SmolyakExperiment: Move out of experimental

ChangeLog

@@ -9,6 +9,7 @@
 ==== New classes ====
 
 ==== API changes ====
+ * SmolyakExperiment left the experimental module
  * Removed deprecated method Study.printLabels
  * Removed deprecated class TimerCallback
  * Removed deprecated MetaModelValidation(inputSample, outputSample, metaModel) ctor

python/doc/examples/reliability_sensitivity/design_of_experiments/plot_smolyak_experiment.py

@@ -8,7 +8,6 @@
 
 # %%
 import openturns as ot
-import openturns.experimental as otexp
 import openturns.viewer as otv
 from matplotlib import pylab as plt
 
@@ -29,7 +28,7 @@
 for i in range(number_of_rows):
     for j in range(number_of_columns):
         level = 1 + j + i * number_of_columns
-        experiment = otexp.SmolyakExperiment(collection, level)
+        experiment = ot.SmolyakExperiment(collection, level)
         nodes, weights = experiment.generateWithWeights()
         sample_size = weights.getDimension()
         graph = ot.Graph(
@@ -160,7 +159,7 @@
     number_of_nodes = ot.Sample(level_max, 1)
     for level in level_list:
         collection = [uniform] * dimension
-        experiment = otexp.SmolyakExperiment(collection, level)
+        experiment = ot.SmolyakExperiment(collection, level)
         nodes, weights = experiment.generateWithWeights()
         size = nodes.getSize()
         number_of_nodes[level - 1, 0] = size

python/doc/examples/reliability_sensitivity/design_of_experiments/plot_smolyak_indices.py

@@ -25,23 +25,22 @@
 
 # %%
 import openturns as ot
-import openturns.experimental as otexp
 import openturns.viewer as otv
 from matplotlib import pylab as plt
 
 # %%
 # In the first example, we print the indices involved in
 # Smolyak-Legendre quadrature of level 3.
 # The multi-indices are computed using the
-# :py:meth:`openturns.experimental.SmolyakExperiment.computeCombination` method.
+# :py:meth:`openturns.SmolyakExperiment.computeCombination` method.
 # Actually, the multi-indices do not actually depend on the
 # underlying univariate quadratures, but this is required for
-# the :class:`openturns.experimental.SmolyakExperiment` class.
+# the :class:`openturns.SmolyakExperiment` class.
 
 collection = [ot.GaussProductExperiment()] * 2
 level = 3
 print("level = ", level)
-experiment = otexp.SmolyakExperiment(collection, level)
+experiment = ot.SmolyakExperiment(collection, level)
 indices = experiment.computeCombination()
 print(indices)
 
@@ -57,7 +56,7 @@
 def drawSmolyakIndices(level):
     # Plot Smolyak indices of given level in 2 dimensions
     collection = [ot.GaussProductExperiment()] * 2
-    experiment = otexp.SmolyakExperiment(collection, level)
+    experiment = ot.SmolyakExperiment(collection, level)
     indices = experiment.computeCombination()
     sample = indices
     graph = ot.Graph("L = %d" % (level), "k1", "k2", True)

python/doc/examples/reliability_sensitivity/design_of_experiments/plot_smolyak_merge.py

@@ -4,15 +4,14 @@
 """
 # %%
 # The goal of this example is to see the effect of the merge algorithm in
-# Smolyak's quadrature implemented in :class:`openturns.experimental.SmolyakExperiment`.
+# Smolyak's quadrature implemented in :class:`openturns.SmolyakExperiment`.
 # We analyse the sensitivity of the number of nodes to the relative
 # and absolute tolerances.
 # Then we analyse the effect of the merge algorithm on the number of nodes.
 
 # %%
 import numpy as np
 import openturns as ot
-import openturns.experimental as otexp
 import openturns.viewer as otv
 import matplotlib.pyplot as plt
 
@@ -46,7 +45,7 @@ def computeNumberOfSmolyakNodes(level, epsilon_a, epsilon_r):
     ot.ResourceMap.SetAsScalar("SmolyakExperiment-MergeAbsoluteEpsilon", epsilon_a)
     uniform = ot.GaussProductExperiment(ot.Uniform(0.0, 1.0))
     collection = [uniform] * 2
-    experiment = otexp.SmolyakExperiment(collection, level)
+    experiment = ot.SmolyakExperiment(collection, level)
     nodes, weights = experiment.generateWithWeights()
     size = nodes.getSize()
     return size

python/doc/examples/reliability_sensitivity/design_of_experiments/plot_smolyak_quadrature.py

@@ -14,7 +14,6 @@
 # %%
 import numpy as np
 import openturns as ot
-import openturns.experimental as otexp
 import openturns.viewer as otv
 from matplotlib import pylab as plt
 
@@ -26,7 +25,7 @@
 collection = [uniform] * 2
 level = 3
 print("level = ", level)
-experiment = otexp.SmolyakExperiment(collection, level)
+experiment = ot.SmolyakExperiment(collection, level)
 nodes, weights = experiment.generateWithWeights()
 print(nodes)
 print(weights)
@@ -98,7 +97,7 @@ def g_function_py(x):
 collection = [uniform] * dimension
 level = 5
 print("level = ", level)
-experiment = otexp.SmolyakExperiment(collection, level)
+experiment = ot.SmolyakExperiment(collection, level)
 nodes, weights = experiment.generateWithWeights()
 print("size = ", nodes.getSize())
 
@@ -178,7 +177,7 @@ def smolyakQuadrature(g_function, level):
     dimension = g_function.getInputDimension()
     uniform = ot.GaussProductExperiment(ot.Uniform(0.0, 1.0))
     collection = [uniform] * dimension
-    experiment = otexp.SmolyakExperiment(collection, level)
+    experiment = ot.SmolyakExperiment(collection, level)
     nodes, weights = experiment.generateWithWeights()
     size = nodes.getSize()
     g_values = g_function(nodes)

python/doc/pyplots/SmolyakExperiment.py

@@ -1,5 +1,4 @@
 import openturns as ot
-import openturns.experimental as otexp
 from matplotlib import pyplot as plt
 from openturns.viewer import View
 
@@ -9,7 +8,7 @@
 experiment2 = ot.GaussProductExperiment(ot.Uniform(0.0, 1.0))
 collection = [experiment1, experiment2]
 level = 3
-smolyak = otexp.SmolyakExperiment(collection, level)
+smolyak = ot.SmolyakExperiment(collection, level)
 
 sample = smolyak.generate()
 

python/doc/user_manual/designs_of_experiments.rst

@@ -69,7 +69,7 @@ Deterministic weighted experiments
     GaussProductExperiment
     LowDiscrepancyExperiment
     TensorProductExperiment
-    experimental.SmolyakExperiment
+    SmolyakExperiment
 
 .. _low_discrepancy_sequence_api:
 

python/src/CMakeLists.txt

@@ -807,6 +807,7 @@ ot_add_python_module(weightedexperiment weightedexperiment_module.i
                       TemperatureProfileImplementation.i TemperatureProfileImplementation_doc.i.in
                       LowDiscrepancyExperiment.i LowDiscrepancyExperiment_doc.i.in
                       MonteCarloExperiment.i MonteCarloExperiment_doc.i.in
+		      SmolyakExperiment.i SmolyakExperiment_doc.i.in
                       SplitterImplementation.i SplitterImplementation_doc.i.in
                       KFoldSplitter.i KFoldSplitter_doc.i.in
                       LeaveOneOutSplitter.i LeaveOneOutSplitter_doc.i.in
@@ -1026,7 +1027,6 @@ ot_add_python_module(experimental experimental_module.i
                      PointToFieldFunctionalChaosAlgorithm.i PointToFieldFunctionalChaosAlgorithm_doc.i.in
                      UserDefinedMetropolisHastings.i UserDefinedMetropolisHastings_doc.i.in
                      SamplePartition.i SamplePartition_doc.i.in
-                     SmolyakExperiment.i SmolyakExperiment_doc.i.in
                      SmoothedUniformFactory.i SmoothedUniformFactory_doc.i.in
                      StudentCopula.i StudentCopula_doc.i.in
                      StudentCopulaFactory.i StudentCopulaFactory_doc.i.in

python/src/SmolyakExperiment_doc.i.in

@@ -1,10 +1,6 @@
 %feature("docstring") OT::SmolyakExperiment
 "Smolyak experiment.
 
-.. warning::
-    This class is experimental and likely to be modified in future releases.
-    To use it, import the ``openturns.experimental`` submodule.
-
 Parameters
 ----------
 experiments : list of :class:`~openturns.WeightedExperiment`
@@ -22,7 +18,7 @@ Smolyak experiment has a significantly lower number of nodes [petras2003]_.
 This implementation uses the combination technique ([gerstner1998]_ page 215).
 Smolyak quadrature involve weights which are negative ([sullivan2015]_ page 177).
 The size of the experiment is only known after the nodes and weights have been computed,
-that is, after the :meth:`~openturns.experimental.SmolyakExperiment.generateWithWeights` method is called.
+that is, after the :meth:`generateWithWeights` method is called.
 
 **Method**
 
@@ -124,7 +120,7 @@ for any :math:`\ell \geq 1`.
 As shown by the previous equation, for a given multi-index :math:`\vect{k}`
 the Smolyak quadrature requires to set the level of each marginal experiment to
 an integer which depends on the multi-index.
-This is done using the :meth:`~openturns.experimental.SmolyakExperiment.setLevel` method of the marginal quadrature.
+This is done using the :meth:`setLevel` method of the marginal quadrature.
 
 The following formula expresses the multivariate quadrature in terms of
 combinations univariate quadratures, known as the *combination technique*.
@@ -217,20 +213,19 @@ Therefore,
 
 See Also
 --------
-openturns.WeightedExperiment
+WeightedExperiment
 
 Examples
 --------
 In the following example, we create Smolyak quadrature using two Gauss-Legendre
 marginal quadratures.
 
 >>> import openturns as ot
->>> import openturns.experimental as otexp
 >>> experiment1 = ot.GaussProductExperiment(ot.Uniform(0.0, 1.0))
 >>> experiment2 = ot.GaussProductExperiment(ot.Uniform(0.0, 1.0))
 >>> collection = [experiment1, experiment2]
 >>> level = 3
->>> multivariate_experiment = otexp.SmolyakExperiment(collection, level)
+>>> multivariate_experiment = ot.SmolyakExperiment(collection, level)
 >>> nodes, weights = multivariate_experiment.generateWithWeights()"
 
 // ---------------------------------------------------------------------

python/src/experimental_module.i

@@ -81,9 +81,5 @@
 %include TruncatedOverMesh.i
 %include PosteriorDistribution.i
 %include UniformOrderStatistics.i
-
-// duplicate helpers
-OTDefaultCollectionConvertFunctions(WeightedExperiment)
-%include SmolyakExperiment.i
 %include GeneralizedExtremeValueValidation.i
 %include GeneralizedParetoValidation.i

python/src/weightedexperiment_module.i

@@ -50,6 +50,7 @@
 %include KFoldSplitter.i
 %include LeaveOneOutSplitter.i
 %include TensorProductExperiment.i
+%include SmolyakExperiment.i
 
 /* At last we include template definitions */
 %include UncertaintyWeightedExperimentTemplateDefs.i

python/test/t_SmolyakExperiment_std.py

@@ -1,7 +1,6 @@
 #! /usr/bin/env python
 
 import openturns as ot
-import openturns.experimental as otexp
 import openturns.testing as ott
 
 ot.TESTPREAMBLE()
@@ -112,7 +111,7 @@ def testSmolyakExperiment1():
     experiment2 = ot.GaussProductExperiment(ot.Uniform(0.0, 1.0))
     collection = [experiment1, experiment2]
     level = 3
-    smolyak = otexp.SmolyakExperiment(collection, level)
+    smolyak = ot.SmolyakExperiment(collection, level)
     nodes, weights = smolyak.generateWithWeights()
     numberOfDigits = 14
     nodes = roundSample(nodes, numberOfDigits)
@@ -197,7 +196,7 @@ def testSmolyakExperiment2():
     experiment2 = ot.GaussProductExperiment(ot.Uniform(0.0, 1.0))
     collection = [experiment1, experiment2]
     level = 3
-    smolyak = otexp.SmolyakExperiment(collection, level)
+    smolyak = ot.SmolyakExperiment(collection, level)
     nodes = smolyak.generate()
     numberOfDigits = 14
     nodes = roundSample(nodes, numberOfDigits)
@@ -239,7 +238,7 @@ def testSmolyakExperiment3():
         marginalExperiment = ot.GaussProductExperiment(ot.Uniform(0.0, 1.0))
         collection.append(marginalExperiment)
     level = 3
-    smolyak = otexp.SmolyakExperiment(collection, level)
+    smolyak = ot.SmolyakExperiment(collection, level)
     nodes, weights = smolyak.generateWithWeights()
     # Check size and dimension
     assert nodes.getDimension() == 3
@@ -256,7 +255,7 @@ def testSmolyakExperiment4():
     experiment2 = ot.GaussProductExperiment(ot.Uniform(0.0, 1.0))
     collection = [experiment1, experiment2]
     level = 1
-    smolyak = otexp.SmolyakExperiment(collection, level)
+    smolyak = ot.SmolyakExperiment(collection, level)
     nodes, weights = smolyak.generateWithWeights()
     numberOfDigits = 14
     nodes = roundSample(nodes, numberOfDigits)
@@ -293,7 +292,7 @@ def testSmolyakExperiment5():
     experiment2 = ot.GaussProductExperiment(ot.Uniform(0.0, 1.0))
     collection = [experiment1, experiment2]
     level = 3
-    smolyak = otexp.SmolyakExperiment(collection, level)
+    smolyak = ot.SmolyakExperiment(collection, level)
     indicesCollection = smolyak.computeCombination()
     print("indicesCollection = ", indicesCollection)
     expected = [[2, 1], [1, 2], [3, 1], [2, 2], [1, 3]]