[ENH] Update ruff linting standards to py310

Closes #649

Changes:
- pyproject.toml: move deprecated top-level ruff keys to lint section
  - [tool.ruff.per-file-ignores] -> [tool.ruff.lint.per-file-ignores]
  - [tool.ruff.pydocstyle] -> [tool.ruff.lint.pydocstyle]
  - Remove deprecated ignore-init-module-imports option
- Apply pyupgrade (UP) rules across the codebase for py310 target:
  - UP006: Replace typing.List/Dict/Tuple with list/dict/tuple
  - UP007: Replace Optional[X]/Union[X,Y] with X|None / X|Y
  - UP035: Remove deprecated typing imports, use built-ins directly
  - UP045: Replace Optional[X] with X|None in annotations
- Fix import ordering (isort) after typing imports cleanup

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Author: Jayant-kernel

pyproject.toml

@@ -149,14 +149,14 @@ exclude = [
     "dist",
     "docs"
 ]
-ignore-init-module-imports = true
 
-[tool.ruff.per-file-ignores]
+
+[tool.ruff.lint.per-file-ignores]
 "setup.py" = ["D100", "SIM115"]
 "__about__.py" = ["D100"]
 "__init__.py" = ["D100"]
 
-[tool.ruff.pydocstyle]
+[tool.ruff.lint.pydocstyle]
 # Use numpy-style docstrings.
 convention = "numpy"
 

src/pytorch_tabular/config/config.py

@@ -5,8 +5,9 @@
 
 import os
 import re
+from collections.abc import Iterable
 from dataclasses import MISSING, dataclass, field
-from typing import Any, Dict, Iterable, List, Optional
+from typing import Any
 
 from omegaconf import OmegaConf
 
@@ -103,22 +104,22 @@ class DataConfig:
 
     """
 
-    target: Optional[List[str]] = field(
+    target: list[str] | None = field(
         default=None,
         metadata={
             "help": "A list of strings with the names of the target column(s)."
             " It is mandatory for all except SSL tasks."
         },
     )
-    continuous_cols: List = field(
+    continuous_cols: list = field(
         default_factory=list,
         metadata={"help": "Column names of the numeric fields. Defaults to []"},
     )
-    categorical_cols: List = field(
+    categorical_cols: list = field(
         default_factory=list,
         metadata={"help": "Column names of the categorical fields to treat differently. Defaults to []"},
     )
-    date_columns: List = field(
+    date_columns: list = field(
         default_factory=list,
         metadata={
             "help": "(Column names, Freq) tuples of the date fields. For eg. a field named"
@@ -131,14 +132,14 @@ class DataConfig:
         default=True,
         metadata={"help": "Whether or not to encode the derived variables from date"},
     )
-    validation_split: Optional[float] = field(
+    validation_split: float | None = field(
         default=0.2,
         metadata={
             "help": "Percentage of Training rows to keep aside as validation."
             " Used only if Validation Data is not given separately"
         },
     )
-    continuous_feature_transform: Optional[str] = field(
+    continuous_feature_transform: str | None = field(
         default=None,
         metadata={
             "help": "Whether or not to transform the features before modelling. By default it is turned off.",
@@ -164,7 +165,7 @@ class DataConfig:
             " the noise is only applied for QuantileTransformer"
         },
     )
-    num_workers: Optional[int] = field(
+    num_workers: int | None = field(
         default=0,
         metadata={"help": "The number of workers used for data loading. For windows always set to 0"},
     )
@@ -186,7 +187,7 @@ class DataConfig:
         metadata={"help": "pickle protocol version passed to `torch.save` for dataset caching to disk"},
     )
 
-    dataloader_kwargs: Dict[str, Any] = field(
+    dataloader_kwargs: dict[str, Any] = field(
         default_factory=dict,
         metadata={"help": "Additional kwargs to be passed to PyTorch DataLoader."},
     )
@@ -229,19 +230,19 @@ class InferredConfig:
     continuous_dim: int = field(
         metadata={"help": "The number of continuous features"},
     )
-    output_dim: Optional[int] = field(
+    output_dim: int | None = field(
         default=None,
         metadata={"help": "The number of output targets"},
     )
-    output_cardinality: Optional[List[int]] = field(
+    output_cardinality: list[int] | None = field(
         default=None,
         metadata={"help": "The number of unique values in classification output"},
     )
-    categorical_cardinality: Optional[List[int]] = field(
+    categorical_cardinality: list[int] | None = field(
         default=None,
         metadata={"help": "The number of unique values in categorical features"},
     )
-    embedding_dims: Optional[List] = field(
+    embedding_dims: list | None = field(
         default=None,
         metadata={
             "help": "The dimensions of the embedding for each categorical column as a list of tuples "
@@ -384,30 +385,30 @@ class TrainerConfig:
         },
     )
     max_epochs: int = field(default=10, metadata={"help": "Maximum number of epochs to be run"})
-    min_epochs: Optional[int] = field(
+    min_epochs: int | None = field(
         default=1,
         metadata={"help": "Force training for at least these many epochs. 1 by default"},
     )
-    max_time: Optional[int] = field(
+    max_time: int | None = field(
         default=None,
         metadata={"help": "Stop training after this amount of time has passed. Disabled by default (None)"},
     )
-    accelerator: Optional[str] = field(
+    accelerator: str | None = field(
         default="auto",
         metadata={
             "help": "The accelerator to use for training. Can be one of 'cpu','gpu','tpu','ipu','auto'."
             " Defaults to 'auto'",
             "choices": ["cpu", "gpu", "tpu", "ipu", "mps", "auto"],
         },
     )
-    devices: Optional[int] = field(
+    devices: int | None = field(
         default=-1,
         metadata={
             "help": "Number of devices to train on. -1 uses all available devices."
             " By default uses all available devices (-1)",
         },
     )
-    devices_list: Optional[List[int]] = field(
+    devices_list: list[int] | None = field(
         default=None,
         metadata={
             "help": "List of devices to train on (list). If specified, takes precedence over `devices` argument."
@@ -454,15 +455,15 @@ class TrainerConfig:
             "help": "If true enables cudnn.deterministic. Might make your system slower, but ensures reproducibility."
         },
     )
-    profiler: Optional[str] = field(
+    profiler: str | None = field(
         default=None,
         metadata={
             "help": "To profile individual steps during training and assist in identifying bottlenecks."
             " None, simple or advanced, pytorch",
             "choices": [None, "simple", "advanced", "pytorch"],
         },
     )
-    early_stopping: Optional[str] = field(
+    early_stopping: str | None = field(
         default="valid_loss",
         metadata={
             "help": "The loss/metric that needed to be monitored for early stopping."
@@ -484,14 +485,14 @@ class TrainerConfig:
         default=3,
         metadata={"help": "The number of epochs to wait until there is no further improvements in loss/metric"},
     )
-    early_stopping_kwargs: Optional[Dict[str, Any]] = field(
+    early_stopping_kwargs: dict[str, Any] | None = field(
         default_factory=lambda: {},
         metadata={
             "help": "Additional keyword arguments for the early stopping callback."
             " See the documentation for the PyTorch Lightning EarlyStopping callback for more details."
         },
     )
-    checkpoints: Optional[str] = field(
+    checkpoints: str | None = field(
         default="valid_loss",
         metadata={
             "help": "The loss/metric that needed to be monitored for checkpoints. If None, there will be no checkpoints"
@@ -505,7 +506,7 @@ class TrainerConfig:
         default=1,
         metadata={"help": "Number of training steps between checkpoints"},
     )
-    checkpoints_name: Optional[str] = field(
+    checkpoints_name: str | None = field(
         default=None,
         metadata={
             "help": "The name under which the models will be saved. If left blank,"
@@ -521,7 +522,7 @@ class TrainerConfig:
         default=1,
         metadata={"help": "The number of best models to save"},
     )
-    checkpoints_kwargs: Optional[Dict[str, Any]] = field(
+    checkpoints_kwargs: dict[str, Any] | None = field(
         default_factory=lambda: {},
         metadata={
             "help": "Additional keyword arguments for the checkpoints callback. See the documentation"
@@ -553,7 +554,7 @@ class TrainerConfig:
         default=42,
         metadata={"help": "Seed for random number generators. Defaults to 42"},
     )
-    trainer_kwargs: Dict[str, Any] = field(
+    trainer_kwargs: dict[str, Any] = field(
         default_factory=dict,
         metadata={"help": "Additional kwargs to be passed to PyTorch Lightning Trainer."},
     )
@@ -611,14 +612,14 @@ class ExperimentConfig:
         },
     )
 
-    run_name: Optional[str] = field(
+    run_name: str | None = field(
         default=None,
         metadata={
             "help": "The name of the run; a specific identifier to recognize the run."
             " If left blank, will be assigned a auto-generated name"
         },
     )
-    exp_watch: Optional[str] = field(
+    exp_watch: str | None = field(
         default=None,
         metadata={
             "help": "The level of logging required.  Can be `gradients`, `parameters`, `all` or `None`."
@@ -690,29 +691,29 @@ class OptimizerConfig:
             " for example 'torch_optimizer.RAdam'."
         },
     )
-    optimizer_params: Dict = field(
+    optimizer_params: dict = field(
         default_factory=lambda: {},
         metadata={"help": "The parameters for the optimizer. If left blank, will use default parameters."},
     )
-    lr_scheduler: Optional[str] = field(
+    lr_scheduler: str | None = field(
         default=None,
         metadata={
             "help": "The name of the LearningRateScheduler to use, if any, from"
             " https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate."
             " If None, will not use any scheduler. Defaults to `None`",
         },
     )
-    lr_scheduler_params: Optional[Dict] = field(
+    lr_scheduler_params: dict | None = field(
         default_factory=lambda: {},
         metadata={"help": "The parameters for the LearningRateScheduler. If left blank, will use default parameters."},
     )
 
-    lr_scheduler_monitor_metric: Optional[str] = field(
+    lr_scheduler_monitor_metric: str | None = field(
         default="valid_loss",
         metadata={"help": "Used with ReduceLROnPlateau, where the plateau is decided based on this metric"},
     )
 
-    lr_scheduler_interval: Optional[str] = field(
+    lr_scheduler_interval: str | None = field(
         default="epoch",
         metadata={"help": "Interval at which to step the LR Scheduler, one of `epoch` or `step`. Defaults to `epoch`."},
     )
@@ -823,7 +824,7 @@ class ModelConfig:
         }
     )
 
-    head: Optional[str] = field(
+    head: str | None = field(
         default="LinearHead",
         metadata={
             "help": "The head to be used for the model. Should be one of the heads defined"
@@ -832,14 +833,14 @@ class ModelConfig:
         },
     )
 
-    head_config: Optional[Dict] = field(
+    head_config: dict | None = field(
         default_factory=lambda: {"layers": ""},
         metadata={
             "help": "The config as a dict which defines the head."
             " If left empty, will be initialized as default linear head."
         },
     )
-    embedding_dims: Optional[List] = field(
+    embedding_dims: list | None = field(
         default=None,
         metadata={
             "help": "The dimensions of the embedding for each categorical column as a list of tuples "
@@ -860,15 +861,15 @@ class ModelConfig:
         default=1e-3,
         metadata={"help": "The learning rate of the model. Defaults to 1e-3."},
     )
-    loss: Optional[str] = field(
+    loss: str | None = field(
         default=None,
         metadata={
             "help": "The loss function to be applied. By Default it is MSELoss for regression "
             "and CrossEntropyLoss for classification. Unless you are sure what you are doing, "
             "leave it at MSELoss or L1Loss for regression and CrossEntropyLoss for classification"
         },
     )
-    metrics: Optional[List[str]] = field(
+    metrics: list[str] | None = field(
         default=None,
         metadata={
             "help": "the list of metrics you need to track during training. The metrics should be one "
@@ -877,23 +878,23 @@ class ModelConfig:
             "and mean_squared_error for regression"
         },
     )
-    metrics_prob_input: Optional[List[bool]] = field(
+    metrics_prob_input: list[bool] | None = field(
         default=None,
         metadata={
             "help": "Is a mandatory parameter for classification metrics defined in the config. This defines "
             "whether the input to the metric function is the probability or the class. Length should be same "
             "as the number of metrics. Defaults to None."
         },
     )
-    metrics_params: Optional[List] = field(
+    metrics_params: list | None = field(
         default=None,
         metadata={
             "help": "The parameters to be passed to the metrics function. `task` is forced to be `multiclass`` "
             "because the multiclass version can handle binary as well and for simplicity we are only using "
             "`multiclass`."
         },
     )
-    target_range: Optional[List] = field(
+    target_range: list | None = field(
         default=None,
         metadata={
             "help": "The range in which we should limit the output variable. "
@@ -902,7 +903,7 @@ class ModelConfig:
         },
     )
 
-    virtual_batch_size: Optional[int] = field(
+    virtual_batch_size: int | None = field(
         default=None,
         metadata={
             "help": "If not None, all BatchNorms will be converted to GhostBatchNorm's "
@@ -1001,23 +1002,23 @@ class SSLModelConfig:
 
     task: str = field(init=False, default="ssl")
 
-    encoder_config: Optional[ModelConfig] = field(
+    encoder_config: ModelConfig | None = field(
         default=None,
         metadata={
             "help": "The config of the encoder to be used for the model."
             " Should be one of the model configs defined in PyTorch Tabular",
         },
     )
 
-    decoder_config: Optional[ModelConfig] = field(
+    decoder_config: ModelConfig | None = field(
         default=None,
         metadata={
             "help": "The config of decoder to be used for the model."
             " Should be one of the model configs defined in PyTorch Tabular. Defaults to nn.Identity",
         },
     )
 
-    embedding_dims: Optional[List] = field(
+    embedding_dims: list | None = field(
         default=None,
         metadata={
             "help": "The dimensions of the embedding for each categorical column as a list of tuples "
@@ -1033,7 +1034,7 @@ class SSLModelConfig:
         default=True,
         metadata={"help": "If True, we will normalize the continuous layer by passing it through a BatchNorm layer."},
     )
-    virtual_batch_size: Optional[int] = field(
+    virtual_batch_size: int | None = field(
         default=None,
         metadata={
             "help": "If not None, all BatchNorms will be converted to GhostBatchNorm's "