$SSSD^{S4}$ 模型修改

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wavenet:
  # WaveNet model parameters
  input_channels: 24  # Number of input channels
  output_channels: 24  # Number of output channels
  residual_layers: 32  # Number of residual layers
  residual_channels: 32  # Number of channels in residual blocks
  skip_channels: 32  # Number of channels in skip connections

  # Diffusion step embedding dimensions
  diffusion_step_embed_dim_input: 64  # Input dimension
  diffusion_step_embed_dim_hidden: 64  # Middle dimension
  diffusion_step_embed_dim_output: 64  # Output dimension

  # Structured State Spaces sequence model (S4) configurations
  s4_max_sequence_length: 292
    # Maximum sequence length
  s4_state_dim: 64  # State dimension
  s4_dropout: 0.0  # Dropout rate
  s4_bidirectional: true  # Whether to use bidirectional layers
  s4_use_layer_norm: true  # Whether to use layer normalization

diffusion:
  # Diffusion model parameters
  T: 200  # Number of diffusion steps
  beta_0: 0.0001  # Initial beta value
  beta_T: 0.02  # Final beta value

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# Training configuration
batch_size: 64  # Batch size
output_directory: "/home/u6025091/SSSD_CP/results/NASA-GES-DISC-fast-rectangular"  # Output directory for checkpoints and logs
ckpt_iter: "max"  # Checkpoint mode (max or min)
iters_per_ckpt: 1000  # Checkpoint frequency (number of epochs)
iters_per_logging: 200  # Log frequency (number of iterations)
n_iters: 38000  # Maximum number of iterations
learning_rate: 0.0005  # Learning rate

# Additional training settings
only_generate_missing: true  # Generate missing values only
use_model: 2  # Model to use for training
masking: "forecast"  # Masking strategy for missing values
missing_k: 22  # Number of missing values

# Data paths
data:
  train_path: "/home/u6025091/SSSD_CP/datasets/NASA-GES-DISC/data_train_known_real.npy"  # Path to training data

# autoFRK config
enable_spatial_prediction: false  # Enable spatial prediction step
autoFRK_period: 20  # Frequency of autoFRK updates (in how many iterations)
location_path: "/home/u6025091/SSSD_CP/datasets/NASA-GES-DISC/stations_known_locations.npy"  # Path to known locations
AFRK_method: "fast"
AFRK_tps_method: "rectangular"

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# Inference configuration
batch_size: 64  # Batch size for inference
output_directory: "/home/u6025091/SSSD_CP/results/NASA-GES-DISC-fast-rectangular/inference"  # Output directory for inference results
ckpt_path: "/home/u6025091/SSSD_CP/results/NASA-GES-DISC-fast-rectangular"  # Path to checkpoint for inference
trials: 1 # Replications

# Additional training settings
only_generate_missing: true  # Generate missing values only
use_model: 2  # Model to use for training
masking: "forecast"  # Masking strategy for missing values
missing_k: 22  # Number of missing values

# Data paths
data:
  test_path: "/home/u6025091/SSSD_CP/datasets/NASA-GES-DISC/data_test_missing.npy"  # Path to test data

# autoFRK config
enable_spatial_inference: true  # Enable spatial prediction step
known_location_path: "/home/u6025091/SSSD_CP/datasets/NASA-GES-DISC/stations_known_locations.npy"  # Path to known locations
unknown_location_path: "/home/u6025091/SSSD_CP/datasets/NASA-GES-DISC/stations_unknown_locations.npy"  # Path to unknown locations
AFRK_method: "fast"
AFRK_tps_method: "rectangular"

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import logging
import os
from typing import Any, Dict, Optional, Union, Tuple

import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from torch.utils.checkpoint import checkpoint  # New
from tqdm import tqdm
import numpy as np  # New
import csv  # New

from sssd.core.model_specs import MASK_FN
from sssd.training.utils import training_loss
from sssd.data.utils import get_dataloader
from sssd.utils.logger import setup_logger
from sssd.utils.utils import find_max_epoch, std_normal  # New
from autoFRK import AutoFRK, to_tensor, garbage_cleaner  # New
from autoFRK.utils.helper import cbrt  # New

LOGGER = setup_logger()


class DiffusionTrainer:
    """
    Train Diffusion Models

    Args:
        dataloader (DataLoader): The training dataloader.
        diffusion_hyperparams (Dict[str, Any]): Hyperparameters for the diffusion process.
        net (nn.Module): The neural network model to be trained.
        device (torch.device): The device to be used for training.
        output_directory (str): Directory to save model checkpoints.
        ckpt_iter (Optional[int, str]): The checkpoint iteration to be loaded; 'max' selects the maximum iteration.
        n_iters (int): Number of iterations to train.
        iters_per_ckpt (int): Number of iterations to save checkpoint.
        iters_per_logging (int): Number of iterations to save training log and compute validation loss.
        learning_rate (float): Learning rate for training.
        only_generate_missing (int): Option to generate missing portions of the signal only.
        masking (str): Type of masking strategy: 'mnr' for Missing Not at Random, 'bm' for Blackout Missing, 'rm' for Random Missing.
        missing_k (int): K missing time steps for each feature across the sample length.
        batch_size (int): Size of each training batch.
        logger (Optional[logging.Logger]): Logger object for logging, defaults to None.
    """

    def __init__(
        self,
        data_path: str,
        diffusion_hyperparams: Dict[str, Any],
        net: nn.Module,
        device: Optional[Union[torch.device, str]],
        output_directory: str,
        ckpt_iter: Union[str, int],
        n_iters: int,
        iters_per_ckpt: int,
        iters_per_logging: int,
        learning_rate: float,
        only_generate_missing: int,
        masking: str,
        missing_k: int,
        batch_size: int,
        enable_spatial_prediction: bool,  # New
        autoFRK_period: int,  # New
        location_path: str,  # New
        AFRK_method: str,  # New
        AFRK_tps_method: str,  # New
        logger: Optional[logging.Logger] = None,
    ) -> None:
        loader, ts_mean, ts_std = get_dataloader(
            path=data_path,
            batch_size=batch_size,
            device=device,
        )
        self.dataloader = loader
        self.ts_mean = ts_mean
        self.ts_std = ts_std
        self.diffusion_hyperparams = diffusion_hyperparams
        self.net = nn.DataParallel(net).to(device)
        self.device = device
        self.output_directory = output_directory
        self.ckpt_iter = ckpt_iter
        self.n_iters = n_iters
        self.iters_per_ckpt = iters_per_ckpt
        self.iters_per_logging = iters_per_logging
        self.learning_rate = learning_rate
        self.only_generate_missing = only_generate_missing
        self.masking = masking
        self.missing_k = missing_k
        self.writer = SummaryWriter(f"{output_directory}/log")
        self.batch_size = batch_size
        self.optimizer = torch.optim.Adam(self.net.parameters(), lr=self.learning_rate)
        self.enable_spatial_prediction = enable_spatial_prediction  # New
        self.autoFRK_period = autoFRK_period  # New
        self.location_path = location_path  # New
        self.real_data = self.dataloader.dataset.tensors[0].to(self.device) # * self.ts_std + self.ts_mean # New
        self.real_data_shape = self.real_data.shape  # New
        self.logger = logger or LOGGER
        self.AFRK_mrts = None  # New
        self.AFRK_method = AFRK_method  # New
        self.AFRK_tps_method = AFRK_tps_method  # New
        self.loc = to_tensor(np.load(self.location_path), dtype=self.real_data.dtype, device=device)

        frk = AutoFRK(
            logger_level=30,
            dtype=self.real_data.dtype,
            device=self.real_data.device,
        )
        
        # autoFRK
        def frk_step(data_slice, mrts):
            _ = frk.forward(
                data=data_slice,
                loc=self.loc,
                G=mrts,
                method=self.AFRK_method,
                tps_method=self.AFRK_tps_method,
                requires_grad=True
            )
            pred = frk.predict()['pred.value']
            return pred.T, frk.obj['G']
        #self.frk_step = torch.compile(frk_step)
        self.frk_step = frk_step

        if self.masking not in MASK_FN:
            raise KeyError(f"Please enter a correct masking, but got {self.masking}")

    def _load_checkpoint(self) -> None:
        if self.ckpt_iter == "max":
            self.ckpt_iter = find_max_epoch(self.output_directory)
        if self.ckpt_iter >= 0:
            try:
                model_path = os.path.join(
                    self.output_directory, f"{self.ckpt_iter}.pkl"
                )
                checkpoint = torch.load(model_path, map_location="cpu")

                self.net.load_state_dict(checkpoint["model_state_dict"])
                if "optimizer_state_dict" in checkpoint:
                    self.optimizer.load_state_dict(checkpoint["optimizer_state_dict"])

                self.logger.info(
                    f"Successfully loaded model at iteration {self.ckpt_iter}"
                )
            except Exception as e:
                self.ckpt_iter = -1
                self.logger.error(f"No valid checkpoint model found. Error: {e}")
        else:
            self.ckpt_iter = -1
            self.logger.info(
                "No valid checkpoint model found, start training from initialization."
            )

    def _save_model(self, n_iter: int) -> None:
        if n_iter > 0 and n_iter % self.iters_per_ckpt == 0:
            torch.save(
                {
                    "model_state_dict": self.net.state_dict(),
                    "optimizer_state_dict": self.optimizer.state_dict(),
                },
                os.path.join(self.output_directory, f"{n_iter}.pkl"),
            )

    def _update_mask(self, batch: torch.Tensor) -> torch.Tensor:
        transposed_mask = MASK_FN[self.masking](batch[0], self.missing_k)
        return (
            transposed_mask.permute(1, 0)
            .repeat(batch.size()[0], 1, 1)
            .to(self.device, dtype=torch.float32)
        )
    
    def _sampling_differentiable(
        self,
        net: torch.nn.Module,
        size: Tuple[int, int, int],
        diffusion_hyperparams: Dict[str, torch.Tensor],
        cond: torch.Tensor,
        mask: torch.Tensor,
        only_generate_missing: int = 0,
        device: Union[torch.device, str] = "cpu",
        sampling_mode: str = "ddpm",  # ✅ "ddpm" 或 "ddim"
        step_skip: Union[int, None] = None  # ✅ 若為 None，則自動偵測
    ) -> torch.Tensor:
        _dh = diffusion_hyperparams
        T, Alpha, Alpha_bar, Sigma = _dh["T"], _dh["Alpha"], _dh["Alpha_bar"], _dh["Sigma"]
        x = torch.randn(size, device=device)
        mask = mask.to(device)
        cond = cond.to(device)

        # ✅ 自動偵測最適 step_skip
        if step_skip is None:
            if T <= 200:
                step_skip = 1       # 小模型：全步精細推論
            elif T <= 1000:
                step_skip = max(1, T // 200)  # 一般模型：約 200 步內完成
            else:
                step_skip = max(1, T // 400)  # 大型模型：控制在 400 步以內
            #LOGGER.info(f"T={T}, using step_skip={step_skip}")
        step_skip = max(1, step_skip)

        # ✅ 建立跳步時間表
        timesteps = list(range(T - 1, -1, -step_skip))
        if timesteps[-1] != 0:
            timesteps.append(0)

        noise_list = []
        if sampling_mode == "ddpm":
            for _ in timesteps:
                # pre-generate noise with same shape as x; deterministic for forward+recompute
                noise_list.append(torch.randn(size, device=device))
        else:
            # keep placeholder list (not used for ddim)
            noise_list = [None] * len(timesteps)

        for idx, t in enumerate(timesteps):
            noise_t = noise_list[idx]
            def net_step(x_inner, noise=noise_t, tt=t):
                if only_generate_missing == 1:
                    x_inner = x_inner * (1 - mask).float() + cond * mask.float()
                diffusion_steps = (tt * torch.ones((size[0], 1), device=device))
                epsilon_theta = net((x_inner, cond, mask, diffusion_steps))

                # ✅ 根據 sampling_mode 決定取樣公式
                if sampling_mode == "ddpm":
                    # DDPM：隨機性版本
                    x_next = (x_inner - (1 - Alpha[tt]) / torch.sqrt(1 - Alpha_bar[tt]) * epsilon_theta) / torch.sqrt(Alpha[tt])
                    if tt > 0:
                        x_next = x_next + Sigma[tt] * noise
                elif sampling_mode == "ddim":
                    # DDIM：確定性版本，支援跳步
                    if tt == 0:
                        x_next = torch.sqrt(Alpha_bar[tt]) * (
                            (x_inner - torch.sqrt(1 - Alpha_bar[tt]) * epsilon_theta)
                            / torch.sqrt(Alpha_bar[tt])
                        )
                    else:
                        Alpha_bar_prev = Alpha_bar[max(tt - step_skip, 0)]
                        x0_pred = (x_inner - torch.sqrt(1 - Alpha_bar[tt]) * epsilon_theta) / torch.sqrt(Alpha_bar[tt])
                        x_next = torch.sqrt(Alpha_bar_prev) * x0_pred + torch.sqrt(1 - Alpha_bar_prev) * epsilon_theta
                else:
                    raise ValueError(f"Unknown sampling_mode: {sampling_mode}")

                return x_next
            x = checkpoint(net_step, x, use_reentrant=False)
            #x = net_step(x)

        return x
    
    def _sssd_prediction_step(self) -> torch.Tensor:
        self.logger.info(f"Start SSSD prediction step")

        all_batches = []
        for (batch,) in tqdm(self.dataloader, desc=f"{self.n_iter}-th predicting TS"):
            mask = self._update_mask(batch)
            batch = batch.permute(0, 2, 1)

            batch_generated = self._sampling_differentiable(
                net=self.net,
                size=batch.shape,
                diffusion_hyperparams=self.diffusion_hyperparams,
                cond=batch,
                mask=mask,
                only_generate_missing=self.only_generate_missing,
                device=self.device,
                sampling_mode=getattr(self, "sampling_mode", "ddim"),
                step_skip=getattr(self, "step_skip", 10)
            )
            all_batches.append(batch_generated)

        sssd_prediction = torch.cat(all_batches, dim=0).permute(1, 2, 0)

        return sssd_prediction

    def _autoFRK_step(self,
                      sssd_prediction,
                      ) -> torch.Tensor:
        # autoFRK
        self.logger.info(f"Start autoFRK inference step")
        dtype = sssd_prediction.dtype
        device = sssd_prediction.device
        
        V, T, N = sssd_prediction.shape
        autoFRK_result = torch.empty_like(sssd_prediction, dtype=dtype, device=device)
        frk_step = self.frk_step

        # mrts
        if self.AFRK_mrts is None:
            data_slice = sssd_prediction[0, :, :].T
            _, mrts = frk_step(data_slice, None)
            self.AFRK_mrts = mrts
        else:
            mrts = self.AFRK_mrts

        # Flatten 迴圈
        for idx in tqdm(range(V), desc=f"{self.n_iter}-th predicting autoFRK"):
            data_slice = sssd_prediction[idx, :, :].T
            try:
                pred, _ = frk_step(data_slice, mrts)
            except Exception as e:
                self.logger.warning(f"Variable {idx} numerical issue: {e}; using fallback clone")
                pred = data_slice.clone()
            autoFRK_result[idx, :, :] = pred
        nan_idx = torch.isnan(autoFRK_result)
        if nan_idx.any():
            self.logger.info(f"Replacing total {nan_idx.sum().item()} NaNs in autoFRK_result with original sssd_prediction values")
            autoFRK_result = torch.where(nan_idx, sssd_prediction, autoFRK_result)
        autoFRK_result = autoFRK_result.permute(2, 1, 0)

        # return
        if autoFRK_result.shape != self.real_data_shape:
            error_msg = f"Shape mismatch: autoFRK_result {autoFRK_result.shape} != real_data {self.real_data_shape}"
            self.logger.error(error_msg)
            raise ValueError(error_msg)

        return autoFRK_result

    def _train_per_epoch(self) -> torch.Tensor:
        loss_function=nn.MSELoss()
        #diffusion_loss = torch.tensor(0.0, device=self.device, dtype=torch.float32)
        #n_batches = 0

        # SSSD training
        for batch_idx, (batch,) in enumerate(tqdm(self.dataloader, desc=f"{self.n_iter}-th training TS")):
            batch = batch.to(self.device)

            # New
            mask = self._update_mask(batch)
            loss_mask = ~mask.bool()
            self.optimizer.zero_grad()

            if loss_mask.sum() == 0:
                self.logger.warning(f"Batch {batch_idx} has no valid elements for loss")
                continue

            batch = batch.permute(0, 2, 1)
            assert batch.size() == mask.size() == loss_mask.size()
            
            loss = training_loss(
                model=self.net,
                loss_function=loss_function,
                training_data=(batch, batch, mask, loss_mask),
                diffusion_parameters=self.diffusion_hyperparams,
                generate_only_missing=self.only_generate_missing,
                device=self.device,
            )

            if not self.enable_spatial_prediction or self.n_iter % self.autoFRK_period != 0:
                loss.backward()
            else:
                loss.backward(retain_graph=True)
            self.optimizer.step()

            #diffusion_loss = diffusion_loss + loss
            #n_batches += 1

        if self.enable_spatial_prediction and self.n_iter % self.autoFRK_period == 0:
            self.logger.info(f"Iteration {self.n_iter}: Start Spatial Prediction step")
            sssd_prediction = self._sssd_prediction_step()
            autoFRK_result = self._autoFRK_step(sssd_prediction=sssd_prediction)

            # compute loss
            self.optimizer.zero_grad()
            spatial_loss = loss_function(
                autoFRK_result,
                self.real_data
            )
            #loss = spatial_loss + diffusion_loss / n_batches
            loss = spatial_loss

            # update model
            loss.backward()
            self.optimizer.step()
            
        self.logger.info(f"Iteration {self.n_iter}: loss: {loss.item()}")
        
        return loss

    def train(self) -> None:
        self._load_checkpoint()

        n_iter_start = (
            self.ckpt_iter + 2 if self.ckpt_iter == -1 else self.ckpt_iter + 1
        )
        self.logger.info(f"Start the {n_iter_start} iteration")

        for n_iter in range(n_iter_start, self.n_iters + 1):
            self.n_iter = n_iter
            loss = self._train_per_epoch()
            self.writer.add_scalar("Train/Loss", loss.item(), n_iter)
            if n_iter % self.iters_per_logging == 0:
                self.logger.info(f"Iteration: {n_iter} \tLoss: { loss.item()}")
            self._save_model(n_iter)

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from typing import Dict, Tuple

import torch

from sssd.utils.utils import std_normal
from sssd.utils.logger import setup_logger

LOGGER = setup_logger()

def training_loss(
    model: torch.nn.Module,
    loss_function: torch.nn.Module,
    training_data: Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor],
    diffusion_parameters: Dict[str, torch.Tensor],
    generate_only_missing: int = 1,
    device: str = "cpu",
) -> torch.Tensor:
    """
    Compute the training loss of epsilon and epsilon_theta.

    Args:
        model (torch.nn.Module): The neural network model.
        loss_function (torch.nn.Module): The loss function, default is nn.MSELoss().
        training_data (tuple): Training data tuple containing (time_series, condition, mask, loss_mask).
        diffusion_parameters (dict): Dictionary of diffusion hyperparameters returned by calc_diffusion_hyperparams.
                                     Note, the tensors need to be cuda tensors.
        generate_only_missing (int): Flag to indicate whether to only generate missing values (default=1).
        device (str): Device to run the computations on (default="cuda").

    Returns:
        torch.Tensor: Training loss.
    """

    # Unpack diffusion hyperparameters
    T, alpha_bar = diffusion_parameters["T"], diffusion_parameters["Alpha_bar"]

    # Unpack training data
    time_series, condition, mask, loss_mask = training_data

    batch_size = time_series.shape[0]

    # Sample random diffusion steps for each batch element
    diffusion_steps = torch.randint(T, size=(batch_size, 1, 1)).to(device)
    if torch.isnan(diffusion_steps).any():
        LOGGER.warning("diffusion_steps contains NaN")

    # Generate Gaussian noise, applying mask if specified
    noise = (
        time_series * mask.float()
        + std_normal(time_series.shape, device) * (1 - mask).float()
        if generate_only_missing
        else std_normal(time_series.shape, device)
    )
    if torch.isnan(noise).any():
        LOGGER.warning("noise contains NaN")
        LOGGER.info(f"noise stats: min={noise.min().item()}, max={noise.max().item()}, mean={noise.mean().item()}")

    # Compute x_t from q(x_t|x_0)
    transformed_series = (
        torch.sqrt(alpha_bar[diffusion_steps]) * time_series
        + torch.sqrt(1 - alpha_bar[diffusion_steps]) * noise
    )
    if torch.isnan(transformed_series).any():
        LOGGER.warning("transformed_series contains NaN")
        LOGGER.info(f"transformed_series stats: min={transformed_series.min().item()}, max={transformed_series.max().item()}, mean={transformed_series.mean().item()}")

    # Predict epsilon according to epsilon_theta
    epsilon_theta = model(
        (transformed_series, condition, mask, diffusion_steps.view(batch_size, 1))
    )
    if torch.isnan(epsilon_theta).any():
        LOGGER.warning("epsilon_theta contains NaN")
        LOGGER.info(f"epsilon_theta stats: min={epsilon_theta.min().item()}, max={epsilon_theta.max().item()}, mean={epsilon_theta.mean().item()}")

    # # Compute loss
    # if generate_only_missing:
    #     return loss_function(epsilon_theta[loss_mask], noise[loss_mask])
    # else:
    #     return loss_function(epsilon_theta, noise)
    # Compute loss
    if generate_only_missing:
        loss = loss_function(epsilon_theta[loss_mask], noise[loss_mask])
    else:
        loss = loss_function(epsilon_theta, noise)

    if torch.isnan(loss):
        LOGGER.warning("loss contains NaN")
        LOGGER.info(f"loss value: {loss.item()}")

    return loss

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import logging
import os
from typing import Dict, Iterable, Optional, Union

import numpy as np
import torch
from sklearn.metrics import mean_absolute_percentage_error, mean_squared_error
from torch.utils.data import DataLoader
from tqdm import tqdm  # New

from sssd.core.model_specs import MASK_FN
from sssd.data.utils import get_dataloader  # New
from sssd.utils.logger import setup_logger
from sssd.utils.utils import find_max_epoch, sampling
from autoFRK import AutoFRK, to_tensor, garbage_cleaner  # New

LOGGER = setup_logger()


class DiffusionGenerator:
    """
    Generate data based on ground truth.

    Args:
        net (torch.nn.Module): The neural network model.
        device (Optional[Union[torch.device, str]]): The device to run the model on (e.g., 'cuda' or 'cpu').
        diffusion_hyperparams (dict): Dictionary of diffusion hyperparameters.
        local_path (str): Local path format for the model.
        testing_data (torch.Tensor): Tensor containing testing data.
        output_directory (str): Path to save generated samples.
        batch_size (int): Number of samples to generate.
        ckpt_path (str): Checkpoint directory.
        ckpt_iter (str): Pretrained checkpoint to load; 'max' selects the maximum iteration.
        masking (str): Type of masking: 'mnr' (missing not at random), 'bm' (black-out), 'rm' (random missing).
        missing_k (int): Number of missing time points for each channel across the length.
        only_generate_missing (int): Whether to generate only missing portions of the signal:
                                      - 0 (all sample diffusion),
                                      - 1 (generate missing portions only).
        saved_data_names (Iterable[str], optional): Names of data arrays to save (default is ("imputation", "original", "mask")).
        logger (Optional[logging.Logger], optional): Logger object for logging messages (default is None).
    """

    def __init__(
        self,
        net: torch.nn.Module,
        device: Optional[Union[torch.device, str]],
        diffusion_hyperparams: dict,
        local_path: str,
        data_path: str,
        output_directory: str,
        batch_size: int,
        ckpt_path: str,
        ckpt_iter: str,
        masking: str,
        missing_k: int,
        only_generate_missing: int,
        enable_spatial_prediction: bool,  # New
        known_location_path: str,  # New
        unknown_location_path: str,  # New
        AFRK_method: str,  # New
        AFRK_tps_method: str,  # New
        saved_data_names: Iterable[str] = ("imputation", "original", "mask"),
        logger: Optional[logging.Logger] = None,
    ):
        self.net = net
        self.device = device
        self.diffusion_hyperparams = diffusion_hyperparams
        self.local_path = local_path
        loader, ts_mean, ts_std = get_dataloader(
            path=data_path,
            batch_size=batch_size,
            device=device,
            inference=True,
            missing_k=missing_k
        )
        self.dataloader = loader
        self.ts_mean = ts_mean
        self.ts_std = ts_std
        self.batch_size = batch_size
        self.masking = masking
        self.missing_k = missing_k
        self.only_generate_missing = only_generate_missing
        self.enable_spatial_prediction = enable_spatial_prediction  # New
        self.known_location_path = known_location_path  # New
        self.unknown_location_path = unknown_location_path  # New
        self.missing_k = missing_k  # New
        self.AFRK_method = AFRK_method  # New
        self.AFRK_tps_method = AFRK_tps_method # New
        self.logger = logger or LOGGER

        self.output_directory = self._prepare_output_directory(
            output_directory, local_path, ckpt_iter
        )
        self.saved_data_names = saved_data_names
        self._load_checkpoint(ckpt_path, ckpt_iter)

    def _load_checkpoint(self, ckpt_path: str, ckpt_iter: str) -> None:
        """Load a checkpoint for the given neural network model."""
        ckpt_path = os.path.join(ckpt_path, self.local_path)
        if ckpt_iter == "max":
            ckpt_iter = find_max_epoch(ckpt_path)
        model_path = os.path.join(ckpt_path, f"{ckpt_iter}.pkl")
        try:
            checkpoint = torch.load(model_path, map_location="cpu")
            self.net.load_state_dict(checkpoint["model_state_dict"])
            self.logger.info(f"Successfully loaded model at iteration {ckpt_iter}")
        except FileNotFoundError as e:
            raise FileNotFoundError(f"Model file not found at {model_path}") from e
        except Exception as e:
            raise Exception(f"Failed to load model: {e}")

    def _prepare_output_directory(
        self, output_directory: str, local_path: str, ckpt_iter: str
    ) -> str:
        """Prepare the output directory to save generated samples."""
        ckpt_iter_str = (
            "max"
            if ckpt_iter == "max"
            else f"imputation_multiple_{int(ckpt_iter) // 1000}k"
        )
        output_directory = os.path.join(output_directory, local_path, ckpt_iter_str)
        os.makedirs(output_directory, exist_ok=True)
        os.chmod(output_directory, 0o775)
        self.logger.info(f"Output directory: {output_directory}")
        return output_directory

    def _update_mask(self, batch: torch.Tensor) -> torch.Tensor:
        """Update mask based on the given batch."""
        transposed_mask = MASK_FN[self.masking](batch[0], self.missing_k)
        return (
            transposed_mask.permute(1, 0)
            .repeat(batch.size()[0], 1, 1)
            .to(self.device, dtype=torch.float32)
        )

    def _save_data(
        self,
        results: Dict[str, np.ndarray],
        index: int,
    ) -> None:
        """Save generated_series, batch, and mask data arrays."""

        for name, data in results.items():
            if name in self.saved_data_names:
                filename = f"{name}{index}.npy"
                np.save(os.path.join(self.output_directory, filename), data)

    def _autoFRK_generate(
        self,
        sssd_inference,
        with_known_loc: bool = True
    ) -> torch.Tensor:
        LOGGER.info(f"Start autoFRK inference step")
        dtype = torch.float32
        device = sssd_inference.device
        sssd_inference = to_tensor(sssd_inference, dtype = dtype, device = device)

        N, T, V = sssd_inference.shape

        known_loc = to_tensor(np.load(self.known_location_path), dtype=dtype, device=device)
        unknown_loc = to_tensor(np.load(self.unknown_location_path), dtype=dtype, device=device)
        if known_loc.ndim == 1:
            known_loc = known_loc.view(-1, 1)
        if unknown_loc.ndim == 1:
            unknown_loc = unknown_loc.view(-1, 1)
        missing_loc = unknown_loc.shape[0]
        autoFRK_inference = torch.zeros((missing_loc, T, V), dtype=dtype, device=device)
        frk = AutoFRK(
            logger_level=30,
            dtype=dtype,
            device=device,
        )

        mrts = None
        for variable in tqdm(range(V), desc=f"inferencing autoFRK"):
            data_slice = sssd_inference[:, :, variable]
            try:
                _ = frk.forward(
                    data=data_slice,
                    loc=known_loc,
                    G = mrts,
                    method=self.AFRK_method,
                    tps_method=self.AFRK_tps_method,
                    requires_grad=False
                )
                pred = frk.predict(
                    newloc = unknown_loc
                )['pred.value']
                mrts = frk.obj['G'] if mrts is None else mrts

            except torch._C._LinAlgError:
                LOGGER.warning(f"Skipped variable={variable} due to ill-conditioned matrix")
                pred = torch.zeros((missing_loc, T), dtype=dtype, device=device)
            
            autoFRK_inference[:, :, variable] = pred

        if with_known_loc:
            autoFRK_inference = torch.cat([sssd_inference, autoFRK_inference], dim=0)

        return autoFRK_inference

    def generate(self) -> list:
        """Generate samples using the given neural network model."""
        all_generated = []
        for index, (batch,) in tqdm(enumerate(self.dataloader), total=len(self.dataloader), desc="inferencing sssd"):
            batch = batch.to(self.device)
            mask = self._update_mask(batch)
            batch = batch.permute(0, 2, 1)

            generated_series = (
                sampling(
                    net=self.net,
                    size=batch.shape,
                    diffusion_hyperparams=self.diffusion_hyperparams,
                    cond=batch,
                    mask=mask,
                    only_generate_missing=self.only_generate_missing,
                    device=self.device,
                )
            )
            all_generated.append(generated_series)
        sssd_inference = torch.cat(all_generated, dim=0).permute(0, 2, 1)
        ts_mean = to_tensor(self.ts_mean, dtype = torch.float32, device = sssd_inference.device)
        ts_std = to_tensor(self.ts_std, dtype = torch.float32, device = sssd_inference.device)
        sssd_inference = sssd_inference * ts_std + ts_mean

        if self.enable_spatial_prediction:
            autoFRK_inference = self._autoFRK_generate(
                sssd_inference = sssd_inference,
                with_known_loc = True
            )
            results = {'imputation': autoFRK_inference.detach().cpu().numpy()}
        else:
            results = {'imputation': sssd_inference.detach().cpu().numpy()}
        self._save_data(results, 0)

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import os

import numpy as np


def read_multiple_imputations(folder_path: str, missing_k: int) -> np.ndarray:
    """
    Read multiple imputations generated from 'inference_multiples.py'.

    Args:
        folder_path (str): The folder containing the imputation files.
        missing_k (int): The number of the last elements to be predicted.

    Returns:
        np.ndarray: An array containing imputations with shape (num_files, obs, channel, missing_k).
    """
    # Check if the folder exists
    if not os.path.exists(folder_path):
        raise FileNotFoundError(f"Folder '{folder_path}' does not exist.")

    # Get a list of files in the folder
    file_list = os.listdir(folder_path)

    # Filter out only imputation0.npy files
    npy_files = [file for file in file_list if file.endswith("imputation0.npy")]

    if not npy_files:
        raise FileNotFoundError(f"No imputation0.npy files found in '{folder_path}'.")

    # Initialize stack array
    stack_array_data = []

    # Loop through all imputation0.npy files and read them
    for npy_file in npy_files:
        # shape = (obs, channel, length) -> (1, obs, channel, length)
        array_data = read_missing_k_data(folder_path, npy_file, missing_k)
        if array_data is not None:
            # Add a new axis for stacking
            array_data = np.expand_dims(array_data, axis=0)
            stack_array_data.append(array_data)

    if not stack_array_data:
        raise ValueError("No valid data found in the imputation files.")

    # Stack the arrays vertically
    stack_array_data = np.vstack(stack_array_data)
    return stack_array_data


def read_missing_k_data(folder_path: str, npy_file: str, missing_k: int) -> np.ndarray:
    """
    Read the last 'missing_k' elements of each observation from a NumPy file.

    Args:
        folder_path (str): The folder containing the file.
        npy_file (str): The file name to read.
        missing_k (int): The number of the last elements to be read.

    Returns:
        np.ndarray: An array containing the last 'missing_k' elements of each observation.
    """
    file_path = os.path.join(folder_path, npy_file)
    data = np.load(file_path)
    last_k_elements = data[:, :, (-missing_k):]
    return last_k_elements


def predict_interval(
    pred: np.ndarray, alpha: float = 0.05
) -> tuple[np.ndarray, np.ndarray]:
    """
    Compute the (1-alpha) quantile prediction interval of imputation ecdf.

    Args:
        pred (np.ndarray): All data with shape (num_imputations, obs, channel, length).
        alpha (float, optional): Significance level of the prediction interval. Defaults to 0.05.

    Returns:
        tuple[np.ndarray, np.ndarray]: Lower and upper bounds of the prediction interval with shape (obs, channel, length).
    """
    # Compute original prediction intervals
    L = np.quantile(pred, alpha / 2, axis=0)
    U = np.quantile(pred, 1 - alpha / 2, axis=0)

    return L, U


def compute_E_star(
    L: np.ndarray, U: np.ndarray, true: np.ndarray, alpha: float = 0.05
) -> np.ndarray:
    """
    Compute the (1-alpha) quantile of conformity scores, i.e., E_star.

    Args:
        L (np.ndarray): Lower bound to be adjusted with shape (obs, channel, length).
        U (np.ndarray): Upper bound to be adjusted with shape (obs, channel, length).
        true (np.ndarray): True values with shape (obs, channel, length).
        alpha (float, optional): Mis-coverage rate of conformal prediction. Defaults to 0.05.

    Returns:
        np.ndarray: E_star with shape (channel, length).
    """
    # Compute the conformity scores
    E = np.maximum(L - true, true - U)

    # Compute the (1-alpha) quantile of conformity scores
    CP_PAR = (1 + 1 / true.shape[0]) * (1 - alpha)
    E_star = np.quantile(E, CP_PAR, axis=0)
    return E_star


def adjust_PI(
    L: np.ndarray, U: np.ndarray, E_star: np.ndarray
) -> tuple[np.ndarray, np.ndarray]:
    """
    Adjust prediction interval using conformal prediction.

    Args:
        L (np.ndarray): Lower bound to be adjusted with shape (obs, channel, length).
        U (np.ndarray): Upper bound to be adjusted with shape (obs, channel, length).
        E_star (np.ndarray): Scores with shape (channel, length).

    Returns:
        tuple[np.ndarray, np.ndarray]: Adjusted lower and upper bound with shape (obs, channel, length).
    """
    E_star_exd = np.expand_dims(E_star, axis=0)
    adjusted_L = L - E_star_exd
    adjusted_U = U + E_star_exd
    return adjusted_L, adjusted_U


def coverage_rate(L: np.ndarray, U: np.ndarray, true: np.ndarray) -> np.ndarray:
    """
    Compute the coverage rate, which is the proportion of [L, U] containing true data.

    Args:
        L (np.ndarray): Lower bound with shape (obs, channel, length).
        U (np.ndarray): Upper bound with shape (obs, channel, length).
        true (np.ndarray): True data with shape (obs, channel, length).

    Returns:
        np.ndarray: Coverage rate with shape (1, length).
    """
    coverage = np.sum(np.logical_and(true > L, true < U), axis=0) / true.shape[0]
    return coverage