FunctionalChaosValidation and LinearModelValidation

Moved to experimental

python/doc/pyplots/FunctionalChaosValidation.py

@@ -1,4 +1,5 @@
 import openturns as ot
+import openturns.experimental as otexp
 from openturns.viewer import View
 from openturns.usecases import ishigami_function
 
@@ -20,6 +21,6 @@
 )
 chaosalgo.run()
 result = chaosalgo.getResult()
-validation = ot.FunctionalChaosValidation(result)
+validation = otexp.FunctionalChaosValidation(result)
 graph = validation.drawValidation()
 View(graph)

python/doc/pyplots/LinearModelValidation.py

@@ -1,4 +1,5 @@
 import openturns as ot
+import openturns.experimental as otexp
 from openturns.viewer import View
 
 func = ot.SymbolicFunction(
@@ -16,6 +17,6 @@
 algo.run()
 result = algo.getResult()
 splitter = ot.LeaveOneOutSplitter(sampleSize)
-validation = ot.LinearModelValidation(result, splitter)
+validation = otexp.LinearModelValidation(result, splitter)
 graph = validation.drawValidation()
 View(graph)

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/CMakeLists.txt

@@ -872,7 +872,6 @@ ot_add_python_module(metamodel metamodel_module.i
                       LinearModelStepwiseAlgorithm.i LinearModelStepwiseAlgorithm_doc.i.in
                       LinearModelAlgorithm.i LinearModelAlgorithm_doc.i.in
                       LinearModelAnalysis.i LinearModelAnalysis_doc.i.in
-                      LinearModelValidation.i LinearModelValidation_doc.i.in
                       RandomVector.i RandomVector_doc.i.in
                       PythonEvaluation.cxx
                       PythonGradient.cxx
@@ -887,7 +886,6 @@ ot_add_python_module(metamodel metamodel_module.i
                       UnionEvent.i UnionEvent_doc.i.in
                       FunctionalChaosRandomVector.i FunctionalChaosRandomVector_doc.i.in
                       FunctionalChaosSobolIndices.i FunctionalChaosSobolIndices_doc.i.in
-                      FunctionalChaosValidation.i FunctionalChaosValidation_doc.i.in
                       KrigingRandomVector.i KrigingRandomVector_doc.i.in
 		      LeastSquaresExpansion.i LeastSquaresExpansion_doc.i.in
                       IntegrationExpansion.i IntegrationExpansion_doc.i.in
@@ -1042,6 +1040,8 @@ ot_add_python_module(experimental experimental_module.i
                      RankSobolSensitivityAlgorithm.i RankSobolSensitivityAlgorithm_doc.i.in
                      CubaIntegration.i CubaIntegration_doc.i.in
                      ExperimentIntegration.i ExperimentIntegration_doc.i.in
+                     LinearModelValidation.i LinearModelValidation_doc.i.in
+                     FunctionalChaosValidation.i FunctionalChaosValidation_doc.i.in
                     )
 set (OPENTURNS_PYTHON_MODULES ${OPENTURNS_PYTHON_MODULES} PARENT_SCOPE) # for the docstring test
 

python/src/FunctionalChaosValidation_doc.i.in

@@ -1,6 +1,9 @@
 %feature("docstring") OT::FunctionalChaosValidation
 "Validate a functional chaos metamodel.
 
+.. warning::
+    This class is experimental and likely to be modified in future releases.
+    To use it, import the ``openturns.experimental`` submodule.
 
 Parameters
 ----------
@@ -13,7 +16,7 @@ splitter : :class:`~openturns.SplitterImplementation`, optional
 
 See also
 --------
-FunctionalChaosAlgorithm, FunctionalChaosResult
+openturns.experimental.LinearModelValidation, FunctionalChaosAlgorithm, FunctionalChaosResult
 
 Notes
 -----
@@ -47,7 +50,7 @@ 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
@@ -68,8 +71,8 @@ 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
@@ -102,6 +105,7 @@ prevents us from using the fast analytical formulas and get an
 accurate estimator of the MSE.
 
 >>> import openturns as ot
+>>> import openturns.experimental as otexp
 >>> from math import pi
 >>> from openturns.usecases import ishigami_function
 >>> im = ishigami_function.IshigamiModel()
@@ -124,7 +128,7 @@ accurate estimator of the MSE.
 Validate the polynomial chaos expansion using leave-one-out cross-validation.
 
 >>> splitterLOO = ot.LeaveOneOutSplitter(sampleSize)
->>> validation = ot.FunctionalChaosValidation(result, splitterLOO)
+>>> validation = otexp.FunctionalChaosValidation(result, splitterLOO)
 >>> r2Score = validation.computeR2Score()
 >>> print('R2 = ', r2Score[0])
 R2 =  0.99...
@@ -134,7 +138,7 @@ and set the :math:`k` parameter.
 
 >>> kParameter = 10
 >>> splitterKF = ot.KFoldSplitter(sampleSize, kParameter)
->>> validation = ot.FunctionalChaosValidation(
+>>> validation = otexp.FunctionalChaosValidation(
 ...     result, splitterKF
 ... )
 

python/src/LinearModelValidation_doc.i.in

@@ -1,6 +1,10 @@
 %feature("docstring") OT::LinearModelValidation
 "Validate a linear regression metamodel.
 
+.. warning::
+    This class is experimental and likely to be modified in future releases.
+    To use it, import the ``openturns.experimental`` submodule.
+
 Parameters
 ----------
 result : :class:`~openturns.LinearModelResult`
@@ -12,7 +16,7 @@ splitter : :class:`~openturns.SplitterImplementation`
 
 See also
 --------
-LinearModelResult
+openturns.experimental.FunctionalChaosValidation, LinearModelResult
 
 Notes
 -----
@@ -39,7 +43,7 @@ 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 +64,16 @@ 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 +89,26 @@ 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.
 

python/src/experimental_module.i

@@ -59,11 +59,14 @@
 %include CubaIntegration.i
 
 /* Uncertainty/Algorithm/Metamodel */
+%import weightedexperiment_module.i
 %include UserDefinedMetropolisHastings.i
 %include FieldFunctionalChaosResult.i
 %include FieldToPointFunctionalChaosAlgorithm.i
 %include FieldFunctionalChaosSobolIndices.i
 %include PointToFieldFunctionalChaosAlgorithm.i
+%include FunctionalChaosValidation.i
+%include LinearModelValidation.i
 
 /* Uncertainty/Algorithm/EventSimulation */
 %include CrossEntropyResult.i

python/src/metamodel_module.i

@@ -60,14 +60,12 @@
 %include FunctionalChaosAlgorithm.i
 %include FunctionalChaosSobolIndices.i
 %include MetaModelValidation.i
-%include FunctionalChaosValidation.i
 %include GeneralLinearModelResult.i
 %include GeneralLinearModelAlgorithm.i
 %include KrigingAlgorithm.i
 %include LinearModelStepwiseAlgorithm.i
 %include LinearModelAlgorithm.i
 %include LinearModelAnalysis.i
-%include LinearModelValidation.i
 
 /* Uncertainty/Model */
 %include RandomVector.i

python/test/t_FunctionalChaosValidation_std.py

@@ -1,6 +1,7 @@
 #! /usr/bin/env python
 
 import openturns as ot
+import openturns.experimental as otexp
 from openturns.usecases import ishigami_function
 from openturns.testing import assert_almost_equal
 
@@ -170,7 +171,7 @@ def computeMSENaiveKFold(
 #
 print("1. Analytical leave-one-out")
 splitterLOO = ot.LeaveOneOutSplitter(sampleSize)
-validationLOO = ot.FunctionalChaosValidation(
+validationLOO = otexp.FunctionalChaosValidation(
     chaosResult, splitterLOO
 )
 mseLOOAnalytical = validationLOO.computeMeanSquaredError()
@@ -206,7 +207,7 @@ def computeMSENaiveKFold(
 #
 print("2. Analytical K-Fold")
 splitterKF = ot.KFoldSplitter(sampleSize, kFoldParameter)
-validationKFold = ot.FunctionalChaosValidation(
+validationKFold = otexp.FunctionalChaosValidation(
     chaosResult, splitterKF
 )
 print("KFold with K = ", kFoldParameter)
@@ -261,7 +262,7 @@ def computeMSENaiveKFold(
 
 # Analytical leave-one-out
 splitterLOO = ot.LeaveOneOutSplitter(sampleSize)
-validationLOO = ot.FunctionalChaosValidation(
+validationLOO = otexp.FunctionalChaosValidation(
     chaosResult, splitterLOO
 )
 mseLOOAnalytical = validationLOO.computeMeanSquaredError()

python/test/t_LinearModelValidation_std.py

@@ -1,6 +1,7 @@
 #! /usr/bin/env python
 
 import openturns as ot
+import openturns.experimental as otexp
 from openturns.testing import assert_almost_equal
 
 ot.TESTPREAMBLE()
@@ -29,7 +30,7 @@
 
 # Create LOO validation
 splitterLOO = ot.LeaveOneOutSplitter(sampleSize)
-validationLOO = ot.LinearModelValidation(result, splitterLOO)
+validationLOO = otexp.LinearModelValidation(result, splitterLOO)
 print(validationLOO)
 
 # Compute analytical LOO MSE
@@ -73,7 +74,7 @@
 
 # Create KFold validation
 splitterKFold = ot.KFoldSplitter(sampleSize, kFoldParameter)
-validationKFold = ot.LinearModelValidation(
+validationKFold = otexp.LinearModelValidation(
     result, splitterKFold
 )
 print(validationKFold)