Merge pull request #86 from arokem/upgrade_numpy_1_22_4

Try to upgrade numpy to 1.22.4

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
   - repo: https://github.com/python/black
-    rev: 22.1.0
+    rev: 24.2.0
     hooks:
       - id: black
         language_version: python3.9

examples/compare_group_lasso_performance.py

@@ -14,6 +14,7 @@
 pip install group-lasso
 ```
 """
+
 import groupyr as gpr
 import matplotlib.pyplot as plt
 import numpy as np

examples/plot_balanced_classifier.py

@@ -11,6 +11,7 @@
 scoring shows a modest increase in both F1 score and accuracy.
 
 """
+
 import numpy as np
 from matplotlib import pyplot as plt
 from groupyr import LogisticSGLCV

examples/plot_classifier.py

@@ -8,6 +8,7 @@
 ground-truth.
 
 """
+
 import numpy as np
 from matplotlib import pyplot as plt
 from groupyr import LogisticSGLCV

examples/plot_regressor.py

@@ -8,6 +8,7 @@
 the results of a Lasso model.
 
 """
+
 import groupyr as gpr
 import matplotlib.pyplot as plt
 import numpy as np

examples/plot_sgl_path.py

@@ -19,6 +19,7 @@
 In practise it is necessary to tune alpha (and l1_ratio) to best fit the
 training data.
 """
+
 import groupyr as gpr
 import matplotlib.pyplot as plt
 import numpy as np

groupyr/__init__.py

@@ -4,6 +4,7 @@
 It provides scikit-learn compatible estimators, including cross-validation
 estimators. See https://richford.github.io/groupyr for more details.
 """
+
 from . import datasets  # noqa
 from . import utils  # noqa
 from .sgl import *  # noqa

groupyr/_base.py

@@ -1,4 +1,5 @@
 """Create base classes based on the sparse group lasso."""
+
 import contextlib
 import copt as cp
 import numpy as np

groupyr/_prox.py

@@ -1,4 +1,5 @@
 """Define custom proximal operators for use with copt package."""
+
 from __future__ import absolute_import, division, print_function
 
 import numpy as np

groupyr/datasets.py

@@ -1,4 +1,5 @@
 """Generate samples of synthetic data sets."""
+
 import numpy as np
 
 from sklearn.datasets import make_classification, make_regression

groupyr/decomposition.py

@@ -1,4 +1,5 @@
 """Perform group-wise functional PCA of feature matrices with grouped covariates."""
+
 import inspect
 import numpy as np
 

groupyr/logistic.py

@@ -1,4 +1,5 @@
 """Create logistic estimators based on the sparse group lasso."""
+
 import contextlib
 import logging
 import numpy as np

groupyr/sgl.py

@@ -1,4 +1,5 @@
 """Create regression estimators based on the sparse group lasso."""
+
 import contextlib
 import logging
 import numpy as np
@@ -244,7 +245,7 @@ def _alpha_grid(
     n_samples = len(y)
     if Xy is None:
         X = check_array(X, accept_sparse=False, copy=(copy_X and fit_intercept))
-        X, y, _, _, _ = _preprocess_data(X, y, fit_intercept, normalize, copy=False)
+        X, y, _, _, _ = _preprocess_data(X, y, fit_intercept=fit_intercept, copy=False)
         Xy = safe_sparse_dot(X.T, y, dense_output=True)
 
     if Xy.ndim == 1:

groupyr/tests/test_transform.py

@@ -297,12 +297,12 @@ def test_GroupAggregator():
     X_ref = np.array([np.array([1, 4, 7, 9]) + i * 10 for i in range(10)])
     assert np.allclose(X_tr, X_ref)  # nosec
 
-    ga = GroupAggregator(func=["mean", np.max], groups=groups, group_names=group_names)
+    ga = GroupAggregator(func=["mean", np.amax], groups=groups, group_names=group_names)
     X_tr = ga.fit_transform(X)
     feature_names_ref = []
     for grp in group_names:
         feature_names_ref.append("__".join([grp, "mean"]))
-        feature_names_ref.append("__".join([grp, "max"]))
+        feature_names_ref.append("__".join([grp, "amax"]))
 
     assert ga.feature_names_out_ == feature_names_ref  # nosec
     X_ref = np.array([np.array([1, 2, 4, 5, 7, 8, 9, 9]) + i * 10 for i in range(10)])

groupyr/transform.py

@@ -1,4 +1,5 @@
 """Transform feature matrices with grouped covariates."""
+
 import logging
 import numpy as np
 

groupyr/utils.py

@@ -1,4 +1,5 @@
 """Utility functions for SGL-based estimators."""
+
 import numpy as np
 
 from collections.abc import Sequence

setup.cfg

@@ -37,6 +37,7 @@ install_requires =
     scipy
     scikit-optimize==0.9.0
     tqdm
+    numpy>=1.22.4
 zip_safe = False
 include_package_data = True
 packages = find:

setup.py

@@ -1,4 +1,5 @@
 """A scikit-learn compatible library for penalized regression with grouped data."""
+
 from setuptools import setup
 import string
 import os.path as op