We might need to restrict sklearn dependency to earlier versions

[arokem]

With current version setting (i.e., latest sklearn), the following code:

pca = GroupPCA(groups=group_indices, n_components=0.95)
gpca = gpca.fit(X_train)
groups_pca = gpca.groups_out_

X_train = gpca.transform(X_train)

model = make_afq_regressor_pipeline(
    imputer_kwargs={"strategy": "median"},
    use_cv_estimator=True,
    scaler="standard",
    groups=groups_pca,
    verbose=0,
    pipeline_verbosity=False,
    tuning_strategy="bayes",
    cv=3,
    n_bayes_points=9,
    n_jobs=28,
    l1_ratio=[0.0, 1.0],
    eps=5e-2,
    n_alphas=100,
)

model.fit(X_train, y_train)

Raises:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[40], line 1
----> 1 model.fit(X_train, y_train)

File ~/miniconda3/envs/age_trt/lib/python3.10/site-packages/sklearn/pipeline.py:405, in Pipeline.fit(self, X, y, **fit_params)
    403     if self._final_estimator != "passthrough":
    404         fit_params_last_step = fit_params_steps[self.steps[-1][0]]
--> 405         self._final_estimator.fit(Xt, y, **fit_params_last_step)
    407 return self

File ~/miniconda3/envs/age_trt/lib/python3.10/site-packages/groupyr/sgl.py:1024, in SGLCV.fit(self, X, y)
   1022 n_l1_ratio = len(l1_ratios)
   1023 if alphas is None:
-> 1024     alphas = [
   1025         _alpha_grid(
   1026             X=X,
   1027             y=y,
   1028             groups=groups,
   1029             scale_l2_by=self.scale_l2_by,
   1030             l1_ratio=l1_ratio,
   1031             fit_intercept=self.fit_intercept,
   1032             eps=self.eps,
   1033             n_alphas=self.n_alphas,
   1034             normalize=self.normalize,
   1035             copy_X=self.copy_X,
   1036         )
   1037         for l1_ratio in l1_ratios
   1038     ]
   1039 else:
   1040     # Making sure alphas is properly ordered.
   1041     alphas = np.tile(np.sort(alphas)[::-1], (n_l1_ratio, 1))

File ~/miniconda3/envs/age_trt/lib/python3.10/site-packages/groupyr/sgl.py:1025, in <listcomp>(.0)
   1022 n_l1_ratio = len(l1_ratios)
   1023 if alphas is None:
   1024     alphas = [
-> 1025         _alpha_grid(
   1026             X=X,
   1027             y=y,
   1028             groups=groups,
   1029             scale_l2_by=self.scale_l2_by,
   1030             l1_ratio=l1_ratio,
   1031             fit_intercept=self.fit_intercept,
   1032             eps=self.eps,
   1033             n_alphas=self.n_alphas,
   1034             normalize=self.normalize,
   1035             copy_X=self.copy_X,
   1036         )
   1037         for l1_ratio in l1_ratios
   1038     ]
   1039 else:
   1040     # Making sure alphas is properly ordered.
   1041     alphas = np.tile(np.sort(alphas)[::-1], (n_l1_ratio, 1))

File ~/miniconda3/envs/age_trt/lib/python3.10/site-packages/groupyr/sgl.py:233, in _alpha_grid(X, y, Xy, groups, scale_l2_by, l1_ratio, fit_intercept, eps, n_alphas, normalize, copy_X, model)
    166 """Compute the grid of alpha values for elastic net parameter search.
    167 
    168 Parameters
   (...)
    230     for classification.
    231 """
    232 if l1_ratio == 1.0:
--> 233     return _lasso_alpha_grid(
    234         X=X,
    235         y=y,
    236         Xy=Xy,
    237         l1_ratio=l1_ratio,
    238         fit_intercept=fit_intercept,
    239         eps=eps,
    240         n_alphas=n_alphas,
    241         normalize=normalize,
    242         copy_X=copy_X,
    243     )
    245 n_samples = len(y)
    246 if Xy is None:

TypeError: _alpha_grid() got an unexpected keyword argument 'normalize'

But if I roll back to sklearn==1.0 I no longer get that error.

Might be related to scikit-learn/scikit-learn#24391