, Loss: {avg_loss}")

    finetune_model.eval()

plot(dataset, horizon_len, quantile_levels, **kwargs)

Plot the forecast results. Args: dataset: dataset for plotting, call get_data_loader() to get the dataloader horizon_len: int, forecast horizon length quantile_levels: list, list of quantile levels

Source code in Samay\src\samay\model.py 
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def plot(self, dataset, horizon_len, quantile_levels, **kwargs):
    """
    Plot the forecast results.
    Args:
        dataset: dataset for plotting, call get_data_loader() to get the dataloader
        horizon_len: int, forecast horizon length
        quantile_levels: list, list of quantile levels
    """
    # Todo: forecast
    dataloader = dataset.get_data_loader()
    trues, preds, histories, quantiles = [], [], [], []
    for i, data in enumerate(dataloader):
        input_seq = data["input_seq"]
        forecast_seq = data["forecast_seq"]
        shape = input_seq.shape
        input_seq = input_seq.reshape(shape[0] * shape[1], shape[2])
        input_seq = torch.tensor(input_seq)
        quantile_outputs, mean = self.pipeline.predict_quantiles(
            context=input_seq,
            prediction_length=horizon_len,
            quantile_levels=quantile_levels,
        )
        trues.append(forecast_seq.detach().cpu().numpy())
        mean = mean.reshape(
            forecast_seq.shape[0], forecast_seq.shape[1], forecast_seq.shape[2]
        )
        preds.append(mean.detach().cpu().numpy())
        input_seq = input_seq.reshape(shape[0], shape[1], shape[2])
        histories.append(input_seq.detach().cpu().numpy())
        quantiles.append(quantile_outputs.detach().cpu().numpy().transpose(2, 0, 1))  # q, b, h

    trues = np.concatenate(trues, axis=0)
    preds = np.concatenate(preds, axis=0)
    histories = np.concatenate(histories, axis=0)
    quantiles = np.concatenate(quantiles, axis=1)

    visualize(
        task_name="forecasting",
        trues=trues,
        preds=preds,
        history=histories,
        quantiles=quantiles,
        **kwargs,
    )

MoiraiTSModel

Salesforce's universal time series forecasting transformer.

MoiraiTSModel(config=None, repo=None, model_type='moirai-moe', model_size='small', **kwargs)

Bases: Basemodel

Source code in Samay\src\samay\model.py 
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def __init__(
    self,
    config=None,
    repo=None,
    model_type="moirai-moe",
    model_size="small",
    **kwargs,
):
    super().__init__(config=config, repo=repo)
    # config.get(<key>, <default_value> if key not found)
    self.model_type = config.get("model_type", model_type)
    self.horizon_len = config.get("horizon_len", 32)
    self.context_len = config.get("context_len", 128)
    self.patch_size = config.get("patch_size", 16)
    self.batch_size = config.get("batch_size", 16)
    self.quantiles = config.get("quantiles", [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]) if self.model_type == "moirai2" else []
    self.num_samples = config.get("num_samples", 100) if self.model_type != "moirai2" else len(self.quantiles)    # for moirai2, we regard num_samples as the number of quantiles
    self.target_dim = config.get("target_dim", 1)
    self.feat_dynamic_real_dim = config.get("feat_dynamic_real_dim", 0)
    self.past_feat_dynamic_real_dim = config.get("past_feat_dynamic_real_dim", 0)
    self.finetuned_model = None

    self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    if self.model_type == "moirai":  # standard moirai
        if repo is None:
            repo = f"Salesforce/moirai-1.1-R-{model_size}"
        self.repo = repo

        self.model = MoiraiForecast(
            module=MoiraiModule.from_pretrained(self.repo),
            prediction_length=self.horizon_len,
            context_length=self.context_len,
            patch_size=self.patch_size,
            num_samples=self.num_samples,
            target_dim=self.target_dim,
            feat_dynamic_real_dim=self.feat_dynamic_real_dim,
            past_feat_dynamic_real_dim=self.past_feat_dynamic_real_dim,
        )

    elif self.model_type == "moirai-moe":  # moirai with Mixture of Experts
        if repo is None:
            repo = f"Salesforce/moirai-moe-1.0-R-{model_size}"
        self.repo = repo

        self.model = MoiraiMoEForecast(
            module=MoiraiMoEModule.from_pretrained(self.repo),
            prediction_length=self.horizon_len,
            context_length=self.context_len,
            patch_size=self.patch_size,
            num_samples=self.num_samples,
            target_dim=self.target_dim,
            feat_dynamic_real_dim=self.feat_dynamic_real_dim,
            past_feat_dynamic_real_dim=self.past_feat_dynamic_real_dim,
        )

    elif self.model_type == "moirai2":
        if repo is None:
            repo = f"Salesforce/moirai-2.0-R-{model_size}"
        self.repo = repo

        self.model = Moirai2Forecast(
            module=Moirai2Module.from_pretrained(self.repo),
            prediction_length=self.horizon_len,
            context_length=self.context_len,
            target_dim=self.target_dim,
            feat_dynamic_real_dim=self.feat_dynamic_real_dim,
            past_feat_dynamic_real_dim=self.past_feat_dynamic_real_dim,
        )
    self.model.to(self.device)

evaluate(dataset: MoiraiDataset, metrics: list[str] = ['MSE'], output_transforms: transforms.Compose = None, num_sample_flag: bool = False, zero_shot: bool = True, leaderboard: bool = False, **kwargs)

For a given test dataset, we evaluate the model using the given metrics.

Parameters:

Name Type Description Default
dataset MoiraiDataset

Dataset to evaluate the model on.

 required
metrics list

Metrics you want to evaluate the model on. Defaults to ["MSE"].

 ['MSE']
output_transforms Compose

A set of transforms to be applied on the model output. Defaults to None.

 None
num_sample_flage bool

If True, the model will use number of samples to sample from the distribution for forecasting. Defaults to False.

 required
zero_shot bool

If True, the standard model will be used, else the finetuned model will be used. Defaults to True.

 True
leaderboard bool

If True, only the metrics will be returned. Defaults to False.

 False

Raises:

Type Description
ValueError

Any metric other than "MSE" or "MASE is not supported.

Returns:

Name Type Description
dict

Evaluation results for each column (variate).
dict

True values for each column (variate).
dict

Predictions for each column (variate).
dict

Histories for each column (variate).
Source code in Samay\src\samay\model.py 
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def evaluate(
    self,
    dataset: MoiraiDataset,
    metrics: list[str] = ["MSE"],
    output_transforms: transforms.Compose = None,
    num_sample_flag: bool = False,
    zero_shot: bool = True,
    leaderboard: bool = False,
    **kwargs,
):
    """For a given test dataset, we evaluate the model using the given metrics.

    Args:
        dataset (MoiraiDataset): Dataset to evaluate the model on.
        metrics (list, optional): Metrics you want to evaluate the model on. Defaults to ["MSE"].
        output_transforms (transforms.Compose, optional): A set of transforms to be applied on the model output. Defaults to None.
        num_sample_flage (bool, optional): If True, the model will use number of samples to sample from the distribution for forecasting. Defaults to False.
        zero_shot (bool, optional): If True, the standard model will be used, else the finetuned model will be used. Defaults to True.
        leaderboard (bool, optional): If True, only the metrics will be returned. Defaults to False.

    Raises:
        ValueError: Any metric other than "MSE" or "MASE is not supported.

    Returns:
        dict: Evaluation results for each column (variate).
        dict: True values for each column (variate).
        dict: Predictions for each column (variate).
        dict: Histories for each column (variate).
    """
    # required fields for the forecast
    inp_names = [
        "past_target",
        "past_observed_target",
        "past_is_pad",
    ]
    if self.feat_dynamic_real_dim > 0:
        inp_names.extend(["feat_dynamic_real", "observed_feat_dynamic_real"])
    if self.past_feat_dynamic_real_dim > 0:
        inp_names.extend(
            ["past_feat_dynamic_real", "past_observed_feat_dynamic_real"]
        )

    # get the batched data
    inference_loader = dataset.get_dataloader()

    # set model in eval mode
    self.model.eval()

    # predict
    forecast = []
    with torch.no_grad():
        for batch in inference_loader:
            for k, v in batch.items():
                if isinstance(v, torch.Tensor):
                    batch[k] = v.to(self.device)
            inputs = filter_dict(batch, inp_names)
            if (
                zero_shot
            ):  # Finetune forward asks for keys like target, observed_mask, etc.
                outputs = (
                    self.model.forward(**inputs).detach().cpu().numpy()
                )  # convert the tensor output to numpy array
            elif not zero_shot and self.finetuned_model is not None:
                # get the context and prediction token lengths
                context_token_len = math.ceil(self.context_len / self.patch_size)
                pred_token_len = math.ceil(self.horizon_len / self.patch_size)
                num_context_tokens = context_token_len * self.target_dim
                num_pred_tokens = pred_token_len * self.target_dim
                num_token_generate = self.model.module.num_predict_token if self.model_type == 'moirai2' else 1

                # prepare the inputs
                pred_index = torch.arange(
                    start=context_token_len - 1,
                    end=num_context_tokens,
                    step=context_token_len,
                )
                assign_index = torch.arange(
                    start=num_context_tokens,
                    end=num_context_tokens + num_pred_tokens,
                    step=pred_token_len,
                )

                old_keys = list(inputs.keys())
                inputs = self.preprocess_inputs(inputs)
                new_keys = list(inputs.keys())
                inputs = filter_dict(inputs, list(set(new_keys) - set(old_keys)))
                for k, v in inputs.items():
                    if isinstance(v, torch.Tensor):
                        inputs[k] = v.to(self.device)

                if self.model_type == "moirai2":
                    quantile_prediction = repeat(
                        inputs["target"],
                        "... patch_size -> ... num_quantiles patch_size",
                        num_quantiles=len(self.model.module.quantile_levels),
                        patch_size=self.model.module.patch_size,
                    ).clone()

                # get the forecast
                if pred_token_len <= num_token_generate:
                    if self.model_type in ["moirai", "moirai-moe"]:
                        # old version models generate distribution outputs
                        distr = self.finetuned_model.forward(**inputs)
                        preds = distr.sample(torch.Size((self.num_samples,)))
                        preds[..., assign_index, :] = preds[..., pred_index, :]
                        outputs = self._format_preds(
                            preds=preds,
                            context_token_len=context_token_len,
                            pred_token_len=pred_token_len,
                        )

                    elif self.model_type == "moirai2":
                        # new version model directly generates prediction outputs
                        preds = self.finetuned_model.forward(**inputs)  # (batch, all_pred_idx, num_tokens*quantiles*patch_size) # all_pred_idx = (c1/ps, c2/ps, ..., cN/ps, p1/ps, p2/ps, ..., pM/ps)
                        preds, adjusted_assign_index = self.structure_multi_predict(
                            per_var_predict_token=pred_token_len,
                            pred_index=pred_index,
                            assign_index=assign_index,
                            preds=preds,
                        )
                        quantile_prediction[..., adjusted_assign_index, :, :] = preds
                        outputs = self._format_preds(
                            preds=quantile_prediction,
                            context_token_len=context_token_len,
                            pred_token_len=pred_token_len,
                            quantile_prediction=True,
                            target_dim=self.target_dim,
                        )
                else:
                    if self.model_type in ["moirai", "moirai-moe"]:
                        # old version models generate distribution outputs
                        distr = self.finetuned_model.forward(**inputs)
                        preds = distr.sample(torch.Size((self.num_samples,))) # (num_samples, batch, combine_seq, patch), combined_seq = all pred index
                    elif self.model_type == "moirai2":
                        # new version model directly generates prediction outputs
                        preds = self.finetuned_model.forward(**inputs)  # (batch, all_pred_idx, num_tokens*quantiles*patch_size)
                        preds, adjusted_assign_index = self.structure_multi_predict(
                            self.model.module.num_predict_token,
                            pred_index,
                            assign_index,
                            preds,
                        )
                        quantile_prediction[..., adjusted_assign_index, :, :] = preds

                    expand_target = (
                        inputs["target"]
                        .unsqueeze(0)
                        .repeat(self.num_samples, 1, 1, 1)
                    )
                    if self.model_type == "moirai2":
                        expand_target = expand_target.permute(1, 0, 2, 3)  # (batch, num_samples, seq_len, patch_size), here num_samples = num_quantiles
                    expand_prediction_mask = (
                        inputs["prediction_mask"]
                        .unsqueeze(0)
                        .repeat(self.num_samples, 1, 1)
                    )
                    if self.model_type == "moirai2":
                        expand_prediction_mask = expand_prediction_mask.permute(1, 0, 2)  # (batch, num_samples, seq_len), here num_samples = num_quantiles
                    expand_observed_mask = (
                        inputs["observed_mask"]
                        .unsqueeze(0)
                        .expand(self.num_samples, -1, -1, -1)
                    )
                    if self.model_type == "moirai2":
                        expand_observed_mask = expand_observed_mask.permute(1, 0, 2, 3)  # (batch, num_samples, seq_len, patch_size), here num_samples = num_quantiles
                    expand_sample_id = (
                        inputs["sample_id"]
                        .unsqueeze(0)
                        .expand(self.num_samples, -1, -1)
                    )
                    if self.model_type == "moirai2":
                        expand_sample_id = expand_sample_id.permute(1, 0, 2)  # (batch, num_samples, seq_len), here num_samples = num_quantiles
                    expand_time_id = (
                        inputs["time_id"]
                        .unsqueeze(0)
                        .expand(self.num_samples, -1, -1)
                    )
                    if self.model_type == "moirai2":
                        expand_time_id = expand_time_id.permute(1, 0, 2)  # (batch, num_samples, seq_len), here num_samples = num_quantiles
                    expand_variate_id = (
                        inputs["variate_id"]
                        .unsqueeze(0)
                        .expand(self.num_samples, -1, -1)
                    )
                    if self.model_type == "moirai2":
                        expand_variate_id = expand_variate_id.permute(1, 0, 2)  # (batch, num_samples, seq_len), here num_samples = num_quantiles
                    expand_patch_size = (
                        inputs["patch_size"]
                        .unsqueeze(0)
                        .expand(self.num_samples, -1, -1)
                    )

                    if self.model_type == "moirai2":
                        expand_target[..., adjusted_assign_index, :, :] = preds.permute(0, 2, 1, 3)  # (batch, num_quantiles, seq_len, patch_size)
                        expand_prediction_mask[..., adjusted_assign_index] = False
                    else:
                        expand_target[..., assign_index, :] = preds[..., pred_index, :]
                        expand_prediction_mask[..., assign_index] = False

                    remain_step = pred_token_len - num_token_generate
                    while remain_step > 0:
                        if self.model_type == "moirai2":
                            preds = self.model.module(
                                expand_target,
                                expand_observed_mask,
                                expand_sample_id,
                                expand_time_id,
                                expand_variate_id,
                                expand_prediction_mask,
                                training_mode=False,
                            )
                            pred_index = assign_index + self.model.module.num_predict_token - 1
                            assign_index = pred_index + 1
                            preds, adjusted_assign_index = self.structure_multi_predict(
                                (
                                    self.model.module.num_predict_token
                                    if remain_step - self.model.module.num_predict_token > 0
                                    else remain_step
                                ),
                                pred_index,
                                assign_index,
                                preds,
                            )
                            quantile_prediction_next_step = rearrange(
                                preds,
                                "... num_quantiles_prev pred_index num_quantiles patch_size -> ... pred_index (num_quantiles_prev num_quantiles) patch_size",
                                num_quantiles=self.model.module.num_quantiles,
                                patch_size=self.model.module.patch_size,
                            )
                            # compute quantile forecast based on quantile forecast from quantile samples
                            quantile_prediction_next_step = torch.quantile(
                                quantile_prediction_next_step,
                                torch.tensor(
                                    self.model.module.quantile_levels,
                                    device=self.device,
                                    dtype=torch.float32,
                                ),
                                dim=-2,
                            )
                            quantile_prediction[..., adjusted_assign_index, :, :] = rearrange(
                                quantile_prediction_next_step,
                                "num_quantiles ... patch_size -> ... num_quantiles patch_size",
                            )

                            # choose current quantiles values as point forecast for next-step prediction
                            expand_target[..., adjusted_assign_index, :] = rearrange(
                                quantile_prediction_next_step,
                                "num_quantiles batch_size predict_token patch_size -> batch_size num_quantiles predict_token patch_size",
                                num_quantiles=self.model.module.num_quantiles,
                                patch_size=self.model.module.patch_size,
                                predict_token=len(adjusted_assign_index),
                            )
                            expand_prediction_mask[..., adjusted_assign_index] = False

                            remain_step -= self.model.module.num_predict_token
                        else:
                            distr = self.finetuned_model.forward(
                                expand_target,
                                expand_observed_mask,
                                expand_sample_id,
                                expand_time_id,
                                expand_variate_id,
                                expand_prediction_mask,
                                expand_patch_size,
                            )
                            preds = distr.sample(torch.Size((1,)))
                            _, _, bs, token, ps = preds.shape
                            preds = preds.view(-1, bs, token, ps)

                            pred_index = assign_index
                            assign_index = assign_index + 1
                            expand_target[..., assign_index, :] = preds[
                                ..., pred_index, :
                            ]
                            expand_prediction_mask[..., assign_index] = False

                            remain_step -= 1

                    if self.model_type == "moirai2":
                        outputs = self._format_preds(
                            preds=quantile_prediction,
                            context_token_len=context_token_len,
                            pred_token_len=pred_token_len,
                            quantile_prediction=True,
                            target_dim=self.target_dim,
                        )
                    else:
                        outputs = self._format_preds(
                            preds=expand_target,
                            context_token_len=context_token_len,
                            pred_token_len=pred_token_len,
                        )

            # Apply output transforms
            if output_transforms is not None:
                outputs = output_transforms(outputs)

            # sample if needed
            if num_sample_flag:
                num_collected_samples = outputs[0].shape[0]
                collected_samples = [outputs]
                # do so until we have enough samples
                while num_collected_samples < self.num_samples:
                    outputs = self.model.forward(**inputs).detach().cpu().numpy()
                    # Apply output transforms
                    if output_transforms is not None:
                        outputs = output_transforms(outputs)
                    collected_samples.append(outputs)
                    num_collected_samples += outputs[0].shape[0]
                # stack the collected samples
                outputs = np.stack(
                    [
                        np.concatenate(s)[: self.num_samples]
                        for s in zip(*collected_samples)
                    ]
                )
                # assert that we have the right number of samples
                assert len(outputs[0]) == self.num_samples, (
                    "We do not have enough samples"
                )
            forecast.extend([np.array(x) for x in outputs.tolist()])

    # Iterators for input, label and forecast
    print("Forecasting done....now testing")
    input_it = iter(dataset.dataset.input)
    label_it = iter(dataset.dataset.label)
    forecast_it = iter(forecast)
    # Quantile levels obtained from cli/conf/eval/default.yaml of MOIRAI repository
    if self.model_type == "moirai2":
        quantile_levels = self.quantiles
    else:
        quantile_levels = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]

    trues = {}
    preds = {}
    histories = {}
    quantile_preds = {}
    eval_windows = []

    with torch.no_grad():  # No need to compute gradients
        # Iterate over each window
        for input, label, forecast in zip(input_it, label_it, forecast_it):
            true_values = (
                label["target"].squeeze()
                if isinstance(label["target"], np.ndarray)
                else np.array(label["target"])
            )
            past_values = (
                input["target"].squeeze()
                if isinstance(input["target"], np.ndarray)
                else np.array(input["target"])
            )
            if self.model_type == 'moirai2':
                quantiles = np.array(forecast)
                if len(quantiles.shape) == 3:  # (quantiles, horizon, target_dim)
                    quantiles = quantiles[: len(quantile_levels), : min(self.horizon_len, true_values.shape[0]), :]  # (quantiles, horizon, target_dim)
                elif len(quantiles.shape) == 2:  # (quantiles, horizon)
                    quantiles = quantiles[: len(quantile_levels), : min(self.horizon_len, true_values.shape[0])]  # (quantiles, horizon)
            else:
                quantiles = np.percentile(
                    forecast[:, : min(self.horizon_len, true_values.shape[0])],
                    [q * 100 for q in quantile_levels],
                    axis=0,
                )
            pred_values = np.median(forecast, axis=0)[
                : min(self.horizon_len, true_values.shape[0])
            ]  # Median of the forecasted values

            length = len(past_values)

            eval = []
            for metric in metrics:
                if metric == "MSE":
                    eval.append(mean_squared_error(true_values, pred_values))

                # MASE = current model's MAE / naive model's MAE
                elif metric == "MASE":
                    forecast_error = np.mean(np.abs(true_values - pred_values))
                    naive_error = np.mean(
                        np.abs(true_values[1:] - true_values[:-1])
                    )
                    if naive_error == 0:  # Avoid division by zero
                        eval.append(np.inf)
                    else:
                        eval.append(forecast_error / naive_error)
                else:
                    raise ValueError(f"Unsupported metric: {metric}")
            eval_windows.append(eval)

            # Update history, true values and predictions
            if length not in histories.keys():
                histories[length] = []
                trues[length] = []
                preds[length] = []
                quantile_preds[length] = []
            histories[length].append(past_values)
            trues[length].append(true_values)
            preds[length].append(pred_values)
            quantile_preds[length].append(quantiles)

    eval_windows = np.mean(np.array(eval_windows), axis=0)
    eval_results = {}
    for i in range(len(metrics)):
        eval_results[metrics[i]] = eval_windows[i]

    # Convert to numpy arrays
    histories = [np.array(histories[key]) for key in histories.keys()]
    trues = [np.array(trues[key]) for key in trues.keys()]
    preds = [np.array(preds[key]) for key in preds.keys()]
    quantile_preds = [
        np.array(quantile_preds[key]) for key in quantile_preds.keys()
    ]
    quantile_preds = [np.transpose(q, (1, 0, 2)) for q in quantile_preds]

    # print([h.shape for h in histories])
    # denormalize the data
    if dataset.normalize:
        trues = dataset._denormalize_data(trues)
        preds = dataset._denormalize_data(preds)
        histories = dataset._denormalize_data(histories)
        quantile_preds = [
            dataset._denormalize_data(q)
            for q in quantile_preds
        ]

    mse = np.mean(np.array([MSE(t, p) for t, p in zip(trues, preds)]), axis=0)
    mae = np.mean(np.array([MAE(t, p) for t, p in zip(trues, preds)]), axis=0)
    mase = np.mean(np.array([MASE(t, p) for t, p in zip(trues, preds)]), axis=0)
    mape = np.mean(np.array([MAPE(t, p) for t, p in zip(trues, preds)]), axis=0)
    rmse = np.mean(np.array([RMSE(t, p) for t, p in zip(trues, preds)]), axis=0)
    nrmse = np.mean(np.array([NRMSE(t, p) for t, p in zip(trues, preds)]), axis=0)
    smape = np.mean(np.array([SMAPE(t, p) for t, p in zip(trues, preds)]), axis=0)
    msis = np.mean(np.array([MSIS(t, p) for t, p in zip(trues, preds)]), axis=0)
    nd = np.mean(np.array([ND(t, p) for t, p in zip(trues, preds)]), axis=0)

    mwsq = np.mean(
        np.array([MWSQ(t, p, q) for t, p, q in zip(trues, quantile_preds, quantile_levels)],),
        axis=0,
    )
    crps = np.mean(
        np.array([CRPS(t, p, q) for t, p, q in zip(trues, quantile_preds, quantile_levels)],),
        axis=0,
    )

    leaderboard_metrics = {
        "mse": mse,
        "mae": mae,
        "mase": mase,
        "mape": mape,
        "rmse": rmse,
        "nrmse": nrmse,
        "smape": smape,
        "msis": msis,
        "nd": nd,
        "mwsq": mwsq,
        "crps": crps,
    }

    cleanup_dataloader(inference_loader)

    if leaderboard:
        return leaderboard_metrics
    else:
        return leaderboard_metrics, trues, preds, histories, quantile_preds

finetune(dataset, **kwargs)

Finetune the model on the given dataset.

Parameters:

Name Type Description Default
dataset MoiraiDataset

Dataset containing the input data and relevant functions like dataloaders etc.

 required

Returns:

Name Type Description
_type_

description
Source code in Samay\src\samay\model.py 
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def finetune(self, dataset, **kwargs):
    """Finetune the model on the given dataset.

    Args:
        dataset (MoiraiDataset): Dataset containing the input data and relevant functions like dataloaders etc.

    Returns:
        _type_: _description_
    """
    model_size = self.repo.split("-")[-1]
    if self.model_type == "moirai":
        model_config = (
            f"../src/uni2ts/cli/conf/finetune/model/moirai_1.1_R_{model_size}.yaml"
        )
    elif self.model_type == "moirai-moe":
        model_config = f"../src/uni2ts/cli/conf/finetune/model/moirai_moe_1.0_R_{model_size}.yaml"
    elif self.model_type == "moirai2":
        # only small version is available for moirai2 for now
        model_config = f"../src/uni2ts/cli/conf/finetune/model/moirai_2.0_R_{model_size}.yaml" 

    with open(model_config, "r") as file:
        fin_model_config = yaml.load(file, Loader=yaml.FullLoader)

    # lr = 1e-4 if "lr" not in fin_model_config else float(fin_model_config["lr"])
    lr = 5e-6
    weight_decay = (
        1e-1
        if "weight_decay" not in fin_model_config
        else float(fin_model_config["weight_decay"])
    )
    self.batch_size = (
        kwargs["batch_size"] if "batch_size" in kwargs else self.batch_size
    )
    epochs = 5
    assert epochs <= kwargs["max_epochs"], (
        "epochs should be less than or equal to max_epochs"
    )

    # Number of batches per epoch required for calculating the number of training steps
    num_batches = len(dataset.dataset) // self.batch_size
    if (
        "num_batches_per_epoch" in kwargs.keys()
    ):  # If num_batches_per_epoch is provided
        num_batches_per_epoch = kwargs["num_batches_per_epoch"]
        epochs = min(epochs, num_batches // num_batches_per_epoch)
    else:
        num_batches_per_epoch = num_batches // epochs

    training_steps = num_batches_per_epoch * kwargs["max_epochs"]
    module_args = convert_module_kwargs(
        fin_model_config["module_kwargs"]
    )  # remove _target_ fields
    if self.model_type == "moirai2":
        self.patch_size = self.model.module.patch_size  # update patch_size
    else:
        self.patch_size = self.model.module.in_proj.in_features_ls[
            0
        ]  # update patch_size

    # Trainer configuration (from uni2ts/cli/train.py)
    # mod_torch is the trainer configuration without _target_ fields or any key
    # whose value is neither a list or dictionary
    if kwargs["tf32"]:
        assert kwargs["mod_torch"]["precision"] == 32, (
            "Precision should be 32 for tf32"
        )
        torch.backends.cuda.matmul.allow_tf32 = True
        torch.backends.cudnn.allow_tf32 = True

    # For now, self.model.module.patch_sizes is just [16] from the config file
    # But in finetune, we are using patch_sizes as [8,16,32,64,128]
    # So, we need to update the patch_sizes in the model
    # self.model.module.patch_sizes = list(module_args["patch_sizes"])

    # instantiate loss function
    spec = fin_model_config["loss_func"]
    def locate(path: str):
        """Locate a class from a string path."""
        module_path, class_name = path.rsplit(".", 1)
        module = importlib.import_module(module_path)
        return getattr(module, class_name)   
    cls = locate(spec["_target_"])
    kwargs = {k: v for k, v in spec.items() if k != "_target_"}
    fin_model_config["loss_func"] = cls(**kwargs)

    # Load the model
    FinetunedModel = MoiraiFinetune(
        min_patches=fin_model_config["min_patches"],
        min_mask_ratio=fin_model_config["min_mask_ratio"],
        max_mask_ratio=fin_model_config["max_mask_ratio"],
        max_dim=fin_model_config["max_dim"],
        num_training_steps=training_steps,
        num_warmup_steps=fin_model_config["num_warmup_steps"],
        module_kwargs=module_args,
        beta1=fin_model_config["beta1"],
        beta2=fin_model_config["beta2"],
        val_metric=fin_model_config["val_metric"],
        weight_decay=fin_model_config["weight_decay"],
        model_type=self.model_type,
        model_size=model_size,
        loss_func=fin_model_config["loss_func"],
    )

    # Pytorch version
    FinetunedModel.to(self.device)
    FinetunedModel.train()  # Set model to training mode

    # Freeze the transformer layers
    # First we finetune the whole model - Not good
    # Freeze the last two encoder layers and param_proj
    for mn, m in FinetunedModel.named_modules():
        for pn, p in m.named_parameters():
            if not p.requires_grad:
                continue

            fpn = f"{mn}.{pn}" if mn else pn
            # print(f"Checking fpn before freezing: {fpn}")

            # Freeze everything except the last 2 encoder layers and param_proj
            if fpn.split(".")[1] in [
                "in_proj",
                "res_proj",
                "feat_proj",
            ]:  # Freeze all initial layers
                p.requires_grad = False
            elif fpn.split(".")[1] == "encoder":
                if (
                    len(fpn.split(".")) > 3
                    and fpn.split(".")[2] == "layers"
                    and int(fpn.split(".")[3]) < 5
                ):  # Freeze all but last two encoder layers
                    p.requires_grad = False

    # Load the dataset
    dataloader = (
        dataset.get_dataloader()
    )  # look at if mode=="train" case for more info

    decay = set()
    no_decay = set()

    whitelist_params = (
        LearnedProjection,
        MultiInSizeLinear,
        MultiOutSizeLinear,
        nn.Linear,
    )
    blacklist_params = (
        BinaryAttentionBias,
        LearnedEmbedding,
        RMSNorm,
        nn.Embedding,
        nn.LayerNorm,
    )

    for mn, m in FinetunedModel.named_modules():
        for pn, p in m.named_parameters():
            if not p.requires_grad:
                continue

            fpn = f"{mn}.{pn}" if mn else pn

            if pn.endswith("bias"):
                no_decay.add(fpn)
            elif pn.endswith("weight") and isinstance(m, whitelist_params):
                decay.add(fpn)
            elif pn.endswith("weight") and isinstance(m, blacklist_params):
                no_decay.add(fpn)

    # validate that we considered every parameter
    param_dict = {
        pn: p for pn, p in FinetunedModel.named_parameters() if p.requires_grad
    }

    optim_groups = [
        {
            "params": filter(
                lambda p: p.requires_grad,
                [v for k, v in param_dict.items() if k in (list(no_decay))],
            ),
            "weight_decay": weight_decay,
        },
        {
            "params": filter(
                lambda p: p.requires_grad,
                [v for k, v in param_dict.items() if k not in (list(no_decay))],
            ),
            "weight_decay": 0.0,
        },
    ]
    optimizer = torch.optim.AdamW(
        optim_groups,
        lr=lr,
        betas=(FinetunedModel.hparams.beta1, FinetunedModel.hparams.beta2),
        eps=1e-6,
    )

    # scheduler = get_scheduler(
    #     SchedulerType.COSINE_WITH_RESTARTS,
    #     optimizer,
    #     num_warmup_steps=FinetunedModel.hparams.num_warmup_steps,
    #     num_training_steps=FinetunedModel.hparams.num_training_steps,
    # )

    loss_vals = []
    for epoch in range(epochs):
        avg_loss = 0
        for i, (inputs) in enumerate(dataloader):  # each batch is processed
            inputs = self.preprocess_inputs(
                inputs
            )  # patchify and other fields added
            for k, v in inputs.items():
                if isinstance(v, torch.Tensor):
                    inputs[k] = v.to(self.device)
                    if v.dtype == torch.float32 or v.dtype == torch.float64:
                        inputs[k] = inputs[k].requires_grad_()
            optimizer.zero_grad()  # reset gradients
            # distribution of predictions
            torch.autograd.set_detect_anomaly(True)
            outputs = FinetunedModel.forward(
                target=inputs["target"],
                observed_mask=inputs["observed_mask"],
                sample_id=inputs["sample_id"],
                time_id=inputs["time_id"],
                variate_id=inputs["variate_id"],
                prediction_mask=inputs["prediction_mask"],
                patch_size=inputs["patch_size"],
            )

            if self.model_type == "moirai2":
                quantile_prediction = repeat(
                    inputs["target"],
                    "... patch_size -> ... num_quantiles patch_size",
                    num_quantiles=len(self.model.module.quantile_levels),
                    patch_size=self.model.module.patch_size,
                ).clone()
                context_token_len = math.ceil(self.context_len / self.patch_size)
                pred_token_len = math.ceil(self.horizon_len / self.patch_size)
                num_context_tokens = context_token_len * self.target_dim
                num_pred_tokens = pred_token_len * self.target_dim
                pred_index = torch.arange(
                    start=context_token_len - 1,
                    end=num_context_tokens,
                    step=context_token_len,
                )
                assign_index = torch.arange(
                    start=num_context_tokens,
                    end=num_context_tokens + num_pred_tokens,
                    step=pred_token_len,
                )
                preds, adjusted_assign_index = self.structure_multi_predict(
                    per_var_predict_token=pred_token_len,
                    pred_index=pred_index,
                    assign_index=assign_index,
                    preds=outputs,
                )
                quantile_prediction[..., adjusted_assign_index, :, :] = preds
                outputs = rearrange(
                    quantile_prediction,
                    "... num_quantiles patch_size -> ... (num_quantiles patch_size)",
                )

            loss = FinetunedModel.hparams.loss_func(
                pred=outputs,
                **{
                    field: inputs[field]
                    for field in [
                        "target",
                        "prediction_mask",
                        "observed_mask",
                        "sample_id",
                        "variate_id",
                    ]
                },
            )

            loss.backward()
            optimizer.step()
            # scheduler.step()
            avg_loss += loss.item()
        avg_loss /= len(dataloader)
        print(f"Epoch {epoch}: Loss: {avg_loss:.3f}")
        loss_vals.append(avg_loss)
    print("Finetuning done")

    # Plot the loss values
    plt.grid(True)
    plt.plot(loss_vals)
    plt.xlabel("Epoch")
    plt.ylabel("Loss")
    plt.title("Training Loss")
    # plt.savefig(f"training_loss_{epochs}_epochs_wo_scheduler.png")

    self.finetuned_model = FinetunedModel
    print("Fineuned model updated")
    cleanup_dataloader(dataloader)

plot(dataset, zero_shot=False, **kwargs)

Plot the results of the model on the given dataset.

Parameters:

Name Type Description Default
dataset MoiraiDataset

Dataset containing the input data and relevant functions like dataloaders etc.

 required
Source code in Samay\src\samay\model.py 
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def plot(self, dataset, zero_shot=False, **kwargs):
    """Plot the results of the model on the given dataset.

    Args:
        dataset (MoiraiDataset): Dataset containing the input data and relevant functions like dataloaders etc.
    """
    _, trues, preds, history = self.evaluate(dataset, leaderboard=False, zero_shot=zero_shot)
    visualize(
        task_name="forecasting",
        trues=np.concatenate(trues, axis=0),
        preds=np.concatenate(preds, axis=0),
        history=np.concatenate(history, axis=0
        ),
    )

preprocess_inputs(inputs: dict)

Preprocess the inputs to the model - specifically adds the following fields: +--------------------+--------------------------------------+-----------------------+----------------------------------+ | FIELD | DESCRIPTION | TYPE | SHAPE | +--------------------+--------------------------------------+-----------------------+----------------------------------+ | target | Batched time series data | torch.tensor[float] | (batch_size, seq_len, max_patch) | | observed_mask | Binary mask for the context part | torch.tensor[bool] | (batch_size, seq_len, max_patch) | | prediction_mask | Binary mask for the prediction part | torch.tensor[bool] | (batch_size, seq_len) | | time_id | Time index | torch.tensor[int] | (batch_size, seq_len) | | sample_id | Time index | torch.tensor[int] | (batch_size, seq_len) | | variate_id | Index indicating the variate | torch.tensor[int] | (batch_size, seq_len) | | patch_size | Patch size the model should use | torch.tensor[int] | (batch_size, seq_len) | +--------------------+--------------------------------------+-----------------------+----------------------------------+

Parameters:

Name Type Description Default
inputs dict

Dictionary containing the input data.

 required

Returns:

Name Type Description
dict

Preprocessed input data.
Source code in Samay\src\samay\model.py 
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def preprocess_inputs(self, inputs: dict):
    """Preprocess the inputs to the model - specifically adds the following fields:
    +--------------------+--------------------------------------+-----------------------+----------------------------------+
    | FIELD              | DESCRIPTION                          | TYPE                  | SHAPE                            |
    +--------------------+--------------------------------------+-----------------------+----------------------------------+
    | target             | Batched time series data             | torch.tensor[float]   | (batch_size, seq_len, max_patch) |
    | observed_mask      | Binary mask for the context part     | torch.tensor[bool]    | (batch_size, seq_len, max_patch) |
    | prediction_mask    | Binary mask for the prediction part  | torch.tensor[bool]    | (batch_size, seq_len)            |
    | time_id            | Time index                           | torch.tensor[int]     | (batch_size, seq_len)            |
    | sample_id          | Time index                           | torch.tensor[int]     | (batch_size, seq_len)            |
    | variate_id         | Index indicating the variate         | torch.tensor[int]     | (batch_size, seq_len)            |
    | patch_size         | Patch size the model should use      | torch.tensor[int]     | (batch_size, seq_len)            |
    +--------------------+--------------------------------------+-----------------------+----------------------------------+

    Args:
        inputs (dict): Dictionary containing the input data.

    Returns:
        dict: Preprocessed input data.
    """
    (target, observed_mask, sample_id, time_id, variate_id, prediction_mask) = (
        self.model._convert(
            patch_size=self.patch_size,
            past_target=inputs["past_target"],
            past_observed_target=inputs["past_observed_target"],
            past_is_pad=inputs["past_is_pad"],
        )
    )
    inputs["target"] = target
    inputs["observed_mask"] = observed_mask
    inputs["sample_id"] = sample_id
    inputs["time_id"] = time_id
    inputs["variate_id"] = variate_id
    inputs["prediction_mask"] = prediction_mask
    inputs["patch_size"] = torch.tensor(
        np.full(shape=sample_id.shape, fill_value=self.patch_size, dtype=np.int64),
        dtype=torch.int64,
    )

    return inputs

TinyTimeMixerModel

Lightweight and efficient time-series forecasting model.

TinyTimeMixerModel(config=None, repo=None)

Bases: Basemodel

Parameters:

Name Type Description Default
config

dict, model configuration

 None
repo

str, Huggingface model repository id

 None
Source code in Samay\src\samay\model.py 
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def __init__(self, config=None, repo=None):
    """
    Args:
        config: dict, model configuration
        repo: str, Huggingface model repository id
    """
    super().__init__(config=config, repo=repo)
    if repo:
        context_len = config["context_len"]
        horizon_len = config["horizon_len"]
        horizon_list = [96, 192, 336, 720]
        closest_larger_horizon = min([x for x in horizon_list if x >= horizon_len])
        if context_len == 512 and closest_larger_horizon == 96:
            revision = "main"
        else:
            revision = f"{context_len}-{closest_larger_horizon}-r2"
        self.model = TinyTimeMixerForPrediction.from_pretrained(
            repo, revision=revision, prediction_filter_length=horizon_len
        )
        # self.model = self.model.to(self.device)
    else:
        raise ValueError("TinyTimeMixer model requires a repository")

evaluate(dataset, **kwargs)

Evaluate the model. Args: dataset: dataset for evaluation, call get_data_loader() to get the dataloader Returns: Dict[str, float]: evaluation metrics, including mse, mae, mase, mape, rmse, nrmse, smape, msis, nd

Source code in Samay\src\samay\model.py 
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def evaluate(self, dataset, **kwargs):
    """
    Evaluate the model.
    Args:
        dataset: dataset for evaluation, call get_data_loader() to get the dataloader
    Returns:
        Dict[str, float]: evaluation metrics, including mse, mae, mase, mape, rmse, nrmse, smape, msis, nd
    """
    dataloader = dataset.get_data_loader()
    trues, preds, histories = [], [], []
    self.model.to(self.device)
    self.model.eval()
    with torch.no_grad():
        for i, data in enumerate(dataloader):
            context, forecast_seq = data
            context = context.float().permute(0, 2, 1).to(self.device)
            forecast_seq = forecast_seq.float().permute(0, 2, 1).to(self.device)
            output = self.model(past_values=context, future_values=forecast_seq)
            pred = output.prediction_outputs
            trues.append(forecast_seq.permute(0, 2, 1).detach().cpu().numpy())
            preds.append(pred.permute(0, 2, 1).detach().cpu().numpy())
            histories.append(context.permute(0, 2, 1).detach().cpu().numpy())

        trues = np.concatenate(trues, axis=0)
        preds = np.concatenate(preds, axis=0)
        histories = np.concatenate(histories, axis=0)

    print(trues.shape, preds.shape, histories.shape)
    mse = MSE(trues, preds)
    mae = MAE(trues, preds)
    mase = MASE(trues, preds)
    mape = MAPE(trues, preds)
    rmse = RMSE(trues, preds)
    nrmse = NRMSE(trues, preds)
    smape = SMAPE(trues, preds)
    msis = MSIS(trues, preds)
    nd = ND(trues, preds)

    return {
        "mse": mse,
        "mae": mae,
        "mase": mase,
        "mape": mape,
        "rmse": rmse,
        "nrmse": nrmse,
        "smape": smape,
        "msis": msis,
        "nd": nd,
    }

finetune(dataset, **kwargs)

Parameters:

Name Type Description Default
dataset

dataset for finetuning, call get_data_loader() to get the dataloader

 required
Source code in Samay\src\samay\model.py 
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def finetune(self, dataset, **kwargs):
    """
    Args:
        dataset: dataset for finetuning, call get_data_loader() to get the dataloader
    """
    dataloader = dataset.get_data_loader()
    self.model.to(self.device)
    self.model.train()
    optimizer = torch.optim.Adam(self.model.parameters(), lr=1e-4)
    for epoch in range(5):
        total_loss = 0
        for i, data in enumerate(dataloader):
            context, forecast_seq = data
            context = context.float().permute(0, 2, 1).to(self.device)
            forecast_seq = forecast_seq.float().permute(0, 2, 1).to(self.device)
            optimizer.zero_grad()
            output = self.model(past_values=context, future_values=forecast_seq)
            loss = output.loss
            loss.backward()
            optimizer.step()
            total_loss += loss.item()
        avg_loss = total_loss / len(dataloader)
        print(f"Epoch {epoch}, Loss: {avg_loss}")
    self.model.eval()

plot(dataset, **kwargs)

Plot the forecast results. Args: dataset: dataset for plotting, call get_data_loader() to get the dataloader

Source code in Samay\src\samay\model.py 
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def plot(self, dataset, **kwargs):
    """
    Plot the forecast results.
    Args:
        dataset: dataset for plotting, call get_data_loader() to get the dataloader
    """
    dataloader = dataset.get_data_loader()
    trues, preds, histories = [], [], []
    self.model.to(self.device)
    self.model.eval()
    with torch.no_grad():
        for i, data in enumerate(dataloader):
            context, forecast_seq = data
            context = context.float().permute(0, 2, 1).to(self.device)
            forecast_seq = forecast_seq.float().permute(0, 2, 1).to(self.device)
            output = self.model(past_values=context, future_values=forecast_seq)
            pred = output.prediction_outputs
            trues.append(forecast_seq.permute(0, 2, 1).detach().cpu().numpy())
            preds.append(pred.permute(0, 2, 1).detach().cpu().numpy())
            histories.append(context.permute(0, 2, 1).detach().cpu().numpy())

        trues = np.concatenate(trues, axis=0)
        preds = np.concatenate(preds, axis=0)
        histories = np.concatenate(histories, axis=0)

    visualize(
        task_name="forecasting",
        trues=trues,
        preds=preds,
        history=histories,
        **kwargs,
    )

Usage Examples

Loading a Model

from samay.model import LPTMModel

config = {
    "task_name": "forecasting",
    "forecast_horizon": 192,
    "freeze_encoder": True,
    "freeze_embedder": True,
    "freeze_head": False,
}

model = LPTMModel(config)

Fine-Tuning

from samay.dataset import LPTMDataset

train_dataset = LPTMDataset(
    datetime_col="date",
    path="./data/ETTh1.csv",
    mode="train",
    horizon=192,
)

finetuned_model = model.finetune(train_dataset, epochs=10)

Evaluation

test_dataset = LPTMDataset(
    datetime_col="date",
    path="./data/ETTh1.csv",
    mode="test",
    horizon=192,
)

avg_loss, trues, preds, histories = model.evaluate(test_dataset)

Saving and Loading

# Save model
model.save("model_checkpoint.pt")

# Load model
loaded_model = LPTMModel.load("model_checkpoint.pt")

Common Parameters

Most model classes share these common parameters:

Parameter Type Description
config dict Configuration dictionary with model-specific parameters
device str Device to run model on: "cuda" or "cpu"
repo str Hugging Face repository for pre-trained weights (some models)

Common Methods

All models implement these methods:

finetune(dataset, epochs=5, learning_rate=1e-4, **kwargs)

Fine-tune the model on a dataset.

Parameters: - dataset: Training dataset object - epochs (int): Number of training epochs - learning_rate (float): Learning rate for optimizer - **kwargs: Additional training arguments

Returns: - Trained model

evaluate(dataset, metrics=None, **kwargs)

Evaluate the model on a dataset.

Parameters: - dataset: Test dataset object - metrics (list): List of metric names to compute - **kwargs: Additional evaluation arguments

Returns: - avg_loss (float): Average loss - trues (np.ndarray): Ground truth values - preds (np.ndarray): Predicted values - histories (np.ndarray): Historical context

predict(input_data, **kwargs)

Make predictions on new data.

Parameters: - input_data: Input time series data - **kwargs: Additional prediction arguments

Returns: - Predictions array

save(path)

Save model to disk.

Parameters: - path (str): Path to save model

load(path) (class method)

Load model from disk.

Parameters: - path (str): Path to load model from

Returns: - Loaded model instance

See Also