🔨 Update Regression support

experiments/regression/uci_datasets/configs/boston/mlp/batch_ensemble.yaml

@@ -0,0 +1,53 @@
+# lightning.pytorch==2.1.3
+seed_everything: false
+eval_after_fit: true
+trainer:
+  accelerator: gpu
+  devices: 1
+  precision: 16-mixed
+  max_epochs: 40
+  logger:
+    class_path: lightning.pytorch.loggers.TensorBoardLogger
+    init_args:
+      save_dir: logs/boston/mlp/normal
+      name: batch_ensembles
+      default_hp_metric: false
+  callbacks:
+    - class_path: torch_uncertainty.callbacks.TURegCheckpoint
+      init_args:
+        probabilistic: true
+    - class_path: lightning.pytorch.callbacks.LearningRateMonitor
+      init_args:
+        logging_interval: step
+    - class_path: lightning.pytorch.callbacks.EarlyStopping
+      init_args:
+        monitor: val/reg/MSE
+        patience: 1000
+        check_finite: true
+model:
+  model:
+    class_path: torch_uncertainty.models.mlp.batched_mlp
+    init_args:
+      in_features: 13
+      num_outputs: 1
+      hidden_dims:
+        - 50
+      dist_family: normal
+      num_estimators: 5
+  output_dim: 1
+  loss: torch_uncertainty.losses.DistributionNLLLoss
+  dist_family: normal
+  save_in_csv: true
+  format_batch_fn:
+    class_path: torch_uncertainty.transforms.RepeatTarget
+    init_args:
+      num_repeats: 5
+data:
+  root: ./data
+  batch_size: 128
+  dataset_name: boston
+optimizer:
+  class_path: torch.optim.Adam
+  init_args:
+    lr: 5e-3
+    weight_decay: 0

experiments/regression/uci_datasets/configs/boston/mlp/bayesian.yaml

@@ -0,0 +1,55 @@
+# lightning.pytorch==2.1.3
+seed_everything: false
+eval_after_fit: true
+trainer:
+  accelerator: gpu
+  devices: 1
+  precision: 16-mixed
+  max_epochs: 40
+  logger:
+    class_path: lightning.pytorch.loggers.TensorBoardLogger
+    init_args:
+      save_dir: logs/boston/mlp/normal
+      name: bayesian
+      default_hp_metric: false
+  callbacks:
+    - class_path: torch_uncertainty.callbacks.TURegCheckpoint
+      init_args:
+        probabilistic: true
+    - class_path: lightning.pytorch.callbacks.LearningRateMonitor
+      init_args:
+        logging_interval: step
+    - class_path: lightning.pytorch.callbacks.EarlyStopping
+      init_args:
+        monitor: val/reg/MSE
+        patience: 1000
+        check_finite: true
+model:
+  model:
+    class_path: torch_uncertainty.models.mlp.bayesian_mlp
+    init_args:
+      in_features: 13
+      num_outputs: 1
+      hidden_dims:
+        - 50
+      dist_family: normal
+      num_samples: 10
+  output_dim: 1
+  loss:
+    class_path: torch_uncertainty.losses.ELBOLoss
+    init_args:
+      kl_weight: 0.00002
+      inner_loss: torch_uncertainty.losses.DistributionNLLLoss
+      num_samples: 3
+      dist_family: normal
+  dist_family: normal
+  save_in_csv: true
+data:
+  root: ./data
+  batch_size: 128
+  dataset_name: boston
+optimizer:
+  class_path: torch.optim.Adam
+  init_args:
+    lr: 5e-3
+    weight_decay: 0

experiments/regression/uci_datasets/configs/boston/mlp/cauchy.yaml

@@ -0,0 +1,49 @@
+# lightning.pytorch==2.1.3
+seed_everything: false
+eval_after_fit: true
+trainer:
+  accelerator: gpu
+  devices: 1
+  precision: 16-mixed
+  max_epochs: 40
+  logger:
+    class_path: lightning.pytorch.loggers.TensorBoardLogger
+    init_args:
+      save_dir: logs/boston/mlp/cauchy
+      name: standard
+      default_hp_metric: false
+  callbacks:
+    - class_path: torch_uncertainty.callbacks.TURegCheckpoint
+      init_args:
+        probabilistic: true
+    - class_path: lightning.pytorch.callbacks.LearningRateMonitor
+      init_args:
+        logging_interval: step
+    - class_path: lightning.pytorch.callbacks.EarlyStopping
+      init_args:
+        monitor: val/reg/MSE
+        patience: 1000
+        check_finite: true
+model:
+  model:
+    class_path: torch_uncertainty.models.mlp.mlp
+    init_args:
+      in_features: 13
+      num_outputs: 1
+      hidden_dims:
+        - 50
+      dist_family: cauchy
+  output_dim: 1
+  loss: torch_uncertainty.losses.DistributionNLLLoss
+  dist_family: cauchy
+  dist_estimate: mode
+  save_in_csv: true
+data:
+  root: ./data
+  batch_size: 128
+  dataset_name: boston
+optimizer:
+  class_path: torch.optim.Adam
+  init_args:
+    lr: 5e-3
+    weight_decay: 0

experiments/regression/uci_datasets/configs/boston/mlp/deep_ensembles.yaml

@@ -0,0 +1,58 @@
+# lightning.pytorch==2.1.3
+seed_everything: false
+eval_after_fit: true
+trainer:
+  accelerator: gpu
+  devices: 1
+  precision: 16-mixed
+  max_epochs: 40
+  logger:
+    class_path: lightning.pytorch.loggers.TensorBoardLogger
+    init_args:
+      save_dir: logs/boston/mlp/normal
+      name: deep_ensembles
+      default_hp_metric: false
+  callbacks:
+    - class_path: torch_uncertainty.callbacks.TURegCheckpoint
+      init_args:
+        probabilistic: true
+    - class_path: lightning.pytorch.callbacks.LearningRateMonitor
+      init_args:
+        logging_interval: step
+    - class_path: lightning.pytorch.callbacks.EarlyStopping
+      init_args:
+        monitor: val/reg/MSE
+        patience: 1000
+        check_finite: true
+model:
+  model:
+    class_path: torch_uncertainty.models.deep_ensembles
+    init_args:
+      models:
+        class_path: torch_uncertainty.models.mlp.mlp
+        init_args:
+          in_features: 13
+          num_outputs: 1
+          hidden_dims:
+            - 50
+          dist_family: normal
+      num_estimators: 5
+      task: regression
+      probabilistic: true
+  output_dim: 1
+  loss: torch_uncertainty.losses.DistributionNLLLoss
+  dist_family: normal
+  save_in_csv: true
+  format_batch_fn:
+    class_path: torch_uncertainty.transforms.RepeatTarget
+    init_args:
+      num_repeats: 5
+data:
+  root: ./data
+  batch_size: 128
+  dataset_name: boston
+optimizer:
+  class_path: torch.optim.Adam
+  init_args:
+    lr: 5e-3
+    weight_decay: 0

experiments/regression/uci_datasets/configs/boston/mlp/laplace.yaml

@@ -21,21 +21,28 @@ trainer:
         logging_interval: step
     - class_path: lightning.pytorch.callbacks.EarlyStopping
       init_args:
-        monitor: val/reg/NLL
+        monitor: val/reg/MSE
         patience: 1000
         check_finite: true
 model:
+  model:
+    class_path: torch_uncertainty.models.mlp.mlp
+    init_args:
+      in_features: 13
+      num_outputs: 1
+      hidden_dims:
+        - 50
+      dist_family: laplace
   output_dim: 1
-  in_features: 13
-  hidden_dims:
-    - 50
   loss: torch_uncertainty.losses.DistributionNLLLoss
-  version: std
-  distribution: laplace
+  dist_family: laplace
+  save_in_csv: true
 data:
   root: ./data
   batch_size: 128
   dataset_name: boston
 optimizer:
-  lr: 5e-3
-  weight_decay: 0
+  class_path: torch.optim.Adam
+  init_args:
+    lr: 5e-3
+    weight_decay: 0

experiments/regression/uci_datasets/configs/boston/mlp/masksemble.yaml

@@ -0,0 +1,54 @@
+# lightning.pytorch==2.1.3
+seed_everything: false
+eval_after_fit: true
+trainer:
+  accelerator: gpu
+  devices: 1
+  precision: 16-mixed
+  max_epochs: 40
+  logger:
+    class_path: lightning.pytorch.loggers.TensorBoardLogger
+    init_args:
+      save_dir: logs/boston/mlp/normal
+      name: masksemble
+      default_hp_metric: false
+  callbacks:
+    - class_path: torch_uncertainty.callbacks.TURegCheckpoint
+      init_args:
+        probabilistic: true
+    - class_path: lightning.pytorch.callbacks.LearningRateMonitor
+      init_args:
+        logging_interval: step
+    - class_path: lightning.pytorch.callbacks.EarlyStopping
+      init_args:
+        monitor: val/reg/MSE
+        patience: 1000
+        check_finite: true
+model:
+  model:
+    class_path: torch_uncertainty.models.mlp.masked_mlp
+    init_args:
+      in_features: 13
+      num_outputs: 1
+      hidden_dims:
+        - 50
+      dist_family: normal
+      num_estimators: 5
+      scale: 2.0
+  output_dim: 1
+  loss: torch_uncertainty.losses.DistributionNLLLoss
+  dist_family: normal
+  save_in_csv: true
+  format_batch_fn:
+    class_path: torch_uncertainty.transforms.RepeatTarget
+    init_args:
+      num_repeats: 5
+data:
+  root: ./data
+  batch_size: 128
+  dataset_name: boston
+optimizer:
+  class_path: torch.optim.Adam
+  init_args:
+    lr: 5e-3
+    weight_decay: 0

experiments/regression/uci_datasets/configs/boston/mlp/mc_dropout.yaml

@@ -0,0 +1,56 @@
+# lightning.pytorch==2.1.3
+seed_everything: false
+eval_after_fit: true
+trainer:
+  accelerator: gpu
+  devices: 1
+  precision: 16-mixed
+  max_epochs: 40
+  logger:
+    class_path: lightning.pytorch.loggers.TensorBoardLogger
+    init_args:
+      save_dir: logs/boston/mlp/normal
+      name: mc_dropout
+      default_hp_metric: false
+  callbacks:
+    - class_path: torch_uncertainty.callbacks.TURegCheckpoint
+      init_args:
+        probabilistic: true
+    - class_path: lightning.pytorch.callbacks.LearningRateMonitor
+      init_args:
+        logging_interval: step
+    - class_path: lightning.pytorch.callbacks.EarlyStopping
+      init_args:
+        monitor: val/reg/MSE
+        patience: 1000
+        check_finite: true
+model:
+  model:
+    class_path: torch_uncertainty.models.mc_dropout
+    init_args:
+      model:
+        class_path: torch_uncertainty.models.mlp.mlp
+        init_args:
+          in_features: 13
+          num_outputs: 1
+          hidden_dims:
+            - 50
+          dist_family: normal
+          dropout_rate: 0.1
+      num_estimators: 10
+      on_batch: false
+      task: regression
+      probabilistic: true
+  output_dim: 1
+  loss: torch_uncertainty.losses.DistributionNLLLoss
+  dist_family: normal
+  save_in_csv: true
+data:
+  root: ./data
+  batch_size: 128
+  dataset_name: boston
+optimizer:
+  class_path: torch.optim.Adam
+  init_args:
+    lr: 5e-3
+    weight_decay: 0