Merge branch 'develop' into fpca_references_fixed

skfda/exploratory/depth/multivariate.py

@@ -22,8 +22,8 @@
 
 
 class _DepthOrOutlyingness(
-    BaseEstimator,
     InductiveTransformerMixin[Input, NDArrayFloat, object],
+    BaseEstimator,
 ):
     """Abstract class representing a depth or outlyingness function."""
 

skfda/exploratory/outliers/_boxplot.py

@@ -8,8 +8,8 @@
 
 
 class BoxplotOutlierDetector(
-    BaseEstimator,
     OutlierMixin[FDataGrid],
+    BaseEstimator,
 ):
     r"""
     Outlier detector using the interquartile range.

skfda/exploratory/outliers/_directional_outlyingness.py

@@ -238,8 +238,8 @@ def directional_outlyingness_stats(  # noqa: WPS218
 
 
 class MSPlotOutlierDetector(  # noqa: WPS230
-    BaseEstimator,
     OutlierMixin[FDataGrid],
+    BaseEstimator,
 ):
     r"""Outlier detector using directional outlyingness.
 

skfda/exploratory/outliers/_outliergram.py

@@ -10,8 +10,8 @@
 
 
 class OutliergramOutlierDetector(
-    BaseEstimator,
     OutlierMixin[FDataGrid],
+    BaseEstimator,
 ):
     r"""
     Outlier detector using the relation between MEI and MBD.

skfda/misc/operators/_srvf.py

@@ -15,8 +15,8 @@
 
 class SRSF(
     Operator[FDataGrid, FDataGrid],
-    BaseEstimator,
     InductiveTransformerMixin[FDataGrid, FDataGrid, object],
+    BaseEstimator,
 ):
     r"""Square-Root Slope Function (SRSF) transform.
 

skfda/misc/scoring.py

@@ -750,12 +750,17 @@ def mean_squared_error(
         multioutput = 'raw_values', ndarray.
 
     """
-    return sklearn.metrics.mean_squared_error(  # type: ignore [no-any-return]
+    function = (
+        sklearn.metrics.mean_squared_error
+        if squared
+        else sklearn.metrics.root_mean_squared_error
+    )
+
+    return function(  # type: ignore [no-any-return]
         y_true,
         y_pred,
         sample_weight=sample_weight,
         multioutput=multioutput,
-        squared=squared,
     )
 
 
@@ -919,13 +924,18 @@ def mean_squared_log_error(
         multioutput = 'raw_values', ndarray.
 
     """
+    function = (
+        sklearn.metrics.mean_squared_log_error
+        if squared
+        else sklearn.metrics.root_mean_squared_log_error
+    )
+
     return (  # type: ignore [no-any-return]
-        sklearn.metrics.mean_squared_log_error(
+        function(
             y_true,
             y_pred,
             sample_weight=sample_weight,
             multioutput=multioutput,
-            squared=squared,
         )
     )
 

skfda/ml/classification/_centroid_classifiers.py

@@ -20,8 +20,8 @@
 
 
 class NearestCentroid(
-    BaseEstimator,
     ClassifierMixin[Input, Target],
+    BaseEstimator,
 ):
     """
     Nearest centroid classifier for functional data.

skfda/ml/classification/_depth_classifiers.py

@@ -64,8 +64,8 @@ def _classifier_fit_depth_methods(
 
 
 class DDClassifier(
-    BaseEstimator,
     ClassifierMixin[Input, Target],
+    BaseEstimator,
 ):
     """
     Depth-versus-depth (DD) classifer for functional data.
@@ -215,8 +215,8 @@ def predict(self, X: Input) -> Target:
 
 
 class DDGClassifier(
-    BaseEstimator,
     ClassifierMixin[Input, Target],
+    BaseEstimator,
 ):
     r"""
     Generalized depth-versus-depth (DD) classifier for functional data.
@@ -451,8 +451,8 @@ def predict(self, X: Input) -> Target:
 
 
 class _ArgMaxClassifier(
-    BaseEstimator,
     ClassifierMixin[NDArrayFloat, Target],
+    BaseEstimator,
 ):
     r"""Arg max classifier for multivariate data.
 

skfda/ml/classification/_logistic_regression.py

@@ -16,8 +16,8 @@
 
 
 class LogisticRegression(
-    BaseEstimator,
     ClassifierMixin[FDataGrid, NDArrayAny],
+    BaseEstimator,
 ):
     r"""Logistic Regression classifier for functional data.
 

skfda/ml/classification/_qda.py

@@ -17,8 +17,8 @@
 
 
 class QuadraticDiscriminantAnalysis(
-    BaseEstimator,
     ClassifierMixin[FDataGrid, Target],
+    BaseEstimator,
 ):
     """
     Functional quadratic discriminant analysis.

skfda/ml/clustering/_kmeans.py

@@ -32,9 +32,9 @@
 
 
 class BaseKMeans(
-    BaseEstimator,
     ClusterMixin[Input],
     TransformerMixin[Input, NDArrayFloat, object],
+    BaseEstimator,
     Generic[Input, MembershipType],
 ):
     """Base class to implement K-Means clustering algorithms.

skfda/ml/regression/_fpca_regression.py

@@ -16,8 +16,8 @@
 
 
 class FPCARegression(
-    BaseEstimator,
     RegressorMixin,
+    BaseEstimator,
 ):
     r"""Regression using Functional Principal Components Analysis.
 

skfda/ml/regression/_fpls_regression.py

@@ -23,8 +23,8 @@
 
 
 class FPLSRegression(
-    BaseEstimator,
     RegressorMixin[InputType, OutputType],
+    BaseEstimator,
 ):
     r"""
     Regression using Functional Partial Least Squares.

skfda/ml/regression/_historical_linear_model.py

@@ -214,8 +214,8 @@ def _create_fem_basis(
 
 
 class HistoricalLinearRegression(
-    BaseEstimator,
     RegressorMixin[FDataGrid, FDataGrid],
+    BaseEstimator,
 ):
     r"""
     Historical functional linear regression.

skfda/ml/regression/_kernel_regression.py

@@ -16,8 +16,8 @@
 
 
 class KernelRegression(
-    BaseEstimator,
     RegressorMixin[Input, Prediction],
+    BaseEstimator,
 ):
     r"""Kernel regression with scalar response.
 

skfda/ml/regression/_linear_regression.py

@@ -60,11 +60,11 @@
 
 
 class LinearRegression(
-    BaseEstimator,
     RegressorMixin[
         Union[AcceptedDataType, Sequence[AcceptedDataType]],
         NDArrayFloat,
     ],
+    BaseEstimator,
 ):
     r"""Linear regression with multivariate and functional response.
 

skfda/preprocessing/dim_reduction/_fpca.py

@@ -21,8 +21,8 @@
 
 
 class FPCA(  # noqa: WPS230 (too many public attributes)
-    BaseEstimator,
     InductiveTransformerMixin[FData, NDArrayFloat, object],
+    BaseEstimator,
 ):
     r"""
     Principal component analysis.

skfda/preprocessing/dim_reduction/variable_selection/_rkvs.py

@@ -102,8 +102,8 @@ def _rkhs_vs(
 
 
 class RKHSVariableSelection(
-    BaseEstimator,
     InductiveTransformerMixin[FDataGrid, NDArrayFloat, NDArrayInt],
+    BaseEstimator,
 ):
     r"""
     Reproducing kernel variable selection.

skfda/preprocessing/dim_reduction/variable_selection/maxima_hunting.py

@@ -109,12 +109,12 @@ def __call__(self, X: FDataGrid) -> NDArrayInt:
 
 
 class MaximaHunting(
-    BaseEstimator,
     InductiveTransformerMixin[
         FDataGrid,
         NDArrayFloat,
         NDArrayReal,
     ],
+    BaseEstimator,
 ):
     r"""
     Maxima Hunting variable selection.

skfda/preprocessing/dim_reduction/variable_selection/mrmr.py

@@ -185,12 +185,12 @@ def _mrmr(
 
 
 class MinimumRedundancyMaximumRelevance(
-    BaseEstimator,
     InductiveTransformerMixin[
         FDataGrid,
         NDArrayFloat,
         Union[NDArrayInt, NDArrayFloat],
     ],
+    BaseEstimator,
     Generic[dtype_y_T],
 ):
     r"""

skfda/preprocessing/dim_reduction/variable_selection/recursive_maxima_hunting.py

@@ -778,12 +778,12 @@ def update_mask(
 
 
 class RecursiveMaximaHunting(
-    BaseEstimator,
     InductiveTransformerMixin[
         FDataGrid,
         NDArrayFloat,
         Union[NDArrayInt, NDArrayFloat],
     ],
+    BaseEstimator,
 ):
     """
     Recursive Maxima Hunting variable selection.

skfda/preprocessing/feature_construction/_coefficients_transformer.py

@@ -10,8 +10,8 @@
 
 
 class CoefficientsTransformer(
-    BaseEstimator,
     TransformerMixin[FDataBasis, NDArrayFloat, object],
+    BaseEstimator,
 ):
     r"""
     Transformer returning the coefficients of FDataBasis objects as a matrix.

skfda/preprocessing/feature_construction/_evaluation_trasformer.py

@@ -21,8 +21,8 @@
 
 
 class EvaluationTransformer(
-    BaseEstimator,
     InductiveTransformerMixin[Input, NDArrayFloat, object],
+    BaseEstimator,
 ):
     r"""
     Transformer returning the evaluations of FData objects as a matrix.

skfda/preprocessing/feature_construction/_function_transformers.py

@@ -14,8 +14,8 @@
 
 
 class LocalAveragesTransformer(
-    BaseEstimator,
     TransformerMixin[FData, NDArrayFloat, object],
+    BaseEstimator,
 ):
     r"""
     Transforms functional data to its local averages.
@@ -102,8 +102,8 @@ def transform(self, X: FData, y: object = None) -> NDArrayFloat:
 
 
 class OccupationMeasureTransformer(
-    BaseEstimator,
     TransformerMixin[FData, NDArrayFloat, object],
+    BaseEstimator,
 ):
     """
     Transformer that works as an adapter for the occupation_measure function.
@@ -175,8 +175,8 @@ def transform(self, X: FData, y: object = None) -> NDArrayFloat:
 
 
 class NumberCrossingsTransformer(
-    BaseEstimator,
     TransformerMixin[FDataGrid, NDArrayInt, object],
+    BaseEstimator,
 ):
     """
     Transformer that works as an adapter for the number_up_crossings function.

skfda/preprocessing/feature_construction/_per_class_transformer.py

@@ -10,7 +10,7 @@
 from sklearn.utils.validation import check_is_fitted as sklearn_check_is_fitted
 
 from ..._utils import _classifier_get_classes
-from ..._utils._sklearn_adapter import TransformerMixin
+from ..._utils._sklearn_adapter import BaseEstimator, TransformerMixin
 from ...representation import FData
 from ...representation.basis import FDataBasis
 from ...representation.grid import FDataGrid
@@ -41,7 +41,10 @@ def _fit_feature_transformer(  # noqa: WPS320 WPS234
     return classes, class_feature_transformers
 
 
-class PerClassTransformer(TransformerMixin[Input, Output, NDArrayInt]):
+class PerClassTransformer(
+    TransformerMixin[Input, Output, NDArrayInt],
+    BaseEstimator,
+):
     r"""Per class feature transformer for functional data.
 
     This class takes a transformer and performs the following map:

skfda/preprocessing/missing/_interpolate.py

@@ -69,8 +69,8 @@ def _interpolate_nans(
 
 
 class MissingValuesInterpolation(
-    BaseEstimator,
     InductiveTransformerMixin[T, T, Any],
+    BaseEstimator,
 ):
     """
     Class to interpolate missing values.

skfda/preprocessing/registration/_base.py

@@ -22,8 +22,8 @@
 
 
 class RegistrationTransformer(
-    BaseEstimator,
     TransformerMixin[Input, Output, object],
+    BaseEstimator,
 ):
     """Base class for the registration methods."""
 

skfda/preprocessing/smoothing/_linear.py

@@ -19,8 +19,8 @@
 
 
 class _LinearSmoother(
-    BaseEstimator,
     TransformerMixin[FDataGrid, FDataGrid, object],
+    BaseEstimator,
 ):
     """Linear smoother.