Fix rendering issues and limit depencies

CHANGELOG

@@ -13,6 +13,7 @@ The rules for CHANGELOG file:
 
 0.3.0 (XXXX/XX/XX)
 ------------------
+- Fix rendering issues for `SparseKDE` and `QuickShift` (#236)
 - Updating ``FPS`` to allow a numpy array of ints as an initialize parameter (#145)
 - Supported Python versions are now ranging from 3.9 - 3.12.
 - Updating ``skmatter.datasets`` submodule to support sklearn 1.5.0 (#229)

examples/pcovr/PCovR_Regressors.py

@@ -55,10 +55,11 @@
 # Use a fitted regressor
 # ----------------------
 #
-# You can pass a fitted regressor to PCovR to rely on the predetermined
-# regression parameters. Currently, scikit-matter supports ``scikit-learn``
-# classes ``LinearModel``, ``Ridge``, and ``RidgeCV``, with plans to support anu
-# regressor with similar architecture in the future.
+# You can pass a fitted regressor to ``PCovR`` to rely on the predetermined regression
+# parameters. Currently, scikit-matter supports ``scikit-learn`` classes
+# class:`LinearModel <sklearn.linear_model.LinearModel>`, :class:`Ridge
+# <sklearn.linear_model.Ridge>`, and class:`RidgeCV <sklearn.linear_model.RidgeCV>`,
+# with plans to support any regressor with similar architecture in the future.
 
 regressor = Ridge(alpha=1e-6, fit_intercept=False, tol=1e-12)
 

examples/selection/FeatureSelection-WHODataset.py

@@ -120,7 +120,7 @@
 # ^^^^^^^^
 
 
-pcur = PCovCUR(n_to_select=n_select, progress_bar=True, mixing=0.0)
+pcur = PCovCUR(n_to_select=n_select, progress_bar=True, mixing=1e-3)
 pcur.fit(X_train, yp_train)
 
 # %%

pyproject.toml

@@ -38,7 +38,8 @@ classifiers = [
     "Topic :: Scientific/Engineering",
 ]
 dependencies = [
-    "scikit-learn>=1.1.0",
+    "scikit-learn < 1.6.0",
+    "scipy < 1.15.0",
 ]
 dynamic = ["version"]
 

src/skmatter/clustering/_quick_shift.py

@@ -1,7 +1,6 @@
-from typing import Callable, Union
+from typing import Callable, Optional
 
 import numpy as np
-from numpy.typing import ArrayLike
 from sklearn.base import BaseEstimator
 from tqdm import tqdm
 
@@ -34,19 +33,19 @@ class QuickShift(BaseEstimator):
     scale : float, default=1.0
         Distance cutoff scaling factor used during the QS clustering. It will be squared
         since the squared distance is used in this class.
-    metric : Callable[[ArrayLike, ArrayLike, bool, dict], ArrayLike], \
-        default= :func:`skmatter.metrics.pairwise_euclidean_distances()`
+    metric : Callable, default=None
         The metric to use. Your metric should be able to take at least three arguments
         in secquence: `X`, `Y`, and `squared=True`. Here, `X` and `Y` are two array-like
         of shape (n_samples, n_components). The return of the metric is an array-like of
         shape (n_samples, n_samples). If you want to use periodic boundary
         conditions, be sure to provide the cell length in the ``metric_params`` and
-        provide a metric that can take the cell argument.
+        provide a metric that can take the cell argument. If :obj:`None`, the
+        :func:`skmatter.metrics.periodic_pairwise_euclidean_distances()` is used.
     metric_params : dict, default=None
         Additional parameters to be passed to the use of
         metric.  i.e. the dimension of a rectangular cell of side length :math:`a_i`
-        for :func:`skmatter.metrics.pairwise_euclidean_distances()`
-        `{'cell_length': [a_1, a_2, ..., a_n]}`
+        for :func:`skmatter.metrics.periodic_pairwise_euclidean_distances()`
+        ``{'cell_length': [a_1, a_2, ..., a_n]}``
 
     Attributes
     ----------
@@ -97,13 +96,11 @@ class QuickShift(BaseEstimator):
 
     def __init__(
         self,
-        dist_cutoff_sq: Union[float, None] = None,
-        gabriel_shell: Union[int, None] = None,
+        dist_cutoff_sq: Optional[float] = None,
+        gabriel_shell: Optional[int] = None,
         scale: float = 1.0,
-        metric: Callable[
-            [ArrayLike, ArrayLike, bool, dict], ArrayLike
-        ] = periodic_pairwise_euclidean_distances,
-        metric_params: Union[dict, None] = None,
+        metric: Optional[Callable] = None,
+        metric_params: Optional[dict] = None,
     ):
         if (dist_cutoff_sq is None) and (gabriel_shell is None):
             raise ValueError("Either dist_cutoff or gabriel_depth must be set.")
@@ -115,6 +112,10 @@ def __init__(
         self.metric_params = (
             metric_params if metric_params is not None else {"cell_length": None}
         )
+
+        if metric is None:
+            metric = periodic_pairwise_euclidean_distances
+
         self.metric = lambda X, Y: metric(X, Y, squared=True, **self.metric_params)
         if isinstance(self.metric_params, dict):
             self.cell = self.metric_params["cell_length"]

src/skmatter/neighbors/_sparsekde.py

@@ -1,8 +1,7 @@
 import warnings
-from typing import Callable, Union
+from typing import Callable, Optional, Union
 
 import numpy as np
-from numpy.typing import ArrayLike
 from scipy.special import logsumexp as LSE
 from sklearn.base import BaseEstimator
 from sklearn.utils.validation import check_is_fitted, check_random_state
@@ -33,19 +32,18 @@ class SparseKDE(BaseEstimator):
     weights: numpy.ndarray, default=None
         Weights of the descriptors.
         If None, all weights are set to `1/n_descriptors`.
-    metric : Callable[[ArrayLike, ArrayLike, bool, dict], ArrayLike],
-        default=:func:`skmatter.metrics.pairwise_euclidean_distances()`
+    metric : Callable, default=None
         The metric to use. Your metric should be able to take at least three arguments
         in secquence: `X`, `Y`, and `squared=True`. Here, `X` and `Y` are two array-like
         of shape (n_samples, n_components). The return of the metric is an array-like of
-        shape (n_samples, n_samples). If you want to use periodic boundary
-        conditions, be sure to provide the cell size in the metric_params and
-        provide a metric that can take the cell argument.
+        shape (n_samples, n_samples). If you want to use periodic boundary conditions,
+        be sure to provide the cell size in the metric_params and provide a metric that
+        can take the cell argument. If :obj:`None`, the
+        :func:`skmatter.metrics.periodic_pairwise_euclidean_distances()` is used.
     metric_params : dict, default=None
-        Additional parameters to be passed to the use of
-        metric.  i.e. the cell dimension for
-        :func:`skmatter.metrics.pairwise_euclidean_distances()`
-        `{'cell_length': [side_length_1, ..., side_length_n]}`
+        Additional parameters to be passed to the use of metric.  i.e. the cell
+        dimension for :func:`skmatter.metrics.periodic_pairwise_euclidean_distances()`
+        ``{'cell_length': [side_length_1, ..., side_length_n]}``
     fspread : float, default=-1.0
         The fractional "space" occupied by the voronoi cell of each grid. Use this when
         each cell is of a similar size.
@@ -106,11 +104,9 @@ class SparseKDE(BaseEstimator):
     def __init__(
         self,
         descriptors: np.ndarray,
-        weights: Union[np.ndarray, None] = None,
-        metric: Callable[
-            [ArrayLike, ArrayLike, bool, dict], ArrayLike
-        ] = periodic_pairwise_euclidean_distances,
-        metric_params: Union[dict, None] = None,
+        weights: Optional[np.ndarray] = None,
+        metric: Optional[Callable] = None,
+        metric_params: Optional[dict] = None,
         fspread: float = -1.0,
         fpoints: float = 0.15,
         kernel: str = "gaussian",
@@ -119,6 +115,10 @@ def __init__(
         self.metric_params = (
             metric_params if metric_params is not None else {"cell_length": None}
         )
+
+        if metric is None:
+            metric = periodic_pairwise_euclidean_distances
+
         self.metric = lambda X, Y: metric(X, Y, squared=True, **self.metric_params)
         self.cell = metric_params["cell_length"] if metric_params is not None else None
         self._check_dimension(descriptors)