Use uv to install minimum dependencies

.github/workflows/test.yml

@@ -195,9 +195,8 @@ jobs:
       - name: Set up Python deps
         run: |
           set -e
-          python -m pip install -U pip wheel
-          python -m pip install -c min-constraints.txt .[test]
-          python -m pip install pytest-cov
+          python -m pip install -U 'uv>=0.1.13'
+          uv pip install --system --resolution=lowest-direct -e '.[test]'
 
       - name: Inspect environment
         run: |

docs/conf.py

@@ -6,42 +6,136 @@
 
 import os
 import sys
+from importlib import import_module  # noqa: F401
+
 sys.path.insert(0, os.path.abspath(".."))
 
+import sphinx_rtd_theme  # noqa: F401
+
 import lenskit
 
+# -- Project information -----------------------------------------------------
+
 project = "LensKit"
-copyright = "2023 Michael Ekstrand"
+copyright = "2018–2024 Drexel University, Boise State University, and collaborators"
 author = "Michael D. Ekstrand"
 
+# The short X.Y version
+version = ".".join(lenskit.__version__.split(".")[:2])
+# The full version, including alpha/beta/rc tags
 release = lenskit.__version__
 
+
 extensions = [
+    "myst_nb",
     "sphinx.ext.napoleon",
     "sphinx.ext.autodoc",
     "sphinx.ext.autosummary",
     "sphinx.ext.intersphinx",
+    "sphinx.ext.mathjax",
     "sphinxext.opengraph",
+    "sphinxcontrib.bibtex",
+    "sphinx_rtd_theme",
 ]
 
+# The suffix(es) of source filenames.
+# You can specify multiple suffix as a list of string:
+#
+# source_suffix = ['.rst', '.md']
 source_suffix = ".rst"
 
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path .
+exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
+
+# The name of the Pygments (syntax highlighting) style to use.
 pygments_style = "sphinx"
 highlight_language = "python3"
 
-html_theme = "furo"
+
+html_theme = "sphinx_rtd_theme"
 html_theme_options = {
+    # 'github_user': 'lenskit',
+    # 'github_repo': 'lkpy',
+    # 'travis_button': False,
+    # 'canonical_url': 'https://lkpy.lenskit.org/',
+    # 'font_family': 'Charter, serif'
+    # 'font_family': '"Source Sans Pro", "Georgia Pro", Georgia, serif',
+    # 'font_size': '15px',
+    # 'head_font_family': '"Merriweather Sans", "Arial", sans-serif',
+    # 'code_font_size': '1em',
+    # 'code_font_family': '"Source Code Pro", "Consolas", "Menlo", sans-serif'
 }
+
 templates_path = ["_templates"]
+html_static_path = ["_static"]
+
+# Custom sidebar templates, must be a dictionary that maps document names
+# to template names.
+#
+# The default sidebars (for documents that don't match any pattern) are
+# defined by theme itself.  Builtin themes are using these templates by
+# default: ``['localtoc.html', 'relations.html', 'sourcelink.html',
+# 'searchbox.html']``.
+#
+# html_sidebars = {}
+
+
+# -- Options for HTMLHelp output ---------------------------------------------
+
+# Output file base name for HTML help builder.
+htmlhelp_basename = "LensKitdoc"
 
+
+# -- Options for LaTeX output ------------------------------------------------
+
+latex_elements = {
+    # The paper size ('letterpaper' or 'a4paper').
+    #
+    # 'papersize': 'letterpaper',
+    # The font size ('10pt', '11pt' or '12pt').
+    #
+    # 'pointsize': '10pt',
+    # Additional stuff for the LaTeX preamble.
+    #
+    # 'preamble': '',
+    # Latex figure (float) alignment
+    #
+    # 'figure_align': 'htbp',
+}
+
+# -- Extension configuration -------------------------------------------------
+
+# -- Options for intersphinx extension ---------------------------------------
+
+# Example configuration for intersphinx: refer to the Python standard library.
 intersphinx_mapping = {
     "python": ("https://docs.python.org/3/", None),
+    "pandas": ("http://pandas.pydata.org/pandas-docs/stable/", None),
     "numpy": ("https://docs.scipy.org/doc/numpy/", None),
+    "scipy": ("https://docs.scipy.org/doc/scipy/reference/", None),
+    "scikit": ("https://scikit-learn.org/stable/", None),
     "sklearn": ("https://scikit-learn.org/stable/", None),
+    "binpickle": ("https://binpickle.lenskit.org/en/stable/", None),
+    "csr": ("https://csr.lenskit.org/en/latest/", None),
+    "seedbank": ("https://seedbank.lenskit.org/en/latest/", None),
 }
 
-autodoc_default_options = {
-    "members": True,
-    "member-order": "bysource"
-}
+autodoc_default_options = {"members": True, "member-order": "bysource", "show-inheritance": True}
 autodoc_typehints = "description"
+
+bibtex_bibfiles = ["lenskit.bib"]
+jupyter_execute_notebooks = "off"
+
+# -- Module Canonicalization ------------------------------------------------
+
+# cleanups = {
+#     'lenskit': ['Algorithm', 'Recommender', 'Predictor', 'CandidateSelector']
+# }
+
+# for module, objects in cleanups.items():
+#     mod = import_module(module)
+#     for name in objects:
+#         obj = getattr(mod, name)
+#         obj.__module__ = module

lenskit/__init__.py

@@ -8,7 +8,6 @@
 Toolkit for recommender systems research, teaching, and more.
 """
 
-
 from importlib.metadata import PackageNotFoundError, version
 
 from lenskit.algorithms import *  # noqa: F401,F403

lenskit/algorithms/__init__.py

@@ -13,8 +13,8 @@
 classes (:py:mod:`abc`) representing different algorithm capabilities.
 """
 
-from abc import ABCMeta, abstractmethod
 import inspect
+from abc import ABCMeta, abstractmethod
 
 __all__ = ["Algorithm", "Recommender", "Predictor", "CandidateSelector"]
 
@@ -217,8 +217,8 @@ def rated_items(ratings):
         Utility function for converting a series or array into an array of item
         IDs.  Useful in implementations of :py:meth:`candidates`.
         """
-        import pandas as pd
         import numpy as np
+        import pandas as pd
 
         if isinstance(ratings, pd.Series):
             return ratings.index.values

lenskit/algorithms/als.py

@@ -8,16 +8,15 @@
 from collections import namedtuple
 
 import numpy as np
-from numba import njit, prange
-
 from csr import CSR
+from numba import njit, prange
 from seedbank import numpy_rng
 
-from .bias import Bias
-from .mf_common import MFPredictor
-from ..data import sparse_ratings
 from .. import util
+from ..data import sparse_ratings
 from ..math.solve import _dposv
+from .bias import Bias
+from .mf_common import MFPredictor
 
 _logger = logging.getLogger(__name__)
 
@@ -563,22 +562,26 @@ class ImplicitMF(MFPredictor):
         initially defaulted to ``True``, but with a warning.  In 0.14 it defaults to ``False``.
 
     Args:
-        features(int): the number of features to train
-        iterations(int): the number of iterations to train
-        reg(float): the regularization factor
-        weight(float): the scaling weight for positive samples (:math:`\\alpha` in :cite:p:`Hu2008-li`).
+        features(int):
+            The number of features to train
+        iterations(int):
+            The number of iterations to train
+        reg(float):
+            The regularization factor
+        weight(float):
+            The scaling weight for positive samples (:math:`\\alpha` in :cite:p:`Hu2008-li`).
         use_ratings(bool):
             Whether to use the `rating` column, if present.  Defaults to ``False``; when ``True``,
-            the values from the ``rating`` column are used, and multipled by ``weight``; if ``False``,
-            ImplicitMF treats every rated user-item pair as having a rating of 1.
+            the values from the ``rating`` column are used, and multipled by ``weight``; if
+            ``False``, ImplicitMF treats every rated user-item pair as having a rating of 1.
         method(str):
             the training method.
 
             ``'cg'`` (the default)
                 Conjugate gradient method :cite:p:`Takacs2011-ix`.
             ``'lu'``
-                A direct implementation of the original implicit-feedback ALS concept :cite:p:`Hu2008-li`
-                using LU-decomposition to solve for the optimized matrices.
+                A direct implementation of the original implicit-feedback ALS concept
+                :cite:p:`Hu2008-li` using LU-decomposition to solve for the optimized matrices.
 
         rng_spec:
             Random number generator or state (see :func:`lenskit.util.random.rng`).

lenskit/algorithms/basic.py

@@ -11,13 +11,12 @@
 import logging
 from collections.abc import Iterable, Sequence
 
-import pandas as pd
 import numpy as np
+import pandas as pd
 
 from ..data import sparse_ratings
-from . import Predictor, Recommender, CandidateSelector
 from ..util import derivable_rng
-
+from . import CandidateSelector, Predictor, Recommender
 from .bias import Bias  # noqa: F401
 from .ranking import TopN  # noqa: F401
 

lenskit/algorithms/funksvd.py

@@ -11,19 +11,19 @@
 import logging
 import time
 
-import pandas as pd
-import numpy as np
 import numba as n
+import numpy as np
+import pandas as pd
 from seedbank import numpy_rng
 
 try:
     from numba.experimental import jitclass
 except ImportError:
     from numba import jitclass
 
+from .. import util
 from .bias import Bias
 from .mf_common import MFPredictor
-from .. import util
 
 _logger = logging.getLogger(__name__)