Merge branch 'develop' into feature/irregular_to_basis_mixed_effects_…

…method

pyproject.toml

@@ -37,7 +37,7 @@ dependencies = [
 	"lazy_loader",
 	"matplotlib",
 	"multimethod>=1.5, !=1.11, != 1.11.1",
-	"numpy>=1.16",
+	"numpy>=1.16,<2",
 	"pandas>=1.0",
 	"rdata",
 	"scikit-datasets[cran]>=0.2.2",

skfda/preprocessing/dim_reduction/variable_selection/_rkvs.py

@@ -75,7 +75,7 @@ def _rkhs_vs(
     indexes = np.delete(indexes, selected_features[0])
 
     for i in range(1, n_features_to_select):
-        aux = np.zeros_like(indexes, dtype=np.float_)
+        aux = np.zeros_like(indexes, dtype=np.float64)
 
         for j in range(0, indexes.shape[0]):
             new_selection = np.concatenate([

skfda/preprocessing/dim_reduction/variable_selection/maxima_hunting.py

@@ -6,9 +6,8 @@
 import numpy as np
 import scipy.signal
 import sklearn.utils
-from sklearn.base import clone
-
 from dcor import u_distance_correlation_sqr
+from sklearn.base import clone
 
 from ...._utils._sklearn_adapter import (
     BaseEstimator,
@@ -232,7 +231,7 @@ def fit(  # type: ignore[override] # noqa: D102
         self.features_shape_ = X.data_matrix.shape[1:]
         self.dependence_ = _compute_dependence(
             X,
-            y.astype(np.float_),
+            y.astype(np.float64),
             dependence_measure=self.dependence_measure,
         )
 

skfda/preprocessing/dim_reduction/variable_selection/mrmr.py

@@ -41,10 +41,10 @@
     bound="MinimumRedundancyMaximumRelevance[Any]",
 )
 
-dtype_X_T = TypeVar("dtype_X_T", bound=np.float_, covariant=True)
+dtype_X_T = TypeVar("dtype_X_T", bound=np.float64, covariant=True)
 dtype_y_T = TypeVar(
     "dtype_y_T",
-    bound=Union[np.int_, np.float_],
+    bound=Union[np.int_, np.float64],
     covariant=True,
 )
 
@@ -108,7 +108,7 @@ def mutual_information(
 MethodName = Literal["MID", "MIQ"]
 
 
-def _parse_method(name: MethodName) -> Method[Union[np.int_, np.float_]]:
+def _parse_method(name: MethodName) -> Method[Union[np.int_, np.float64]]:
     if name == "MID":
         return MID
     elif name == "MIQ":
@@ -336,8 +336,8 @@ def __init__(
         *,
         n_features_to_select: int = 1,
         dependence_measure: _DependenceMeasure[
-            np.typing.NDArray[np.float_],
-            np.typing.NDArray[np.float_ | dtype_y_T],
+            np.typing.NDArray[np.float64],
+            np.typing.NDArray[np.float64 | dtype_y_T],
         ],
         criterion: _CriterionLike,
     ) -> None:
@@ -349,12 +349,12 @@ def __init__(
         *,
         n_features_to_select: int = 1,
         relevance_dependence_measure: _DependenceMeasure[
-            np.typing.NDArray[np.float_],
+            np.typing.NDArray[np.float64],
             np.typing.NDArray[dtype_y_T],
         ],
         redundancy_dependence_measure: _DependenceMeasure[
-            np.typing.NDArray[np.float_],
-            np.typing.NDArray[np.float_],
+            np.typing.NDArray[np.float64],
+            np.typing.NDArray[np.float64],
         ],
         criterion: _CriterionLike,
     ) -> None:
@@ -366,16 +366,16 @@ def __init__(
         n_features_to_select: int = 1,
         method: Method[dtype_y_T] | MethodName | None = None,
         dependence_measure: _DependenceMeasure[
-            np.typing.NDArray[np.float_],
-            np.typing.NDArray[np.float_ | dtype_y_T],
+            np.typing.NDArray[np.float64],
+            np.typing.NDArray[np.float64 | dtype_y_T],
         ] | None = None,
         relevance_dependence_measure: _DependenceMeasure[
-            np.typing.NDArray[np.float_],
+            np.typing.NDArray[np.float64],
             np.typing.NDArray[dtype_y_T],
         ] | None = None,
         redundancy_dependence_measure: _DependenceMeasure[
-            np.typing.NDArray[np.float_],
-            np.typing.NDArray[np.float_],
+            np.typing.NDArray[np.float64],
+            np.typing.NDArray[np.float64],
         ] | None = None,
         criterion: _CriterionLike | None = None,
     ) -> None:
@@ -417,7 +417,7 @@ def _validate_parameters(self) -> None:
             )
 
             self.relevance_dependence_measure_: _DependenceMeasure[
-                np.typing.NDArray[np.float_],
+                np.typing.NDArray[np.float64],
                 np.typing.NDArray[dtype_y_T],
             ] = (
                 method.relevance_dependence_measure

skfda/tests/test_interpolation.py

@@ -401,7 +401,7 @@ def test_evaluation_nodes(self) -> None:
 @np.vectorize
 def _coordinate_function(
     *args: float,
-) -> np.typing.NDArray[np.float_]:
+) -> np.typing.NDArray[np.float64]:
     _, *domain_indexes, _ = args
     return np.sum(domain_indexes)  # type: ignore[no-any-return]
 

skfda/tests/test_kernel_regression.py

@@ -18,7 +18,7 @@
 from skfda.representation.basis import FDataBasis, FourierBasis, MonomialBasis
 from skfda.representation.grid import FDataGrid
 
-FloatArray = np.typing.NDArray[np.float_]
+FloatArray = np.typing.NDArray[np.float64]
 
 
 def _nw_alt(
@@ -151,7 +151,7 @@ def test_nadaraya_watson(self) -> None:
         fd_train_grid, fd_test_grid, y_train_grid = _create_data_grid()
 
         # Test NW method with basis representation and bandwidth=1
-        nw_basis = KernelRegression[FDataBasis, np.typing.NDArray[np.float_]](
+        nw_basis = KernelRegression[FDataBasis, np.typing.NDArray[np.float64]](
             kernel_estimator=NadarayaWatsonHatMatrix(bandwidth=1),
         )
         nw_basis.fit(fd_train_basis, y_train_basis)
@@ -168,7 +168,7 @@ def test_nadaraya_watson(self) -> None:
         )
 
         # Test NW method with grid representation and bandwidth=1
-        nw_grid = KernelRegression[FDataGrid, np.typing.NDArray[np.float_]](
+        nw_grid = KernelRegression[FDataGrid, np.typing.NDArray[np.float64]](
             kernel_estimator=NadarayaWatsonHatMatrix(bandwidth=1),
         )
         nw_grid.fit(fd_train_grid, y_train_grid)
@@ -192,7 +192,7 @@ def test_knn(self) -> None:
 
         # Test KNN method with basis representation, n_neighbours=3 and
         # uniform kernel
-        knn_basis = KernelRegression[FDataBasis, np.typing.NDArray[np.float_]](
+        knn_basis = KernelRegression[FDataBasis, np.typing.NDArray[np.float64]](
             kernel_estimator=KNeighborsHatMatrix(n_neighbors=3),
         )
         knn_basis.fit(fd_train_basis, y_train_basis)
@@ -210,7 +210,7 @@ def test_knn(self) -> None:
 
         # Test KNN method with grid representation, n_neighbours=3 and
         # uniform kernel
-        knn_grid = KernelRegression[FDataGrid, np.typing.NDArray[np.float_]](
+        knn_grid = KernelRegression[FDataGrid, np.typing.NDArray[np.float64]](
             kernel_estimator=KNeighborsHatMatrix(n_neighbors=3),
         )
         knn_grid.fit(fd_train_grid, y_train_grid)
@@ -253,7 +253,7 @@ def test_llr(self) -> None:
         # Creating data
         fd_train_basis, fd_test_basis, y_train_basis = _create_data_basis()
 
-        llr_basis = KernelRegression[FDataBasis, np.typing.NDArray[np.float_]](
+        llr_basis = KernelRegression[FDataBasis, np.typing.NDArray[np.float64]](
             kernel_estimator=LocalLinearRegressionHatMatrix(bandwidth=1),
         )
         llr_basis.fit(fd_train_basis, y_train_basis)
@@ -273,7 +273,7 @@ def test_nw_r(self) -> None:
         """Comparison of NW's results with results from fda.usc."""
         X_train, X_test, y_train = _create_data_r()
 
-        nw = KernelRegression[FDataGrid, np.typing.NDArray[np.float_]](
+        nw = KernelRegression[FDataGrid, np.typing.NDArray[np.float64]](
             kernel_estimator=NadarayaWatsonHatMatrix(bandwidth=1),
         )
         nw.fit(X_train, y_train)
@@ -298,7 +298,7 @@ def test_knn_r(self) -> None:
         """Comparison of NW's results with results from fda.usc."""
         X_train, X_test, y_train = _create_data_r()
 
-        knn = KernelRegression[FDataGrid, np.typing.NDArray[np.float_]](
+        knn = KernelRegression[FDataGrid, np.typing.NDArray[np.float64]](
             kernel_estimator=KNeighborsHatMatrix(n_neighbors=3),
         )
         knn.fit(X_train, y_train)
@@ -337,7 +337,7 @@ def test_llr_non_orthonormal(self) -> None:
             bandwidth=100,
             kernel=uniform,
         )
-        kr = KernelRegression[FDataBasis, np.typing.NDArray[np.float_]](
+        kr = KernelRegression[FDataBasis, np.typing.NDArray[np.float64]](
             kernel_estimator=llr,
         )
         kr.fit(X_train, y_train)

skfda/tests/test_math.py

@@ -16,8 +16,8 @@
 
 
 def _ndm(
-    *args: np.typing.NDArray[np.float_],
-) -> Sequence[np.typing.NDArray[np.float_]]:
+    *args: np.typing.NDArray[np.float64],
+) -> Sequence[np.typing.NDArray[np.float64]]:
     return [
         x[(None,) * i + (slice(None),) + (None,) * (len(args) - i - 1)]
         for i, x in enumerate(args)
@@ -30,10 +30,10 @@ class InnerProductTest(unittest.TestCase):
     def test_several_variables(self) -> None:
         """Test inner_product with functions of several variables."""
         def f(  # noqa: WPS430
-            x: np.typing.NDArray[np.float_],
-            y: np.typing.NDArray[np.float_],
-            z: np.typing.NDArray[np.float_],
-        ) -> np.typing.NDArray[np.float_]:
+            x: np.typing.NDArray[np.float64],
+            y: np.typing.NDArray[np.float64],
+            z: np.typing.NDArray[np.float64],
+        ) -> np.typing.NDArray[np.float64]:
             return x * y * z
 
         t = np.linspace(0, 1, 30)
@@ -73,9 +73,9 @@ def f(  # noqa: WPS430
     def test_mixed_several_variables(self) -> None:
         """Test inner_product with basis and grid multivariable functions."""
         def f(  # noqa: WPS430
-            x: np.typing.NDArray[np.float_],
-            y: np.typing.NDArray[np.float_],
-        ) -> np.typing.NDArray[np.float_]:
+            x: np.typing.NDArray[np.float64],
+            y: np.typing.NDArray[np.float64],
+        ) -> np.typing.NDArray[np.float64]:
             return x + y
 
         t = np.linspace(0, 1, 50)
@@ -103,13 +103,13 @@ def f(  # noqa: WPS430
     def test_vector_valued(self) -> None:
         """Test inner_product with vector valued functions."""
         def f(  # noqa: WPS430
-            x: np.typing.NDArray[np.float_],
-        ) -> np.typing.NDArray[np.float_]:
+            x: np.typing.NDArray[np.float64],
+        ) -> np.typing.NDArray[np.float64]:
             return x**2
 
         def g(  # noqa: WPS430
-            y: np.typing.NDArray[np.float_],
-        ) -> np.typing.NDArray[np.float_]:
+            y: np.typing.NDArray[np.float64],
+        ) -> np.typing.NDArray[np.float64]:
             return 3 * y
 
         t = np.linspace(0, 1, 100)

skfda/tests/test_neighbors.py

@@ -134,12 +134,12 @@ def test_predict_regressor(self) -> None:
         """Test scalar regression, predicts mode location."""
         # Dummy test, with weight = distance, only the sample with distance 0
         # will be returned, obtaining the exact location
-        knnr = KNeighborsRegressor[FDataGrid, np.typing.NDArray[np.float_]](
+        knnr = KNeighborsRegressor[FDataGrid, np.typing.NDArray[np.float64]](
             weights='distance',
         )
         rnnr = RadiusNeighborsRegressor[
             FDataGrid,
-            np.typing.NDArray[np.float_],
+            np.typing.NDArray[np.float64],
         ](
             weights='distance',
             radius=0.1,
@@ -168,7 +168,7 @@ def test_kneighbors(self) -> None:
         knn = KNeighborsClassifier()
         knn.fit(self.X, self.y)
 
-        knnr = KNeighborsRegressor[FDataGrid, np.typing.NDArray[np.float_]]()
+        knnr = KNeighborsRegressor[FDataGrid, np.typing.NDArray[np.float64]]()
         knnr.fit(self.X, self.modes_location)
 
         neigh: KNeighborsMixin[FDataGrid, Any]
@@ -201,7 +201,7 @@ def test_radius_neighbors(self) -> None:
 
         knnr = RadiusNeighborsRegressor[
             FDataGrid,
-            np.typing.NDArray[np.float_],
+            np.typing.NDArray[np.float64],
         ](radius=0.1)
         knnr.fit(self.X, self.modes_location)
 
@@ -241,7 +241,7 @@ def test_knn_functional_response_precomputed(self) -> None:
         """Test that precomputed distances work for functional response."""
         # Non-precomputed
         knnr = KNeighborsRegressor[
-            np.typing.NDArray[np.float_],
+            np.typing.NDArray[np.float64],
             FDataGrid,
         ](
             weights='distance',
@@ -252,7 +252,7 @@ def test_knn_functional_response_precomputed(self) -> None:
 
         # Precomputed
         knnr_pre = KNeighborsRegressor[
-            np.typing.NDArray[np.float_],
+            np.typing.NDArray[np.float64],
             FDataGrid,
         ](
             weights='distance',
@@ -395,7 +395,7 @@ def test_score_scalar_response(self) -> None:
         """Test regression with scalar response."""
         neigh = KNeighborsRegressor[
             FDataGrid,
-            np.typing.NDArray[np.float_],
+            np.typing.NDArray[np.float64],
         ]()
 
         neigh.fit(self.X, self.modes_location)
@@ -522,8 +522,8 @@ def test_lof_exceptions(self) -> None:
 
     def _weights(
         self,
-        weights: np.typing.NDArray[np.float_],
-    ) -> np.typing.NDArray[np.float_]:
+        weights: np.typing.NDArray[np.float64],
+    ) -> np.typing.NDArray[np.float64]:
         return np.array([w == np.min(weights) for w in weights], dtype=float)
 
 

skfda/tests/test_pandas_fdatabasis.py

@@ -60,7 +60,7 @@ def data_missing(basis: Basis) -> FDataBasis:
     """Length-2 array with [NA, Valid]."""
     coef_matrix = np.arange(
         2 * basis.n_basis,
-        dtype=np.float_,
+        dtype=np.float64,
     ).reshape(2, basis.n_basis)
     coef_matrix[0, :] = np.NaN
 

skfda/tests/test_pandas_fdatagrid.py

@@ -66,7 +66,7 @@ def data_missing() -> ExtensionArray:
     """Return a length-2 array with [NA, Valid]."""
     data_matrix = np.arange(
         2 * 10 * 10 * 3,
-        dtype=np.float_,
+        dtype=np.float64,
     ).reshape(2, 10, 10, 3)
     data_matrix[0, ...] = np.NaN
     grid_points = [

skfda/tests/test_per_class_transformer.py

@@ -25,7 +25,7 @@ def setUp(self) -> None:
 
     def test_transform(self) -> None:
         """Check the data transformation is done correctly."""
-        t = PerClassTransformer[FDataGrid, np.typing.NDArray[np.float_]](
+        t = PerClassTransformer[FDataGrid, np.typing.NDArray[np.float64]](
             RecursiveMaximaHunting(),
             array_output=True,
         )
@@ -46,7 +46,7 @@ def test_transform(self) -> None:
 
     def test_not_transformer_argument(self) -> None:
         """Check that invalid arguments in fit raise exception."""
-        t = PerClassTransformer[FDataGrid, np.typing.NDArray[np.float_]](
+        t = PerClassTransformer[FDataGrid, np.typing.NDArray[np.float64]](
             KNeighborsClassifier(),
         )
         self.assertRaises(

skfda/tests/test_recursive_maxima_hunting.py

@@ -29,8 +29,8 @@ def test_gaussian_homoscedastic(self) -> None:
         n_features = 101
 
         def mean_1(  # noqa: WPS430
-            t: np.typing.NDArray[np.float_],
-        ) -> np.typing.NDArray[np.float_]:
+            t: np.typing.NDArray[np.float64],
+        ) -> np.typing.NDArray[np.float64]:
 
             return (  # type: ignore[no-any-return]
                 np.abs(t - 0.25)
@@ -86,8 +86,8 @@ def test_fit_exponential(self) -> None:
         n_features = 101
 
         def mean_1(  # noqa: WPS430
-            t: np.typing.NDArray[np.float_],
-        ) -> np.typing.NDArray[np.float_]:
+            t: np.typing.NDArray[np.float64],
+        ) -> np.typing.NDArray[np.float64]:
 
             return (  # type: ignore[no-any-return]
                 np.abs(t - 0.25)