From 56f15bd6409d169316ea76d0552491b79be021a3 Mon Sep 17 00:00:00 2001
From: SIKAI ZHANG <34108862+MatthewSZhang@users.noreply.github.com>
Date: Wed, 11 Dec 2024 11:14:28 +0800
Subject: [PATCH] MNT align with sklearn 1.6

---
 fastcan/_fastcan.py |    5 +-
 fastcan/_narx.py    |   95 +-
 meson.build         |    2 +-
 pixi.lock           | 4678 +++++++++++++++++--------------------------
 pyproject.toml      |   11 +-
 tests/test_narx.py  |    8 +-
 6 files changed, 1882 insertions(+), 2917 deletions(-)

diff --git a/fastcan/_fastcan.py b/fastcan/_fastcan.py
index ff5d760..b5e99cd 100644
--- a/fastcan/_fastcan.py
+++ b/fastcan/_fastcan.py
@@ -12,7 +12,7 @@
 from sklearn.utils import check_array, check_consistent_length
 from sklearn.utils._openmp_helpers import _openmp_effective_n_threads
 from sklearn.utils._param_validation import Interval
-from sklearn.utils.validation import check_is_fitted
+from sklearn.utils.validation import check_is_fitted, validate_data
 
 from ._cancorr_fast import _forward_search  # type: ignore
 
@@ -162,7 +162,8 @@ def fit(self, X, y):
             "dtype": float,
             "force_writeable": True,
         }
-        X, y = self._validate_data(
+        X, y = validate_data(
+            self,
             X=X,
             y=y,
             multi_output=True,
diff --git a/fastcan/_narx.py b/fastcan/_narx.py
index 49562a4..de6f476 100644
--- a/fastcan/_narx.py
+++ b/fastcan/_narx.py
@@ -13,7 +13,11 @@
 from sklearn.linear_model import LinearRegression
 from sklearn.utils import check_array, check_consistent_length, column_or_1d
 from sklearn.utils._param_validation import Interval, StrOptions, validate_params
-from sklearn.utils.validation import check_is_fitted
+from sklearn.utils.validation import (
+    _check_sample_weight,
+    check_is_fitted,
+    validate_data,
+)
 
 from ._fastcan import FastCan
 from ._refine import refine
@@ -52,7 +56,7 @@ def make_time_shift_features(X, ids):
            [5., 3., 4.],
            [7., 5., 6.]])
     """
-    X = check_array(X, ensure_2d=True, dtype=float, force_all_finite="allow-nan")
+    X = check_array(X, ensure_2d=True, dtype=float, ensure_all_finite="allow-nan")
     ids = check_array(ids, ensure_2d=True, dtype=int)
     n_samples = X.shape[0]
     n_outputs = ids.shape[0]
@@ -177,7 +181,7 @@ def make_poly_features(X, ids):
            [ 1.,  5., 25.,  6.],
            [ 1.,  7., 49.,  8.]])
     """
-    X = check_array(X, ensure_2d=True, dtype=float, force_all_finite="allow-nan")
+    X = check_array(X, ensure_2d=True, dtype=float, ensure_all_finite="allow-nan")
     ids = check_array(ids, ensure_2d=True, dtype=int)
     n_samples = X.shape[0]
     n_outputs, degree = ids.shape
@@ -269,7 +273,7 @@ def _mask_missing_value(*arr):
     return tuple([x[mask_nomissing] for x in arr])
 
 
-class Narx(BaseEstimator, RegressorMixin):
+class Narx(RegressorMixin, BaseEstimator):
     """Nonlinear Autoregressive eXogenous model.
     For example, a (polynomial) Narx model is like
     y(t) = y(t-1)*u(t-1) + u(t-1)^2 + u(t-2) + 1.5
@@ -374,10 +378,11 @@ def __init__(
     @validate_params(
         {
             "coef_init": [None, StrOptions({"one_step_ahead"}), "array-like"],
+            "sample_weight": ["array-like", None],
         },
         prefer_skip_nested_validation=True,
     )
-    def fit(self, X, y, coef_init=None, **params):
+    def fit(self, X, y, sample_weight=None, coef_init=None, **params):
         """
         Fit narx model.
 
@@ -389,6 +394,10 @@ def fit(self, X, y, coef_init=None, **params):
         y : array-like of shape (n_samples,)
             Target values. Will be cast to X's dtype if necessary.
 
+        sample_weight : array-like of shape (n_samples,), default=None
+            Individual weights for each sample, which are used for a One-Step-Ahead
+            Narx.
+
         coef_init : array-like of shape (n_terms,), default=None
             The initial values of coefficients and intercept for optimization.
             When `coef_init` is None, the model will be a One-Step-Ahead Narx.
@@ -410,9 +419,15 @@ def fit(self, X, y, coef_init=None, **params):
         self : object
             Fitted Estimator.
         """
-        X = self._validate_data(X, dtype=float, force_all_finite="allow-nan")
+        X = validate_data(
+            self,
+            X,
+            dtype=float,
+            ensure_all_finite="allow-nan",
+        )
         y = column_or_1d(y, dtype=float, warn=True)
         check_consistent_length(X, y)
+        sample_weight = _check_sample_weight(sample_weight, X)
 
         if self.time_shift_ids is None:
             self.time_shift_ids_ = make_time_shift_ids(
@@ -467,9 +482,11 @@ def fit(self, X, y, coef_init=None, **params):
             time_shift_vars = make_time_shift_features(xy_hstack, self.time_shift_ids_)
             poly_terms = make_poly_features(time_shift_vars, self.poly_ids_)
             # Remove missing values
-            poly_terms_masked, y_masked = _mask_missing_value(poly_terms, y)
+            poly_terms_masked, y_masked, sample_weight_masked = _mask_missing_value(
+                poly_terms, y, sample_weight
+            )
 
-            osa_narx.fit(poly_terms_masked, y_masked)
+            osa_narx.fit(poly_terms_masked, y_masked, sample_weight_masked)
             if coef_init is None:
                 self.coef_ = osa_narx.coef_
                 self.intercept_ = osa_narx.intercept_
@@ -545,7 +562,7 @@ def _predict(expression, X, y_init, coef, intercept, max_delay):
             else:
                 y_hat[k] = expression(X, y_hat, coef, intercept, k)
             if np.any(y_hat[k] > 1e20):
-                y_hat[k:] = np.inf
+                y_hat[k:] = 1e20
                 return y_hat
         return y_hat
 
@@ -564,7 +581,7 @@ def _residual(
 
         y_masked, y_hat_masked = _mask_missing_value(y, y_hat)
 
-        return (y_masked - y_hat_masked).flatten()
+        return y_masked - y_hat_masked
 
     @validate_params(
         {
@@ -591,7 +608,7 @@ def predict(self, X, y_init=None):
         """
         check_is_fitted(self)
 
-        X = self._validate_data(X, reset=False, force_all_finite="allow-nan")
+        X = validate_data(self, X, reset=False, ensure_all_finite="allow-nan")
         if y_init is None:
             y_init = np.zeros(self.max_delay_)
         else:
@@ -613,8 +630,10 @@ def predict(self, X, y_init=None):
             self.max_delay_,
         )
 
-    def _more_tags(self):
-        return {"allow_nan": True}
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.input_tags.allow_nan = True
+        return tags
 
 
 @validate_params(
@@ -718,14 +737,13 @@ def _get_term_str(term_id):
         ],
         "include_zero_delay": [None, "array-like"],
         "static_indices": [None, "array-like"],
-        "eta": ["boolean"],
-        "verbose": ["verbose"],
-        "drop": [
+        "refine_verbose": ["verbose"],
+        "refine_drop": [
             None,
             Interval(Integral, 1, None, closed="left"),
             StrOptions({"all"}),
         ],
-        "max_iter": [
+        "refine_max_iter": [
             None,
             Interval(Integral, 1, None, closed="left"),
         ],
@@ -741,10 +759,10 @@ def make_narx(
     *,
     include_zero_delay=None,
     static_indices=None,
-    eta=False,
-    verbose=1,
-    drop=None,
-    max_iter=None,
+    refine_verbose=1,
+    refine_drop=None,
+    refine_max_iter=None,
+    **params,
 ):
     """Find `time_shift_ids` and `poly_ids` for a Narx model.
 
@@ -775,19 +793,20 @@ def make_narx(
             If the corresponding include_zero_delay of the static features is False, the
             static feature will be excluded from candidate features.
 
-    eta : bool, default=False
-        Whether to use eta-cosine method.
+    refine_verbose : int, default=1
+        The verbosity level of refine.
 
-    verbose : int, default=1
-        The verbosity level.
-
-    drop : int or "all", default=None
+    refine_drop : int or "all", default=None
             The number of the selected features dropped for the consequencing
             reselection. If `drop` is None, no refining will be performed.
 
-    max_iter : int, default=None
+    refine_max_iter : int, default=None
         The maximum number of valid iterations in the refining process.
 
+    **params : dict
+            Keyword arguments passed to
+            `fastcan.FastCan`.
+
     Returns
     -------
     narx : Narx
@@ -818,7 +837,8 @@ def make_narx(
     ...     static_indices=[1],
     ...     eta=True,
     ...     verbose=0,
-    ...     drop=1)
+    ...     refine_verbose=0,
+    ...     refine_drop=1)
     >>> print(f"{mean_squared_error(y, narx.fit(X, y).predict(X)):.4f}")
     0.0289
     >>> print_narx(narx)
@@ -830,7 +850,7 @@ def make_narx(
     | X[k-1,0]*X[k-3,0]  |  1.999   |
     | X[k-2,0]*X[k-0,1]  |  1.527   |
     """
-    X = check_array(X, dtype=float, ensure_2d=True, force_all_finite="allow-nan")
+    X = check_array(X, dtype=float, ensure_2d=True, ensure_all_finite="allow-nan")
     y = column_or_1d(y, dtype=float)
     check_consistent_length(X, y)
 
@@ -838,12 +858,14 @@ def make_narx(
     n_features = X.shape[1]
 
     if include_zero_delay is None:
-        include_zero_delay = [True] * n_features + [False]
+        _include_zero_delay = [True] * n_features + [False]
+    else:
+        _include_zero_delay = include_zero_delay + [False]
 
     time_shift_ids_all = make_time_shift_ids(
         n_features=xy_hstack.shape[1],
         max_delay=max_delay,
-        include_zero_delay=include_zero_delay,
+        include_zero_delay=_include_zero_delay,
     )
 
     time_shift_ids_all = np.delete(
@@ -867,11 +889,12 @@ def make_narx(
 
     csf = FastCan(
         n_features_to_select,
-        eta=eta,
-        verbose=0,
+        **params,
     ).fit(poly_terms_masked, y_masked)
-    if drop is not None:
-        indices, _ = refine(csf, drop=drop, max_iter=max_iter, verbose=verbose)
+    if refine_drop is not None:
+        indices, _ = refine(
+            csf, drop=refine_drop, max_iter=refine_max_iter, verbose=refine_verbose
+        )
         support = np.zeros(shape=csf.n_features_in_, dtype=bool)
         support[indices] = True
     else:
diff --git a/meson.build b/meson.build
index d836371..522ef8a 100644
--- a/meson.build
+++ b/meson.build
@@ -1,7 +1,7 @@
 project(
   'fastcan',
   'c', 'cython',
-  version: '0.2.7',
+  version: '0.3.0',
   license: 'MIT',
   meson_version: '>= 1.1.0',
   default_options: [
diff --git a/pixi.lock b/pixi.lock
index dbad1be..be62b15 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -1,4 +1,4 @@
-version: 5
+version: 6
 environments:
   default:
     channels:
@@ -9,18 +9,18 @@ environments:
       linux-64:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/black-24.10.0-py312h7900ff3_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/black-24.10.0-py313h78bf25f_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.8.30-hbcca054_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/click-8.1.7-unix_pyh707e725_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_0.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/coverage-7.6.8-py312h178313f_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cython-3.0.11-py312h8fd2918_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/click-8.1.7-unix_pyh707e725_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/coverage-7.6.9-py313h8060acc_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cython-3.0.11-py313hc66aa0d_3.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cython-lint-0.16.6-pyhff2d567_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.2.2-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.5.0-pyha770c72_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.2.2-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.5.0-pyha770c72_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.43-h712a8e2_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-25_linux64_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-25_linux64_openblas.conda
@@ -32,72 +32,71 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-14.2.0-hd5240d6_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-14.2.0-h77fa898_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-25_linux64_openblas.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hd590300_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.6.3-hb9d3cd8_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libmpdec-4.0.0-h4bc722e_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.28-pthreads_h94d23a6_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.47.0-hadc24fc_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.47.2-hee588c1_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-14.2.0-hc0a3c3a_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-14.2.0-h4852527_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.38.1-h0b41bf4_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/meson-1.6.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/meson-python-0.17.1-pyh70fd9c4_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/mypy-1.13.0-py312h66e93f0_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.0.0-pyha770c72_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/meson-1.6.0-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/meson-python-0.17.1-pyh70fd9c4_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/mypy-1.13.0-py313h536fd9c_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.0.0-pyha770c72_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-he02047a_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ninja-1.12.1-h297d8ca_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.1.3-py312h58c1407_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.2.0-py313hb30382a_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.4.0-hb9d3cd8_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-24.2-pyhff2d567_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/pandas-2.2.3-py312hf9745cd_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/pathspec-0.12.1-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.3.6-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.5.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/psutil-6.1.0-py312h66e93f0_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/pycodestyle-2.12.1-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.9.0-pyh2cfa8aa_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/pyproject_hooks-1.2.0-pyh7850678_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-8.3.4-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-cov-6.0.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.12.7-hc5c86c4_0_cpython.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/python-build-1.2.2.post1-pyhff2d567_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhff2d567_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2024.2-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.12-5_cp312.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-24.2-pyhd8ed1ab_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/pandas-2.2.3-py313ha87cce1_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/pathspec-0.12.1-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.3.6-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.5.0-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/psutil-6.1.0-py313h536fd9c_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/pycodestyle-2.12.1-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.9.0-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/pyproject_hooks-1.2.0-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-8.3.4-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-cov-6.0.0-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.13.1-ha99a958_102_cp313.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/python-build-1.2.2.post1-pyhff2d567_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhff2d567_1.conda
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-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.6-hb46c0d2_0.conda
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.6-hb46c0d2_0.conda
   sha256: 2d4fd1ff7ee79cd954ca8e81abf11d9d49954dd1fef80f27289e2402ae9c2e09
   md5: d96942c06c3e84bfcc5efb038724a7fd
   depends:
   - __osx >=11.0
   - libzlib >=1.2.13,<2.0.0a0
+  arch: arm64
+  platform: osx
   license: BSD-3-Clause
   license_family: BSD
   purls: []
diff --git a/pyproject.toml b/pyproject.toml
index c8ce34a..199c7a9 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "fastcan"
-version = "0.2.7"
+version = "0.3.0"
 description = "A fast canonical-correlation-based feature selection method"
 authors = [
     { name = "Matthew Sikai Zhang", email = "matthew.szhang91@gmail.com" },
@@ -9,7 +9,7 @@ requires-python = ">=3.9"
 readme = "README.rst"
 license = { file = "LICENSE" }
 
-dependencies = ["scikit-learn>=1.5.0"]
+dependencies = ["scikit-learn>=1.6.0"]
 
 classifiers = [
     "Development Status :: 4 - Beta",
@@ -23,6 +23,7 @@ classifiers = [
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
     "Programming Language :: Python :: Implementation :: CPython",
 ]
 
@@ -55,7 +56,7 @@ python-build = "*"
 uv = "*"
 
 [build-system]
-requires = ["meson-python>=0.16.0", "Cython>=3.0.10", "scikit-learn>=1.5.0"]
+requires = ["meson-python>=0.16.0", "Cython>=3.0.10", "scikit-learn>=1.6.0"]
 build-backend = "mesonpy"
 
 [tool.pixi.project]
@@ -79,11 +80,11 @@ compilers = "*"
 [tool.pixi.host-dependencies]
 meson-python = ">=0.16.0"
 cython = ">=3.0.10"
-scikit-learn = ">=1.5.0"
+scikit-learn = ">=1.6.0"
 
 [tool.pixi.dependencies]
 python = ">=3.12.0"
-scikit-learn = ">=1.5.0"
+scikit-learn = ">=1.6.0"
 
 [tool.pixi.tasks]
 time-h = "python -m timeit -n 5 -s 'import numpy as np; from fastcan import FastCan; X = np.random.rand(3000, 100); y = np.random.rand(3000, 20)' 's = FastCan(100, verbose=0).fit(X, y)'"
diff --git a/tests/test_narx.py b/tests/test_narx.py
index ef5f9ed..4c8e062 100644
--- a/tests/test_narx.py
+++ b/tests/test_narx.py
@@ -50,7 +50,7 @@ def test_narx(nan):
     assert narx_default.poly_ids.shape[0] ==\
            params["n_features_to_select"]
 
-    params["include_zero_delay"] = [False, True, False]
+    params["include_zero_delay"] = [False, True]
     narx_0_delay = make_narx(X=X, y=y, **params)
     time_shift_ids = narx_0_delay.time_shift_ids
     time_ids_u0 = time_shift_ids[time_shift_ids[:, 0] == 0]
@@ -67,8 +67,8 @@ def test_narx(nan):
     if time_ids_u1.size != 0:
         assert time_ids_u1[0, 1] == 0
 
-    params["drop"] = 1
-    params["max_iter"] = 10
+    params["refine_drop"] = 1
+    params["refine_max_iter"] = 10
     narx_drop = make_narx(X=X, y=y, **params)
     assert np.any(
         narx_drop.poly_ids !=\
@@ -124,5 +124,3 @@ def test_narx(nan):
     poly_ids = make_poly_ids(time_shift_ids.shape[0]+1, 2)
     with pytest.raises(ValueError, match=r"The element x of poly_ids should .*"):
         narx_osa = Narx(time_shift_ids=time_shift_ids, poly_ids=poly_ids).fit(X, y)
-
-test_narx(True)