pypots tutorial

pypots은 시계열 처리(결측, 예측, 분류, 군집)을 쉽게 처리할 수 있도록 만든 패키지

pypots 설치

# by pip
pip install pypots            # the first time installation
pip install pypots --upgrade  # update pypots to the latest version

# by conda
conda install -c conda-forge pypots  # the first time installation
conda update  -c conda-forge pypots  # update pypots to the latest version

preparing data

from pypots.data.generating import gene_physionet2012
physionet2012_dataset = gene_physionet2012(artificially_missing_rate=0.1)

dataset_for_training = {
    "X": physionet2012_dataset['train_X'],
}

dataset_for_validating = {
    "X": physionet2012_dataset['val_X'],
    "X_intact": physionet2012_dataset['val_X_intact'],
    "indicating_mask": physionet2012_dataset['val_X_indicating_mask'],
}

dataset_for_testing = {
    "X": physionet2012_dataset['test_X'],
}

결측치 처리(impute)

from pypots.optim import Adam
from pypots.imputation import SAITS

# initialize the model
saits = SAITS(
    n_steps=physionet2012_dataset['n_steps'],
    n_features=physionet2012_dataset['n_features'],
    n_layers=2,
    d_model=256,
    d_inner=128,
    n_heads=4,
    d_k=64,
    d_v=64,
    dropout=0.1,
    attn_dropout=0.1,
    diagonal_attention_mask=True,  # otherwise the original self-attention mechanism will be applied
    ORT_weight=1,  # you can adjust the weight values of arguments ORT_weight
    # and MIT_weight to make the SAITS model focus more on one task. Usually you can just leave them to the default values, i.e. 1.
    MIT_weight=1,
    batch_size=32,
    # here we set epochs=10 for a quick demo, you can set it to 100 or more for better performance
    epochs=10,
    # here we set patience=3 to early stop the training if the evaluting loss doesn't decrease for 3 epoches.
    # You can leave it to defualt as None to disable early stopping.
    patience=3,
    # give the optimizer. Different from torch.optim.Optimizer, you don't have to specify model's parameters when
    # initializing pypots.optim.Optimizer. You can also leave it to default. It will initilize an Adam optimizer with lr=0.001.
    optimizer=Adam(lr=1e-3),
    # this num_workers argument is for torch.utils.data.Dataloader. It's the number of subprocesses to use for data loading.
    # Leaving it to default as 0 means data loading will be in the main process, i.e. there won't be subprocesses.
    # You can increase it to >1 if you think your dataloading is a bottleneck to your model training speed
    num_workers=0,
    # just leave it to default, PyPOTS will automatically assign the best device for you.
    # Set it to 'cpu' if you don't have CUDA devices. You can also set it to 'cuda:0' or 'cuda:1' if you have multiple CUDA devices.
    device='cpu',  
    # set the path for saving tensorboard and trained model files 
    saving_path="tutorial_results/imputation/saits",
    # only save the best model after training finished.
    # You can also set it as "better" to save models performing better ever during training.
    model_saving_strategy="best",
)

# train the model on the training set, and validate it on the validating set to select the best model for testing in the next step
saits.fit(train_set=dataset_for_training, val_set=dataset_for_validating)

# the testing stage, impute the originally-missing values and artificially-missing values in the test set
saits_imputation = saits.impute(dataset_for_testing)

from pypots.utils.metrics import cal_mae

# calculate mean absolute error on the ground truth (artificially-missing values)
testing_mae = cal_mae(
    saits_imputation, physionet2012_dataset['test_X_intact'], physionet2012_dataset['test_X_indicating_mask'])
print("Testing mean absolute error: %.4f" % testing_mae)