1140506 meeting

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# Training configuration
batch_size: 1  # Batch size
output_directory: "./results/real_time"  # 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: 100  # Log frequency (number of iterations)
n_iters: 20000  # Maximum number of iterations
learning_rate: 0.001  # Learning rate

# Additional training settings
only_generate_missing: true  # Generate missing values only
use_model: 2  # Model to use for training
masking: "rm"  # Masking strategy for missing values
missing_k: 200  # Number of missing values

# Data paths
data:
  train_path: "./datasets/real_time/pollutants_train.npy"  # Path to training data

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wavenet:
  # WaveNet model parameters
  input_channels: 26  # Number of input channels
  output_channels: 26  # Number of output channels
  residual_layers: 36  # Number of residual layers
  residual_channels: 256  # Number of channels in residual blocks
  skip_channels: 256  # Number of channels in skip connections

  # Diffusion step embedding dimensions
  diffusion_step_embed_dim_input: 128  # Input dimension
  diffusion_step_embed_dim_hidden: 512  # Middle dimension
  diffusion_step_embed_dim_output: 512  # Output dimension

  # Structured State Spaces sequence model (S4) configurations
  s4_max_sequence_length: 2000  # 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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# Inference configuration
batch_size: 1  # Batch size for inference
output_directory: "./results/real_time/12/inference/mnr"  # Output directory for inference results
ckpt_path: "./results/real_time"  # 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: "mnr"  # Masking strategy for missing values
missing_k: 12  # Number of missing values

# Data paths
data:
  test_path: "./datasets/real_time/pollutants_test.npy"  # Path to test data

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def run_job(
    model_config: dict,
    inference_config: dict,
    device: Optional[Union[torch.device, str]],
    ckpt_iter: Union[str, int],
) -> None:

    ...
    dataloader = get_dataloader(
        inference_config["data"]["test_path"],
        batch_size,
        device=device,
    )
    ...

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def get_dataloader(
    path: str,
    batch_size: int,
    is_shuffle: bool = True,
    device: Union[str, torch.device] = "cpu",
    num_workers: int = 0,
) -> DataLoader:
    """
    Get a PyTorch DataLoader for the dataset stored at the given path.

    Args:
        path (str): Path to the dataset file.
        batch_size (int): Size of each batch.
        is_shuffle (bool, optional): Whether to shuffle the dataset. Defaults to True.
        device (Union[str, torch.device], optional): Device to move the data to. Defaults to "cpu".
        num_workers (int, optional): Number of subprocesses to use for data loading. Defaults to 8.

    Returns:
        DataLoader: PyTorch DataLoader for the dataset.
    """
    dataset = TensorDataset(torch.from_numpy(np.load(path)).to(dtype=torch.float32))
    pin_memory = device == "cuda" or device == torch.device("cuda")
    return DataLoader(
        dataset,
        batch_size=batch_size,
        shuffle=is_shuffle,
        pin_memory=pin_memory,
        num_workers=num_workers,
    )

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def generate(self) -> list:
    ...
    for index, (batch,) in enumerate(self.dataloader):
    ...

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# test imputation
import os
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm

test_data = np.load(r'real_time\pollutants_test.npy').transpose(0, 2, 1)

## functions
def imputation_plot(full_data, missing_data, imputation_data, title, save_path):
    """
    Plot the first 3 dimensions of the first sample, comparing full data and missing data.
    """
    os.makedirs(save_path, exist_ok=True)  # 確保保存目錄存在
    show_dims = 2
    fig, axes = plt.subplots(show_dims, 1, figsize=(12, 8), sharex=True)
    #imputation_data = np.where(np.isnan(missing_data), imputation_data, np.nan)
    for j in range(show_dims):
        axes[j].plot(full_data[0, j], color='gray', label='full data', alpha=0.6)
        axes[j].plot(imputation_data[0, j], color='orange', label='imputation data', alpha=0.6)
        axes[j].plot(missing_data[0, j], color='red', label=title)
        axes[j].set_ylabel(f'Dim {j}')
        axes[j].legend()

    plt.suptitle(title)
    plt.xlabel('Time')
    plt.tight_layout()
    plt.savefig(f"{save_path}.png", dpi=300)
    #plt.show()

def imputation_plot_each_dim(full_data, missing_data, imputation_data, title, save_dir):
    """
    Plot each dimension of the first sample separately, comparing full data, missing data, and imputation data.
    Save each plot as a separate file.
    """
    num_dims = full_data.shape[1]
    os.makedirs(save_dir, exist_ok=True)  # 確保保存目錄存在

    for j in tqdm(range(num_dims)):
        fig, ax = plt.subplots(figsize=(12, 4))
        ax.plot(full_data[0, j], color='gray', label='full data', alpha=0.6)
        ax.plot(imputation_data[0, j], color='orange', label='imputation data', alpha=0.6)
        ax.plot(missing_data[0, j], color='red', label=title)
        ax.set_ylabel(f'Dim {j}')
        ax.set_xlabel('Time')
        ax.set_title(f'{title} - Dimension {j}')

        # 圖例放到圖外
        ax.legend(
            loc='center left',           # 圖例在圖的左側中央（搭配下面這行）
            bbox_to_anchor=(1, 0.5)       # (x, y)：x=1是圖的最右邊，往右推一點
        )

        fig.tight_layout(rect=[0, 0, 0.85, 1])  # 調整畫布大小，右邊留空給圖例
        save_path = os.path.join(save_dir, f"{title}_dim{j}.png")
        plt.savefig(save_path, dpi=300)
        plt.close(fig)  # 不然畫一堆圖會記憶體爆掉

def analysis_predict_data(missing_k, path):
    folder_path = f'.\\real_time\\imputation\\{missing_k}\\predict\\{path}\\T200_beta00.0001_betaT0.02\\max'
    os.makedirs(folder_path, exist_ok=True)
    length = len(os.listdir(folder_path))
    imputation = []
    for i in range(length):
        file_path = os.path.join(folder_path, f'imputation{i}.npy')
        arr = np.load(file_path)
        imputation.append(arr)
    predict_data = np.concatenate(imputation, axis=0)
    print(f'Shape: {predict_data.shape}')
    print(f'NAs: {np.isnan(predict_data).sum()}')
    # print(predict_data[0, 0])
    print(f'MSPE for all: {((test_data - predict_data)**2).mean()}')

    test_data_predict = np.load(f'real_time\\{missing_k}\\pollutants_test_{path}.npy').transpose(0, 2, 1)
    print(f'MSPE only for missing: {((test_data[np.isnan(test_data_predict)] - predict_data[np.isnan(test_data_predict)])**2).mean()}')

    imputation_plot(test_data, test_data_predict, predict_data, f'imputation {path} test data', f'real_time\\imputation\\{missing_k}\\result\\predict\\imputation0_{path}')
    imputation_plot_each_dim(test_data, test_data_predict, predict_data, f'imputation {path} test data', f'real_time\\imputation\\{missing_k}\\result\\predict\\imputation0_{path}')
    # test_data[0, 0][1:10]
    # test_data_predict[0, 0][1:10]
    # predict_data[0, 0][1:10]

missing_k = 200

## rm
analysis_predict_data(missing_k, 'rm')

## rbm
analysis_predict_data(missing_k, 'rbm')

## bm
analysis_predict_data(missing_k, 'bm')

## tf
analysis_predict_data(missing_k, 'tf')



# original
def analysis_imputation_data(missing_k, path):
    folder_path = f'.\\real_time\\imputation\\{missing_k}\\inference\\{path}\\T200_beta00.0001_betaT0.02\\max'
    os.makedirs(folder_path, exist_ok=True)
    length = len(os.listdir(folder_path))
    imputation = []
    for i in range(length):
        file_path = os.path.join(folder_path, f'imputation{i}.npy')
        arr = np.load(file_path)
        imputation.append(arr)
    imputation_data = np.concatenate(imputation, axis=0)
    print(f'Shape: {imputation_data.shape}')
    print(f'NAs: {np.isnan(imputation_data).sum()}')
    # print(imputation_data[0, 0])
    print(f'MSPE: {((test_data - imputation_data)**2).mean()}')

    imputation_plot(test_data, test_data, imputation_data, f'imputation {path} test data', f'real_time\\imputation\\{missing_k}\\result\\original\\imputation0_{path}')
    imputation_plot_each_dim(test_data, test_data, imputation_data, f'imputation {path} test data', f'real_time\\imputation\\{missing_k}\\result\\original\\imputation0_{path}')
    # test_data[0, 0][1:10]
    # test_data_imputation[0, 0][1:10]
    # imputation_data[0, 0][1:10]

## rm
analysis_imputation_data(missing_k, 'rm')

## bm
analysis_imputation_data(missing_k, 'bm')

## mnr
analysis_imputation_data(missing_k, 'mnr')

## tf
analysis_imputation_data(missing_k, 'tf')