Allow to use NumPy 2.

All tests are now executed with NumPy 2. This is important, because the representation of arrays (and thus the expected doctest output) has been changed in this version.
GAA-UAM · Aug 31, 2024 · 58796e1 · 58796e1
1 parent 2f1fb20
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diff --git a/pyproject.toml b/pyproject.toml
@@ -37,7 +37,7 @@ dependencies = [
 	"lazy_loader",
 	"matplotlib",
 	"multimethod>=1.5, !=1.11, != 1.11.1",
-	"numpy>=1.16, <2",
+	"numpy>=1.16",
 	"pandas>=1.0",
 	"rdata",
 	"scikit-datasets[cran]>=0.2.2",
@@ -61,6 +61,7 @@ docs = [
   "sphinxcontrib-bibtex",
 ]
 test = [
+  "numpy>=2", # Changes in array representation.
   "pytest",
   "pytest-env",
   "pytest-subtests",

diff --git a/skfda/_utils/_utils.py b/skfda/_utils/_utils.py
@@ -201,22 +201,22 @@ def _cartesian_product(  # noqa: WPS234
         >>> from skfda._utils import _cartesian_product
         >>> axes = [[0,1],[2,3]]
         >>> _cartesian_product(axes)
-        array([[0, 2],
-               [0, 3],
-               [1, 2],
-               [1, 3]])
+        array([[ 0, 2],
+               [ 0, 3],
+               [ 1, 2],
+               [ 1, 3]])
 
         >>> axes = [[0,1],[2,3],[4]]
         >>> _cartesian_product(axes)
-        array([[0, 2, 4],
-               [0, 3, 4],
-               [1, 2, 4],
-               [1, 3, 4]])
+        array([[ 0, 2, 4],
+               [ 0, 3, 4],
+               [ 1, 2, 4],
+               [ 1, 3, 4]])
 
         >>> axes = [[0,1]]
         >>> _cartesian_product(axes)
-        array([[0],
-               [1]])
+        array([[ 0],
+               [ 1]])
     """
     cartesian = np.stack(np.meshgrid(*axes, indexing='ij'), -1)
 

diff --git a/skfda/exploratory/outliers/_directional_outlyingness.py b/skfda/exploratory/outliers/_directional_outlyingness.py
@@ -316,7 +316,7 @@ class MSPlotOutlierDetector(  # noqa: WPS230
         >>> fd = skfda.FDataGrid(data_matrix, grid_points)
         >>> out_detector = MSPlotOutlierDetector()
         >>> out_detector.fit_predict(fd)
-        array([1, 1, 1, 1])
+        array([ 1, 1, 1, 1])
 
     References:
         Dai, Wenlin, and Genton, Marc G. "Multivariate functional data

diff --git a/skfda/misc/_math.py b/skfda/misc/_math.py
@@ -257,14 +257,14 @@ def inner_product(
         >>> array1 = np.array([1, 2, 3])
         >>> array2 = np.array([4, 5, 6])
         >>> inner_product(array1, array2)
-        32
+        np.int64(32)
 
         If the arrays contain more than one sample
 
         >>> array1 = np.array([[1, 2, 3], [2, 3, 4]])
         >>> array2 = np.array([[4, 5, 6], [1, 1, 1]])
         >>> inner_product(array1, array2)
-        array([32, 9])
+        array([ 32, 9])
 
         The inner product of the :math:`f(x) = x` and the constant
         :math:`y=1` defined over the interval :math:`[0,1]` is the area of
@@ -592,7 +592,7 @@ def cosine_similarity(
         >>> array1 = np.array([1, 2, 3])
         >>> array2 = np.array([4, 5, 6])
         >>> cosine_similarity(array1, array2)
-        0.9746318461970762
+        np.float64(0.9746318461970762)
 
         If the arrays contain more than one sample
 

diff --git a/skfda/misc/covariances.py b/skfda/misc/covariances.py
@@ -29,7 +29,7 @@ def _squared_norms(x: NDArrayFloat, y: NDArrayFloat) -> NDArrayFloat:
 
 def _transform_to_2d(t: ArrayLike) -> NDArrayFloat:
     """Transform 1d arrays in column vectors."""
-    t = np.asfarray(t)
+    t = np.asarray(t, dtype=np.float64)
 
     dim = t.ndim
     assert dim <= 2

diff --git a/skfda/misc/operators/_linear_differential_operator.py b/skfda/misc/operators/_linear_differential_operator.py
@@ -616,14 +616,14 @@ def _optimized_operator_evaluation_in_grid(
         [ 0.,  0.,  0.,  0.,  1., -2.,  1.,  4.],
         [ 0.,  0.,  0.,  0.,  0.,  1., -2., -5.],
         [ 0.,  0.,  0.,  0.,  0.,  0.,  1.,  2.]]),
-        array([[0, 1],
-                [0, 2],
-                [0, 3],
-                [0, 4],
-                [3, 7],
-                [4, 7],
-                [5, 7],
-                [6, 7]]))
+        array([[ 0, 1],
+               [ 0, 2],
+               [ 0, 3],
+               [ 0, 4],
+               [ 3, 7],
+               [ 4, 7],
+               [ 5, 7],
+               [ 6, 7]]))
 
     Explanation:
         Each row of the first array contains the values of the linear operator

diff --git a/skfda/ml/classification/_centroid_classifiers.py b/skfda/ml/classification/_centroid_classifiers.py
@@ -63,7 +63,7 @@ class and return a :class:`FData` object with only one sample
         We can predict the class of new samples
 
         >>> neigh.predict(fd[::2]) # Predict labels for even samples
-        array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1])
+        array([ 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1])
 
     See also:
         :class:`~skfda.ml.classification.DTMClassifier`
@@ -164,7 +164,7 @@ class DTMClassifier(NearestCentroid[Input, Target]):
         We can predict the class of new samples
 
         >>> clf.predict(X_test) # Predict labels for test samples
-        array([1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1,
+        array([ 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1,
                 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1])
 
         Finally, we calculate the mean accuracy for the test data

diff --git a/skfda/ml/classification/_depth_classifiers.py b/skfda/ml/classification/_depth_classifiers.py
@@ -106,8 +106,8 @@ class DDClassifier(
         We can predict the class of new samples
 
         >>> clf.predict(X_test)
-        array([1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1,
-               1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1])
+        array([ 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1,
+                1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1])
 
         Finally, we calculate the mean accuracy for the test data
 
@@ -278,8 +278,8 @@ class DDGClassifier(
         We can predict the class of new samples
 
         >>> clf.predict(X_test)
-        array([1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1,
-               1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1])
+        array([ 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1,
+                1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1])
 
         Finally, we calculate the mean accuracy for the test data
 
@@ -299,8 +299,8 @@ class DDGClassifier(
         >>> clf.fit(X_train, y_train)
         DDGClassifier(...)
         >>> clf.predict(X_test)
-        array([1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1,
-               1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1])
+        array([ 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1,
+                1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1])
         >>> clf.score(X_test, y_test)
         0.875
 
@@ -475,7 +475,7 @@ class _ArgMaxClassifier(
         We can predict the class of new samples
 
         >>> clf.predict(X) # Predict labels for test samples
-        array([1, 0, 0])
+        array([ 1, 0, 0])
     """
 
     def fit(self, X: NDArrayFloat, y: Target) -> _ArgMaxClassifier[Target]:
@@ -543,8 +543,8 @@ class MaximumDepthClassifier(DDGClassifier[Input, Target]):
         We can predict the class of new samples
 
         >>> clf.predict(X_test) # Predict labels for test samples
-        array([1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1,
-               1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1])
+        array([ 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1,
+                1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1])
 
         Finally, we calculate the mean accuracy for the test data
 

diff --git a/skfda/ml/classification/_neighbors_classifiers.py b/skfda/ml/classification/_neighbors_classifiers.py
@@ -89,7 +89,7 @@ class KNeighborsClassifier(
         We can predict the class of new samples
 
         >>> neigh.predict(fd[::2]) # Predict labels for even samples
-        array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1])
+        array([ 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1])
 
         And the estimated probabilities.
 
@@ -254,7 +254,7 @@ class RadiusNeighborsClassifier(
         We can predict the class of new samples.
 
         >>> neigh.predict(fd[::2]) # Predict labels for even samples
-        array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1])
+        array([ 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1])
 
     See also:
         :class:`~skfda.ml.classification.KNeighborsClassifier`

diff --git a/skfda/ml/classification/_qda.py b/skfda/ml/classification/_qda.py
@@ -95,9 +95,9 @@ class QuadraticDiscriminantAnalysis(
 
         We can predict the class of new samples.
 
-        >>> list(qda.predict(X_test))
-        [0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1,
-         1, 0, 1, 0, 1, 0, 1, 1]
+        >>> qda.predict(X_test)
+        array([ 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1,
+         1, 0, 1, 0, 1, 0, 1, 1], dtype=int8)
 
         Finally, we calculate the mean accuracy for the test data.
 

diff --git a/skfda/ml/clustering/_hierarchical.py b/skfda/ml/clustering/_hierarchical.py
@@ -144,7 +144,7 @@ class AgglomerativeClustering(  # noqa: WPS230
         >>> clustering.fit(X)
         AgglomerativeClustering(...)
         >>> clustering.labels_.astype(np.int_)
-        array([0, 0, 1, 0, 0, 1])
+        array([ 0, 0, 1, 0, 0, 1])
     """
 
     LinkageCriterion = LinkageCriterion

diff --git a/skfda/ml/regression/_historical_linear_model.py b/skfda/ml/regression/_historical_linear_model.py
@@ -264,19 +264,19 @@ class HistoricalLinearRegression(
         >>> import scipy.integrate
 
         >>> random_state = np.random.RandomState(0)
-        >>> data_matrix = random_state.choice(10, size=(8, 6))
+        >>> data_matrix = random_state.choice(10, size=(8, 6)).astype(float)
         >>> data_matrix
-        array([[5, 0, 3, 3, 7, 9],
-               [3, 5, 2, 4, 7, 6],
-               [8, 8, 1, 6, 7, 7],
-               [8, 1, 5, 9, 8, 9],
-               [4, 3, 0, 3, 5, 0],
-               [2, 3, 8, 1, 3, 3],
-               [3, 7, 0, 1, 9, 9],
-               [0, 4, 7, 3, 2, 7]])
-        >>> intercept = random_state.choice(10, size=(1, 6))
+        array([[ 5., 0., 3., 3., 7., 9.],
+               [ 3., 5., 2., 4., 7., 6.],
+               [ 8., 8., 1., 6., 7., 7.],
+               [ 8., 1., 5., 9., 8., 9.],
+               [ 4., 3., 0., 3., 5., 0.],
+               [ 2., 3., 8., 1., 3., 3.],
+               [ 3., 7., 0., 1., 9., 9.],
+               [ 0., 4., 7., 3., 2., 7.]])
+        >>> intercept = random_state.choice(10, size=(1, 6)).astype(float)
         >>> intercept
-        array([[2, 0, 0, 4, 5, 5]])
+        array([[ 2., 0., 0., 4., 5., 5.]])
         >>> y_data = scipy.integrate.cumulative_trapezoid(
         ...              data_matrix,
         ...              initial=0,

diff --git a/skfda/preprocessing/dim_reduction/variable_selection/recursive_maxima_hunting.py b/skfda/preprocessing/dim_reduction/variable_selection/recursive_maxima_hunting.py
@@ -44,7 +44,7 @@
 
 
 def _transform_to_2d(t: ArrayLike) -> NDArrayFloat:
-    t = np.asfarray(t)
+    t = np.asarray(t, dtype=np.float64)
 
     dim = t.ndim
     assert dim <= 2
@@ -907,7 +907,7 @@ def fit(  # type: ignore[override] # noqa: D102
         """Recursive maxima hunting algorithm."""
         self.features_shape_ = X.data_matrix.shape[1:]
 
-        y = np.asfarray(y)
+        y = np.asarray(y, dtype=np.float64)
 
         self.correction_ = (
             UniformCorrection()

diff --git a/skfda/preprocessing/feature_construction/_function_transformers.py b/skfda/preprocessing/feature_construction/_function_transformers.py
@@ -226,7 +226,7 @@ class NumberCrossingsTransformer(
         FDataGrid created.
         >>> tf = NumberCrossingsTransformer(levels=0, direction="up")
         >>> tf.fit_transform(fd_grid)
-        array([[2]])
+        array([[ 2]])
     """
 
     def __init__(

diff --git a/skfda/preprocessing/feature_construction/_functions.py b/skfda/preprocessing/feature_construction/_functions.py
@@ -349,7 +349,7 @@ def number_crossings(
         FDataGrid created.
 
         >>> number_crossings(fd_grid, levels=0, direction="up")
-        array([[2]])
+        array([[ 2]])
     """
     # This is only defined for univariate functions
     check_fdata_dimensions(fd, dim_domain=1, dim_codomain=1)

diff --git a/skfda/preprocessing/feature_construction/_per_class_transformer.py b/skfda/preprocessing/feature_construction/_per_class_transformer.py
@@ -107,7 +107,7 @@ class PerClassTransformer(TransformerMixin[Input, Output, NDArrayInt]):
         Finally we can predict and check the score:
 
         >>> neigh1.predict(X_test1)
-        array([0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1,
+        array([ 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1,
             1, 1, 1], dtype=int8)
 
         >>> round(neigh1.score(X_test1, y_test1), 3)
@@ -145,7 +145,7 @@ class PerClassTransformer(TransformerMixin[Input, Output, NDArrayInt]):
         >>> neigh2 = KNeighborsClassifier()
         >>> neigh2 = neigh2.fit(X_train2, y_train2)
         >>> neigh2.predict(X_test2)
-        array([1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1,
+        array([ 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1,
                1, 1, 1], dtype=int8)
 
         >>> round(neigh2.score(X_test2, y_test2), 3)

diff --git a/skfda/preprocessing/registration/validation.py b/skfda/preprocessing/registration/validation.py
@@ -103,10 +103,10 @@ class AmplitudePhaseDecompositionStats():
     :func:`mse_r_squared`, returned when `return_stats` is `True`.
 
     Args:
-        r_square (float): Squared correlation index :math:`R^2`.
-        mse_amplitude (float): Mean square error of amplitude
+        r_square: Squared correlation index :math:`R^2`.
+        mse_amplitude: Mean square error of amplitude
             :math:`\text{MSE}_{amp}`.
-        mse_phase (float): Mean square error of phase :math:`\text{MSE}_{pha}`.
+        mse_phase: Mean square error of phase :math:`\text{MSE}_{pha}`.
         c_r (float): Constant :math:`C_R`.
 
     """
@@ -297,10 +297,10 @@ def stats(
         X_mean = X.mean()
         y_mean = y.mean()
 
-        c_r = np.sum(l2_norm(X)**2) / np.sum(l2_norm(y)**2)
+        c_r = float(np.sum(l2_norm(X)**2) / np.sum(l2_norm(y)**2))
 
-        mse_amplitude = c_r * np.mean(l2_distance(y, y.mean())**2)
-        mse_phase = (c_r * l2_norm(y_mean)**2 - l2_norm(X_mean)**2).item()
+        mse_amplitude = float(c_r * np.mean(l2_distance(y, y.mean())**2))
+        mse_phase = float(c_r * l2_norm(y_mean)**2 - l2_norm(X_mean)**2)
 
         # Should be equal to np.mean(l2_distance(X, X_mean)**2)
         mse_total = mse_amplitude + mse_phase

diff --git a/skfda/preprocessing/smoothing/validation.py b/skfda/preprocessing/smoothing/validation.py
@@ -210,7 +210,7 @@ class SmoothingParameterSearch(
         >>> np.array(grid.cv_results_['mean_test_score']).round(2)
         array([-11.67, -12.37])
         >>> round(grid.best_score_, 2)
-        -11.67
+        np.float64(-11.67)
         >>> grid.best_params_['kernel_estimator__n_neighbors']
         2
         >>> grid.best_estimator_.hat_matrix().round(2)

diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py
@@ -435,7 +435,7 @@ def sum(  # noqa: WPS125
         if min_count > 0:
             valid = ~np.isnan(self.coefficients)
             n_valid = np.sum(valid, axis=0)
-            coefs[n_valid < min_count] = np.NaN
+            coefs[n_valid < min_count] = np.nan
 
         return self.copy(
             coefficients=coefs,
@@ -1026,7 +1026,7 @@ def construct_array_type(cls) -> Type[FDataBasis]:  # noqa: D102
     def _na_repr(self) -> FDataBasis:
         return FDataBasis(
             basis=self.basis,
-            coefficients=((np.NaN,) * self.basis.n_basis,),
+            coefficients=((np.nan,) * self.basis.n_basis,),
         )
 
     def __eq__(self, other: Any) -> bool:

diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py
@@ -591,7 +591,7 @@ def sum(  # noqa: WPS125
         if min_count > 0:
             valid = ~np.isnan(self.data_matrix)
             n_valid = np.sum(valid, axis=0)
-            data[n_valid < min_count] = np.NaN
+            data[n_valid < min_count] = np.nan
 
         return self.copy(
             data_matrix=data,
@@ -1518,7 +1518,7 @@ def _na_repr(self) -> FDataGrid:
             + (self.dim_codomain,)
         )
 
-        data_matrix = np.full(shape=shape, fill_value=np.NaN)
+        data_matrix = np.full(shape=shape, fill_value=np.nan)
 
         return FDataGrid(
             grid_points=self.grid_points,