fix: lazy loading error for classification CSAI;

pypots/classification/csai/data.py

@@ -6,6 +6,7 @@
 # License: BSD-3-Clause
 
 from typing import Union
+
 from ...imputation.csai.data import DatasetForCSAI as DatasetForCSAI_Imputation
 
 

pypots/classification/csai/model.py

@@ -13,8 +13,11 @@
 from .core import _BCSAI
 from .data import DatasetForCSAI
 from ..base import BaseNNClassifier
+from ...data.checking import key_in_data_set
+from ...data.saving.h5 import load_dict_from_h5
 from ...optim.adam import Adam
 from ...optim.base import Optimizer
+from ...utils.logging import logger
 
 
 class CSAI(BaseNNClassifier):
@@ -171,6 +174,7 @@ def __init__(
 
         # set up the optimizer
         self.optimizer = optimizer
+        self.optimizer.init_optimizer(self.model.parameters())
 
     def _assemble_input_for_training(self, data: list, training=True) -> dict:
         # extract data
@@ -245,6 +249,12 @@ def fit(
         file_type: str = "hdf5",
     ) -> None:
         # Create dataset
+        if isinstance(train_set, str):
+            logger.warning(
+                "CSAI does not support lazy loading because normalise mean and std need to be calculated ahead. "
+                "Hence the whole train set will be loaded into memory."
+            )
+            train_set = load_dict_from_h5(train_set)
         training_set = DatasetForCSAI(
             data=train_set,
             file_type=file_type,
@@ -267,6 +277,15 @@ def fit(
         )
         val_loader = None
         if val_set is not None:
+            if isinstance(val_set, str):
+                logger.warning(
+                    "CSAI does not support lazy loading because normalise mean and std need to be calculated ahead. "
+                    "Hence the whole val set will be loaded into memory."
+                )
+                val_set = load_dict_from_h5(val_set)
+
+            if not key_in_data_set("X_ori", val_set):
+                raise ValueError("val_set must contain 'X_ori' for model validation.")
             val_set = DatasetForCSAI(
                 data=val_set,
                 file_type=file_type,
@@ -284,24 +303,6 @@ def fit(
                 shuffle=False,
                 num_workers=self.num_workers,
             )
-        # Create model
-        self.model = _BCSAI(
-            n_steps=self.n_steps,
-            n_features=self.n_features,
-            rnn_hidden_size=self.rnn_hidden_size,
-            imputation_weight=self.imputation_weight,
-            consistency_weight=self.consistency_weight,
-            classification_weight=self.classification_weight,
-            n_classes=self.n_classes,
-            step_channels=self.step_channels,
-            dropout=self.dropout,
-            intervals=self.intervals,
-        )
-        self._send_model_to_given_device()
-        self._print_model_size()
-
-        # set up the optimizer
-        self.optimizer.init_optimizer(self.model.parameters())
 
         # train the model
         self._train_model(train_loader, val_loader)
@@ -317,6 +318,13 @@ def predict(
     ) -> dict:
 
         self.model.eval()
+
+        if isinstance(test_set, str):
+            logger.warning(
+                "CSAI does not support lazy loading because normalise mean and std need to be calculated ahead. "
+                "Hence the whole test set will be loaded into memory."
+            )
+            test_set = load_dict_from_h5(test_set)
         test_set = DatasetForCSAI(
             data=test_set,
             file_type=file_type,

pypots/imputation/csai/model.py

@@ -6,7 +6,6 @@
 # License: BSD-3-Clause
 
 from typing import Union, Optional
-from venv import logger
 
 import numpy as np
 import torch
@@ -19,6 +18,7 @@
 from ...data.saving.h5 import load_dict_from_h5
 from ...optim.adam import Adam
 from ...optim.base import Optimizer
+from ...utils.logging import logger
 
 
 class CSAI(BaseNNImputer):