Build a Pipeline abstraction

.vscode/ltex.dictionary.en-US.txt

@@ -10,3 +10,8 @@ invoker
 CUDA
 subpackages
 recomputation
+Higley
+POPROX
+rankers
+Scikit-Learn
+unpickle

docs/index.rst

@@ -50,6 +50,7 @@ Resources
     :caption: Algorithms
 
     interfaces
+    pipeline
     algorithms
     basic
     ranking

docs/pipeline.rst

@@ -0,0 +1,331 @@
+Recommendation Pipelines
+========================
+
+.. module:: lenskit.pipeline
+
+.. todo::
+    None of this has been implemented yet.
+
+Since version :ref:`2024.1`, LensKit uses a flexible “pipeline” abstraction to
+wire together different components such as candidate selectors, personalized
+item scorers, and rankers to produce predictions, recommendations, or other
+recommender system outputs.  This is a significant change from the LensKit 0.x
+design of monolithic and composable components based on the Scikit-Learn API,
+allowing new recommendation designs to be composed without writing new classes
+just for the composition.  It also makes recommender definition code more explicit
+by laying out the pipeline instead of burying composition logic in the definitions
+of different composition classes.
+
+If all you want to do is build a standard top-N recommendation pipeline from an
+item scorer, see :func:`topn_pipeline`; this is the equivalent to
+``Recommender.adapt`` in the old LensKit API.  If you want more flexibility, you
+can write out the pipeline configuration yourself; the equivalent to
+``topn_pipeline(scorer)`` is:
+
+.. code:: python
+
+    pipe = Pipeline()
+    # define an input parameter for the user ID
+    user = pipe.create_input('user', EntityId)
+    # allow candidate items to be optionally specified
+    items = pipe.create_input('items', list[EntityId], None)
+    # look up a user's history in the training data
+    history = pipe.add_component('lookup-user', LookupTrainingHistory(), user=user)
+    # find candidates from the training data
+    lookup_candidates = pipe.add_component(
+        'select-candidates',
+        UnratedTrainingItemsCandidateSelector(),
+        user=history,
+    )
+    # if the client provided items as a pipeline input, use those; otherwise
+    # use the candidate selector we just configured.
+    candidates = pipe.use_first_of('candidates', items, lookup_candidates)
+    # score the candidate items using the specified scorer
+    score = pipe.add_component('score', scorer, user=user, items=candidates)
+    # rank the items by score
+    recommend = pipe.add_component('recommend', TopNRanker(50), items=score)
+
+You can then run this pipeline to produce recommendations with:
+
+.. code:: python
+
+    user_recs = pipe.run(recommend, user=user_id)
+
+.. todo::
+    Redo some of those types with user & item data, etc.
+
+.. todo::
+    Provide utility functions to make more common wiring operations easy so there
+    is middle ground between “give me a standard pipeline” and “make me do everything
+    myself”.
+
+.. todo::
+    Rethink the “keyword inputs only” constraint in view of the limitation it
+    places on fallback or other compositional components — it's hard to specify
+    a component that implements fallback logic for an arbitrary number of
+    inputs.
+
+Pipeline components are not limited to looking things up from training data —
+they can query databases, load files, and any other operations.  A runtime
+pipeline can use some components (especially the scorer) trained from training
+data, and other components that query a database or REST services for things
+like user history and candidate set lookup.
+
+The LensKit pipeline design is heavily inspired by Haystack_ and by the pipeline
+abstraction Karl Higley created for POPROX_.
+
+.. _Haystack: https://docs.haystack.deepset.ai/docs/pipelines
+.. _POPROX: https://ccri-poprox.github.io/poprox-researcher-manual/reference/recommender/poprox_recommender.pipeline.html
+
+Common Pipelines
+~~~~~~~~~~~~~~~~
+
+These functions make it easy to create common pipeline designs.
+
+.. autofunction:: topn_pipeline
+
+.. _pipeline-model:
+
+Pipeline Model
+~~~~~~~~~~~~~~
+
+A pipeline has a couple key concepts:
+
+* An **input** is data that needs to be provided to the pipeline when it is run,
+  such as the user to generate recommendations for.  Inputs have specified data
+  types, and it is an error to provide an input value of an unexpected type.
+* A **component** processes input data and produces an output.  It can be either
+  a Python function or object (anything that implements the :class:`Component`
+  protocol) that takes inputs as keyword arguments and returns an output.
+
+These are arranged in a directed acyclic graph, consisting of:
+
+* **Nodes** (represented by :class:`Node`), which correspond to either *inputs*
+  or *components*.
+* **Connections** from one node's input to another node's data (or to a fixed
+  data value).  This is how the pipeline knows which components depend on other
+  components and how to provide each component with the inputs it requires; see
+  :ref:`pipeline-connections` for details.
+
+Each node has a name that can be used to look up the node with
+:meth:`Pipeline.node` and appears in serialization and logging situations. Names
+must be unique within a pipeline.
+
+.. _pipeline-connections:
+
+Connections
+-----------
+
+Components declare their inputs as keyword arguments on their call signatures
+(either the function call signature, if it is a bare function, or the
+``__call__`` method if it is implemented by a class).  In a pipeline, these
+inputs can be connected to a source, which the pipeline will use to obtain a
+value for that parameter when running the pipeline.  Inputs can be connected to
+the following types:
+
+* A :class:`Node`, in which case the input will be provided from the
+  corresponding pipeline input or component return value.  Nodes are
+  returned by :meth:`create_input` or :meth:`add_component`, and can be
+  looked up after creation with :meth:`node`.
+* A Python object, in which case that value will be provided directly to
+  the component input argument.
+
+These input connections are specified via keyword arguments to the
+:meth:`Pipeline.add_component` or :meth:`Pipeline.connect` methods — specify the
+component's input name(s) and the node or data to which each input should be
+wired.
+
+You can also use :meth:`Pipeline.add_default` to specify default connections. For example,
+you can specify a default for ``user``::
+
+    pipe.add_default('user', user_history)
+
+With this default in place, if a component has an input named ``user`` and that
+input is not explicitly connected to a node, then the ``user_history`` node will
+be used to supply its value.  Judicious use of defaults can reduce the amount of
+code overhead needed to wire common pipelines.
+
+.. note::
+
+    You cannot directly wire an input another component using only that
+    component's name; if you only have a name, pass it to :meth:`node`
+    to obtain the node.  This is because it would be impossible to
+    distinguish between a string component name and a string data value.
+
+.. note::
+
+    You do not usually need to call this method directly; when possible,
+    provide the wirings when calling :meth:`add_component`.
+
+.. _pipeline-execution:
+
+Execution
+---------
+
+Once configured, a pipeline can be run with :meth:`Pipeline.run`.  This
+method takes two types of inputs:
+
+*   Positional arguments specifying the node(s) to run and whose results should
+    be returned.  This is to allow partial runs of pipelines (e.g. to only score
+    items without ranking them), and to allow multiple return values to be
+    obtained (e.g. initial item scores and final rankings, which may have
+    altered scores).
+
+    If no components are specified, it is the same as specifying the last
+    component added to the pipeline.
+
+*   Keyword arguments specifying the values for the pipeline's inputs, as defined by
+    calls to :meth:`create_input`.
+
+Pipeline execution logically proceeds in the following steps:
+
+1.  Determine the full list of pipeline components that need to be run
+    in order to run the specified components.
+2.  Run those components in order, taking their inputs from pipeline
+    inputs or previous components as specified by the pipeline
+    connections and defaults.
+3.  Return the values of the specified components.  If a single
+    component is specified, its value is returned directly; if two or
+    more components are specified, their values are returned in a tuple.
+
+.. _pipeline-names:
+
+Component Names
+---------------
+
+As noted above, each component (and pipeline input) has a *name* that is unique
+across the pipeline.  For consistency and clarity, we recommend naming
+components with a verb or kebab-case verb phrase that captures the action that component performs, such as:
+
+* ``recommend``
+* ``rerank``
+* ``score``
+* ``lookup-user-history``
+* ``embed-items``
+
+Component nodes can also have *aliases*, allowing them to be accessed by more
+than one name. Use :meth:`Pipeline.alias` to define these aliases.
+
+Various LensKit facilities recognize several standard component names that we
+recommend you use when applicable:
+
+* ``score`` — compute (usually personalized) scores for items for a given user.
+* ``rank`` — compute a (ranked) list of recommendations for a user.  If you are
+  configuring a pipeline with rerankers whose outputs are also rankings, this
+  name should usually be used for the last such ranker, and downstream
+  components (if any) transform that ranking into another layout; that way the
+  evaluation tools will operate on the last such ranking.
+* ``recommend`` — compute recommendations for a user.  This will often be an
+  alias for ``rank``, as in a top-*N* recommender, but may return other formats
+  such as grids or unordered slates.
+* ``predict-ratings`` — predict a user's ratings for the specified items.  When
+  present, this is usually an alias for ``score``, but in some pipelines it will
+  be a different component that transforms the scores into rating predictions.
+
+These component names replace the task-specific interfaces in pre-2024 LensKit;
+a ``Recommender`` is now just a pipeline with ``recommend`` and/or ``rank``
+components.
+
+.. _pipeline-serialization:
+
+Pipeline Serialization
+----------------------
+
+Pipelines are defined by the following:
+
+* The components and inputs (nodes)
+* The component input connections (edges)
+* The component configurations (see :class:`ConfigurableComponent`)
+* The components' learned parameters (see :class:`TrainableComponent`)
+
+.. todo::
+    Serialization support other than ``pickle`` is not yet implemented.
+
+LensKit supports serializing both pipeline descriptions (components,
+connections, and configurations) and pipeline parameters.  There are
+three ways to save a pipeline or part thereof:
+
+1.  Pickle the entire pipeline.  This is easy, and saves everything pipeline; it
+    has the usual downsides of pickling (arbitrary code execution, etc.).
+    LensKit uses pickling to share pipelines with worker processes for parallel
+    batch operations.
+2.  Save the pipeline configuration with :meth:`Pipeline.save_config`.  This saves
+    the components, their configurations, and their connections, but **not** any
+    learned parameter data.  A new pipeline can be constructed from such a
+    configuration can be reloaded with :meth:`Pipeline.from_config`.
+3.  Save the pipeline parameters with :meth:`Pipeline.save_params`.  This saves
+    the learned parameters but **not** the configuration or connections.  The
+    parameters can be reloaded into a compatible pipeline with
+    :meth:`Pipeline.load_params`; a compatible pipeline can be created by
+    running the pipeline setup code or using a saved pipeline configuration.
+
+These can be mixed and matched; if you pickle an untrained pipeline, you can
+unpickle it and use :meth:`~Pipeline.load_params` to infuse it with parameters.
+
+Component implementations need to support the configuration and/or parameter
+values, as needed, in addition to functioning correctly with pickle (no specific
+logic is usually needed for this).
+
+LensKit knows how to safely save the following object types from
+:meth:`Component.get_params`:
+
+*   :class:`torch.Tensor` (dense, CSR, and COO tensors).
+*   :class:`numpy.ndarray`.
+*   :class:`scipy.sparse.csr_array`, :class:`scipy.sparse.~coo_array`,
+    :class:`scipy.sparse.~csc_array`, and the corresponding ``*_matrix``
+    versions.
+
+Other objects (including Pandas dataframes) are serialized by pickling, and the
+pipeline will emit a warning (or fail, if ``allow_pickle=False`` is passed to
+:meth:`~Pipeline.save_params`).
+
+.. note::
+    The load/save parameter operations are modeled after PyTorch's
+    :meth:`~torch.nn.Module.state_dict` and the needs of ``safetensors``.
+
+Pipeline Class
+~~~~~~~~~~~~~~
+
+.. autoclass:: Pipeline
+
+Pipeline Nodes
+~~~~~~~~~~~~~~
+
+Pipeline nodes are represented by :class:`Node` objects.  For the purposes of
+client code, these should be considered opaque objects usable only to reference
+a node.
+
+.. autoclass:: Node
+    :members: name
+
+Component Interface
+~~~~~~~~~~~~~~~~~~~
+
+Pipeline components are callable objects that can optionally provide training
+and serialization capabilities.  In the simplest case, a component that requires
+no training or configuration can simply be a Python function; more sophisticated
+components can implement the :class:`TrainableComponent` and/or
+:class:`ConfigurableComponent` protocols to support flexible model training and
+pipeline serialization.
+
+Components also need to be pickleable, as LensKit uses pickling for shared
+memory parallelism in its batch-inference code.
+
+.. note::
+
+    The component interfaces are simply protocol definitions (defined using
+    :class:`typing.Protocol` with :func:`~typing.runtime_checkable`), so
+    implementations can directly implement the specified methods and do not need
+    to explicitly inherit from the protocol classes, although they are free to
+    do so.
+
+.. todo::
+
+    Is it clear to write these capabilities as separate protocols, or would it be
+    better to write a single ``Component`` :class:`~abc.ABC`?
+
+.. autoclass:: Component
+
+.. autoclass:: ConfigurableComponent
+
+.. autoclass:: TrainableComponent

lenskit/lenskit/data/dataset.py

@@ -915,7 +915,7 @@ def normalize_interactions_df(
     df = df.rename(columns=renames)
     kc = [c for c in known_columns if c in df.columns]
     oc = [c for c in df.columns if c not in known_columns]
-    _log.debug("final columns: %s", kc, oc)
+    _log.debug("final columns: %s", kc + oc)
     return df[kc + oc]  # type: ignore