Merge pull request #260 from WenjieDu/dev

Adding get_random_seed(), and adding func calc_quantile_crps()

pypots/clustering/crli/modules/core.py

@@ -18,7 +18,7 @@
 from sklearn.cluster import KMeans
 
 from .submodules import Generator, Decoder, Discriminator
-from ....utils.metrics import cal_mse
+from ....utils.metrics import calc_mse
 
 
 class _CRLI(nn.Module):
@@ -89,8 +89,8 @@ def forward(
  l_G = F.binary_cross_entropy_with_logits(
  inputs["discrimination"], 1 - missing_mask, weight=1 - missing_mask
  )
- l_pre = cal_mse(inputs["imputation_latent"], X, missing_mask)
- l_rec = cal_mse(inputs["reconstruction"], X, missing_mask)
+ l_pre = calc_mse(inputs["imputation_latent"], X, missing_mask)
+ l_rec = calc_mse(inputs["reconstruction"], X, missing_mask)
  HTH = torch.matmul(inputs["fcn_latent"], inputs["fcn_latent"].permute(1, 0))
 
  if (

pypots/clustering/vader/modules/core.py

@@ -21,7 +21,7 @@
  PeepholeLSTMCell,
  ImplicitImputation,
 )
-from ....utils.metrics import cal_mse
+from ....utils.metrics import calc_mse
 
 
 class _VaDER(nn.Module):
@@ -184,7 +184,7 @@ def forward(
  }
 
  # calculate the reconstruction loss
- unscaled_reconstruction_loss = cal_mse(X_reconstructed, X, missing_mask)
+ unscaled_reconstruction_loss = calc_mse(X_reconstructed, X, missing_mask)
  reconstruction_loss = (
  unscaled_reconstruction_loss
  * self.n_steps

pypots/imputation/base.py

@@ -16,7 +16,7 @@
 
 from ..base import BaseModel, BaseNNModel
 from ..utils.logging import logger
-from ..utils.metrics import cal_mse
+from ..utils.metrics import calc_mse
 
 try:
  import nni
@@ -299,7 +299,7 @@ def _train_model(
  imputation_collector = torch.cat(imputation_collector)
  imputation_collector = imputation_collector.cpu().detach().numpy()
 
- mean_val_loss = cal_mse(
+ mean_val_loss = calc_mse(
  imputation_collector,
  val_loader.dataset.data["X_intact"],
  val_loader.dataset.data["indicating_mask"],

pypots/imputation/brits/modules/core.py

@@ -21,7 +21,7 @@
 
 from .submodules import FeatureRegression
 from ....modules.rnn import TemporalDecay
-from ....utils.metrics import cal_mae
+from ....utils.metrics import calc_mae
 
 
 class RITS(nn.Module):
@@ -150,17 +150,17 @@ def impute(
 
  hidden_states = hidden_states * gamma_h # decay hidden states
  x_h = self.hist_reg(hidden_states)
- reconstruction_loss += cal_mae(x_h, x, m)
+ reconstruction_loss += calc_mae(x_h, x, m)
 
  x_c = m * x + (1 - m) * x_h
 
  z_h = self.feat_reg(x_c)
- reconstruction_loss += cal_mae(z_h, x, m)
+ reconstruction_loss += calc_mae(z_h, x, m)
 
  alpha = torch.sigmoid(self.combining_weight(torch.cat([gamma_x, m], dim=1)))
 
  c_h = alpha * z_h + (1 - alpha) * x_h
- reconstruction_loss += cal_mae(c_h, x, m)
+ reconstruction_loss += calc_mae(c_h, x, m)
 
  c_c = m * x + (1 - m) * c_h
  estimations.append(c_h.unsqueeze(dim=1))

pypots/imputation/csdi/model.py

@@ -32,7 +32,7 @@
 from ...optim.adam import Adam
 from ...optim.base import Optimizer
 from ...utils.logging import logger
-from ...utils.metrics import cal_mse
+from ...utils.metrics import calc_mse
 
 
 class CSDI(BaseNNImputer):
@@ -256,7 +256,7 @@ def _train_model(
  imputation_collector = torch.cat(imputation_collector)
  imputation_collector = imputation_collector.cpu().detach().numpy()
 
- mean_val_loss = cal_mse(
+ mean_val_loss = calc_mse(
  imputation_collector,
  val_loader.dataset.data["X_intact"],
  val_loader.dataset.data["indicating_mask"],

pypots/imputation/mrnn/modules/core.py

@@ -12,7 +12,7 @@
 import torch.nn as nn
 
 from .submodules import FCN_Regression
-from ....utils.metrics import cal_rmse
+from ....utils.metrics import calc_rmse
 
 
 class _MRNN(nn.Module):
@@ -74,7 +74,7 @@ def forward(self, inputs: dict, training: bool = True) -> dict:
  FCN_estimation = self.fcn_regression(
  x, m, RNN_imputed_data
  ) # FCN estimation is output estimation
- reconstruction_loss += cal_rmse(FCN_estimation, x, m) + cal_rmse(
+ reconstruction_loss += calc_rmse(FCN_estimation, x, m) + calc_rmse(
  RNN_estimation, x, m
  )
  estimations.append(FCN_estimation.unsqueeze(dim=1))

pypots/imputation/saits/model.py

@@ -27,7 +27,7 @@
 from ...optim.adam import Adam
 from ...optim.base import Optimizer
 from ...utils.logging import logger
-from ...utils.metrics import cal_mae
+from ...utils.metrics import calc_mae
 
 
 class SAITS(BaseNNImputer):
@@ -145,7 +145,7 @@ def __init__(
  batch_size: int = 32,
  epochs: int = 100,
  patience: Optional[int] = None,
- customized_loss_func: Callable = cal_mae,
+ customized_loss_func: Callable = calc_mae,
  optimizer: Optional[Optimizer] = Adam(),
  num_workers: int = 0,
  device: Optional[Union[str, torch.device, list]] = None,

pypots/imputation/saits/modules/core.py

@@ -20,7 +20,7 @@
 import torch.nn.functional as F
 
 from ....modules.transformer import EncoderLayer, PositionalEncoding
-from ....utils.metrics import cal_mae
+from ....utils.metrics import calc_mae
 
 
 class _SAITS(nn.Module):
@@ -39,7 +39,7 @@ def __init__(
  diagonal_attention_mask: bool = True,
  ORT_weight: float = 1,
  MIT_weight: float = 1,
- customized_loss_func: Callable = cal_mae,
+ customized_loss_func: Callable = calc_mae,
  ):
  super().__init__()
  self.n_layers = n_layers

pypots/imputation/timesnet/modules/core.py

@@ -11,7 +11,7 @@
 
 from .embedding import DataEmbedding
 from .layer import TimesBlock
-from ....utils.metrics import cal_mse
+from ....utils.metrics import calc_mse
 
 
 class _TimesNet(nn.Module):
@@ -88,7 +88,7 @@ def forward(self, inputs: dict, training: bool = True) -> dict:
 
  if training:
  # `loss` is always the item for backward propagating to update the model
- loss = cal_mse(dec_out, inputs["X_intact"], inputs["indicating_mask"])
+ loss = calc_mse(dec_out, inputs["X_intact"], inputs["indicating_mask"])
  results["loss"] = loss
 
  return results

pypots/imputation/transformer/modules/core.py

@@ -19,7 +19,7 @@
 import torch.nn as nn
 
 from ....modules.transformer import EncoderLayer, PositionalEncoding
-from ....utils.metrics import cal_mae
+from ....utils.metrics import calc_mae
 
 
 class _TransformerEncoder(nn.Module):
@@ -89,8 +89,8 @@ def forward(self, inputs: dict, training: bool = True) -> dict:
 
  # if in training mode, return results with losses
  if training:
- ORT_loss = cal_mae(learned_presentation, X, masks)
- MIT_loss = cal_mae(
+ ORT_loss = calc_mae(learned_presentation, X, masks)
+ MIT_loss = calc_mae(
  learned_presentation, inputs["X_intact"], inputs["indicating_mask"]
  )
  results["ORT_loss"] = ORT_loss

pypots/utils/metrics/__init__.py

@@ -6,13 +6,29 @@
 # License: BSD-3-Clause
 
 from .classification import (
+ calc_binary_classification_metrics,
+ calc_precision_recall_f1,
+ calc_pr_auc,
+ calc_roc_auc,
+ calc_acc,
+ # deprecated
  cal_binary_classification_metrics,
  cal_precision_recall_f1,
  cal_pr_auc,
  cal_roc_auc,
  cal_acc,
 )
 from .clustering import (
+ calc_rand_index,
+ calc_adjusted_rand_index,
+ calc_cluster_purity,
+ calc_nmi,
+ calc_chs,
+ calc_dbs,
+ calc_silhouette,
+ calc_internal_cluster_validation_metrics,
+ calc_external_cluster_validation_metrics,
+ # deprecated
  cal_rand_index,
  cal_adjusted_rand_index,
  cal_cluster_purity,
@@ -23,21 +39,49 @@
  cal_internal_cluster_validation_metrics,
  cal_external_cluster_validation_metrics,
 )
-from .error import cal_mae, cal_mse, cal_rmse, cal_mre
+from .error import (
+ calc_mae,
+ calc_mse,
+ calc_rmse,
+ calc_mre,
+ calc_quantile_crps,
+ calc_quantile_crps_sum,
+ # deprecated
+ cal_mae,
+ cal_mse,
+ cal_rmse,
+ cal_mre,
+)
 
 __all__ = [
  # error
+ "calc_mae",
+ "calc_mse",
+ "calc_rmse",
+ "calc_mre",
+ "calc_quantile_crps",
+ "calc_quantile_crps_sum",
+ # classification
+ "calc_binary_classification_metrics",
+ "calc_precision_recall_f1",
+ "calc_pr_auc",
+ "calc_roc_auc",
+ "calc_acc",
+ # clustering
+ "calc_rand_index",
+ "calc_adjusted_rand_index",
+ "calc_cluster_purity",
+ "calc_nmi",
+ "calc_chs",
+ "calc_dbs",
+ "calc_silhouette",
+ "calc_internal_cluster_validation_metrics",
+ "calc_external_cluster_validation_metrics",
+ # deprecated
  "cal_mae",
  "cal_mse",
  "cal_rmse",
  "cal_mre",
- # classification
- "cal_binary_classification_metrics",
- "cal_precision_recall_f1",
- "cal_pr_auc",
- "cal_roc_auc",
- "cal_acc",
- # clustering
  "cal_rand_index",
  "cal_adjusted_rand_index",
  "cal_cluster_purity",
@@ -47,4 +91,9 @@
  "cal_silhouette",
  "cal_internal_cluster_validation_metrics",
  "cal_external_cluster_validation_metrics",
+ "cal_binary_classification_metrics",
+ "cal_precision_recall_f1",
+ "cal_pr_auc",
+ "cal_roc_auc",
+ "cal_acc",
 ]