Moved FCValidation and LMValidation into experimental

mbaudin47 · May 7, 2024 · d056915 · d056915
1 parent 5442546
commit d056915
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Showing 7 changed files with 22 additions and 13 deletions.
diff --git a/python/doc/pyplots/FunctionalChaosValidation.py b/python/doc/pyplots/FunctionalChaosValidation.py
@@ -1,6 +1,7 @@
 import openturns as ot
 from openturns.viewer import View
 from openturns.usecases import ishigami_function
+import openturns.experimental as otexp
 
 im = ishigami_function.IshigamiModel()
 sampleSize = 500
@@ -20,6 +21,6 @@
 )
 chaosalgo.run()
 result = chaosalgo.getResult()
-validation = ot.FunctionalChaosValidation(result)
+validation = otexp.FunctionalChaosValidation(result)
 graph = validation.drawValidation()
 View(graph)
diff --git a/python/doc/pyplots/LinearModelValidation.py b/python/doc/pyplots/LinearModelValidation.py
@@ -1,5 +1,6 @@
 import openturns as ot
 from openturns.viewer import View
+import openturns.experimental as otexp
 
 func = ot.SymbolicFunction(
     ['x1', 'x2', 'x3'],
@@ -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)
diff --git a/python/src/FunctionalChaosValidation_doc.i.in b/python/src/FunctionalChaosValidation_doc.i.in
@@ -102,6 +102,7 @@ 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 +125,7 @@ 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 +135,7 @@ and set the :math:`k` parameter.
 
 >>> kParameter = 10
 >>> splitterKF = ot.KFoldSplitter(sampleSize, kParameter)
->>> validation = ot.FunctionalChaosValidation(
+>>> validation = otexp.FunctionalChaosValidation(
 ...     result, splitterKF
 ... )
 

diff --git a/python/src/experimental_module.i b/python/src/experimental_module.i
@@ -81,5 +81,13 @@
 %include TruncatedOverMesh.i
 %include PosteriorDistribution.i
 %include UniformOrderStatistics.i
+
+/* Uncertainty/Algorithm/Metamodel */
+%include FunctionalChaosValidation.i
+%include LinearModelValidation.i
+
+// duplicate helpers
+OTDefaultCollectionConvertFunctions(WeightedExperiment)
+%include SmolyakExperiment.i
 %include GeneralizedExtremeValueValidation.i
 %include GeneralizedParetoValidation.i
diff --git a/python/src/metamodel_module.i b/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

diff --git a/python/test/t_FunctionalChaosValidation_std.py b/python/test/t_FunctionalChaosValidation_std.py
@@ -3,6 +3,7 @@
 import openturns as ot
 from openturns.usecases import ishigami_function
 from openturns.testing import assert_almost_equal
+import openturns.experimental as otexp
 
 
 def computeMSENaiveLOO(
@@ -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()
@@ -206,13 +207,11 @@ 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)
 assert validationKFold.getSplitter().getN() == sampleSize
-# TODO: fix this
-# assert validationKFold.getSplitter().getSize() == kFoldParameter
 
 # Compute mean squared error
 mseKFoldAnalytical = validationKFold.computeMeanSquaredError()
@@ -261,7 +260,7 @@ def computeMSENaiveKFold(
 
 # Analytical leave-one-out
 splitterLOO = ot.LeaveOneOutSplitter(sampleSize)
-validationLOO = ot.FunctionalChaosValidation(
+validationLOO = otexp.FunctionalChaosValidation(
     chaosResult, splitterLOO
 )
 mseLOOAnalytical = validationLOO.computeMeanSquaredError()

diff --git a/python/test/t_LinearModelValidation_std.py b/python/test/t_LinearModelValidation_std.py
@@ -2,6 +2,7 @@
 
 import openturns as ot
 from openturns.testing import assert_almost_equal
+import openturns.experimental as otexp
 
 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)