provide generic type for component subclasses

docs/guide/examples/blendcomp.py

@@ -14,7 +14,7 @@ class LinearBlendConfig(BaseModel):
     """
 
 
-class LinearBlendScorer(Component):
+class LinearBlendScorer(Component[ItemList]):
     r"""
     Score items with a linear blend of two other scores.
 

lenskit-funksvd/lenskit/funksvd.py

@@ -231,7 +231,7 @@ def _align_add_bias(bias, index, keys, series):
     return bias, series
 
 
-class FunkSVDScorer(Trainable, Component):
+class FunkSVDScorer(Trainable, Component[ItemList]):
     """
     FunkSVD explicit-feedback matrix factoriation.  FunkSVD is a regularized
     biased matrix factorization technique trained with featurewise stochastic

lenskit-hpf/lenskit/hpf.py

@@ -16,7 +16,7 @@
 _logger = logging.getLogger(__name__)
 
 
-class HPFScorer(Component, Trainable):
+class HPFScorer(Component[ItemList], Trainable):
     """
     Hierarchical Poisson factorization, provided by
     `hpfrec <https://hpfrec.readthedocs.io/en/latest/>`_.

lenskit-implicit/lenskit/implicit.py

@@ -34,7 +34,7 @@ class ImplicitALSConfig(ImplicitConfig, extra="allow"):
     weight: float = 40.0
 
 
-class BaseRec(Component, Trainable):
+class BaseRec(Component[ItemList], Trainable):
     """
     Base class for Implicit-backed recommenders.
 

lenskit-sklearn/lenskit/sklearn/svd.py

@@ -38,7 +38,7 @@ class BiasedSVDConfig:
     n_iter: int = 5
 
 
-class BiasedSVDScorer(Component, Trainable):
+class BiasedSVDScorer(Component[ItemList], Trainable):
     """
     Biased matrix factorization for explicit feedback using SciKit-Learn's
     :class:`~sklearn.decomposition.TruncatedSVD`.  It operates by first

lenskit/lenskit/als/_common.py

@@ -130,7 +130,7 @@ def to(self, device):
         return self._replace(ui_rates=self.ui_rates.to(device), iu_rates=self.iu_rates.to(device))
 
 
-class ALSBase(ABC, Component, Trainable):
+class ALSBase(ABC, Component[ItemList], Trainable):
     """
     Base class for ALS models.
 

lenskit/lenskit/basic/bias.py

@@ -270,7 +270,7 @@ def entity_damping(self, entity: Literal["user", "item"]) -> float:
         return entity_damping(self.damping, entity)
 
 
-class BiasScorer(Component):
+class BiasScorer(Component[ItemList]):
     """
     A user-item bias rating prediction model.  This component uses
     :class:`BiasModel` to predict ratings for users and items.

lenskit/lenskit/basic/candidates.py

@@ -11,7 +11,7 @@
 _logger = logging.getLogger(__name__)
 
 
-class TrainingCandidateSelectorBase(Component, Trainable):
+class TrainingCandidateSelectorBase(Component[ItemList], Trainable):
     """
     Base class for candidate selectors using the training data.
 

lenskit/lenskit/basic/composite.py

@@ -10,7 +10,7 @@
 _logger = logging.getLogger(__name__)
 
 
-class FallbackScorer(Component):
+class FallbackScorer(Component[ItemList]):
     """
     Scoring component that fills in missing scores using a fallback.
 

lenskit/lenskit/basic/history.py

@@ -20,7 +20,7 @@
 _logger = logging.getLogger(__name__)
 
 
-class UserTrainingHistoryLookup(Component, Trainable):
+class UserTrainingHistoryLookup(Component[ItemList], Trainable):
     """
     Look up a user's history from the training data.
 
@@ -57,7 +57,7 @@ def __str__(self):
         return self.__class__.__name__
 
 
-class KnownRatingScorer(Component, Trainable):
+class KnownRatingScorer(Component[ItemList], Trainable):
     """
     Score items by returning their values from the training data.
 

lenskit/lenskit/basic/popularity.py

@@ -27,7 +27,7 @@ class PopConfig(BaseModel):
     """
 
 
-class PopScorer(Component, Trainable):
+class PopScorer(Component[ItemList], Trainable):
     """
     Score items by their popularity.  Use with :py:class:`TopN` to get a
     most-popular-items recommender.

lenskit/lenskit/basic/random.py

@@ -20,7 +20,7 @@ class RandomConfig(BaseModel, arbitrary_types_allowed=True):
     """
 
 
-class RandomSelector(Component):
+class RandomSelector(Component[ItemList]):
     """
     Randomly select items from a candidate list.
 
@@ -74,7 +74,7 @@ def __call__(
             return items[np.zeros(0, dtype=np.int32)]
 
 
-class SoftmaxRanker(Component):
+class SoftmaxRanker(Component[ItemList]):
     """
     Stochastic top-N ranking with softmax sampling.
 

lenskit/lenskit/basic/topn.py

@@ -24,7 +24,7 @@ class TopNConfig(BaseModel):
     """
 
 
-class TopNRanker(Component):
+class TopNRanker(Component[ItemList]):
     """
     Rank scored items by their score and take the top *N*.  The ranking length
     can be passed either at runtime or at component instantiation time, with the

lenskit/lenskit/knn/item.py

@@ -79,7 +79,7 @@ def explicit(self) -> bool:
         return self.feedback == "explicit"
 
 
-class ItemKNNScorer(Component, Trainable):
+class ItemKNNScorer(Component[ItemList], Trainable):
     """
     Item-item nearest-neighbor collaborative filtering feedback. This item-item
     implementation is based on the description of item-based CF by

lenskit/lenskit/knn/user.py

@@ -69,7 +69,7 @@ def explicit(self) -> bool:
         return self.feedback == "explicit"
 
 
-class UserKNNScorer(Component, Trainable):
+class UserKNNScorer(Component[ItemList], Trainable):
     """
     User-user nearest-neighbor collaborative filtering with ratings. This
     user-user implementation is not terribly configurable; it hard-codes design

lenskit/tests/pipeline/test_component_config.py

@@ -32,14 +32,14 @@ class PrefixConfigPYDC:
     prefix: str = "UNDEFINED"
 
 
-class PrefixerDC(Component):
+class PrefixerDC(Component[str]):
     config: PrefixConfigDC
 
     def __call__(self, msg: str) -> str:
         return self.config.prefix + msg
 
 
-class PrefixerM(Component):
+class PrefixerM(Component[str]):
     config: PrefixConfigM
 
     def __call__(self, msg: str) -> str:
@@ -51,7 +51,7 @@ class PrefixerM2(PrefixerM):
     config: PrefixConfigM
 
 
-class PrefixerPYDC(Component):
+class PrefixerPYDC(Component[str]):
     config: PrefixConfigPYDC
 
     def __call__(self, msg: str) -> str:

lenskit/tests/pipeline/test_pipeline_clone.py

@@ -17,7 +17,7 @@ class PrefixConfig:
     prefix: str
 
 
-class Prefixer(Component):
+class Prefixer(Component[str]):
     config: PrefixConfig
 
     def __call__(self, msg: str) -> str:

lenskit/tests/pipeline/test_save_load.py

@@ -28,7 +28,7 @@ class PrefixConfig:
     prefix: str
 
 
-class Prefixer(Component):
+class Prefixer(Component[str]):
     config: PrefixConfig
 
     def __call__(self, msg: str) -> str: