Convert task classes to more generic so they can be re-used (#504)

* Convert task classes to more generic so they can be re-used

* Fix imports in tutorial notebooks, and model report desc

* Small fix to metrics var

.pre-commit-config.yaml

@@ -21,7 +21,7 @@ repos:
     - id: black
 
   - repo: https://github.com/charliermarsh/ruff-pre-commit
-    rev: 'v0.1.0'
+    rev: 'v0.1.5'
     hooks:
     - id: ruff
       args: [--fix, --exit-non-zero-on-fix]
@@ -34,7 +34,7 @@ repos:
       entry: python3 -m mypy --config-file pyproject.toml
       language: system
       types: [python]
-      exclude: "use_cases|tests|cyclops/(process|models|tasks|monitor|report/plot)"
+      exclude: "use_cases|tests|cyclops/(process|models|monitor|report/plot)"
 
   - repo: local
     hooks:

cyclops/models/wrappers/utils.py

@@ -4,7 +4,7 @@
 import os
 import random
 from collections import defaultdict
-from typing import Mapping, Sequence, Union
+from typing import Any, Mapping, Sequence, Union
 
 import numpy as np
 import torch
@@ -65,7 +65,7 @@ def to_tensor(
     )
 
 
-def to_numpy(X):
+def to_numpy(X) -> Union[np.typing.NDArray[Any], Sequence, Mapping]:
     """Convert the input to a numpy array.
 
     Parameters

cyclops/tasks/__init__.py

@@ -4,5 +4,3 @@
     BinaryTabularClassificationTask,
     MultilabelImageClassificationTask,
 )
-from cyclops.tasks.cxr_classification import CXRClassificationTask
-from cyclops.tasks.mortality_prediction import MortalityPredictionTask

cyclops/tasks/base.py

@@ -44,18 +44,16 @@ def __init__(
         """
         self.models = prepare_models(models)
         self._validate_models()
-        self.task_features = (
-            [task_features] if isinstance(task_features, str) else task_features
-        )
+        self.task_features = task_features
         self.task_target = (
             [task_target] if isinstance(task_target, str) else task_target
         )
         self.device = get_device()
-        self.trained_models = []
-        self.pretrained_models = []
+        self.trained_models: List[str] = []
+        self.pretrained_models: List[str] = []
 
     @property
-    def models_count(self):
+    def models_count(self) -> int:
         """Number of models in the task.
 
         Returns
@@ -92,7 +90,7 @@ def data_type(self) -> str:
         """
         raise NotImplementedError
 
-    def list_models(self):
+    def list_models(self) -> List[str]:
         """List the names of the models in the task.
 
         Returns
@@ -104,14 +102,14 @@ def list_models(self):
         return list(self.models.keys())
 
     @abstractmethod
-    def _validate_models(self):
+    def _validate_models(self) -> None:
         """Validate the models for the task data type."""
         raise NotImplementedError
 
     def add_model(
         self,
         model: Union[str, WrappedModel, Dict[str, WrappedModel]],
-    ):
+    ) -> None:
         """Add a model to the task.
 
         Parameters
@@ -167,7 +165,7 @@ def save_model(
         self,
         filepath: Union[str, Dict[str, str]],
         model_names: Optional[Union[str, List[str]]] = None,
-        **kwargs,
+        **kwargs: Any,
     ) -> None:
         """Save the model to a specified filepath.
 
@@ -219,7 +217,7 @@ def load_model(
         self,
         filepath: str,
         model_name: Optional[str] = None,
-        **kwargs,
+        **kwargs: Any,
     ) -> WrappedModel:
         """Load a pretrained model.