diff --git a/CHANGELOG b/CHANGELOG
index 679df31ae..7d9c0e581 100644
--- a/CHANGELOG
+++ b/CHANGELOG
@@ -13,6 +13,8 @@ The rules for CHANGELOG file:
0.3.0 (XXXX/XX/XX)
------------------
+- Fixed moved function import from scipy and bump scipy dependency to 1.15.0 (#236)
+- 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)
diff --git a/pyproject.toml b/pyproject.toml
index 20399c980..4290b257a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -39,6 +39,7 @@ classifiers = [
]
dependencies = [
"scikit-learn>=1.1.0",
+ "scipy >= 1.15.0", # explicit here since need a newer version as scikit-learn
]
dynamic = ["version"]
diff --git a/src/skmatter/clustering/_quick_shift.py b/src/skmatter/clustering/_quick_shift.py
index 92fc4eb6e..1e7b1c0d3 100644
--- a/src/skmatter/clustering/_quick_shift.py
+++ b/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"]
diff --git a/src/skmatter/neighbors/_sparsekde.py b/src/skmatter/neighbors/_sparsekde.py
index b98c6a9e1..bbb7c1ea9 100644
--- a/src/skmatter/neighbors/_sparsekde.py
+++ b/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)
diff --git a/src/skmatter/sample_selection/_base.py b/src/skmatter/sample_selection/_base.py
index f5531d897..67d5f0472 100644
--- a/src/skmatter/sample_selection/_base.py
+++ b/src/skmatter/sample_selection/_base.py
@@ -4,7 +4,7 @@
import numpy as np
from scipy.interpolate import LinearNDInterpolator, interp1d
-from scipy.interpolate.interpnd import _ndim_coords_from_arrays
+from scipy.interpolate._interpnd import _ndim_coords_from_arrays
from scipy.spatial import ConvexHull
from sklearn.utils.validation import check_array, check_is_fitted, check_X_y