refactor: normalize some code;

pypots/classification/csai/core.py

@@ -11,28 +11,6 @@
 
 from ...nn.modules.csai import BackboneBCSAI
 
-# class DiceBCELoss(nn.Module):
-#     def __init__(self, weight=None, size_average=True):
-#         super(DiceBCELoss, self).__init__()
-#         self.bcelogits = nn.BCEWithLogitsLoss()
-
-#     def forward(self, y_score, y_out, targets, smooth=1):
-
-#         #comment out if your model contains a sigmoid or equivalent activation layer
-#         # inputs = F.sigmoid(inputs)
-
-#         #flatten label and prediction tensors
-#         BCE = self.bcelogits(y_out, targets)
-
-#         y_score = y_score.view(-1)
-#         targets = targets.view(-1)
-#         intersection = (y_score * targets).sum()
-#         dice_loss = 1 - (2.*intersection + smooth)/(y_score.sum() + targets.sum() + smooth)
-
-#         Dice_BCE = BCE + dice_loss
-
-#         return BCE, Dice_BCE
-
 
 class _BCSAI(nn.Module):
     def __init__(

pypots/classification/csai/model.py

@@ -6,7 +6,7 @@
 # License: BSD-3-Clause
 
 from typing import Optional, Union
-import numpy as np
+
 import torch
 from torch.utils.data import DataLoader
 
@@ -60,31 +60,43 @@ class CSAI(BaseNNClassifier):
     The batch size for training and evaluating the model.
 
     epochs :
-    The number of epochs for training the model.
-
-    dropout :
-    The dropout rate for the model to prevent overfitting. Default is 0.5.
+        The number of epochs for training the model.
 
     patience :
-    The patience for the early-stopping mechanism. Given a positive integer, the training process will be stopped when the model does not perform better after that number of epochs. Leaving it default as None will disable the early-stopping.
+        The patience for the early-stopping mechanism. Given a positive integer, the training process will be
+        stopped when the model does not perform better after that number of epochs.
+        Leaving it default as None will disable the early-stopping.
 
     optimizer :
-    The optimizer for model training. If not given, will use a default Adam optimizer.
+        The optimizer for model training.
+        If not given, will use a default Adam optimizer.
 
     num_workers :
-    The number of subprocesses to use for data loading. 0 means data loading will be in the main process, i.e. there won't be subprocesses.
+        The number of subprocesses to use for data loading.
+        `0` means data loading will be in the main process, i.e. there won't be subprocesses.
 
     device :
-    The device for the model to run on. It can be a string, a :class:torch.device object, or a list of them. If not given, will try to use CUDA devices first (will use the default CUDA device if there are multiple), then CPUs, considering CUDA and CPU are so far the main devices for people to train ML models. If given a list of devices, e.g. ['cuda:0', 'cuda:1'], or [torch.device('cuda:0'), torch.device('cuda:1')], the model will be parallely trained on the multiple devices (so far only support parallel training on CUDA devices). Other devices like Google TPU and Apple Silicon accelerator MPS may be added in the future.
+        The device for the model to run on. It can be a string, a :class:`torch.device` object, or a list of them.
+        If not given, will try to use CUDA devices first (will use the default CUDA device if there are multiple),
+        then CPUs, considering CUDA and CPU are so far the main devices for people to train ML models.
+        If given a list of devices, e.g. ['cuda:0', 'cuda:1'], or [torch.device('cuda:0'), torch.device('cuda:1')] , the
+        model will be parallely trained on the multiple devices (so far only support parallel training on CUDA devices).
+        Other devices like Google TPU and Apple Silicon accelerator MPS may be added in the future.
 
     saving_path :
-    The path for automatically saving model checkpoints and tensorboard files (i.e. loss values recorded during training into a tensorboard file). Will not save if not given.
+        The path for automatically saving model checkpoints and tensorboard files (i.e. loss values recorded during
+        training into a tensorboard file). Will not save if not given.
 
     model_saving_strategy :
-    The strategy to save model checkpoints. It has to be one of [None, "best", "better", "all"]. No model will be saved when it is set as None. The "best" strategy will only automatically save the best model after the training finished. The "better" strategy will automatically save the model during training whenever the model performs better than in previous epochs. The "all" strategy will save every model after each epoch training.
+        The strategy to save model checkpoints. It has to be one of [None, "best", "better", "all"].
+        No model will be saved when it is set as None.
+        The "best" strategy will only automatically save the best model after the training finished.
+        The "better" strategy will automatically save the model during training whenever the model performs
+        better than in previous epochs.
+        The "all" strategy will save every model after each epoch training.
 
     verbose :
-    Whether to print out the training logs during the training process.
+        Whether to print out the training logs during the training process.
 
     """
 
@@ -136,6 +148,9 @@ def __init__(
         self.compute_intervals = compute_intervals
         self.dropout = dropout
         self.intervals = None
+        self.replacement_probabilities = None
+        self.mean_set = None
+        self.std_set = None
 
         # Initialise empty model
         self.model = _BCSAI(
@@ -230,7 +245,7 @@ def fit(
         file_type: str = "hdf5",
     ) -> None:
         # Create dataset
-        self.training_set = DatasetForCSAI(
+        training_set = DatasetForCSAI(
             data=train_set,
             file_type=file_type,
             return_y=True,
@@ -239,13 +254,13 @@ def fit(
             compute_intervals=self.compute_intervals,
         )
 
-        self.intervals = self.training_set.intervals
-        self.replacement_probabilities = self.training_set.replacement_probabilities
-        self.mean_set = self.training_set.mean_set
-        self.std_set = self.training_set.std_set
+        self.intervals = training_set.intervals
+        self.replacement_probabilities = training_set.replacement_probabilities
+        self.mean_set = training_set.mean_set
+        self.std_set = training_set.std_set
 
         train_loader = DataLoader(
-            self.training_set,
+            training_set,
             batch_size=self.batch_size,
             shuffle=True,
             num_workers=self.num_workers,
@@ -321,15 +336,15 @@ def predict(
             num_workers=self.num_workers,
         )
 
-        classificaion_results = []
+        classification_results = []
 
         with torch.no_grad():
             for idx, data in enumerate(test_loader):
                 inputs = self._assemble_input_for_testing(data)
                 results = self.model.forward(inputs, training=False)
-                classificaion_results.append(results["classification_pred"])
+                classification_results.append(results["classification_pred"])
 
-        classification = torch.cat(classificaion_results).cpu().detach().numpy()
+        classification = torch.cat(classification_results).cpu().detach().numpy()
         result_dict = {
             "classification": classification,
         }

pypots/imputation/csai/core.py

@@ -62,7 +62,10 @@ class _BCSAI(nn.Module):
 
     Notes
     -----
-    BCSAI is a bidirectional imputation model that uses forward and backward GRU cells to handle time-series data. It computes consistency and reconstruction losses to improve imputation accuracy. During training, the forward and backward reconstructions are combined, and losses are used to update the model. In evaluation mode, the model also outputs original data and indicating masks for further analysis.
+    CSAI is a bidirectional imputation model that uses forward and backward GRU cells to handle time-series data.
+    It computes consistency and reconstruction losses to improve imputation accuracy.
+    During training, the forward and backward reconstructions are combined, and losses are used to update the model.
+    In evaluation mode, the model also outputs original data and indicating masks for further analysis.
 
     """
 

pypots/imputation/csai/data.py

@@ -5,15 +5,17 @@
 # Created by Linglong Qian, Joseph Arul Raj <[email protected], [email protected]>
 # License: BSD-3-Clause
 
+import copy
 from typing import Iterable
-from ...data.dataset import BaseDataset
+from typing import Union
+
 import numpy as np
 import torch
-from typing import Union
-import copy
-from ...data.utils import parse_delta
 from sklearn.preprocessing import StandardScaler
 
+from ...data.dataset import BaseDataset
+from ...data.utils import parse_delta
+
 
 def normalize_csai(
     data,
@@ -22,7 +24,8 @@ def normalize_csai(
     compute_intervals: bool = False,
 ):
     """
-    Normalize the data based on the given mean and standard deviation, and optionally compute time intervals between observations.
+    Normalize the data based on the given mean and standard deviation,
+    and optionally compute time intervals between observations.
 
     Parameters
     ----------
@@ -33,7 +36,8 @@ def normalize_csai(
         The mean values for each variable, used for normalization. If empty, means will be computed from the data.
 
     std : list of float, optional
-        The standard deviation values for each variable, used for normalization. If empty, std values will be computed from the data.
+        The standard deviation values for each variable, used for normalization.
+        If empty, std values will be computed from the data.
 
     compute_intervals : bool, optional, default=False
         Whether to compute the time intervals between observations for each variable.
@@ -47,10 +51,12 @@ def normalize_csai(
         The mean values for each variable after normalization, either computed from the data or passed as input.
 
     std_set : np.ndarray
-        The standard deviation values for each variable after normalization, either computed from the data or passed as input.
+        The standard deviation values for each variable after normalization,
+        either computed from the data or passed as input.
 
     intervals_list : dict of int to float, optional
-        If `compute_intervals` is True, this will return the median time intervals between observations for each variable.
+        If `compute_intervals` is True, this will return the median time intervals between observations
+        for each variable.
     """
 
     # Convert data to numpy array if it is a torch tensor
@@ -296,13 +302,23 @@ class DatasetForCSAI(BaseDataset):
     Parameters
     ----------
     data :
-        The dataset for model input, which can be either a dictionary or a path string to a data file. If it's a dictionary, `X` should be an array-like structure with shape [n_samples, sequence length (n_steps), n_features], containing the time-series data, and it can have missing values. Optionally, the dictionary can include `y`, an array-like structure with shape [n_samples], representing the labels of `X`. If `data` is a path string, it should point to a data file (e.g., h5 file) that contains key-value pairs like a dictionary, including keys for `X` and possibly `y`.
+        The dataset for model input, which can be either a dictionary or a path string to a data file.
+        If it's a dictionary, `X` should be an array-like structure
+        with shape [n_samples, sequence length (n_steps), n_features], containing the time-series data,
+        and it can have missing values. Optionally, the dictionary can include `y`,
+        an array-like structure with shape [n_samples], representing the labels of `X`.
+        If `data` is a path string, it should point to a data file (e.g., h5 file) that contains key-value pairs like
+        a dictionary, including keys for `X` and possibly `y`.
 
     return_X_ori :
-        Whether to return the original time-series data (`X_ori`) when fetching data samples, useful for evaluation purposes.
+        Whether to return the original time-series data (`X_ori`) when fetching data samples,
+        useful for evaluation purposes.
 
     return_y :
-        Whether to return classification labels in the `__getitem__()` method if they exist in the dataset. If `True`, labels will be included in the returned data samples, which is useful for training classification models. If `False`, the labels won't be returned, suitable for testing or validation stages.
+        Whether to return classification labels in the `__getitem__()` method if they exist in the dataset.
+        If `True`, labels will be included in the returned data samples,
+        which is useful for training classification models.
+        If `False`, the labels won't be returned, suitable for testing or validation stages.
 
     file_type :
         The type of the data file if `data` is a path string, such as "hdf5".
@@ -317,20 +333,29 @@ class DatasetForCSAI(BaseDataset):
         Whether to compute time intervals between observations for handling irregular time-series data.
 
     replacement_probabilities :
-        Optional precomputed probabilities for sampling missing values. If not provided, they will be calculated during the initialization of the dataset.
+        Optional precomputed probabilities for sampling missing values.
+        If not provided, they will be calculated during the initialization of the dataset.
 
     normalise_mean :
-        A list of mean values for normalizing the input features. If not provided, they will be computed during initialization.
+        A list of mean values for normalizing the input features.
+        If not provided, they will be computed during initialization.
 
     normalise_std :
-        A list of standard deviation values for normalizing the input features. If not provided, they will be computed during initialization.
+        A list of standard deviation values for normalizing the input features.
+        If not provided, they will be computed during initialization.
 
     training :
-        Whether the dataset is used for training. If `False`, it will adjust how data is processed, particularly for evaluation and testing phases.
+        Whether the dataset is used for training.
+        If `False`, it will adjust how data is processed, particularly for evaluation and testing phases.
 
     Notes
     -----
-    The DatasetForCSAI class is designed for bidirectional imputation of time-series data, handling both forward and backward directions to improve imputation accuracy. It supports on-the-fly data normalization and missing value simulation, making it suitable for training and evaluating deep learning models like CSAI. The class can work with large datasets stored on disk, leveraging lazy-loading to minimize memory usage, and supports both training and testing scenarios, adjusting data handling as needed.
+    The DatasetForCSAI class is designed for bidirectional imputation of time-series data,
+    handling both forward and backward directions to improve imputation accuracy.
+    It supports on-the-fly data normalization and missing value simulation,
+    making it suitable for training and evaluating deep learning models like CSAI.
+    The class can work with large datasets stored on disk, leveraging lazy-loading to minimize memory usage,
+    and supports both training and testing scenarios, adjusting data handling as needed.
 
     """
 

pypots/imputation/csai/model.py

@@ -11,11 +11,9 @@
 import torch
 from torch.utils.data import DataLoader
 
-
 from .core import _BCSAI
 from .data import DatasetForCSAI
 from ..base import BaseNNImputer
-from ...data.checking import key_in_data_set
 from ...optim.adam import Adam
 from ...optim.base import Optimizer
 
@@ -146,6 +144,9 @@ def __init__(
         self.step_channels = step_channels
         self.compute_intervals = compute_intervals
         self.intervals = None
+        self.replacement_probabilities = None
+        self.mean_set = None
+        self.std_set = None
 
         # Initialise model
         self.model = _BCSAI(
@@ -238,16 +239,16 @@ def fit(
         file_type: str = "hdf5",
     ) -> None:
 
-        self.training_set = DatasetForCSAI(
+        training_set = DatasetForCSAI(
             train_set, False, False, file_type, self.removal_percent, self.increase_factor, self.compute_intervals
         )
-        self.intervals = self.training_set.intervals
-        self.replacement_probabilities = self.training_set.replacement_probabilities
-        self.mean_set = self.training_set.mean_set
-        self.std_set = self.training_set.std_set
+        self.intervals = training_set.intervals
+        self.replacement_probabilities = training_set.replacement_probabilities
+        self.mean_set = training_set.mean_set
+        self.std_set = training_set.std_set
 
         training_loader = DataLoader(
-            self.training_set,
+            training_set,
             batch_size=self.batch_size,
             shuffle=True,
             num_workers=self.num_workers,

pypots/imputation/segrnn/core.py

@@ -5,13 +5,10 @@
 
 # Created by Shengsheng Lin
 
-from typing import Optional
-
-from typing import Callable
 import torch.nn as nn
 
-from ...nn.modules.segrnn import BackboneSegRNN
 from ...nn.modules.saits import SaitsLoss
+from ...nn.modules.segrnn import BackboneSegRNN
 
 
 class _SegRNN(nn.Module):

pypots/nn/modules/csai/backbone.py

@@ -5,10 +5,11 @@
 # Created by Linglong Qian, Joseph Arul Raj <[email protected], [email protected]>
 # License: BSD-3-Clause
 
+import math
+
 import torch
 import torch.nn as nn
-import torch.nn.functional as F
-import math
+
 from .layers import FeatureRegression, Decay, Decay_obs, PositionalEncoding, Conv1dWithInit, TorchTransformerEncoder
 from ....utils.metrics import calc_mae
 
@@ -91,7 +92,7 @@ class BackboneCSAI(nn.Module):
     """
 
     def __init__(self, n_steps, n_features, rnn_hidden_size, step_channels, medians_df=None):
-        super(BackboneCSAI, self).__init__()
+        super().__init__()
 
         if medians_df is not None:
             self.medians_tensor = torch.tensor(list(medians_df.values())).float()
@@ -134,7 +135,7 @@ def forward(self, x, mask, deltas, last_obs, h=None):
 
         decay_factor = self.weighted_obs(deltas - medians.unsqueeze(1))
 
-        if h == None:
+        if h is None:
             data_last_obs = self.input_projection(last_obs.permute(0, 2, 1)).permute(0, 2, 1)
             data_decay_factor = self.input_projection(decay_factor.permute(0, 2, 1)).permute(0, 2, 1)
 
@@ -191,7 +192,7 @@ def forward(self, x, mask, deltas, last_obs, h=None):
 
 class BackboneBCSAI(nn.Module):
     def __init__(self, n_steps, n_features, rnn_hidden_size, step_channels, medians_df=None):
-        super(BackboneBCSAI, self).__init__()
+        super().__init__()
 
         self.model_f = BackboneCSAI(n_steps, n_features, rnn_hidden_size, step_channels, medians_df)
         self.model_b = BackboneCSAI(n_steps, n_features, rnn_hidden_size, step_channels, medians_df)