20251007 meeting

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# Training configuration
batch_size: 80  # Batch size
output_directory: "./results/NYISO_4/Zone/NYISO_4_MILLWD_test"  # 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: 1000  # Log frequency (number of iterations)
n_iters: 60000  # Maximum number of iterations
learning_rate: 0.0002  # 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: 24  # Number of missing values

# Data paths
data:
  train_path: "./datasets/NYISO/test-normalization/MILLWD_train.npy"  # Path to training data, for known locations

# autoFRK config
enable_spatial_prediction: true  # Enable spatial prediction step
n_cores: 4  # Number of CPU cores to use (int)
autoFRK_period: 100  # Frequency of autoFRK updates (in how many iterations)
location_path: "./datasets/NYISO/test-normalization/MILLWD_known_location.npy"  # Path to known locations

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import argparse
import os
from typing import Optional, Union

import torch
import yaml

from sssd.core.model_specs import MODEL_PATH_FORMAT, setup_model
from sssd.data.utils import get_dataloader
from sssd.training.trainer import DiffusionTrainer
from sssd.utils.logger import setup_logger
from sssd.utils.utils import calc_diffusion_hyperparams, display_current_time

LOGGER = setup_logger()


def fetch_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "-m",
        "--model_config",
        type=str,
        default="configs/model.yaml",
        help="Model configuration",
    )
    parser.add_argument(
        "-t",
        "--training_config",
        type=str,
        default="configs/training.yaml",
        help="Training configuration",
    )
    return parser.parse_args()


def setup_output_directory(
    model_config: dict,
    training_config: dict,
) -> str:
    # Build output directory
    local_path = MODEL_PATH_FORMAT.format(
        T=model_config["diffusion"]["T"],
        beta_0=model_config["diffusion"]["beta_0"],
        beta_T=model_config["diffusion"]["beta_T"],
    )
    output_directory = os.path.join(training_config["output_directory"], local_path)

    if not os.path.isdir(output_directory):
        os.makedirs(output_directory)
        os.chmod(output_directory, 0o775)
    LOGGER.info("Output directory %s", output_directory)
    return output_directory


def run_job(
    model_config: dict,
    training_config: dict,
    device: Optional[Union[torch.device, str]],
) -> None:
    output_directory = setup_output_directory(model_config, training_config)
    dataloader = get_dataloader(
        training_config["data"]["train_path"],
        batch_size=training_config.get("batch_size"),
        device=device,
    )

    diffusion_hyperparams = calc_diffusion_hyperparams(
        **model_config["diffusion"], device=device
    )
    net = setup_model(training_config["use_model"], model_config, device)

    LOGGER.info(display_current_time())
    trainer = DiffusionTrainer(
        dataloader=dataloader,
        diffusion_hyperparams=diffusion_hyperparams,
        net=net,
        device=device,
        output_directory=output_directory,
        ckpt_iter=training_config.get("ckpt_iter"),
        n_iters=training_config.get("n_iters"),
        iters_per_ckpt=training_config.get("iters_per_ckpt"),
        iters_per_logging=training_config.get("iters_per_logging"),
        learning_rate=training_config.get("learning_rate"),
        only_generate_missing=training_config.get("only_generate_missing"),
        masking=training_config.get("masking"),
        missing_k=training_config.get("missing_k"),
        batch_size=training_config.get("batch_size"),
        enable_spatial_prediction=training_config.get("enable_spatial_prediction", True),  # New
        n_cores=training_config.get("n_cores"),  # New
        autoFRK_period=training_config.get("autoFRK_period"),  # New
        location_path=os.path.abspath(training_config["location_path"]),  # New
        logger=LOGGER,
    )
    trainer.train()

    LOGGER.info(display_current_time())


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

    with open(args.model_config, "rt") as f:
        model_config = yaml.safe_load(f.read())
    with open(args.training_config, "rt") as f:
        training_config = yaml.safe_load(f.read())

    LOGGER.info(f"Model spec: {model_config}")
    LOGGER.info(f"Training spec: {training_config}")

    if torch.cuda.device_count() > 0:
        LOGGER.info(f"Using {torch.cuda.device_count()} GPUs!")
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    run_job(model_config, training_config, device)

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import logging
import os
from typing import Any, Dict, Optional, Union
import subprocess  # New
import numpy as np  # New
import yaml  # New

import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm

from sssd.core.model_specs import MASK_FN
from sssd.training.utils import training_loss
from sssd.utils.logger import setup_logger
from sssd.utils.utils import find_max_epoch, sampling  # 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,
        dataloader: DataLoader,
        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
        n_cores: Union[int, str],  # New
        autoFRK_period: int,  # New
        location_path: str,  # New
        logger: Optional[logging.Logger] = None,
    ) -> None:
        self.dataloader = dataloader
        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.n_cores = n_cores  # New
        self.autoFRK_period = autoFRK_period  # New
        self.location_path = location_path  # New
        self.real_data = self.dataloader.dataset.tensors[0].to(self.device)  # New
        self.real_data_shape = self.real_data.shape  # New
        self.logger = logger or LOGGER

        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 _sssd_prediction_step(self,
                              ) -> torch.Tensor:
        
        # SSSD prediction
        LOGGER.info(f"Start SSSD prediction step")
        all_generated = []
        with torch.no_grad():
            for (batch,) in tqdm(self.dataloader, desc=f"{self.n_iter}-th predicting TS"):
                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_prediction = torch.cat(all_generated, dim=0).permute(1, 2, 0)
            del all_generated
        return sssd_prediction

    def _autoFRK_step(self,
                      sssd_prediction,
                      ) -> torch.Tensor:

        # autoFRK
        LOGGER.info(f"Start autoFRK inference step")
        ## config paths
        sssd_pred_save_path = "sssd_prediction.npy"
        autoFRK_config_path = "autoFRK_config.yaml"
        autoFRK_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "utils", "autoFRK.R")
        autoFRK_result_path = "autoFRK_result.npy"

        ## save sssd prediction and location config
        sssd_prediction = sssd_prediction.detach().cpu().numpy()
        np.save(sssd_pred_save_path, sssd_prediction)
        del sssd_prediction
        autoFRK_config = {
            'ncores': self.n_cores,
            'known_location_path': self.location_path
        }
        with open(autoFRK_config_path, "w") as f:
            yaml.dump(autoFRK_config, f)

        ## run autoFRK
        subprocess.run(["Rscript", autoFRK_path],
                       stdout=subprocess.DEVNULL,   # 忽略標準輸出
                       #stderr=subprocess.DEVNULL    # 忽略錯誤輸出
                       )

        ## load autoFRK inference result
        autoFRK_result = np.load(autoFRK_result_path).transpose(2, 1, 0).astype(np.float32)
        autoFRK_result = torch.from_numpy(autoFRK_result).to(self.device)

        ## clean up
        if os.path.exists(sssd_pred_save_path):
            os.remove(sssd_pred_save_path)
        if os.path.exists(autoFRK_config_path):
            os.remove(autoFRK_config_path)
        if os.path.exists(autoFRK_result_path):
            os.remove(autoFRK_result_path)


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

        return autoFRK_result
    
    def _autoFRK_surrogate_layer(self,
                                 sssd_prediction: torch.Tensor,
                                 autoFRK_result: torch.Tensor,
                                 epochs: int = 0,
                                 lr: float = 1e-3,
                                 element_wise: bool = True,
                                 loss_function: nn.Module = nn.MSELoss(),
                                 ) -> torch.Tensor:
        """
        Surrogate layer: 將 sssd_prediction 逼近 autoFRK_result。

        Args:
            sssd_prediction: Tensor 或 list of Tensors, shape (V,T,L)
            autoFRK_result: Tensor, shape (V,T,L)，用作監督目標
            epochs: int，微調 surrogate 的迭代次數
            lr: float，Adam optimizer learning rate
            element_wise: bool，如果 True，對每個元素使用單獨 scale/bias
            loss_function: torch loss function, 預設使用 MSELoss

        Returns:
            result: Tensor, shape (V,T,L)，經 surrogate 層逼近 autoFRK_result
        """
        LOGGER.info(f"Start autoFRK surrogate step")
        # -----------------------------
        # 取最後一個 batch 或 tensor
        # -----------------------------
        last_sssd = sssd_prediction[-1] if isinstance(sssd_prediction, list) else sssd_prediction

        # -----------------------------
        # 確認 shape 與 real_data_shape 一致
        # -----------------------------
        if (last_sssd.shape != self.real_data_shape) or (self.real_data_shape != autoFRK_result.shape):
            msg = (
                f"Shape mismatch: sssd_prediction {last_sssd.shape}, "
                f"expected {self.real_data_shape}, "
                f"autoFRK_result {None if autoFRK_result is None else autoFRK_result.shape}"
            )
            LOGGER.error(msg)
            raise ValueError(msg)

        V, T, L = self.real_data_shape

        # -----------------------------
        # 初始化 surrogate 參數
        # -----------------------------
        if not hasattr(self, 'surrogate_scale'):
            if element_wise:
                # 每個元素對應一個 scale/bias
                self.surrogate_scale = nn.Parameter(torch.ones((V, T, L), device=self.device))
                self.surrogate_bias = nn.Parameter(torch.zeros((V, T, L), device=self.device))
            else:
                # 每個變數一個 scale/bias
                self.surrogate_scale = nn.Parameter(torch.ones(V, device=self.device))
                self.surrogate_bias = nn.Parameter(torch.zeros(V, device=self.device))

        # -----------------------------
        # 定義 surrogate 前向函數
        # -----------------------------
        def surrogate_forward(x: torch.Tensor) -> torch.Tensor:
            if element_wise:
                # element-wise scale/bias
                return x * self.surrogate_scale + self.surrogate_bias
            else:
                # per-variable scale/bias (broadcast)
                return x * self.surrogate_scale[:, None, None] + self.surrogate_bias[:, None, None]

        # -----------------------------
        # closed-form 初始化（不進梯度圖）
        # -----------------------------
        x = last_sssd.detach()
        y = autoFRK_result.detach()

        if element_wise:
            # 對每個元素單獨計算 scale/bias: y = a*x + b
            a = torch.ones_like(x)
            b = torch.zeros_like(x)
            # 避免除以 0
            mask = x != 0
            a[mask] = y[mask] / x[mask]
            b = y - a * x
        else:
            # 對每個變數計算 scale/bias
            x_flat = x.reshape(V, -1)
            y_flat = y.reshape(V, -1)
            x_mean = x_flat.mean(dim=1, keepdim=True)
            y_mean = y_flat.mean(dim=1, keepdim=True)
            cov = ((x_flat - x_mean) * (y_flat - y_mean)).sum(dim=1)
            var = ((x_flat - x_mean)**2).sum(dim=1) + 1e-8
            a = cov / var
            b = (y_mean.squeeze() - a * x_mean.squeeze())

        with torch.no_grad():
            self.surrogate_scale.copy_(a.to(self.device))
            self.surrogate_bias.copy_(b.to(self.device))

        # -----------------------------
        # 微調 (保持可微分)
        # -----------------------------
        if epochs > 0:
            optimizer = torch.optim.Adam([self.surrogate_scale, self.surrogate_bias], lr=lr)
            with tqdm(range(epochs), desc="[autoFRK surrogate]") as pbar:
                for epoch in pbar:
                    optimizer.zero_grad()
                    surrogate_out = surrogate_forward(last_sssd)
                    loss = loss_function(surrogate_out, autoFRK_result)
                    loss.backward()
                    optimizer.step()
                    pbar.set_postfix({"Loss": f"{loss.item():.6f}"})

        # -----------------------------
        # 前向傳遞 (保持可微)
        # -----------------------------
        result = surrogate_forward(last_sssd)
        return result

    def _train_per_epoch(self) -> torch.Tensor:

        # SSSD training
        for (batch,) in tqdm(self.dataloader, desc=f"{self.n_iter}-th   training TS"):
            batch = batch.to(self.device)
            mask = self._update_mask(batch)
            loss_mask = ~mask.bool()
            loss_function=nn.MSELoss()

            batch = batch.permute(0, 2, 1)
            assert batch.size() == mask.size() == loss_mask.size()

            self.optimizer.zero_grad()
            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,
            )
            loss.backward()
            self.optimizer.step()

        if self.enable_spatial_prediction and self.n_iter % self.autoFRK_period == 0:
            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)
            autoFRK_surrogate = self._autoFRK_surrogate_layer(sssd_prediction=sssd_prediction.permute(2, 1, 0),
                                                              autoFRK_result=autoFRK_result,
                                                              loss_function=loss_function,
                                                              epochs=50,
                                                              lr=1e-3
                                                              )

            # compute loss
            self.optimizer.zero_grad()
            loss = loss_function(
                autoFRK_surrogate,
                self.real_data
            )

            # update model
            loss.backward()
            self.optimizer.step()
            LOGGER.info(f"Iteration {self.n_iter}: Spatial Prediction step done, 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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##########################################
# This script is for inference the value for SSSD to the unknown locations, and the return values are for calculating loss to update time series parameters.
# version: 1141003
# author: Yao-Chih Hsu
##########################################

# library
library(reticulate)
library(dplyr)
library(yaml)
library(parallel)
library(autoFRK)
library(foreach)
np <- import("numpy")

# load config
path <- "autoFRK_config.yaml"
config <- yaml.load_file(path)

# load data
sssd_pred_path  <- "sssd_prediction.npy"
sssd_prediction <- np$load(sssd_pred_path)
sssd_prediction <- py_to_r(sssd_prediction)

# load locations
known_location_path   <- config$known_location_path
known_locs            <- np$load(known_location_path)
known_locs            <- py_to_r(known_locs)

# load n_cores
n_cores <- min(config$n_cores, max(1, detectCores() - 1))

# initial parameters
locs <- known_locs  # for testing, use known locations as unknown locations
n_locs <- if (!is.null(dim(locs))) dim(locs)[1] else length(locs)
result_shape  <- c(dim(sssd_prediction)[1:2], n_locs)
result        <- array(NA, dim = result_shape)
mrts_basis    <- NULL

# get MRTS basis
variable_index  <- 1L
time_index      <- 1L
temp_data       <- sssd_prediction[variable_index, time_index, ]
if (length(temp_data) != nrow(known_locs)) {
  stop("Length of temp_data does not match number of known locations!")
}

cat("Calculating first model to get MRST basis...\n")
model <- autoFRK(data = temp_data, loc = known_locs)
mrts_basis <- model$G
cat("Get MRST basis\n")

# save first result
pred <- predict.FRK(object = model, newloc = locs)
result[variable_index, time_index, ] <- pred$pred.value

# expand tasks
tasks <- expand.grid(
  variable = 1:dim(sssd_prediction)[1],
  ts       = 1:dim(sssd_prediction)[2]
) %>%
  filter(!(variable == variable_index & ts == time_index))

# parallel
cat("Calculating...\n")
if (.Platform$OS.type == "windows") {
  # Windows → doParallel (socket cluster)
  library(doParallel)
  cl <- makeCluster(n_cores)
  registerDoParallel(cl)
} else {
  # Linux/macOS → doMC (fork, 記憶體共享)
  library(doMC)
  registerDoMC(cores = n_cores)
}

# compute all tasks
tryCatch({
  results <- foreach(i = 1:nrow(tasks),
                     .packages = c("autoFRK")
                     ) %dopar% {
    variable_index  <- tasks$variable[i]
    time_index  <- tasks$ts[i]
    temp_data <- sssd_prediction[variable_index, time_index, ]

    # calculate with MRTS basis
    model <- autoFRK(data = temp_data, loc = known_locs, G = mrts_basis)
    pred  <- predict.FRK(object = model, newloc = locs)
    
    # return results
    list(variable = variable_index,
         ts       = time_index,
         pred     = pred$pred.value
         )
  }
}, finally = {
  if (.Platform$OS.type == "windows") {
    stopCluster(cl)
  }
  registerDoSEQ()
})

# results
if (!exists("results")) stop("Parallel computation failed: 'results' does not exist.")
for (res in results) {
  result[res$variable, res$ts, ] <- res$pred
}

# save and back to Python
path <- "autoFRK_result.npy"
result <- r_to_py(result)
np$save(path, result)
cat("Finish calculated autoFRK\n")