From 722d582f9d88ecefb486dae90e0ca6d835d42024 Mon Sep 17 00:00:00 2001
From: Philip Loche <ploche@physik.fu-berlin.de>
Date: Tue, 28 Jan 2025 17:49:55 +0100
Subject: [PATCH] limitt scikit-learn < 1.6.0

---
 CHANGELOG                                       |   1 -
 examples/pcovr/PCovR_Regressors.py              |   9 +++++----
 .../selection/FeatureSelection-WHODataset.py    |   2 +-
 pyproject.toml                                  |   4 ++--
 src/skmatter/sample_selection/_base.py          |   2 +-
 ...age.tsf-492-wpa-0-247.epfl.ch.11311.XpjwIfdx | Bin 0 -> 53248 bytes
 6 files changed, 9 insertions(+), 9 deletions(-)
 create mode 100644 tests/.coverage.tsf-492-wpa-0-247.epfl.ch.11311.XpjwIfdx

diff --git a/CHANGELOG b/CHANGELOG
index 7d9c0e581..46008bb47 100644
--- a/CHANGELOG
+++ b/CHANGELOG
@@ -13,7 +13,6 @@ 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.
diff --git a/examples/pcovr/PCovR_Regressors.py b/examples/pcovr/PCovR_Regressors.py
index 0b62cac2b..777009d56 100644
--- a/examples/pcovr/PCovR_Regressors.py
+++ b/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)
 
diff --git a/examples/selection/FeatureSelection-WHODataset.py b/examples/selection/FeatureSelection-WHODataset.py
index afbdb519e..e7abbc6ab 100644
--- a/examples/selection/FeatureSelection-WHODataset.py
+++ b/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)
 
 # %%
diff --git a/pyproject.toml b/pyproject.toml
index 4290b257a..e05c76026 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -38,8 +38,8 @@ classifiers = [
     "Topic :: Scientific/Engineering",
 ]
 dependencies = [
-    "scikit-learn>=1.1.0",
-    "scipy >= 1.15.0",  # explicit here since need a newer version as scikit-learn
+    "scikit-learn < 1.6.0",
+    "scipy < 1.15.0",
 ]
 dynamic = ["version"]
 
diff --git a/src/skmatter/sample_selection/_base.py b/src/skmatter/sample_selection/_base.py
index 67d5f0472..f5531d897 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
 
diff --git a/tests/.coverage.tsf-492-wpa-0-247.epfl.ch.11311.XpjwIfdx b/tests/.coverage.tsf-492-wpa-0-247.epfl.ch.11311.XpjwIfdx
new file mode 100644
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