Remove normalize parameter from sindy (#292)

* Remove normalize parameter from sindy

deeptime/sindy/_sindy.py

@@ -407,16 +407,18 @@ class STLSQ(LinearRegression):
     .. footbibliography::
     """
 
-    def __init__(self, threshold=0.1, alpha=0.05, max_iter=20, ridge_kw=None, normalize=False, fit_intercept=False,
-                 copy_X=True):
+    def __init__(self, threshold=0.1, alpha=0.05, max_iter=20, ridge_kw=None, fit_intercept=False,
+                 copy_X=True, **kw):
         super().__init__(fit_intercept=fit_intercept, copy_X=copy_X)
         self.threshold = threshold
         self.alpha = alpha
         self.max_iter = max_iter
         self.ridge_kw = ridge_kw
-        self.normalize = normalize
         self.fit_intercept = fit_intercept
         self.copy_X = copy_X
+        if "normalize" in kw.keys():
+            raise RuntimeError("`normalize` was removed in more recent versions of scikit-learn (>=1.2.2), "
+                               "please perform normalization a priori if desired.")
 
     def fit(self, x_, y):
         r"""Fit to the data.
@@ -442,7 +444,6 @@ def fit(self, x_, y):
             x_,
             y,
             fit_intercept=self.fit_intercept,
-            normalize=self.normalize,
             copy=self.copy_X,
         )
 

pyproject.toml

@@ -24,7 +24,7 @@ requires-python = ">= 3.8"
 dependencies = [
     'numpy>=1.20',
     'scipy>=1.9.0',
-    'scikit-learn<=1.2.2',
+    'scikit-learn>=1.2.2',
     'threadpoolctl>=3.1.0'
 ]
 dynamic = ['version']

tests/sindy/test_sindy.py

@@ -1,6 +1,6 @@
 import numpy as np
 import pytest
-from numpy.testing import assert_equal, assert_
+from numpy.testing import assert_equal, assert_, assert_raises
 from scipy.integrate import odeint
 from sklearn.preprocessing import PolynomialFeatures
 
@@ -54,6 +54,11 @@ def test_learned_coefficients(data_lorenz):
     np.testing.assert_almost_equal(true_coef, model_coef, decimal=3)
 
 
+def test_removed_args_error():
+    with assert_raises(RuntimeError):
+        STLSQ(normalize=True)
+
+
 def test_score(data_lorenz):
     x, x_dot, t = data_lorenz
     model = SINDy().fit(x, y=x_dot).fetch_model()