Moved to experimental

python/doc/theory/meta_modeling/cross_validation.rst

@@ -635,16 +635,16 @@ The generic cross-validation method can be implemented using the following class
   to split the data set.
 
 Since the :class:`~openturns.LinearModelResult` is based on linear least
-squares, fast methods are implemented in the :class:`~openturns.LinearModelValidation`.
+squares, fast methods are implemented in the :class:`~openturns.experimental.LinearModelValidation`.
 
-See :ref:`pce_cross_validation` and :class:`~openturns.FunctionalChaosValidation`
+See :ref:`pce_cross_validation` and :class:`~openturns.experimental.FunctionalChaosValidation`
 for specific methods for the the cross-validation of a polynomial chaos expansion.
 
 .. topic:: API:
 
     - See :class:`~openturns.MetaModelValidation`
-    - See :class:`~openturns.LinearModelValidation`
-    - See :class:`~openturns.FunctionalChaosValidation`
+    - See :class:`~openturns.experimental.LinearModelValidation`
+    - See :class:`~openturns.experimental.FunctionalChaosValidation`
     - See :class:`~openturns.KFoldSplitter`
     - See :class:`~openturns.LeaveOneOutSplitter`
 

python/doc/theory/meta_modeling/pce_cross_validation.rst

@@ -52,11 +52,11 @@ then the fast methods presented in :ref:`cross_validation` can be applied:
 - the fast leave-one-out cross-validation,
 - the fast K-Fold cross-validation.
 
-Fast methods are implemented in :class:`~openturns.FunctionalChaosValidation`.
+Fast methods are implemented in :class:`~openturns.experimental.FunctionalChaosValidation`.
 
 .. topic:: API:
 
-    - See :class:`~openturns.FunctionalChaosValidation`
+    - See :class:`~openturns.experimental.FunctionalChaosValidation`
 
 .. topic:: References:
 

python/doc/user_manual/response_surface/functional_chaos_expansion.rst

@@ -81,7 +81,7 @@ Results
     FunctionalChaosSobolIndices
 
     :template: classWithPlot.rst_t
-    FunctionalChaosValidation
+    experimental.FunctionalChaosValidation
 
 Functional chaos on fields
 ==========================

python/doc/user_manual/response_surface/lm.rst

@@ -26,4 +26,4 @@ Post-processing
 
     :template: classWithPlot.rst_t
 
-    LinearModelValidation
+    experimental.LinearModelValidation

python/src/FunctionalChaosValidation_doc.i.in

@@ -47,7 +47,7 @@ conditions are met.
   If model selection is involved, the naive methods based on the
   :class:`~openturns.LeaveOneOutSplitter` and :class:`~openturns.KFoldSplitter`
   classes can be used, but this can be much slower than the
-  analytical methods implemented in the :class:`~openturns.FunctionalChaosValidation`
+  analytical methods implemented in the :class:`~openturns.experimental.FunctionalChaosValidation`
   class.
   In many cases, however, the order of magnitude of the estimate from the
   analytical formula applied to a sparse model is correct: the estimate of
@@ -68,15 +68,15 @@ the :math:`i`-th prediction is the prediction of the linear model
 trained using the hold-out sample where the :math:`i`-th observation
 was removed.
 This produces a sample of residuals which can be retrieved using
-the :class:`~openturns.FunctionalChaosValidation.getResidualSample` method.
-The :class:`~openturns.FunctionalChaosValidation.drawValidation` performs
+the :class:`~openturns.experimental.FunctionalChaosValidation.getResidualSample` method.
+The :class:`~openturns.experimental.FunctionalChaosValidation.drawValidation` performs
 similarly.
 
 If the weights of the observations are not equal, the analytical method
 may not necessarily provide an accurate estimator of the mean squared error (MSE).
 This is because LOO and K-Fold cross-validation do not take the weights
 into account.
-Since the :class:`~openturns.FunctionalChaosResult` object does not know
+Since the :class:`~openturns.experimental.FunctionalChaosResult` object does not know
 if the weights are equal, no exception can be generated.
 
 If the sample was not produced from Monte Carlo, then the leave-one-out

python/src/LinearModelValidation_doc.i.in

@@ -39,7 +39,7 @@ cross-validation methods can be used.
 If model selection is involved, the naive methods based on the
 :class:`~openturns.LeaveOneOutSplitter` and :class:`~openturns.KFoldSplitter`
 classes can be used directly, but this can be much slower than the
-analytical methods implemented in the :class:`~openturns.LinearModelValidation`
+analytical methods implemented in the :class:`~openturns.experimental.LinearModelValidation`
 class.
 In many cases, however, the order of magnitude of the estimate from the
 analytical formula applied to a sparse model is correct: the estimate of
@@ -60,15 +60,16 @@ the :math:`i`-th prediction is the prediction of the linear model
 trained using the hold-out sample where the :math:`i`-th observation
 was removed.
 This produces a sample of residuals which can be retrieved using
-the :class:`~openturns.LinearModelValidation.getResidualSample` method.
-The :class:`~openturns.LinearModelValidation.drawValidation` performs
+the :class:`~openturns.experimental.LinearModelValidation.getResidualSample` method.
+The :class:`~openturns.experimental.LinearModelValidation.drawValidation` performs
 similarly.
 
 Examples
 --------
 Create a linear model.
 
 >>> import openturns as ot
+>>> import openturns.experimental as otexp
 >>> func = ot.SymbolicFunction(
 ...     ['x1', 'x2', 'x3'],
 ...     ['x1 + x2 + sin(x2 * 2 * pi_) / 5 + 1e-3 * x3^2']
@@ -84,26 +85,26 @@ Create a linear model.
 
 Validate the linear model using leave-one-out cross-validation.
 
->>> validation = ot.LinearModelValidation(result)
+>>> validation = otexp.LinearModelValidation(result)
 
 We can use a specific cross-validation splitter if needed.
 
 >>> splitterLOO = ot.LeaveOneOutSplitter(sampleSize)
->>> validation = ot.LinearModelValidation(result, splitterLOO)
+>>> validation = otexp.LinearModelValidation(result, splitterLOO)
 >>> r2Score = validation.computeR2Score()
 >>> print('R2 = ', r2Score[0])
 R2 =  0.98...
 
 Validate the linear model using K-Fold cross-validation.
 
 >>> splitterKFold = ot.KFoldSplitter(sampleSize)
->>> validation = ot.LinearModelValidation(result, splitterKFold)
+>>> validation = otexp.LinearModelValidation(result, splitterKFold)
 
 Validate the linear model using K-Fold cross-validation and set K.
 
 >>> kFoldParameter = 10
 >>> splitterKFold = ot.KFoldSplitter(sampleSize, kFoldParameter)
->>> validation = ot.LinearModelValidation(result, splitterKFold)
+>>> validation = otexp.LinearModelValidation(result, splitterKFold)
 
 Draw the validation graph.