This is my pull request for the issue 647

docs/modules/misc/scoring.rst

@@ -16,3 +16,11 @@ Only scores that support multioutput are included.
      skfda.misc.scoring.mean_squared_error
      skfda.misc.scoring.mean_squared_log_error
      skfda.misc.scoring.r2_score
+     skfda.misc.scoring.root_mean_squared_error
+     skfda.misc.scoring.root_mean_squared_log_error
+
+.. warning::
+
+    The `squared` parameter in `mean_squared_error` and `mean_squared_log_error`
+    is deprecated and will be removed in a future version. Use
+    `root_mean_squared_error` and `root_mean_squared_log_error` instead.

docs/sg_execution_times.rst

@@ -0,0 +1,151 @@
+
+:orphan:
+
+.. _sphx_glr_sg_execution_times:
+
+
+Computation times
+=================
+**00:07.828** total execution time for 39 files **from all galleries**:
+
+.. container::
+
+  .. raw:: html
+
+    <style scoped>
+    <link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
+    <link href="https://cdn.datatables.net/1.13.6/css/dataTables.bootstrap5.min.css" rel="stylesheet" />
+    </style>
+    <script src="https://code.jquery.com/jquery-3.7.0.js"></script>
+    <script src="https://cdn.datatables.net/1.13.6/js/jquery.dataTables.min.js"></script>
+    <script src="https://cdn.datatables.net/1.13.6/js/dataTables.bootstrap5.min.js"></script>
+    <script type="text/javascript" class="init">
+    $(document).ready( function () {
+        $('table.sg-datatable').DataTable({order: [[1, 'desc']]});
+    } );
+    </script>
+
+  .. list-table::
+   :header-rows: 1
+   :class: table table-striped sg-datatable
+
+   * - Example
+     - Time
+     - Mem (MB)
+   * - :ref:`sphx_glr_auto_examples_plot_aemet_unsupervised.py` (``..\examples\plot_aemet_unsupervised.py``)
+     - 00:07.828
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_expand_skfda_plot_basis_subclass.py` (``..\examples\expand_skfda\plot_basis_subclass.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_expand_skfda_plot_new_evaluator.py` (``..\examples\expand_skfda\plot_new_evaluator.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_boxplot.py` (``..\examples\plot_boxplot.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_classification_methods.py` (``..\examples\plot_classification_methods.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_clustering.py` (``..\examples\plot_clustering.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_composition.py` (``..\examples\plot_composition.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_depth_classification.py` (``..\examples\plot_depth_classification.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_discrete_representation.py` (``..\examples\plot_discrete_representation.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_elastic_registration.py` (``..\examples\plot_elastic_registration.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_explore.py` (``..\examples\plot_explore.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_extrapolation.py` (``..\examples\plot_extrapolation.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_fpca.py` (``..\examples\plot_fpca.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_fpca_inverse_transform_outl_detection.py` (``..\examples\plot_fpca_inverse_transform_outl_detection.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_fpca_regression.py` (``..\examples\plot_fpca_regression.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_functional_regression.py` (``..\examples\plot_functional_regression.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_interpolation.py` (``..\examples\plot_interpolation.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_k_neighbors_classification.py` (``..\examples\plot_k_neighbors_classification.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_kernel_regression.py` (``..\examples\plot_kernel_regression.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_kernel_smoothing.py` (``..\examples\plot_kernel_smoothing.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_landmark_registration.py` (``..\examples\plot_landmark_registration.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_landmark_shift.py` (``..\examples\plot_landmark_shift.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_magnitude_shape.py` (``..\examples\plot_magnitude_shape.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_magnitude_shape_synthetic.py` (``..\examples\plot_magnitude_shape_synthetic.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_neighbors_functional_regression.py` (``..\examples\plot_neighbors_functional_regression.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_neighbors_scalar_regression.py` (``..\examples\plot_neighbors_scalar_regression.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_oneway.py` (``..\examples\plot_oneway.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_oneway_synthetic.py` (``..\examples\plot_oneway_synthetic.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_pairwise_alignment.py` (``..\examples\plot_pairwise_alignment.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_phonemes_classification.py` (``..\examples\plot_phonemes_classification.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_radius_neighbors_classification.py` (``..\examples\plot_radius_neighbors_classification.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_representation.py` (``..\examples\plot_representation.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_shift_registration.py` (``..\examples\plot_shift_registration.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_surface_boxplot.py` (``..\examples\plot_surface_boxplot.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_tecator_regression.py` (``..\examples\plot_tecator_regression.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_tutorial_plot_basis_representation.py` (``..\tutorial\plot_basis_representation.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_tutorial_plot_getting_data.py` (``..\tutorial\plot_getting_data.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_tutorial_plot_introduction.py` (``..\tutorial\plot_introduction.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_tutorial_plot_skfda_sklearn.py` (``..\tutorial\plot_skfda_sklearn.py``)
+     - 00:00.000
+     - 0.0

skfda/misc/scoring.py

@@ -731,6 +731,8 @@
             are taken.
         multioutput: Defines format of the return.
         squared: If True returns MSE value, if False returns RMSE value.
+            Deprecated since version 1.X: The 'squared' parameter will be removed in a future version.
+            Use `root_mean_squared_error` instead if `squared=False` was used.
 
     Returns:
         Mean squared error.
@@ -750,6 +752,13 @@
         multioutput = 'raw_values', ndarray.
 
     """
+    if not squared:
+        warnings.warn(
+            "The 'squared' parameter is deprecated and will be removed in a future version. "
+            "Use `root_mean_squared_error` instead.",
+            FutureWarning,
+            stacklevel=2,
+        )
     function = (
         sklearn.metrics.mean_squared_error
         if squared
@@ -826,6 +835,24 @@
 
     return _multioutput_score_basis(y_true, multioutput, _mse_func)
 
+@singledispatch
+def root_mean_squared_error(
+    y_true: DataType,
+    y_pred: DataType,
+    *,
+    sample_weight: NDArrayFloat | None = None,
+    multioutput: MultiOutputType = "uniform_average",
+) -> float | DataType:
+    """Root Mean Squared Error for functional data."""
+
+    return mean_squared_error(
+        y_true,
+        y_pred,
+        sample_weight=sample_weight,
+        multioutput=multioutput,
+        squared=False,  # Apply the square root directly
+    )
+
 
 @overload
 def mean_squared_log_error(
@@ -924,6 +951,13 @@
         multioutput = 'raw_values', ndarray.
 
     """
+    if not squared:
+        warnings.warn(
+            "The 'squared' parameter is deprecated and will be removed in a future version. "
+            "Use `root_mean_squared_log_error` instead.",
+            FutureWarning,
+            stacklevel=2,
+        )
     function = (
         sklearn.metrics.mean_squared_log_error
         if squared
@@ -1025,6 +1059,132 @@
 
     return _multioutput_score_basis(y_true, multioutput, _msle_func)
 
+@overload
+def root_mean_squared_log_error(
+    y_true: DataType,
+    y_pred: DataType,
+    *,
+    sample_weight: NDArrayFloat | None = None,
+    multioutput: Literal['uniform_average'] = 'uniform_average',
+) -> float:
+    pass  # noqa: WPS428
+
+
+@overload
+def root_mean_squared_log_error(
+    y_true: DataType,
+    y_pred: DataType,
+    *,
+    sample_weight: NDArrayFloat | None = None,
+    multioutput: Literal['raw_values'],
+) -> DataType:
+    pass  # noqa: WPS428
+
+
+@singledispatch
+def root_mean_squared_log_error(
+    y_true: DataType,
+    y_pred: DataType,
+    *,
+    sample_weight: NDArrayFloat | None = None,
+    multioutput: MultiOutputType = 'uniform_average',
+) -> float | DataType:
+    r"""Root Mean Squared Log Error for :class:`~skfda.representation.FData`.
+
+    This function applies the same logic as `mean_squared_log_error`, but
+    directly takes the square root of the result.
+
+    Args:
+        y_true: True target values.
+        y_pred: Predicted values.
+        sample_weight: Sample weights.
+        multioutput: Return format (raw values or uniform average).
+
+    Returns:
+        Root mean squared logarithmic error.
+    """
+    return sklearn.metrics.root_mean_squared_log_error(
+        y_true,
+        y_pred,
+        sample_weight=sample_weight,
+        multioutput=multioutput,
+    )
+
+@root_mean_squared_log_error.register  # type: ignore[attr-defined, misc]
+def _root_mean_squared_log_error_fdatagrid(
+    y_true: FDataGrid,
+    y_pred: FDataGrid,
+    *,
+    sample_weight: NDArrayFloat | None = None,
+    multioutput: MultiOutputType = 'uniform_average',
+) -> float | FDataGrid:
+
+    if np.any(y_true.data_matrix < 0) or np.any(y_pred.data_matrix < 0):
+        raise ValueError(
+            "Root Mean Squared Logarithmic Error cannot be used when "
+            "targets functions have negative values.",
+        )
+
+    return root_mean_squared_error(
+        np.log1p(y_true),
+        np.log1p(y_pred),
+        sample_weight=sample_weight,
+        multioutput=multioutput,
+    )
+
+@root_mean_squared_log_error.register  # type: ignore[attr-defined, misc]
+def _root_mean_squared_log_error_fdatairregular(
+    y_true: FDataIrregular,
+    y_pred: FDataIrregular,
+    *,
+    sample_weight: NDArrayFloat | None = None,
+    multioutput: MultiOutputType = 'uniform_average',
+) -> float:
+
+    if np.any(y_true.values < 0) or np.any(y_pred.values < 0):
+        raise ValueError(
+            "Root Mean Squared Logarithmic Error cannot be used when "
+            "targets functions have negative values.",
+        )
+
+    return root_mean_squared_error(
+        np.log1p(y_true),
+        np.log1p(y_pred),
+        sample_weight=sample_weight,
+        multioutput=multioutput,
+    )
+
+@root_mean_squared_log_error.register  # type: ignore[attr-defined, misc]
+def _root_mean_squared_log_error_fdatabasis(
+    y_true: FDataBasis,
+    y_pred: FDataBasis,
+    *,
+    sample_weight: NDArrayFloat | None = None,
+    multioutput: MultiOutputType = 'uniform_average',
+) -> float:
+
+    def _rmsle_func(x: EvalPointsType) -> NDArrayFloat:
+        y_true_eval = y_true(x)
+        y_pred_eval = y_pred(x)
+
+        if np.any(y_true_eval < 0) or np.any(y_pred_eval < 0):
+            raise ValueError(
+                "Root Mean Squared Logarithmic Error cannot be used when "
+                "targets functions have negative values.",
+            )
+
+        error: NDArrayFloat = np.sqrt(np.average(
+            (np.log1p(y_true_eval) - np.log1p(y_pred_eval)) ** 2,
+            weights=sample_weight,
+            axis=0,
+        ))
+
+        # Verify that the error only contains 1 input point
+        assert error.shape[0] == 1
+        return error[0]  # type: ignore [no-any-return]
+
+    return _multioutput_score_basis(y_true, multioutput, _rmsle_func)
+
 
 @overload
 def r2_score(