From ff52c6f67817ae5c04967a2f66785afd6de5fcfe Mon Sep 17 00:00:00 2001 From: GitHub Actions Date: Thu, 19 Sep 2024 15:33:51 +0000 Subject: [PATCH] GitHub Actions build openturns/otbenchmark 10943979829 --- .../plot_borgonovo_sensitivity.ipynb | 235 +++++++ .../plot_nloscillator_sensitivity.zip | Bin 0 -> 8962 bytes .../auto_examples_python.zip | Bin 20006 -> 54769 bytes .../plot_dirichlet_sensitivity.zip | Bin 0 -> 8931 bytes .../plot_flood_sensitivity.zip | Bin 0 -> 8943 bytes .../plot_crosscut_distribution_2d.zip | Bin 4067 -> 4067 bytes .../plot_benchmark_sensitivity_methods.ipynb | 365 +++++++++++ .../plot_flood_sensitivity.py | 95 +++ .../plot_convergence_reliability_mc.zip | Bin 10056 -> 10056 bytes .../plot_flood_sensitivity.ipynb | 246 ++++++++ .../plot_ishigami_sensitivity.py | 97 +++ .../plot_gaussian_product_sensitivity.ipynb | 246 ++++++++ .../plot_benchmark_sensitivity_methods.py | 191 ++++++ .../plot_gsobol_sensitivity.zip | Bin 0 -> 9479 bytes 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mode 100644 otbenchmark/master/auto_examples/sensitivity_problems/plot_borehole_sensitivity.html create mode 100644 otbenchmark/master/auto_examples/sensitivity_problems/plot_borgonovo_sensitivity.html create mode 100644 otbenchmark/master/auto_examples/sensitivity_problems/plot_dirichlet_sensitivity.html create mode 100644 otbenchmark/master/auto_examples/sensitivity_problems/plot_flood_sensitivity.html create mode 100644 otbenchmark/master/auto_examples/sensitivity_problems/plot_gaussian_product_sensitivity.html create mode 100644 otbenchmark/master/auto_examples/sensitivity_problems/plot_gaussian_sum.html create mode 100644 otbenchmark/master/auto_examples/sensitivity_problems/plot_gsobol_sensitivity.html create mode 100644 otbenchmark/master/auto_examples/sensitivity_problems/plot_ishigami_sensitivity.html create mode 100644 otbenchmark/master/auto_examples/sensitivity_problems/plot_morris_sensitivity.html create mode 100644 otbenchmark/master/auto_examples/sensitivity_problems/plot_nloscillator_sensitivity.html create mode 100644 otbenchmark/master/auto_examples/sensitivity_problems/plot_oakleyohagan_sensitivity.html diff --git a/otbenchmark/master/_downloads/018bebf23004eb4c852157b01b94c189/plot_borgonovo_sensitivity.ipynb b/otbenchmark/master/_downloads/018bebf23004eb4c852157b01b94c189/plot_borgonovo_sensitivity.ipynb new file mode 100644 index 00000000000..63ff75b7401 --- /dev/null +++ b/otbenchmark/master/_downloads/018bebf23004eb4c852157b01b94c189/plot_borgonovo_sensitivity.ipynb @@ -0,0 +1,235 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Benchmark the Borgonovo test function\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import openturns as ot\nimport otbenchmark as otb\nimport openturns.viewer as otv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "problem = otb.BorgonovoSensitivity()\nprint(problem)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "distribution = problem.getInputDistribution()\nmodel = problem.getFunction()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Exact first and total order\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "exact_first_order = problem.getFirstOrderIndices()\nexact_first_order" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "exact_total_order = problem.getTotalOrderIndices()\nexact_total_order" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot the function\n\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create X/Y data\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ot.RandomGenerator.SetSeed(0)\nsize = 200\ninputDesign = ot.MonteCarloExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "dimension = distribution.getDimension()\nfull_sample = ot.Sample(size, 1 + dimension)\nfull_sample[:, range(dimension)] = inputDesign\nfull_sample[:, dimension] = outputDesign\nfull_description = list(inputDesign.getDescription())\nfull_description.append(outputDesign.getDescription()[0])\nfull_sample.setDescription(full_description)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "marginal_distribution = ot.ComposedDistribution(\n [\n ot.KernelSmoothing().build(full_sample.getMarginal(i))\n for i in range(1 + dimension)\n ]\n)\nclouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution)\n_ = otv.View(clouds, figure_kw={\"figsize\": (6.0, 6.0)})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "output_distribution = ot.KernelSmoothing().build(outputDesign)\n_ = otv.View(output_distribution.drawPDF())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Perform sensitivity analysis\n\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create X/Y data\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ot.RandomGenerator.SetSeed(0)\nsize = 10000\ninputDesign = ot.SobolIndicesExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute first order indices using the Saltelli estimator\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)\ncomputed_first_order = sensitivityAnalysis.getFirstOrderIndices()\ncomputed_total_order = sensitivityAnalysis.getTotalOrderIndices()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compare with exact results\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "print(\"Sample size : \", size)\n# First order\n# Compute absolute error (the LRE cannot be computed,\n# because S can be zero)\nprint(\"Computed first order = \", computed_first_order)\nprint(\"Exact first order = \", exact_first_order)\n# Total order\nprint(\"Computed total order = \", computed_total_order)\nprint(\"Exact total order = \", exact_total_order)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "_ = otv.View(sensitivityAnalysis.draw())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "otv.View.ShowAll()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.20" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git 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Qij?=@=MVV%lZ*fT3qp;F6#xJL literal 0 HcmV?d00001 diff --git a/otbenchmark/master/_downloads/0ee134db52089df8699dbb596527cd27/plot_crosscut_distribution_2d.zip b/otbenchmark/master/_downloads/0ee134db52089df8699dbb596527cd27/plot_crosscut_distribution_2d.zip index 4d0a458d62f4c18479efe28aef4cf6958720f803..d7ee759b6552e5694c0828193986b1367f3976b6 100644 GIT binary patch delta 43 vcmaDX|5%a2cV`e&0w0R2iGH#}0MU!p#eSqA_UHp*%6-W)C diff --git a/otbenchmark/master/_downloads/13b9169f4ad20d57745bd3f8f0cb6282/plot_benchmark_sensitivity_methods.ipynb b/otbenchmark/master/_downloads/13b9169f4ad20d57745bd3f8f0cb6282/plot_benchmark_sensitivity_methods.ipynb new file mode 100644 index 00000000000..fc04b1ce438 --- /dev/null +++ b/otbenchmark/master/_downloads/13b9169f4ad20d57745bd3f8f0cb6282/plot_benchmark_sensitivity_methods.ipynb @@ -0,0 +1,365 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Benchmark sensitivity analysis methods\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import openturns as ot\nimport otbenchmark as otb" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## When we estimate Sobol' indices, we may encounter the following warning messages:\nWRN - The estimated first order Sobol index (2) is greater than its total order index ...\nWRN - The estimated total order Sobol index (2) is lesser than first order index ...\n```\nLots of these messages are printed in the current Notebook. This is why we disable them with:\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ot.Log.Show(ot.Log.NONE)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use Borgonovo problem\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "problem = otb.BorgonovoSensitivity()\ndistribution = problem.getInputDistribution()\nmodel = problem.getFunction()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Exact first and total order\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "exact_first_order = problem.getFirstOrderIndices()\nexact_total_order = problem.getTotalOrderIndices()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Saltelli estimator with Monte-Carlo sample\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sample_size = 10000" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "inputDesign = ot.SobolIndicesExperiment(distribution, sample_size).generate()\noutputDesign = model(inputDesign)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute first order indices using the Saltelli estimator\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(\n inputDesign, outputDesign, sample_size\n)\ncomputed_first_order = sensitivityAnalysis.getFirstOrderIndices()\ncomputed_total_order = sensitivityAnalysis.getTotalOrderIndices()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compare with exact results\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "print(\"Sample size : \", sample_size)\n# First order\nprint(\"Computed first order = \", computed_first_order)\nprint(\"Exact first order = \", exact_first_order)\n# Total order\nprint(\"Computed total order = \", computed_total_order)\nprint(\"Exact total order = \", exact_total_order)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Saltelli estimator with Quasi Monte-Carlo sample\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sample_size = 500" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "dimension = distribution.getDimension()\nsequence = ot.SobolSequence(dimension)\nrestart = True\nexperiment = ot.LowDiscrepancyExperiment(sequence, distribution, sample_size, restart)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "inputDesign = ot.SobolIndicesExperiment(experiment).generate()\noutputDesign = model(inputDesign)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute first order indices using the Saltelli estimator\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(\n inputDesign, outputDesign, sample_size\n)\nfirst_order = sensitivityAnalysis.getFirstOrderIndices()\ntotal_order = sensitivityAnalysis.getTotalOrderIndices()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compare with exact results\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "print(\"Sample size : \", sample_size)\n# First order\nprint(\"Computed first order = \", computed_first_order)\nprint(\"Exact first order = \", exact_first_order)\n# Total order\nprint(\"Computed total order = \", computed_total_order)\nprint(\"Exact total order = \", exact_total_order)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loop over the estimators\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "print(\"Available estimators:\")\nestimators_list = otb.SensitivityBenchmarkMetaAlgorithm.GetEstimators()\nfor sobolAlgorithm in estimators_list:\n name = sobolAlgorithm.getClassName()\n print(\" - \", name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "metaSAAlgorithm = otb.SensitivityBenchmarkMetaAlgorithm(problem)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "print(\"Monte-Carlo sampling\")\nfor sobolAlgorithm in estimators_list:\n (\n computed_first_order,\n computed_total_order,\n ) = metaSAAlgorithm.runSamplingEstimator(sample_size)\n name = sobolAlgorithm.getClassName()\n print(name)\n print(\" S = \", computed_first_order)\n print(\" T = \", computed_total_order)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "print(\"Quasi Monte-Carlo sampling\")\nfor estimator in [\"Saltelli\", \"Martinez\", \"Jansen\", \"MauntzKucherenko\"]:\n (\n computed_first_order,\n computed_total_order,\n ) = metaSAAlgorithm.runSamplingEstimator(\n sample_size, estimator=estimator, sampling_method=\"QMC\"\n )\n name = sobolAlgorithm.getClassName()\n print(name)\n print(\" S = \", computed_first_order)\n print(\" T = \", computed_total_order)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "print(\"Polynomial chaos\")\nsample_size = 500\n(\n computed_first_order,\n computed_total_order,\n) = metaSAAlgorithm.runPolynomialChaosEstimator(\n sample_size_train=sample_size,\n sample_size_test=2,\n total_degree=5,\n hyperbolic_quasinorm=0.5,\n)\nprint(\" S = \", computed_first_order)\nprint(\" T = \", computed_total_order)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define the metric\n\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We consider the following accuracy metrics:\n* the vector or log relative errors for a given index (first order or total order),\n* the mean log relative error, as the mean of the LRE vector (first order or total order),\n* the average mean log relative error, as the mean of the first and total order mean log relative error.\n\nLarger LRE values are prefered.\n\nThe first order (resp. total order) mean LRE represents the mean number of digits for all components\nof the first order indices (resp. total order indices).\nThe average mean LRE represents the mean LRE for both first and total order indices.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "S_LRE = ot.Point(dimension)\nT_LRE = ot.Point(dimension)\nfor i in range(dimension):\n S_LRE[i] = otb.ComputeLogRelativeError(\n computed_first_order[i], exact_first_order[i]\n )\n T_LRE[i] = otb.ComputeLogRelativeError(\n computed_total_order[i], exact_total_order[i]\n )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "print(\"LRE S = \", S_LRE)\nprint(\"LRE T = \", T_LRE)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "mean_LRE_S = sum(S_LRE) / dimension\nmean_LRE_T = sum(T_LRE) / dimension\nmean_LRE = (mean_LRE_S + mean_LRE_T) / 2.0\nprint(\"Mean LRE S = %.2f\" % (mean_LRE_S))\nprint(\"Mean LRE T = %.2f\" % (mean_LRE_T))\nprint(\"Mean LRE = %.2f\" % (mean_LRE))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The digit per point ratio measure the number of digits relatively to the sample size. A greater value is prefered.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "digit_per_point_ratio = mean_LRE / sample_size\nprint(\"Digit / point = %.3e\" % (digit_per_point_ratio))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.20" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/otbenchmark/master/_downloads/15180aff63b94e352470b9af8a67b807/plot_flood_sensitivity.py b/otbenchmark/master/_downloads/15180aff63b94e352470b9af8a67b807/plot_flood_sensitivity.py new file mode 100644 index 00000000000..7c6bc90ee00 --- /dev/null +++ b/otbenchmark/master/_downloads/15180aff63b94e352470b9af8a67b807/plot_flood_sensitivity.py @@ -0,0 +1,95 @@ +""" +Benchmark the Flooding test function +==================================== +""" + +# %% +import openturns as ot +import otbenchmark as otb +import openturns.viewer as otv + +# %% +problem = otb.FloodingSensitivity() + +# %% +print(problem) + +# %% +distribution = problem.getInputDistribution() +model = problem.getFunction() + +# %% +# Exact first and total order +exact_first_order = problem.getFirstOrderIndices() +exact_first_order + +# %% +exact_total_order = problem.getTotalOrderIndices() +exact_total_order + +# %% +# Plot the function +# ----------------- + +# %% +# Create X/Y data +ot.RandomGenerator.SetSeed(0) +size = 200 +inputDesign = ot.MonteCarloExperiment(distribution, size).generate() +outputDesign = model(inputDesign) + +# %% +dimension = distribution.getDimension() +full_sample = ot.Sample(size, 1 + dimension) +full_sample[:, range(dimension)] = inputDesign +full_sample[:, dimension] = outputDesign +full_description = list(inputDesign.getDescription()) +full_description.append(outputDesign.getDescription()[0]) +full_sample.setDescription(full_description) + +# %% +marginal_distribution = ot.ComposedDistribution( + [ + ot.KernelSmoothing().build(full_sample.getMarginal(i)) + for i in range(1 + dimension) + ] +) +clouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution) +_ = otv.View(clouds, figure_kw={"figsize": (10.0, 10.0)}) + +# %% +output_distribution = ot.KernelSmoothing().build(outputDesign) +_ = otv.View(output_distribution.drawPDF()) + +# %% +# Perform sensitivity analysis +# ---------------------------- + +# %% +# Create X/Y data +ot.RandomGenerator.SetSeed(0) +size = 10000 +inputDesign = ot.SobolIndicesExperiment(distribution, size).generate() +outputDesign = model(inputDesign) + +# %% +# Compute first order indices using the Saltelli estimator +sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size) +computed_first_order = sensitivityAnalysis.getFirstOrderIndices() +computed_total_order = sensitivityAnalysis.getTotalOrderIndices() + +# %% +# Compare with exact results +print("Sample size : ", size) +# First order +print("Computed first order = ", computed_first_order) +print("Exact first order = ", exact_first_order) +# Total order +print("Computed total order = ", computed_total_order) +print("Exact total order = ", exact_total_order) + +# %% +_ = otv.View(sensitivityAnalysis.draw()) + +# %% +otv.View.ShowAll() diff --git a/otbenchmark/master/_downloads/177f69a01b2091371b1393b3784ee85e/plot_convergence_reliability_mc.zip b/otbenchmark/master/_downloads/177f69a01b2091371b1393b3784ee85e/plot_convergence_reliability_mc.zip index f3140bda73cae551fdf153cf84daf9ea11570f56..71700da848ae47bf21955e3289319f55527cc2e0 100644 GIT binary patch delta 43 vcmX@%cfyY+z?+#xgaHJ0RBz--=40Afy}6K2OM+=f_2ju~en9Tzw`$P<6y^>J delta 43 vcmX@%cfyY+z?+#xgaHIj7H#B7=3_clw7HN^OM>ZS(d4;men9Tzw`$P<7b*@a diff --git a/otbenchmark/master/_downloads/39c987f12c082b6f6c2b1c199aed0518/plot_flood_sensitivity.ipynb b/otbenchmark/master/_downloads/39c987f12c082b6f6c2b1c199aed0518/plot_flood_sensitivity.ipynb new file mode 100644 index 00000000000..1974475fc3d --- /dev/null +++ b/otbenchmark/master/_downloads/39c987f12c082b6f6c2b1c199aed0518/plot_flood_sensitivity.ipynb @@ -0,0 +1,246 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Benchmark the Flooding test function\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import openturns as ot\nimport otbenchmark as otb\nimport openturns.viewer as otv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "problem = otb.FloodingSensitivity()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "print(problem)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "distribution = problem.getInputDistribution()\nmodel = problem.getFunction()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Exact first and total order\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "exact_first_order = problem.getFirstOrderIndices()\nexact_first_order" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "exact_total_order = problem.getTotalOrderIndices()\nexact_total_order" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot the function\n\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create X/Y data\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ot.RandomGenerator.SetSeed(0)\nsize = 200\ninputDesign = ot.MonteCarloExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "dimension = distribution.getDimension()\nfull_sample = ot.Sample(size, 1 + dimension)\nfull_sample[:, range(dimension)] = inputDesign\nfull_sample[:, dimension] = outputDesign\nfull_description = list(inputDesign.getDescription())\nfull_description.append(outputDesign.getDescription()[0])\nfull_sample.setDescription(full_description)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "marginal_distribution = ot.ComposedDistribution(\n [\n ot.KernelSmoothing().build(full_sample.getMarginal(i))\n for i in range(1 + dimension)\n ]\n)\nclouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution)\n_ = otv.View(clouds, figure_kw={\"figsize\": (10.0, 10.0)})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "output_distribution = ot.KernelSmoothing().build(outputDesign)\n_ = otv.View(output_distribution.drawPDF())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Perform sensitivity analysis\n\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create X/Y data\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ot.RandomGenerator.SetSeed(0)\nsize = 10000\ninputDesign = ot.SobolIndicesExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute first order indices using the Saltelli estimator\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)\ncomputed_first_order = sensitivityAnalysis.getFirstOrderIndices()\ncomputed_total_order = sensitivityAnalysis.getTotalOrderIndices()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compare with exact results\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "print(\"Sample size : \", size)\n# First order\nprint(\"Computed first order = \", computed_first_order)\nprint(\"Exact first order = \", exact_first_order)\n# Total order\nprint(\"Computed total order = \", computed_total_order)\nprint(\"Exact total order = \", exact_total_order)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "_ = otv.View(sensitivityAnalysis.draw())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "otv.View.ShowAll()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.20" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/otbenchmark/master/_downloads/3b23190f79b9a4829b68307be5e836d7/plot_ishigami_sensitivity.py b/otbenchmark/master/_downloads/3b23190f79b9a4829b68307be5e836d7/plot_ishigami_sensitivity.py new file mode 100644 index 00000000000..09864ade9ce --- /dev/null +++ b/otbenchmark/master/_downloads/3b23190f79b9a4829b68307be5e836d7/plot_ishigami_sensitivity.py @@ -0,0 +1,97 @@ +""" +Benchmark the Ishigami test function +==================================== +""" + +# %% +import openturns as ot +import otbenchmark as otb +import openturns.viewer as otv + +# %% +problem = otb.IshigamiSensitivity() + +# %% +print(problem) + +# %% +distribution = problem.getInputDistribution() +model = problem.getFunction() + +# %% +# Exact first and total order +exact_first_order = problem.getFirstOrderIndices() +exact_first_order + +# %% +exact_total_order = problem.getTotalOrderIndices() +exact_total_order + +# %% +# Plot the function +# ----------------- + +# %% +# Create X/Y data +ot.RandomGenerator.SetSeed(0) +size = 200 +inputDesign = ot.MonteCarloExperiment(distribution, size).generate() +outputDesign = model(inputDesign) + +# %% +dimension = distribution.getDimension() +full_sample = ot.Sample(size, 1 + dimension) +full_sample[:, range(dimension)] = inputDesign +full_sample[:, dimension] = outputDesign +full_description = list(inputDesign.getDescription()) +full_description.append(outputDesign.getDescription()[0]) +full_sample.setDescription(full_description) + +# %% +marginal_distribution = ot.ComposedDistribution( + [ + ot.KernelSmoothing().build(full_sample.getMarginal(i)) + for i in range(1 + dimension) + ] +) +clouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution) +_ = otv.View(clouds, figure_kw={"figsize": (6.0, 6.0)}) + +# %% +output_distribution = ot.KernelSmoothing().build(outputDesign) +_ = otv.View(output_distribution.drawPDF()) + +# %% +# Perform sensitivity analysis +# ---------------------------- + +# %% +# Create X/Y data +ot.RandomGenerator.SetSeed(0) +size = 10000 +inputDesign = ot.SobolIndicesExperiment(distribution, size).generate() +outputDesign = model(inputDesign) + +# %% +# Compute first order indices using the Saltelli estimator +sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size) +computed_first_order = sensitivityAnalysis.getFirstOrderIndices() +computed_total_order = sensitivityAnalysis.getTotalOrderIndices() + +# %% +# Compare with exact results +print("Sample size : ", size) +# First order +# Compute absolute error (the LRE cannot be computed, +# because S can be zero) +print("Computed first order = ", computed_first_order) +print("Exact first order = ", exact_first_order) +# Total order +print("Computed total order = ", computed_total_order) +print("Exact total order = ", exact_total_order) + +# %% +_ = otv.View(sensitivityAnalysis.draw()) + +# %% +otv.View.ShowAll() diff --git a/otbenchmark/master/_downloads/3bd92b053e8108a3b84f939606e1f584/plot_gaussian_product_sensitivity.ipynb b/otbenchmark/master/_downloads/3bd92b053e8108a3b84f939606e1f584/plot_gaussian_product_sensitivity.ipynb new file mode 100644 index 00000000000..acb757fb745 --- /dev/null +++ b/otbenchmark/master/_downloads/3bd92b053e8108a3b84f939606e1f584/plot_gaussian_product_sensitivity.ipynb @@ -0,0 +1,246 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Benchmark the gaussian product test function\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import openturns as ot\nimport otbenchmark as otb\nimport openturns.viewer as otv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "problem = otb.GaussianProductSensitivity()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "print(problem)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "distribution = problem.getInputDistribution()\nmodel = problem.getFunction()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Exact first and total order\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "exact_first_order = problem.getFirstOrderIndices()\nprint(exact_first_order)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "exact_total_order = problem.getTotalOrderIndices()\nprint(exact_total_order)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot the function\n\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create X/Y data\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ot.RandomGenerator.SetSeed(0)\nsize = 200\ninputDesign = ot.MonteCarloExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "dimension = distribution.getDimension()\nfull_sample = ot.Sample(size, 1 + dimension)\nfull_sample[:, range(dimension)] = inputDesign\nfull_sample[:, dimension] = outputDesign\nfull_description = list(inputDesign.getDescription())\nfull_description.append(outputDesign.getDescription()[0])\nfull_sample.setDescription(full_description)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "marginal_distribution = ot.ComposedDistribution(\n [\n ot.KernelSmoothing().build(full_sample.getMarginal(i))\n for i in range(1 + dimension)\n ]\n)\nclouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution)\nview = otv.View(clouds, figure_kw={\"figsize\": (6.0, 6.0)})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "output_distribution = ot.KernelSmoothing().build(outputDesign)\n_ = otv.View(output_distribution.drawPDF())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Perform sensitivity analysis\n\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create X/Y data\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ot.RandomGenerator.SetSeed(0)\nsize = 10000\ninputDesign = ot.SobolIndicesExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute first order indices using the Saltelli estimator\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)\ncomputed_first_order = sensitivityAnalysis.getFirstOrderIndices()\ncomputed_total_order = sensitivityAnalysis.getTotalOrderIndices()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compare with exact results\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "print(\"Sample size : \", size)\n# First order\n# Compute absolute error (the LRE cannot be computed,\n# because S can be zero)\nprint(\"Computed first order = \", computed_first_order)\nprint(\"Exact first order = \", exact_first_order)\n# Total order\nprint(\"Computed total order = \", computed_total_order)\nprint(\"Exact total order = \", exact_total_order)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "_ = otv.View(sensitivityAnalysis.draw())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "otv.View.ShowAll()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.20" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/otbenchmark/master/_downloads/3e62dca12ac3db00aaba5db1a12e3867/plot_benchmark_sensitivity_methods.py b/otbenchmark/master/_downloads/3e62dca12ac3db00aaba5db1a12e3867/plot_benchmark_sensitivity_methods.py new file mode 100644 index 00000000000..4d0ee641f21 --- /dev/null +++ b/otbenchmark/master/_downloads/3e62dca12ac3db00aaba5db1a12e3867/plot_benchmark_sensitivity_methods.py @@ -0,0 +1,191 @@ +""" +Benchmark sensitivity analysis methods +====================================== +""" + +# %% +import openturns as ot +import otbenchmark as otb + +# %% +# When we estimate Sobol' indices, we may encounter the following warning messages: +# ``` +# WRN - The estimated first order Sobol index (2) is greater than its total order index ... +# WRN - The estimated total order Sobol index (2) is lesser than first order index ... +# ``` +# Lots of these messages are printed in the current Notebook. This is why we disable them with: +ot.Log.Show(ot.Log.NONE) + +# %% +# Use Borgonovo problem +problem = otb.BorgonovoSensitivity() +distribution = problem.getInputDistribution() +model = problem.getFunction() + +# %% +# Exact first and total order +exact_first_order = problem.getFirstOrderIndices() +exact_total_order = problem.getTotalOrderIndices() + +# %% +# Saltelli estimator with Monte-Carlo sample +# ------------------------------------------ + +# %% +sample_size = 10000 + +# %% +inputDesign = ot.SobolIndicesExperiment(distribution, sample_size).generate() +outputDesign = model(inputDesign) + +# %% +# Compute first order indices using the Saltelli estimator +sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm( + inputDesign, outputDesign, sample_size +) +computed_first_order = sensitivityAnalysis.getFirstOrderIndices() +computed_total_order = sensitivityAnalysis.getTotalOrderIndices() + +# %% +# Compare with exact results +print("Sample size : ", sample_size) +# First order +print("Computed first order = ", computed_first_order) +print("Exact first order = ", exact_first_order) +# Total order +print("Computed total order = ", computed_total_order) +print("Exact total order = ", exact_total_order) + +# %% +# Saltelli estimator with Quasi Monte-Carlo sample +# ------------------------------------------------ + +# %% +sample_size = 500 + +# %% +dimension = distribution.getDimension() +sequence = ot.SobolSequence(dimension) +restart = True +experiment = ot.LowDiscrepancyExperiment(sequence, distribution, sample_size, restart) + +# %% +inputDesign = ot.SobolIndicesExperiment(experiment).generate() +outputDesign = model(inputDesign) + +# %% +# Compute first order indices using the Saltelli estimator +sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm( + inputDesign, outputDesign, sample_size +) +first_order = sensitivityAnalysis.getFirstOrderIndices() +total_order = sensitivityAnalysis.getTotalOrderIndices() + +# %% +# Compare with exact results +print("Sample size : ", sample_size) +# First order +print("Computed first order = ", computed_first_order) +print("Exact first order = ", exact_first_order) +# Total order +print("Computed total order = ", computed_total_order) +print("Exact total order = ", exact_total_order) + +# %% +# Loop over the estimators +# ------------------------ + +# %% +print("Available estimators:") +estimators_list = otb.SensitivityBenchmarkMetaAlgorithm.GetEstimators() +for sobolAlgorithm in estimators_list: + name = sobolAlgorithm.getClassName() + print(" - ", name) + +# %% +metaSAAlgorithm = otb.SensitivityBenchmarkMetaAlgorithm(problem) + +# %% +print("Monte-Carlo sampling") +for sobolAlgorithm in estimators_list: + ( + computed_first_order, + computed_total_order, + ) = metaSAAlgorithm.runSamplingEstimator(sample_size) + name = sobolAlgorithm.getClassName() + print(name) + print(" S = ", computed_first_order) + print(" T = ", computed_total_order) + +# %% +print("Quasi Monte-Carlo sampling") +for estimator in ["Saltelli", "Martinez", "Jansen", "MauntzKucherenko"]: + ( + computed_first_order, + computed_total_order, + ) = metaSAAlgorithm.runSamplingEstimator( + sample_size, estimator=estimator, sampling_method="QMC" + ) + name = sobolAlgorithm.getClassName() + print(name) + print(" S = ", computed_first_order) + print(" T = ", computed_total_order) + +# %% +print("Polynomial chaos") +sample_size = 500 +( + computed_first_order, + computed_total_order, +) = metaSAAlgorithm.runPolynomialChaosEstimator( + sample_size_train=sample_size, + sample_size_test=2, + total_degree=5, + hyperbolic_quasinorm=0.5, +) +print(" S = ", computed_first_order) +print(" T = ", computed_total_order) + +# %% +# Define the metric +# ----------------- + +# %% +# We consider the following accuracy metrics: +# * the vector or log relative errors for a given index (first order or total order), +# * the mean log relative error, as the mean of the LRE vector (first order or total order), +# * the average mean log relative error, as the mean of the first and total order mean log relative error. +# +# Larger LRE values are prefered. +# +# The first order (resp. total order) mean LRE represents the mean number of digits for all components +# of the first order indices (resp. total order indices). +# The average mean LRE represents the mean LRE for both first and total order indices. + +# %% +S_LRE = ot.Point(dimension) +T_LRE = ot.Point(dimension) +for i in range(dimension): + S_LRE[i] = otb.ComputeLogRelativeError( + computed_first_order[i], exact_first_order[i] + ) + T_LRE[i] = otb.ComputeLogRelativeError( + computed_total_order[i], exact_total_order[i] + ) + +# %% +print("LRE S = ", S_LRE) +print("LRE T = ", T_LRE) + +# %% +mean_LRE_S = sum(S_LRE) / dimension +mean_LRE_T = sum(T_LRE) / dimension +mean_LRE = (mean_LRE_S + mean_LRE_T) / 2.0 +print("Mean LRE S = %.2f" % (mean_LRE_S)) +print("Mean LRE T = %.2f" % (mean_LRE_T)) +print("Mean LRE = %.2f" % (mean_LRE)) + +# %% +# The digit per point ratio measure the number of digits relatively to the sample size. 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+ +# %% +problem = otb.GaussianProductSensitivity() + +# %% +print(problem) + +# %% +distribution = problem.getInputDistribution() +model = problem.getFunction() + +# %% +# Exact first and total order +exact_first_order = problem.getFirstOrderIndices() +print(exact_first_order) + +# %% +exact_total_order = problem.getTotalOrderIndices() +print(exact_total_order) + +# %% +# Plot the function +# ----------------- + +# %% +# Create X/Y data +ot.RandomGenerator.SetSeed(0) +size = 200 +inputDesign = ot.MonteCarloExperiment(distribution, size).generate() +outputDesign = model(inputDesign) + +# %% +dimension = distribution.getDimension() +full_sample = ot.Sample(size, 1 + dimension) +full_sample[:, range(dimension)] = inputDesign +full_sample[:, dimension] = outputDesign +full_description = list(inputDesign.getDescription()) +full_description.append(outputDesign.getDescription()[0]) +full_sample.setDescription(full_description) + +# %% +marginal_distribution = ot.ComposedDistribution( + [ + ot.KernelSmoothing().build(full_sample.getMarginal(i)) + for i in range(1 + dimension) + ] +) +clouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution) +view = otv.View(clouds, figure_kw={"figsize": (6.0, 6.0)}) + +# %% +output_distribution = ot.KernelSmoothing().build(outputDesign) +_ = otv.View(output_distribution.drawPDF()) + +# %% +# Perform sensitivity analysis +# ---------------------------- + +# %% +# Create X/Y data +ot.RandomGenerator.SetSeed(0) +size = 10000 +inputDesign = ot.SobolIndicesExperiment(distribution, size).generate() +outputDesign = model(inputDesign) + +# %% +# Compute first order indices using the Saltelli estimator +sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size) +computed_first_order = sensitivityAnalysis.getFirstOrderIndices() +computed_total_order = sensitivityAnalysis.getTotalOrderIndices() + +# %% +# Compare with exact results +print("Sample size : ", size) +# First order +# Compute absolute error (the LRE cannot be computed, +# because S can be zero) +print("Computed first order = ", computed_first_order) +print("Exact first order = ", exact_first_order) +# Total order +print("Computed total order = ", computed_total_order) +print("Exact total order = ", exact_total_order) + +# %% +_ = otv.View(sensitivityAnalysis.draw()) + +# %% +otv.View.ShowAll() diff --git a/otbenchmark/master/_downloads/5fab7882172318ff3e3ffcf48ef9eddf/plot_ishigami_sensitivity.zip b/otbenchmark/master/_downloads/5fab7882172318ff3e3ffcf48ef9eddf/plot_ishigami_sensitivity.zip new file mode 100644 index 0000000000000000000000000000000000000000..f283c455f683d714a6f32aa97e30754a31731772 GIT binary patch literal 9111 zcmd^FQES{r5O$NcP(A0N4}~r|0y=V0E^#R|V5l2ACAgs`7ea$&giczY7fZWBTG_ss zJOujO*ZzV0fIhd-Khb~Dm;Qy$thBm4Nxt}OQ+!m$=kxB&?9A-!%GyJ#xSK)YMNh8c0fhdhnV=feb7m-4|e~ zNWQ0>W`Pu$JEC$#Y2*z1R?gl~F!RxHXffqfnzIRd8`z!;F6omXi^cY7N;8%~a5(0q z3<$FKVMdf>3Jwv4#HrCz)TB6ufs{xIY!e;7iwKOrAX7!P9KbY|(XCju6CPNTW? 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[], + "source": [ + "problem = otb.GSobolSensitivity()\nprint(problem)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "distribution = problem.getInputDistribution()\nmodel = problem.getFunction()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Exact first and total order\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "exact_first_order = problem.getFirstOrderIndices()\nexact_first_order" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "exact_total_order = problem.getTotalOrderIndices()\nprint(exact_total_order)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot function\n\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create X/Y data\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ot.RandomGenerator.SetSeed(0)\nsize = 200\ninputDesign = ot.MonteCarloExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "dimension = distribution.getDimension()\nfull_sample = ot.Sample(size, 1 + dimension)\nfull_sample[:, range(dimension)] = inputDesign\nfull_sample[:, dimension] = outputDesign\nfull_description = list(inputDesign.getDescription())\nfull_description.append(outputDesign.getDescription()[0])\nfull_sample.setDescription(full_description)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "marginal_distribution = ot.ComposedDistribution(\n [\n ot.KernelSmoothing().build(full_sample.getMarginal(i))\n for i in range(1 + dimension)\n ]\n)\nclouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution)\n_ = otv.View(clouds, figure_kw={\"figsize\": (6.0, 6.0)})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create X/Y data\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ot.RandomGenerator.SetSeed(0)\nsize = 1000\ninputDesign = ot.MonteCarloExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "output_distribution = ot.KernelSmoothing().build(outputDesign)\n_ = otv.View(output_distribution.drawPDF())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Perform SA\n\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create X/Y data\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ot.RandomGenerator.SetSeed(0)\nsize = 10000\ninputDesign = ot.SobolIndicesExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute first order indices using the Saltelli estimator\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)\ncomputed_first_order = sensitivityAnalysis.getFirstOrderIndices()\ncomputed_total_order = sensitivityAnalysis.getTotalOrderIndices()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compare with exact results\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "print(\"Sample size : \", size)\n# First order\n# Compute absolute error (the LRE cannot be computed,\n# because S can be zero)\nprint(\"Computed first order = \", computed_first_order)\nprint(\"Exact first order = \", exact_first_order)\n# Total order\nprint(\"Computed total order = \", computed_total_order)\nprint(\"Exact total order = \", exact_total_order)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "_ = otv.View(sensitivityAnalysis.draw())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "otv.View.ShowAll()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + 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as ot +import otbenchmark as otb +import openturns.viewer as otv + +# %% +problem = otb.BoreholeSensitivity() +print(problem) + +# %% +distribution = problem.getInputDistribution() +model = problem.getFunction() + +# %% +# Exact first and total order +exact_first_order = problem.getFirstOrderIndices() +exact_first_order + +# %% +exact_total_order = problem.getTotalOrderIndices() +exact_total_order + +# %% +# Plot the function +# ----------------- + +# %% +# Create X/Y data +ot.RandomGenerator.SetSeed(0) +size = 200 +inputDesign = ot.MonteCarloExperiment(distribution, size).generate() +outputDesign = model(inputDesign) + +# %% +dimension = distribution.getDimension() +full_sample = ot.Sample(size, 1 + dimension) +full_sample[:, range(dimension)] = inputDesign +full_sample[:, dimension] = outputDesign +full_description = list(inputDesign.getDescription()) +full_description.append(outputDesign.getDescription()[0]) +full_sample.setDescription(full_description) + +# %% +marginal_distribution = ot.ComposedDistribution( + [ + ot.KernelSmoothing().build(full_sample.getMarginal(i)) + for i in range(1 + dimension) + ] +) +clouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution) +_ = otv.View(clouds, figure_kw={"figsize": (10.0, 10.0)}) + +# %% +output_distribution = ot.KernelSmoothing().build(outputDesign) +_ = otv.View(output_distribution.drawPDF()) + +# %% +# Perform sensitivity analysis +# ---------------------------- + +# %% +# Create X/Y data +ot.RandomGenerator.SetSeed(0) +size = 10000 +inputDesign = ot.SobolIndicesExperiment(distribution, size).generate() +outputDesign = model(inputDesign) + +# %% +# Compute first order indices using the Saltelli estimator +sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size) +computed_first_order = sensitivityAnalysis.getFirstOrderIndices() +computed_total_order = sensitivityAnalysis.getTotalOrderIndices() + +# %% +# Compare with exact results +print("Sample size : ", size) +# First order +# Compute absolute error (the LRE cannot be computed, +# because S can be zero) +print("Computed first order = ", computed_first_order) +print("Exact first order = ", exact_first_order) +# Total order +print("Computed total order = ", computed_total_order) +print("Exact total order = ", exact_total_order) + +# %% +_ = otv.View(sensitivityAnalysis.draw()) + +# %% +otv.View.ShowAll() diff --git a/otbenchmark/master/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip b/otbenchmark/master/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip index 255aeb352ed8b445d13d69c0ae32f83ac3ff3ab8..6b23ff1c558001be27495be27078da31fe192af0 100644 GIT binary patch literal 124721 zcmeHwTZ|*wd0s0k$ABy+GQ76q7>Ej-X)Jq}&F#yKuo|Qt&Ght)Mmv|*bniuIjyXm0 zkgT0z6}PI`-Q3XtUO)x{8!(U;1uzW6fLDGoj6`-|OO_4DFOKDxz!7XCP~han_JbWb z4|xoXeE)yWsX9egbDGn=J>>N1UG_Aq&i(rT|My?E-+bwlFE7x~?>%1s%1^G{+!sHx 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z87vvJ>Lw&$vbDCfAC*XaK9o3a5rWR}%SikNdI0(8qJR5APJL*Kl}?=t=lwL@qE2dri7$z&FC?{C(J%EXkvzkFCOk>jV1uPwMc;eQIo1KXgZ`R zo`eJadJFyDV-fY}u`CYDfyBmFMJgOThOMMNCXhgO=lJ%5{Ho*D^JySBbS*(Vz7axG zl)gxn%x8G)pLr3$`--A8!0=?PzrF5!>)aca{sLo^&ul)$Q%@g{1inVqs|Q}pxIn2R z>&KU;hG(SnyH+ literal 0 HcmV?d00001 diff --git a/otbenchmark/master/_downloads/9ace8485dedabebe7a1c9e1e1009c0ea/plot_gaussian_sum.py b/otbenchmark/master/_downloads/9ace8485dedabebe7a1c9e1e1009c0ea/plot_gaussian_sum.py new file mode 100644 index 00000000000..128199fbe9c --- /dev/null +++ b/otbenchmark/master/_downloads/9ace8485dedabebe7a1c9e1e1009c0ea/plot_gaussian_sum.py @@ -0,0 +1,97 @@ +""" +Benchmark the gaussian sum test function +======================================== +""" + +# %% +import openturns as ot +import otbenchmark as otb +import openturns.viewer as otv + +# %% +problem = otb.GaussianSumSensitivity() + +# %% +print(problem) + +# %% +distribution = problem.getInputDistribution() +model = problem.getFunction() + +# %% +# Exact first and total order +exact_first_order = problem.getFirstOrderIndices() +print(exact_first_order) + +# %% +exact_total_order = problem.getTotalOrderIndices() +print(exact_total_order) + +# %% +# Plot the function +# ----------------- + +# %% +# Create X/Y data +ot.RandomGenerator.SetSeed(0) +size = 200 +inputDesign = ot.MonteCarloExperiment(distribution, size).generate() +outputDesign = model(inputDesign) + +# %% +dimension = distribution.getDimension() +full_sample = ot.Sample(size, 1 + dimension) +full_sample[:, range(dimension)] = inputDesign +full_sample[:, dimension] = outputDesign +full_description = list(inputDesign.getDescription()) +full_description.append(outputDesign.getDescription()[0]) +full_sample.setDescription(full_description) + +# %% +marginal_distribution = ot.ComposedDistribution( + [ + ot.KernelSmoothing().build(full_sample.getMarginal(i)) + for i in range(1 + dimension) + ] +) +clouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution) +view = otv.View(clouds, figure_kw={"figsize": (6.0, 6.0)}) + +# %% +output_distribution = ot.KernelSmoothing().build(outputDesign) +_ = otv.View(output_distribution.drawPDF()) + +# %% +# Perform sensitivity analysis +# ---------------------------- + +# %% +# Create X/Y data +ot.RandomGenerator.SetSeed(0) +size = 10000 +inputDesign = ot.SobolIndicesExperiment(distribution, size).generate() +outputDesign = model(inputDesign) + +# %% +# Compute first order indices using the Saltelli estimator +sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size) +computed_first_order = sensitivityAnalysis.getFirstOrderIndices() +computed_total_order = sensitivityAnalysis.getTotalOrderIndices() + +# %% +# Compare with exact results +print("Sample size : ", size) +# First order +# Compute absolute error (the LRE cannot be computed, +# because S can be zero) +print("Computed first order = ", computed_first_order) +print("Exact first order = ", exact_first_order) +# Total order +print("Computed total order = ", computed_total_order) +print("Exact total order = ", exact_total_order) + +# %% +_ = otv.View(sensitivityAnalysis.draw()) + +# %% +otv.View.ShowAll() diff --git a/otbenchmark/master/_downloads/a707b852149a93fcecac6a74f1fed670/plot_morris_sensitivity.py b/otbenchmark/master/_downloads/a707b852149a93fcecac6a74f1fed670/plot_morris_sensitivity.py new file mode 100644 index 00000000000..39ba62f891b --- /dev/null +++ b/otbenchmark/master/_downloads/a707b852149a93fcecac6a74f1fed670/plot_morris_sensitivity.py @@ -0,0 +1,98 @@ +""" +Benchmark the Morris test function +================================== +""" + +# %% +import openturns as ot +import otbenchmark as otb +import openturns.viewer as otv + +# %% +problem = otb.MorrisSensitivity() +print(problem) + +# %% +distribution = problem.getInputDistribution() +model = problem.getFunction() + +# %% +# Exact first and total order +exact_first_order = problem.getFirstOrderIndices() +exact_first_order + +# %% +exact_total_order = problem.getTotalOrderIndices() +exact_total_order + +# %% +# Plot the function +# ----------------- + +# %% +# Create X/Y data +ot.RandomGenerator.SetSeed(0) +size = 200 +inputDesign = ot.MonteCarloExperiment(distribution, size).generate() +outputDesign = model(inputDesign) + +# %% +dimension = distribution.getDimension() +nbcolumns = 4 +nbrows = int(dimension / nbcolumns) +grid = ot.GridLayout(nbrows, nbcolumns) +inputDescription = distribution.getDescription() +outputDescription = model.getOutputDescription()[0] +index = 0 +for i in range(nbrows): + for j in range(nbcolumns): + graph = ot.Graph( + "n=%d" % (size), inputDescription[index], outputDescription, True, "" + ) + sample = ot.Sample(size, 2) + sample[:, 0] = inputDesign[:, index] + sample[:, 1] = outputDesign[:, 0] + cloud = ot.Cloud(sample) + graph.add(cloud) + grid.setGraph(i, j, graph) + index += 1 +_ = otv.View(grid, figure_kw={"figsize": (10.0, 10.0)}) + +# % +output_distribution = ot.KernelSmoothing().build(outputDesign) +_ = otv.View(output_distribution.drawPDF()) + +# %% +# Perform sensitivity analysis +# ---------------------------- + +# %% +# Create X/Y data +ot.RandomGenerator.SetSeed(0) +size = 30 +inputDesign = ot.SobolIndicesExperiment(distribution, size).generate() +outputDesign = model(inputDesign) + +# %% +# Compute first order indices using the Saltelli estimator +sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size) +computed_first_order = sensitivityAnalysis.getFirstOrderIndices() +computed_total_order = sensitivityAnalysis.getTotalOrderIndices() + +# %% +# Compare with exact results +print("Sample size : ", size) +# First order +# Compute absolute error (the LRE cannot be computed, +# because S can be zero) +print("Computed first order = ", computed_first_order) +print("Exact first order = ", exact_first_order) +# Total order +print("Computed total order = ", computed_total_order) +print("Exact total order = ", exact_total_order) + +# %% +_ = otv.View(sensitivityAnalysis.draw()) + +# %% +otv.View.ShowAll() diff --git a/otbenchmark/master/_downloads/aa932f5940d0a0978383c5019f1a7018/plot_gaussian_sum.ipynb b/otbenchmark/master/_downloads/aa932f5940d0a0978383c5019f1a7018/plot_gaussian_sum.ipynb new file mode 100644 index 00000000000..09c06722cd3 --- /dev/null +++ b/otbenchmark/master/_downloads/aa932f5940d0a0978383c5019f1a7018/plot_gaussian_sum.ipynb @@ -0,0 +1,246 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Benchmark the gaussian sum test function\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import openturns as ot\nimport otbenchmark as otb\nimport openturns.viewer as otv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "problem = otb.GaussianSumSensitivity()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "print(problem)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "distribution = problem.getInputDistribution()\nmodel = problem.getFunction()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Exact first and total order\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "exact_first_order = problem.getFirstOrderIndices()\nprint(exact_first_order)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "exact_total_order = problem.getTotalOrderIndices()\nprint(exact_total_order)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot the function\n\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create X/Y data\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ot.RandomGenerator.SetSeed(0)\nsize = 200\ninputDesign = ot.MonteCarloExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "dimension = distribution.getDimension()\nfull_sample = ot.Sample(size, 1 + dimension)\nfull_sample[:, range(dimension)] = inputDesign\nfull_sample[:, dimension] = outputDesign\nfull_description = list(inputDesign.getDescription())\nfull_description.append(outputDesign.getDescription()[0])\nfull_sample.setDescription(full_description)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "marginal_distribution = ot.ComposedDistribution(\n [\n ot.KernelSmoothing().build(full_sample.getMarginal(i))\n for i in range(1 + dimension)\n ]\n)\nclouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution)\nview = otv.View(clouds, figure_kw={\"figsize\": (6.0, 6.0)})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "output_distribution = ot.KernelSmoothing().build(outputDesign)\n_ = otv.View(output_distribution.drawPDF())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Perform sensitivity analysis\n\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create X/Y data\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ot.RandomGenerator.SetSeed(0)\nsize = 10000\ninputDesign = ot.SobolIndicesExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute first order indices using the Saltelli estimator\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)\ncomputed_first_order = sensitivityAnalysis.getFirstOrderIndices()\ncomputed_total_order = sensitivityAnalysis.getTotalOrderIndices()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compare with exact results\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "print(\"Sample size : \", size)\n# First order\n# Compute absolute error (the LRE cannot be computed,\n# because S can be zero)\nprint(\"Computed first order = \", computed_first_order)\nprint(\"Exact first order = \", exact_first_order)\n# Total order\nprint(\"Computed total order = \", computed_total_order)\nprint(\"Exact total order = \", exact_total_order)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "_ = otv.View(sensitivityAnalysis.draw())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "otv.View.ShowAll()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.20" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/otbenchmark/master/_downloads/ac791b18603462477ad30d69d47bc91f/plot_nloscillator_sensitivity.py b/otbenchmark/master/_downloads/ac791b18603462477ad30d69d47bc91f/plot_nloscillator_sensitivity.py new file mode 100644 index 00000000000..a9f04fd1256 --- /dev/null +++ b/otbenchmark/master/_downloads/ac791b18603462477ad30d69d47bc91f/plot_nloscillator_sensitivity.py @@ -0,0 +1,95 @@ +""" +Benchmark the NLOscillator test function +======================================== +""" + +# %% +import openturns as ot +import otbenchmark as otb +import openturns.viewer as otv + +# %% +problem = otb.NLOscillatorSensitivity() +print(problem) + +# %% +distribution = problem.getInputDistribution() +model = problem.getFunction() + +# %% +# Exact first and total order +exact_first_order = problem.getFirstOrderIndices() +exact_first_order + +# %% +exact_total_order = problem.getTotalOrderIndices() +exact_total_order + +# %% +# Plot the function +# ----------------- + +# %% +# Create X/Y data +ot.RandomGenerator.SetSeed(0) +size = 200 +inputDesign = ot.MonteCarloExperiment(distribution, size).generate() +outputDesign = model(inputDesign) + +# %% +dimension = distribution.getDimension() +full_sample = ot.Sample(size, 1 + dimension) +full_sample[:, range(dimension)] = inputDesign +full_sample[:, dimension] = outputDesign +full_description = list(inputDesign.getDescription()) +full_description.append(outputDesign.getDescription()[0]) +full_sample.setDescription(full_description) + +# %% +marginal_distribution = ot.ComposedDistribution( + [ + ot.KernelSmoothing().build(full_sample.getMarginal(i)) + for i in range(1 + dimension) + ] +) +clouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution) +_ = otv.View(clouds, figure_kw={"figsize": (10.0, 10.0)}) + +# %% +output_distribution = ot.KernelSmoothing().build(outputDesign) +_ = otv.View(output_distribution.drawPDF()) + +# %% +# Perform sensitivity analysis +# ---------------------------- + +# %% +# Create X/Y data +ot.RandomGenerator.SetSeed(0) +size = 10000 +inputDesign = ot.SobolIndicesExperiment(distribution, size).generate() +outputDesign = model(inputDesign) + +# %% +# Compute first order indices using the Saltelli estimator +sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size) +computed_first_order = sensitivityAnalysis.getFirstOrderIndices() +computed_total_order = sensitivityAnalysis.getTotalOrderIndices() + +# %% +# Compare with exact results +print("Sample size : ", size) +# First order +# Compute absolute error (the LRE cannot be computed, +# because S can be zero) +print("Computed first order = ", computed_first_order) +print("Exact first order = ", exact_first_order) +# Total order +print("Computed total order = ", computed_total_order) +print("Exact total order = ", exact_total_order) + +# %% +_ = otv.View(sensitivityAnalysis.draw()) + +# %% +otv.View.ShowAll() diff --git a/otbenchmark/master/_downloads/af08bf3d5aa390154f02f9673b9ee5cb/plot_ishigami_sensitivity.ipynb b/otbenchmark/master/_downloads/af08bf3d5aa390154f02f9673b9ee5cb/plot_ishigami_sensitivity.ipynb new file mode 100644 index 00000000000..d518d6d7b5c --- /dev/null +++ b/otbenchmark/master/_downloads/af08bf3d5aa390154f02f9673b9ee5cb/plot_ishigami_sensitivity.ipynb @@ -0,0 +1,246 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Benchmark the Ishigami test function\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import openturns as ot\nimport otbenchmark as otb\nimport openturns.viewer as otv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "problem = otb.IshigamiSensitivity()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "print(problem)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "distribution = problem.getInputDistribution()\nmodel = problem.getFunction()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Exact first and total order\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "exact_first_order = problem.getFirstOrderIndices()\nexact_first_order" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "exact_total_order = problem.getTotalOrderIndices()\nexact_total_order" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot the function\n\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create X/Y data\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ot.RandomGenerator.SetSeed(0)\nsize = 200\ninputDesign = ot.MonteCarloExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "dimension = distribution.getDimension()\nfull_sample = ot.Sample(size, 1 + dimension)\nfull_sample[:, range(dimension)] = inputDesign\nfull_sample[:, dimension] = outputDesign\nfull_description = list(inputDesign.getDescription())\nfull_description.append(outputDesign.getDescription()[0])\nfull_sample.setDescription(full_description)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "marginal_distribution = ot.ComposedDistribution(\n [\n ot.KernelSmoothing().build(full_sample.getMarginal(i))\n for i in range(1 + dimension)\n ]\n)\nclouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution)\n_ = otv.View(clouds, figure_kw={\"figsize\": (6.0, 6.0)})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "output_distribution = ot.KernelSmoothing().build(outputDesign)\n_ = otv.View(output_distribution.drawPDF())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Perform sensitivity analysis\n\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create X/Y data\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ot.RandomGenerator.SetSeed(0)\nsize = 10000\ninputDesign = ot.SobolIndicesExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute first order indices using the Saltelli estimator\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)\ncomputed_first_order = sensitivityAnalysis.getFirstOrderIndices()\ncomputed_total_order = sensitivityAnalysis.getTotalOrderIndices()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compare with exact results\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "print(\"Sample size : \", size)\n# First order\n# Compute absolute error (the LRE cannot be computed,\n# because S can be zero)\nprint(\"Computed first order = \", computed_first_order)\nprint(\"Exact first order = \", exact_first_order)\n# Total order\nprint(\"Computed total order = \", computed_total_order)\nprint(\"Exact total order = \", exact_total_order)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "_ = otv.View(sensitivityAnalysis.draw())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "otv.View.ShowAll()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.20" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/otbenchmark/master/_downloads/be33b4907cde40d05f3bc08b57b462fc/plot_gsobol_sensitivity.py b/otbenchmark/master/_downloads/be33b4907cde40d05f3bc08b57b462fc/plot_gsobol_sensitivity.py new file mode 100644 index 00000000000..a2fccfcfe79 --- /dev/null +++ b/otbenchmark/master/_downloads/be33b4907cde40d05f3bc08b57b462fc/plot_gsobol_sensitivity.py @@ -0,0 +1,103 @@ +""" +Benchmark the G-Sobol test function +=================================== +""" + +# %% +import openturns as ot +import otbenchmark as otb +import openturns.viewer as otv + +# %% +problem = otb.GSobolSensitivity() +print(problem) + +# %% +distribution = problem.getInputDistribution() +model = problem.getFunction() + +# %% +# Exact first and total order +exact_first_order = problem.getFirstOrderIndices() +exact_first_order + +# %% +exact_total_order = problem.getTotalOrderIndices() +print(exact_total_order) + +# %% +# Plot function +# ------------- + +# %% +# Create X/Y data +ot.RandomGenerator.SetSeed(0) +size = 200 +inputDesign = ot.MonteCarloExperiment(distribution, size).generate() +outputDesign = model(inputDesign) + +# %% +dimension = distribution.getDimension() +full_sample = ot.Sample(size, 1 + dimension) +full_sample[:, range(dimension)] = inputDesign +full_sample[:, dimension] = outputDesign +full_description = list(inputDesign.getDescription()) +full_description.append(outputDesign.getDescription()[0]) +full_sample.setDescription(full_description) + +# %% +marginal_distribution = ot.ComposedDistribution( + [ + ot.KernelSmoothing().build(full_sample.getMarginal(i)) + for i in range(1 + dimension) + ] +) +clouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution) +_ = otv.View(clouds, figure_kw={"figsize": (6.0, 6.0)}) + + +# %% +# Create X/Y data +ot.RandomGenerator.SetSeed(0) +size = 1000 +inputDesign = ot.MonteCarloExperiment(distribution, size).generate() +outputDesign = model(inputDesign) + +# %% +output_distribution = ot.KernelSmoothing().build(outputDesign) +_ = otv.View(output_distribution.drawPDF()) + +# %% +# Perform SA +# ---------- + +# %% +# Create X/Y data +ot.RandomGenerator.SetSeed(0) +size = 10000 +inputDesign = ot.SobolIndicesExperiment(distribution, size).generate() +outputDesign = model(inputDesign) + +# %% +# Compute first order indices using the Saltelli estimator +sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size) +computed_first_order = sensitivityAnalysis.getFirstOrderIndices() +computed_total_order = sensitivityAnalysis.getTotalOrderIndices() + +# %% +# Compare with exact results +print("Sample size : ", size) +# First order +# Compute absolute error (the LRE cannot be computed, +# because S can be zero) +print("Computed first order = ", computed_first_order) +print("Exact first order = ", exact_first_order) +# Total order +print("Computed total order = ", computed_total_order) +print("Exact total order = ", exact_total_order) + +# %% +_ = otv.View(sensitivityAnalysis.draw()) + +# %% +otv.View.ShowAll() diff --git a/otbenchmark/master/_downloads/c1babdce08a3967dd83b589429d3cfb4/plot_dirichlet_sensitivity.py b/otbenchmark/master/_downloads/c1babdce08a3967dd83b589429d3cfb4/plot_dirichlet_sensitivity.py new file mode 100644 index 00000000000..735ea0d657e --- /dev/null +++ b/otbenchmark/master/_downloads/c1babdce08a3967dd83b589429d3cfb4/plot_dirichlet_sensitivity.py @@ -0,0 +1,95 @@ +""" +Benchmark the Dirichlet test function +===================================== +""" + +# %% +import openturns as ot +import otbenchmark as otb +import openturns.viewer as otv + +# %% +problem = otb.DirichletSensitivity() +print(problem) + +# %% +distribution = problem.getInputDistribution() +model = problem.getFunction() + +# %% +# Exact first and total order +exact_first_order = problem.getFirstOrderIndices() +exact_first_order + +# %% +exact_total_order = problem.getTotalOrderIndices() +exact_total_order + +# %% +# Plot the function +# ----------------- + +# %% +# Create X/Y data +ot.RandomGenerator.SetSeed(0) +size = 200 +inputDesign = ot.MonteCarloExperiment(distribution, size).generate() +outputDesign = model(inputDesign) + +# %% +dimension = distribution.getDimension() +full_sample = ot.Sample(size, 1 + dimension) +full_sample[:, range(dimension)] = inputDesign +full_sample[:, dimension] = outputDesign +full_description = list(inputDesign.getDescription()) +full_description.append(outputDesign.getDescription()[0]) +full_sample.setDescription(full_description) + +# %% +marginal_distribution = ot.ComposedDistribution( + [ + ot.KernelSmoothing().build(full_sample.getMarginal(i)) + for i in range(1 + dimension) + ] +) +clouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution) +_ = otv.View(clouds, figure_kw={"figsize": (6.0, 6.0)}) + +# %% +output_distribution = ot.KernelSmoothing().build(outputDesign) +_ = otv.View(output_distribution.drawPDF()) + +# %% +# Perform sensitivity analysis +# ---------------------------- + +# %% +# Create X/Y data +ot.RandomGenerator.SetSeed(0) +size = 10000 +inputDesign = ot.SobolIndicesExperiment(distribution, size).generate() +outputDesign = model(inputDesign) + +# %% +# Compute first order indices using the Saltelli estimator +sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size) +computed_first_order = sensitivityAnalysis.getFirstOrderIndices() +computed_total_order = sensitivityAnalysis.getTotalOrderIndices() + +# %% +# Compare with exact results +print("Sample size : ", size) +# First order +# Compute absolute error (the LRE cannot be computed, +# because S can be zero) +print("Computed first order = ", computed_first_order) +print("Exact first order = ", exact_first_order) +# Total order +print("Computed total order = ", computed_total_order) +print("Exact total order = ", exact_total_order) + +# %% +_ = otv.View(sensitivityAnalysis.draw()) + +# %% +otv.View.ShowAll() diff --git a/otbenchmark/master/_downloads/d005867b51360ee9c2c4914f27adb11b/plot_morris_sensitivity.ipynb b/otbenchmark/master/_downloads/d005867b51360ee9c2c4914f27adb11b/plot_morris_sensitivity.ipynb new file mode 100644 index 00000000000..018ca455715 --- /dev/null +++ b/otbenchmark/master/_downloads/d005867b51360ee9c2c4914f27adb11b/plot_morris_sensitivity.ipynb @@ -0,0 +1,213 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Benchmark the Morris test function\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import openturns as ot\nimport otbenchmark as otb\nimport openturns.viewer as otv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "problem = otb.MorrisSensitivity()\nprint(problem)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "distribution = problem.getInputDistribution()\nmodel = problem.getFunction()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Exact first and total order\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "exact_first_order = problem.getFirstOrderIndices()\nexact_first_order" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "exact_total_order = problem.getTotalOrderIndices()\nexact_total_order" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot the function\n\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create X/Y data\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ot.RandomGenerator.SetSeed(0)\nsize = 200\ninputDesign = ot.MonteCarloExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "dimension = distribution.getDimension()\nnbcolumns = 4\nnbrows = int(dimension / nbcolumns)\ngrid = ot.GridLayout(nbrows, nbcolumns)\ninputDescription = distribution.getDescription()\noutputDescription = model.getOutputDescription()[0]\nindex = 0\nfor i in range(nbrows):\n for j in range(nbcolumns):\n graph = ot.Graph(\n \"n=%d\" % (size), inputDescription[index], outputDescription, True, \"\"\n )\n sample = ot.Sample(size, 2)\n sample[:, 0] = inputDesign[:, index]\n sample[:, 1] = outputDesign[:, 0]\n cloud = ot.Cloud(sample)\n graph.add(cloud)\n grid.setGraph(i, j, graph)\n index += 1\n_ = otv.View(grid, figure_kw={\"figsize\": (10.0, 10.0)})\n\n# %\noutput_distribution = ot.KernelSmoothing().build(outputDesign)\n_ = otv.View(output_distribution.drawPDF())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Perform sensitivity analysis\n\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create X/Y data\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ot.RandomGenerator.SetSeed(0)\nsize = 30\ninputDesign = ot.SobolIndicesExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute first order indices using the Saltelli estimator\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)\ncomputed_first_order = sensitivityAnalysis.getFirstOrderIndices()\ncomputed_total_order = sensitivityAnalysis.getTotalOrderIndices()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compare with exact results\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "print(\"Sample size : \", size)\n# First order\n# Compute absolute error (the LRE cannot be computed,\n# because S can be zero)\nprint(\"Computed first order = \", computed_first_order)\nprint(\"Exact first order = \", exact_first_order)\n# Total order\nprint(\"Computed total order = \", computed_total_order)\nprint(\"Exact total order = \", exact_total_order)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "_ = otv.View(sensitivityAnalysis.draw())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "otv.View.ShowAll()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.20" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/otbenchmark/master/_downloads/d0644c1b59c97e301533d2cc27d6f994/plot_borehole_sensitivity.ipynb b/otbenchmark/master/_downloads/d0644c1b59c97e301533d2cc27d6f994/plot_borehole_sensitivity.ipynb new file mode 100644 index 00000000000..7067cfe78af --- /dev/null +++ b/otbenchmark/master/_downloads/d0644c1b59c97e301533d2cc27d6f994/plot_borehole_sensitivity.ipynb @@ -0,0 +1,235 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Benchmark the Borehole test function\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import openturns as ot\nimport otbenchmark as otb\nimport openturns.viewer as otv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "problem = otb.BoreholeSensitivity()\nprint(problem)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "distribution = problem.getInputDistribution()\nmodel = problem.getFunction()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Exact first and total order\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "exact_first_order = problem.getFirstOrderIndices()\nexact_first_order" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "exact_total_order = problem.getTotalOrderIndices()\nexact_total_order" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot the function\n\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create X/Y data\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ot.RandomGenerator.SetSeed(0)\nsize = 200\ninputDesign = ot.MonteCarloExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "dimension = distribution.getDimension()\nfull_sample = ot.Sample(size, 1 + dimension)\nfull_sample[:, range(dimension)] = inputDesign\nfull_sample[:, dimension] = outputDesign\nfull_description = list(inputDesign.getDescription())\nfull_description.append(outputDesign.getDescription()[0])\nfull_sample.setDescription(full_description)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "marginal_distribution = ot.ComposedDistribution(\n [\n ot.KernelSmoothing().build(full_sample.getMarginal(i))\n for i in range(1 + dimension)\n ]\n)\nclouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution)\n_ = otv.View(clouds, figure_kw={\"figsize\": (10.0, 10.0)})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "output_distribution = ot.KernelSmoothing().build(outputDesign)\n_ = otv.View(output_distribution.drawPDF())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Perform sensitivity analysis\n\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create X/Y data\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ot.RandomGenerator.SetSeed(0)\nsize = 10000\ninputDesign = ot.SobolIndicesExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute first order indices using the Saltelli estimator\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)\ncomputed_first_order = sensitivityAnalysis.getFirstOrderIndices()\ncomputed_total_order = sensitivityAnalysis.getTotalOrderIndices()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compare with exact results\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "print(\"Sample size : \", size)\n# First order\n# Compute absolute error (the LRE cannot be computed,\n# because S can be zero)\nprint(\"Computed first order = \", computed_first_order)\nprint(\"Exact first order = \", exact_first_order)\n# Total order\nprint(\"Computed total order = \", computed_total_order)\nprint(\"Exact total order = \", exact_total_order)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "_ = otv.View(sensitivityAnalysis.draw())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "otv.View.ShowAll()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + 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100644 GIT binary patch delta 43 vcmX?ScFv3^z?+#xgaHJ$S8wD|XJ^_`z1fsKLzroM_2l_d0YL8Lk5aJ!2R#jk delta 43 vcmX?ScFv3^z?+#xgaHIj6m8^DXJa33@Bwls3kXI604miEfdBvi delta 43 wcmaE<^iqi@z?+#xgaHJO6>a3X%EEN2X!B#1yWC92iY6Zt@Bwls3kXI604&uGod5s; diff --git a/otbenchmark/master/_downloads/e830ec759701c1a03cbee2f23827aba1/plot_borgonovo_sensitivity.py b/otbenchmark/master/_downloads/e830ec759701c1a03cbee2f23827aba1/plot_borgonovo_sensitivity.py new file mode 100644 index 00000000000..bc822331e17 --- /dev/null +++ b/otbenchmark/master/_downloads/e830ec759701c1a03cbee2f23827aba1/plot_borgonovo_sensitivity.py @@ -0,0 +1,95 @@ +""" +Benchmark the Borgonovo test function +===================================== +""" + +# %% +import openturns as ot +import otbenchmark as otb +import openturns.viewer as otv + +# %% +problem = otb.BorgonovoSensitivity() +print(problem) + +# %% +distribution = problem.getInputDistribution() +model = problem.getFunction() + +# %% +# Exact first and total order +exact_first_order = problem.getFirstOrderIndices() +exact_first_order + +# %% +exact_total_order = problem.getTotalOrderIndices() +exact_total_order + +# %% +# Plot the function +# ----------------- + +# %% +# Create X/Y data +ot.RandomGenerator.SetSeed(0) +size = 200 +inputDesign = ot.MonteCarloExperiment(distribution, size).generate() +outputDesign = model(inputDesign) + +# %% +dimension = distribution.getDimension() +full_sample = ot.Sample(size, 1 + dimension) +full_sample[:, range(dimension)] = inputDesign +full_sample[:, dimension] = outputDesign +full_description = list(inputDesign.getDescription()) +full_description.append(outputDesign.getDescription()[0]) +full_sample.setDescription(full_description) + +# %% +marginal_distribution = ot.ComposedDistribution( + [ + ot.KernelSmoothing().build(full_sample.getMarginal(i)) + for i in range(1 + dimension) + ] +) +clouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution) +_ = otv.View(clouds, figure_kw={"figsize": (6.0, 6.0)}) + +# %% +output_distribution = ot.KernelSmoothing().build(outputDesign) +_ = otv.View(output_distribution.drawPDF()) + +# %% +# Perform sensitivity analysis +# ---------------------------- + +# %% +# Create X/Y data +ot.RandomGenerator.SetSeed(0) +size = 10000 +inputDesign = ot.SobolIndicesExperiment(distribution, size).generate() +outputDesign = model(inputDesign) + +# %% +# Compute first order indices using the Saltelli estimator +sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size) +computed_first_order = sensitivityAnalysis.getFirstOrderIndices() +computed_total_order = sensitivityAnalysis.getTotalOrderIndices() + +# %% +# Compare with exact results +print("Sample size : ", size) +# First order +# Compute absolute error (the LRE cannot be computed, +# because S can be zero) +print("Computed first order = ", computed_first_order) +print("Exact first order = ", exact_first_order) +# Total order +print("Computed total order = ", computed_total_order) +print("Exact total order = ", exact_total_order) + +# %% +_ = otv.View(sensitivityAnalysis.draw()) + +# %% +otv.View.ShowAll() diff --git a/otbenchmark/master/_downloads/e9685936f0e9649b1b8ff6dcb9f0fc2b/plot_oakleyohagan_sensitivity.zip b/otbenchmark/master/_downloads/e9685936f0e9649b1b8ff6dcb9f0fc2b/plot_oakleyohagan_sensitivity.zip new file mode 100644 index 0000000000000000000000000000000000000000..9473eb25f68282c66c354e8fb97616f34af1a42c GIT binary patch literal 8816 zcmd@aO>f&aG;Oy5)49N|oQz^J=DKdP9u~j@x-{*IbVyJa!-}dfWQ%dsN~AzhN!*}^ zo_F7Mm;QmCdl~j;cHCdsd!#7QlI+G!F*KNhBr@OQ`{Lt!e2?DWz4F=B4SfDQ-F$NC zufP4bUv6yRFM+W%=5ow~7crgl!(bM$aYk7d$>=DO^L{#aUDx@Mvgu(Gh!;d2QnIIz zukU^H6QUDIGf56|HkA=)&hX5SgOr^PxpvKol9UTccuJYf1h?w*W>~1kRj4~;rNsdYZg#y~2 zQTc$Sxx8zEfkeVX8ZV;VD=Y#~tz?JnoCH%aRV2V*0Sk%bGKdKmAr%fq*jT~FYNSRD zC+#8T0~SV8nw4@lCK`Skpm?-Ve2mOjP_!ljMNpyBA^YGf$_2{hHmYq48c0fhz43&E zfeaij`;S0Hp4_L5ia>JFA5l4?H1q~tCyQQE(C^k@;6%!rG>c|vW|;OfXOeCQBIY|M 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openturns.viewer as otv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "problem = otb.OakleyOHaganSensitivity()\nprint(problem)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "distribution = problem.getInputDistribution()\nmodel = problem.getFunction()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Exact first and total order\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "exact_first_order = problem.getFirstOrderIndices()\nprint(exact_first_order)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "exact_total_order = problem.getTotalOrderIndices()\nprint(exact_total_order)\n\n\n# ## Plot the function" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create X/Y data\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ot.RandomGenerator.SetSeed(0)\nsize = 200\ninputDesign = ot.MonteCarloExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "dimension = distribution.getDimension()\nnbcolumns = 5\nnbrows = int(dimension / nbcolumns)\ngrid = ot.GridLayout(nbrows, nbcolumns)\ninputDescription = distribution.getDescription()\noutputDescription = model.getOutputDescription()[0]\nindex = 0\nfor i in range(nbrows):\n for j in range(nbcolumns):\n graph = ot.Graph(\n \"n=%d\" % (size), inputDescription[index], outputDescription, True, \"\"\n )\n sample = ot.Sample(size, 2)\n sample[:, 0] = inputDesign[:, index]\n sample[:, 1] = outputDesign[:, 0]\n cloud = ot.Cloud(sample)\n graph.add(cloud)\n grid.setGraph(i, j, graph)\n index += 1\n_ = otv.View(grid, figure_kw={\"figsize\": (10.0, 10.0)})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "output_distribution = ot.KernelSmoothing().build(outputDesign)\n_ = otv.View(output_distribution.drawPDF())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Perform sensitivity analysis\n\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create X/Y data\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ot.RandomGenerator.SetSeed(0)\nsize = 1000\ninputDesign = ot.SobolIndicesExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute first order indices using the Saltelli estimator\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)\ncomputed_first_order = sensitivityAnalysis.getFirstOrderIndices()\ncomputed_total_order = sensitivityAnalysis.getTotalOrderIndices()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compare with exact results\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "print(\"Sample size : \", size)\n# First order\n# Compute absolute error (the LRE cannot be computed,\n# because S can be zero)\nprint(\"Computed first order = \", computed_first_order)\nprint(\"Exact first order = \", exact_first_order)\n# Total order\nprint(\"Computed total order = \", computed_total_order)\nprint(\"Exact total order = \", exact_total_order)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "_ = otv.View(sensitivityAnalysis.draw())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "otv.View.ShowAll()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.20" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/otbenchmark/master/_downloads/fb9f49f74b26df53e1cc530460c44c35/plot_CrossCutFunction_Demo.zip 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zPt@i?NivNzF{YtJW2w1rnzMzFw!0_T3`s~-K}U^taUr<~tf7%k{>h|o(gX`0kC710 z7;ZwtB$VwlS*)|h(z)sQ1v7BCvFoT=FYg`|>Kf9ivyl-IP#irQnZ$A__HoWf=W@TN zIsoa*h9dY@N!}*rY{T1V%IH+8`3XX%nZM>!l$I?;>QzTKO>@2e_n`XO{D=>w;2eB42t49_n+%ACpqHqi4(!NsD1roK zUs*@KZ0SfQy$UE^eM`%DcQ!!I$Y$r}o@^b?_8s?9U8)2K?L-dvY0P5ARuyuf+;?yi zOf#_C+QWUa5|u!i{Sl`b=M+0jDu2eoo z2>J-NiFwUfV?c6aU)Eu|eY^FRi-Z;l%S_(*csM3l;su%b3NZjVJ54Pt7>28TkR|2x z++B0?x6@ldKZR zTHr6k)XC|tQoSsVbZ45%S6Hj9BauEFte;(jX;fTUndwl3787CZ>G(QG7KmLXTmi!N zM|pc=$>G~VYA0WDcZN7%$@g{olRVR&bblzKBIIq|ljkzv#2H4zqj5kMx*=pD#2FYF zB}2^uPY@xY$NZz!wTGL9>n`32i(}T!i@s#o82JIWk|&KZo)Nk+e9r45LI9&)>y_Rn zkL|!QCb(=t2o0!ow`J<131($t=bbP;u*D?LKJ2G$xd?BSzqkOG>wRw%flB0dN`|ZP zS;HjQ%cz1MHFzdFcG&LFd3vVO$TP?AXV-;-b*a?4x%b-$(DTb*>@Km_ZuQKT_1=Ar z@#(^1Rro$SpAymRYhEe~_}y?7nJQpzm3$ +.. raw:: html + +

+ +.. only:: html + + .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_flood_sensitivity_thumb.png + :alt: + + :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_flood_sensitivity.py` + +.. raw:: html + +
Benchmark the Flooding test function
+
+ + +.. raw:: html + +
+ +.. only:: html + + .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_borgonovo_sensitivity_thumb.png + :alt: + + :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_borgonovo_sensitivity.py` + +.. raw:: html + +
Benchmark the Borgonovo test function
+
+ + +.. raw:: html + +
+ +.. only:: html + + .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_ishigami_sensitivity_thumb.png + :alt: + + :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_ishigami_sensitivity.py` + +.. raw:: html + +
Benchmark the Ishigami test function
+
+ + +.. raw:: html + +
+ +.. only:: html + + .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_dirichlet_sensitivity_thumb.png + :alt: + + :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_dirichlet_sensitivity.py` + +.. raw:: html + +
Benchmark the Dirichlet test function
+
+ + +.. raw:: html + +
+ +.. only:: html + + .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_borehole_sensitivity_thumb.png + :alt: + + :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_borehole_sensitivity.py` + +.. raw:: html + +
Benchmark the Borehole test function
+
+ + +.. raw:: html + +
+ +.. only:: html + + .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_nloscillator_sensitivity_thumb.png + :alt: + + :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_nloscillator_sensitivity.py` + +.. raw:: html + +
Benchmark the NLOscillator test function
+
+ + +.. raw:: html + +
+ +.. only:: html + + .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_gaussian_sum_thumb.png + :alt: + + :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_gaussian_sum.py` + +.. raw:: html + +
Benchmark the gaussian sum test function
+
+ + +.. raw:: html + +
+ +.. only:: html + + .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_gaussian_product_sensitivity_thumb.png + :alt: + + :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_gaussian_product_sensitivity.py` + +.. raw:: html + +
Benchmark the gaussian product test function
+
+ + +.. raw:: html + +
+ +.. only:: html + + .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_morris_sensitivity_thumb.png + :alt: + + :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_morris_sensitivity.py` + +.. raw:: html + +
Benchmark the Morris test function
+
+ + +.. raw:: html + +
+ +.. only:: html + + .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_oakleyohagan_sensitivity_thumb.png + :alt: + + :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_oakleyohagan_sensitivity.py` + +.. raw:: html + +
Benchmark the Oakley-O'Hagan test function
+
+ + +.. raw:: html + +
+ +.. only:: html + + .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_gsobol_sensitivity_thumb.png + :alt: + + :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_gsobol_sensitivity.py` + +.. raw:: html + +
Benchmark the G-Sobol test function
+
+ + .. raw:: html
@@ -174,6 +361,23 @@ Sensitivity examples
+.. raw:: html + +
+ +.. only:: html + + .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_benchmark_sensitivity_methods_thumb.png + :alt: + + :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_benchmark_sensitivity_methods.py` + +.. raw:: html + +
Benchmark sensitivity analysis methods
+
+ + .. thumbnail-parent-div-close .. raw:: html diff --git a/otbenchmark/master/_sources/auto_examples/plot_ConditionalDistribution_Demo.rst b/otbenchmark/master/_sources/auto_examples/plot_ConditionalDistribution_Demo.rst index e476fa9d04f..b2b4ef117d6 100644 --- a/otbenchmark/master/_sources/auto_examples/plot_ConditionalDistribution_Demo.rst +++ b/otbenchmark/master/_sources/auto_examples/plot_ConditionalDistribution_Demo.rst @@ -242,7 +242,7 @@ Create a Punk distribution .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 4.619 seconds) + **Total running time of the script:** (0 minutes 2.577 seconds) .. _sphx_glr_download_auto_examples_plot_ConditionalDistribution_Demo.py: diff --git a/otbenchmark/master/_sources/auto_examples/plot_CrossCutFunction_Demo.rst b/otbenchmark/master/_sources/auto_examples/plot_CrossCutFunction_Demo.rst index 9f5655cf089..a68a90524c7 100644 --- a/otbenchmark/master/_sources/auto_examples/plot_CrossCutFunction_Demo.rst +++ b/otbenchmark/master/_sources/auto_examples/plot_CrossCutFunction_Demo.rst @@ -213,7 +213,7 @@ Let :math:`\bar{x}\in\mathbb{R}^3` be the reference point. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 0.738 seconds) + **Total running time of the script:** (0 minutes 1.229 seconds) .. _sphx_glr_download_auto_examples_plot_CrossCutFunction_Demo.py: diff --git a/otbenchmark/master/_sources/auto_examples/plot_convergence_reliability_mc.rst b/otbenchmark/master/_sources/auto_examples/plot_convergence_reliability_mc.rst index b5c111bf60e..15d485a8174 100644 --- a/otbenchmark/master/_sources/auto_examples/plot_convergence_reliability_mc.rst +++ b/otbenchmark/master/_sources/auto_examples/plot_convergence_reliability_mc.rst @@ -122,7 +122,7 @@ where :math:`n` is the sample size. .. code-block:: none - Number of function calls = 1842 , Pf = 0.0765 , 95.0 % confidence interval :[0.0616,0.0915] + Number of function calls = 1978 , Pf = 0.0713 , 95.0 % confidence interval :[0.0573,0.0852] @@ -186,153 +186,153 @@ where :math:`n` is the sample size. Number of function calls = 4 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000] Number of function calls = 4 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000] Number of function calls = 4 , Pf = 0.2500 , 95.0 % confidence interval :[-0.2978,0.7978] - Number of function calls = 4 , Pf = 0.2500 , 95.0 % confidence interval :[-0.2978,0.7978] + Number of function calls = 4 , Pf = 0.5000 , 95.0 % confidence interval :[0.0100,0.9900] + Number of function calls = 4 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000] + Number of function calls = 4 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000] Number of function calls = 4 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000] - Number of function calls = 4 , Pf = 0.2500 , 95.0 % confidence interval :[-0.2978,0.7978] Number of function calls = 4 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000] Number of function calls = 4 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000] Number of function calls = 4 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000] - Number of function calls = 4 , Pf = 0.2500 , 95.0 % confidence interval :[-0.2978,0.7978] - Number of function calls = 8 , Pf = 0.1250 , 95.0 % confidence interval :[-0.1623,0.4123] - Number of function calls = 8 , Pf = 0.1250 , 95.0 % confidence interval :[-0.1623,0.4123] - Number of function calls = 8 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000] Number of function calls = 8 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000] Number of function calls = 8 , Pf = 0.1250 , 95.0 % confidence interval :[-0.1623,0.4123] + Number of function calls = 8 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000] Number of function calls = 8 , Pf = 0.1250 , 95.0 % confidence interval :[-0.1623,0.4123] - Number of function calls = 8 , Pf = 0.1250 , 95.0 % confidence interval :[-0.1623,0.4123] - Number of function calls = 8 , Pf = 0.2500 , 95.0 % confidence interval :[-0.1374,0.6374] + Number of function calls = 8 , Pf = 0.3750 , 95.0 % confidence interval :[-0.0026,0.7526] + Number of function calls = 8 , Pf = 0.2500 , 95.0 % confidence interval :[-0.0501,0.5501] + Number of function calls = 8 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000] Number of function calls = 8 , Pf = 0.2500 , 95.0 % confidence interval :[-0.1374,0.6374] Number of function calls = 8 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000] - Number of function calls = 16 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000] - Number of function calls = 16 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0844,0.2094] - Number of function calls = 16 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000] + Number of function calls = 8 , Pf = 0.1250 , 95.0 % confidence interval :[-0.1623,0.4123] Number of function calls = 16 , Pf = 0.1250 , 95.0 % confidence interval :[-0.0781,0.3281] Number of function calls = 16 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0844,0.2094] Number of function calls = 16 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0844,0.2094] - Number of function calls = 16 , Pf = 0.2500 , 95.0 % confidence interval :[-0.0239,0.5239] Number of function calls = 16 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0844,0.2094] - Number of function calls = 16 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000] + Number of function calls = 16 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0844,0.2094] + Number of function calls = 16 , Pf = 0.1250 , 95.0 % confidence interval :[-0.0370,0.2870] + Number of function calls = 16 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0844,0.2094] + Number of function calls = 16 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0844,0.2094] + Number of function calls = 16 , Pf = 0.1250 , 95.0 % confidence interval :[-0.0781,0.3281] Number of function calls = 16 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000] Number of function calls = 32 , Pf = 0.0312 , 95.0 % confidence interval :[-0.0430,0.1055] - Number of function calls = 32 , Pf = 0.0938 , 95.0 % confidence interval :[-0.0161,0.2036] - Number of function calls = 32 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0414,0.1664] + Number of function calls = 32 , Pf = 0.0938 , 95.0 % confidence interval :[-0.0321,0.2196] Number of function calls = 32 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0414,0.1664] + Number of function calls = 32 , Pf = 0.0938 , 95.0 % confidence interval :[-0.0321,0.2196] + Number of function calls = 32 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000] + Number of function calls = 32 , Pf = 0.1875 , 95.0 % confidence interval :[0.0390,0.3360] + Number of function calls = 32 , Pf = 0.0938 , 95.0 % confidence interval :[-0.0321,0.2196] Number of function calls = 32 , Pf = 0.1562 , 95.0 % confidence interval :[-0.0025,0.3150] - Number of function calls = 32 , Pf = 0.0312 , 95.0 % confidence interval :[-0.0430,0.1055] - Number of function calls = 32 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0414,0.1664] - Number of function calls = 32 , Pf = 0.0312 , 95.0 % confidence interval :[-0.0430,0.1055] - Number of function calls = 32 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0214,0.1464] Number of function calls = 32 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0414,0.1664] - Number of function calls = 64 , Pf = 0.1406 , 95.0 % confidence interval :[0.0335,0.2477] - Number of function calls = 64 , Pf = 0.0938 , 95.0 % confidence interval :[0.0102,0.1773] - Number of function calls = 64 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0109,0.1359] - Number of function calls = 64 , Pf = 0.1094 , 95.0 % confidence interval :[0.0138,0.2049] - Number of function calls = 64 , Pf = 0.0312 , 95.0 % confidence interval :[-0.0212,0.0837] - Number of function calls = 64 , Pf = 0.1719 , 95.0 % confidence interval :[0.0675,0.2762] + Number of function calls = 32 , Pf = 0.1250 , 95.0 % confidence interval :[-0.0186,0.2686] + Number of function calls = 64 , Pf = 0.0781 , 95.0 % confidence interval :[-0.0035,0.1598] + Number of function calls = 64 , Pf = 0.0938 , 95.0 % confidence interval :[0.0048,0.1827] Number of function calls = 64 , Pf = 0.0469 , 95.0 % confidence interval :[-0.0171,0.1108] + Number of function calls = 64 , Pf = 0.1562 , 95.0 % confidence interval :[0.0440,0.2685] Number of function calls = 64 , Pf = 0.0938 , 95.0 % confidence interval :[0.0048,0.1827] - Number of function calls = 64 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0109,0.1359] - Number of function calls = 64 , Pf = 0.0781 , 95.0 % confidence interval :[-0.0035,0.1598] - Number of function calls = 128 , Pf = 0.0937 , 95.0 % confidence interval :[0.0308,0.1567] - Number of function calls = 128 , Pf = 0.0781 , 95.0 % confidence interval :[0.0204,0.1359] - Number of function calls = 128 , Pf = 0.0781 , 95.0 % confidence interval :[0.0204,0.1359] - Number of function calls = 128 , Pf = 0.1016 , 95.0 % confidence interval :[0.0363,0.1669] - Number of function calls = 128 , Pf = 0.1172 , 95.0 % confidence interval :[0.0475,0.1869] - Number of function calls = 128 , Pf = 0.0703 , 95.0 % confidence interval :[0.0154,0.1252] + Number of function calls = 64 , Pf = 0.0469 , 95.0 % confidence interval :[-0.0171,0.1108] + Number of function calls = 64 , Pf = 0.1719 , 95.0 % confidence interval :[0.0589,0.2849] + Number of function calls = 64 , Pf = 0.0781 , 95.0 % confidence interval :[0.0024,0.1538] + Number of function calls = 64 , Pf = 0.0938 , 95.0 % confidence interval :[0.0102,0.1773] + Number of function calls = 64 , Pf = 0.0469 , 95.0 % confidence interval :[-0.0171,0.1108] + Number of function calls = 128 , Pf = 0.1094 , 95.0 % confidence interval :[0.0418,0.1769] + Number of function calls = 128 , Pf = 0.0312 , 95.0 % confidence interval :[-0.0059,0.0684] + Number of function calls = 128 , Pf = 0.1250 , 95.0 % confidence interval :[0.0548,0.1952] + Number of function calls = 128 , Pf = 0.0312 , 95.0 % confidence interval :[-0.0059,0.0684] + Number of function calls = 128 , Pf = 0.0469 , 95.0 % confidence interval :[0.0017,0.0921] Number of function calls = 128 , Pf = 0.0391 , 95.0 % confidence interval :[-0.0023,0.0804] - Number of function calls = 128 , Pf = 0.0859 , 95.0 % confidence interval :[0.0255,0.1463] - Number of function calls = 128 , Pf = 0.0547 , 95.0 % confidence interval :[0.0060,0.1034] - Number of function calls = 128 , Pf = 0.0938 , 95.0 % confidence interval :[0.0327,0.1548] - Number of function calls = 256 , Pf = 0.0781 , 95.0 % confidence interval :[0.0380,0.1182] + Number of function calls = 128 , Pf = 0.0859 , 95.0 % confidence interval :[0.0275,0.1444] + Number of function calls = 128 , Pf = 0.1172 , 95.0 % confidence interval :[0.0492,0.1852] + Number of function calls = 128 , Pf = 0.0703 , 95.0 % confidence interval :[0.0154,0.1252] + Number of function calls = 128 , Pf = 0.1016 , 95.0 % confidence interval :[0.0381,0.1650] + Number of function calls = 256 , Pf = 0.0820 , 95.0 % confidence interval :[0.0417,0.1224] Number of function calls = 256 , Pf = 0.0859 , 95.0 % confidence interval :[0.0432,0.1286] - Number of function calls = 256 , Pf = 0.0664 , 95.0 % confidence interval :[0.0286,0.1042] - Number of function calls = 256 , Pf = 0.0664 , 95.0 % confidence interval :[0.0286,0.1042] - Number of function calls = 256 , Pf = 0.0742 , 95.0 % confidence interval :[0.0344,0.1141] - Number of function calls = 256 , Pf = 0.0742 , 95.0 % confidence interval :[0.0351,0.1133] - Number of function calls = 256 , Pf = 0.0664 , 95.0 % confidence interval :[0.0286,0.1042] - Number of function calls = 256 , Pf = 0.0586 , 95.0 % confidence interval :[0.0238,0.0934] - Number of function calls = 256 , Pf = 0.1016 , 95.0 % confidence interval :[0.0554,0.1477] - Number of function calls = 256 , Pf = 0.0977 , 95.0 % confidence interval :[0.0536,0.1417] - Number of function calls = 512 , Pf = 0.0508 , 95.0 % confidence interval :[0.0276,0.0740] + Number of function calls = 256 , Pf = 0.0781 , 95.0 % confidence interval :[0.0373,0.1190] + Number of function calls = 256 , Pf = 0.0625 , 95.0 % confidence interval :[0.0266,0.0984] + Number of function calls = 256 , Pf = 0.0781 , 95.0 % confidence interval :[0.0380,0.1182] + Number of function calls = 256 , Pf = 0.0352 , 95.0 % confidence interval :[0.0074,0.0630] + Number of function calls = 256 , Pf = 0.0664 , 95.0 % confidence interval :[0.0302,0.1026] + Number of function calls = 256 , Pf = 0.0547 , 95.0 % confidence interval :[0.0202,0.0891] + Number of function calls = 256 , Pf = 0.0625 , 95.0 % confidence interval :[0.0258,0.0992] + Number of function calls = 256 , Pf = 0.0703 , 95.0 % confidence interval :[0.0315,0.1092] Number of function calls = 512 , Pf = 0.0723 , 95.0 % confidence interval :[0.0450,0.0996] - Number of function calls = 512 , Pf = 0.0762 , 95.0 % confidence interval :[0.0482,0.1042] - Number of function calls = 512 , Pf = 0.0645 , 95.0 % confidence interval :[0.0390,0.0900] - Number of function calls = 512 , Pf = 0.0645 , 95.0 % confidence interval :[0.0381,0.0908] - Number of function calls = 512 , Pf = 0.0762 , 95.0 % confidence interval :[0.0490,0.1034] - Number of function calls = 512 , Pf = 0.0586 , 95.0 % confidence interval :[0.0334,0.0838] - Number of function calls = 512 , Pf = 0.1055 , 95.0 % confidence interval :[0.0729,0.1380] - Number of function calls = 512 , Pf = 0.0781 , 95.0 % confidence interval :[0.0498,0.1065] - Number of function calls = 512 , Pf = 0.0840 , 95.0 % confidence interval :[0.0546,0.1134] - Number of function calls = 1024 , Pf = 0.0850 , 95.0 % confidence interval :[0.0639,0.1060] + Number of function calls = 512 , Pf = 0.0605 , 95.0 % confidence interval :[0.0358,0.0852] + Number of function calls = 512 , Pf = 0.0996 , 95.0 % confidence interval :[0.0686,0.1306] + Number of function calls = 512 , Pf = 0.0938 , 95.0 % confidence interval :[0.0628,0.1247] + Number of function calls = 512 , Pf = 0.0742 , 95.0 % confidence interval :[0.0471,0.1013] + Number of function calls = 512 , Pf = 0.0723 , 95.0 % confidence interval :[0.0450,0.0996] + Number of function calls = 512 , Pf = 0.0684 , 95.0 % confidence interval :[0.0415,0.0952] + Number of function calls = 512 , Pf = 0.0859 , 95.0 % confidence interval :[0.0565,0.1154] + Number of function calls = 512 , Pf = 0.0938 , 95.0 % confidence interval :[0.0628,0.1247] + Number of function calls = 512 , Pf = 0.0566 , 95.0 % confidence interval :[0.0322,0.0811] + Number of function calls = 1024 , Pf = 0.0713 , 95.0 % confidence interval :[0.0519,0.0906] + Number of function calls = 1024 , Pf = 0.0791 , 95.0 % confidence interval :[0.0588,0.0994] + Number of function calls = 1024 , Pf = 0.0850 , 95.0 % confidence interval :[0.0638,0.1061] + Number of function calls = 1024 , Pf = 0.0850 , 95.0 % confidence interval :[0.0640,0.1059] + Number of function calls = 1024 , Pf = 0.0830 , 95.0 % confidence interval :[0.0625,0.1035] + Number of function calls = 1024 , Pf = 0.0869 , 95.0 % confidence interval :[0.0658,0.1080] + Number of function calls = 1024 , Pf = 0.0869 , 95.0 % confidence interval :[0.0657,0.1081] + Number of function calls = 1024 , Pf = 0.0771 , 95.0 % confidence interval :[0.0570,0.0973] + Number of function calls = 1024 , Pf = 0.0830 , 95.0 % confidence interval :[0.0622,0.1038] Number of function calls = 1024 , Pf = 0.0742 , 95.0 % confidence interval :[0.0546,0.0939] - Number of function calls = 1024 , Pf = 0.0889 , 95.0 % confidence interval :[0.0675,0.1102] - Number of function calls = 1024 , Pf = 0.0674 , 95.0 % confidence interval :[0.0485,0.0862] - Number of function calls = 1024 , Pf = 0.0850 , 95.0 % confidence interval :[0.0642,0.1058] - Number of function calls = 1024 , Pf = 0.0635 , 95.0 % confidence interval :[0.0456,0.0814] - Number of function calls = 1024 , Pf = 0.0781 , 95.0 % confidence interval :[0.0580,0.0983] - Number of function calls = 1024 , Pf = 0.0967 , 95.0 % confidence interval :[0.0744,0.1189] - Number of function calls = 1024 , Pf = 0.0820 , 95.0 % confidence interval :[0.0615,0.1026] - Number of function calls = 1024 , Pf = 0.0752 , 95.0 % confidence interval :[0.0553,0.0951] - Number of function calls = 2048 , Pf = 0.0786 , 95.0 % confidence interval :[0.0644,0.0929] - Number of function calls = 2048 , Pf = 0.0776 , 95.0 % confidence interval :[0.0636,0.0917] - Number of function calls = 2048 , Pf = 0.0840 , 95.0 % confidence interval :[0.0692,0.0988] - Number of function calls = 2048 , Pf = 0.0664 , 95.0 % confidence interval :[0.0531,0.0797] + Number of function calls = 2048 , Pf = 0.0840 , 95.0 % confidence interval :[0.0693,0.0987] + Number of function calls = 2048 , Pf = 0.0864 , 95.0 % confidence interval :[0.0716,0.1013] + Number of function calls = 2048 , Pf = 0.0894 , 95.0 % confidence interval :[0.0743,0.1044] + Number of function calls = 2048 , Pf = 0.0864 , 95.0 % confidence interval :[0.0715,0.1014] + Number of function calls = 2048 , Pf = 0.0757 , 95.0 % confidence interval :[0.0618,0.0896] + Number of function calls = 2048 , Pf = 0.0781 , 95.0 % confidence interval :[0.0638,0.0925] Number of function calls = 2048 , Pf = 0.0786 , 95.0 % confidence interval :[0.0643,0.0930] - Number of function calls = 2048 , Pf = 0.0742 , 95.0 % confidence interval :[0.0605,0.0880] - Number of function calls = 2048 , Pf = 0.0791 , 95.0 % confidence interval :[0.0647,0.0935] - Number of function calls = 2048 , Pf = 0.0815 , 95.0 % confidence interval :[0.0670,0.0961] - Number of function calls = 2048 , Pf = 0.0767 , 95.0 % confidence interval :[0.0626,0.0907] - Number of function calls = 2048 , Pf = 0.0801 , 95.0 % confidence interval :[0.0658,0.0944] - Number of function calls = 4096 , Pf = 0.0820 , 95.0 % confidence interval :[0.0717,0.0923] - Number of function calls = 4096 , Pf = 0.0789 , 95.0 % confidence interval :[0.0688,0.0889] - Number of function calls = 4096 , Pf = 0.0754 , 95.0 % confidence interval :[0.0656,0.0853] - Number of function calls = 4096 , Pf = 0.0806 , 95.0 % confidence interval :[0.0703,0.0908] - Number of function calls = 4096 , Pf = 0.0833 , 95.0 % confidence interval :[0.0728,0.0937] - Number of function calls = 4096 , Pf = 0.0786 , 95.0 % confidence interval :[0.0686,0.0887] - Number of function calls = 4096 , Pf = 0.0720 , 95.0 % confidence interval :[0.0623,0.0817] - Number of function calls = 4096 , Pf = 0.0723 , 95.0 % confidence interval :[0.0625,0.0820] - Number of function calls = 4096 , Pf = 0.0754 , 95.0 % confidence interval :[0.0656,0.0853] - Number of function calls = 4096 , Pf = 0.0732 , 95.0 % confidence interval :[0.0635,0.0830] - Number of function calls = 8192 , Pf = 0.0786 , 95.0 % confidence interval :[0.0715,0.0858] + Number of function calls = 2048 , Pf = 0.0747 , 95.0 % confidence interval :[0.0609,0.0885] + Number of function calls = 2048 , Pf = 0.0674 , 95.0 % confidence interval :[0.0542,0.0806] + Number of function calls = 2048 , Pf = 0.0796 , 95.0 % confidence interval :[0.0653,0.0938] + Number of function calls = 4096 , Pf = 0.0735 , 95.0 % confidence interval :[0.0637,0.0833] + Number of function calls = 4096 , Pf = 0.0774 , 95.0 % confidence interval :[0.0674,0.0874] + Number of function calls = 4096 , Pf = 0.0825 , 95.0 % confidence interval :[0.0722,0.0929] + Number of function calls = 4096 , Pf = 0.0764 , 95.0 % confidence interval :[0.0665,0.0863] + Number of function calls = 4096 , Pf = 0.0815 , 95.0 % confidence interval :[0.0713,0.0918] + Number of function calls = 4096 , Pf = 0.0791 , 95.0 % confidence interval :[0.0689,0.0893] + Number of function calls = 4096 , Pf = 0.0784 , 95.0 % confidence interval :[0.0682,0.0885] + Number of function calls = 4096 , Pf = 0.0740 , 95.0 % confidence interval :[0.0641,0.0838] + Number of function calls = 4096 , Pf = 0.0747 , 95.0 % confidence interval :[0.0649,0.0845] + Number of function calls = 4096 , Pf = 0.0815 , 95.0 % confidence interval :[0.0712,0.0918] + Number of function calls = 8192 , Pf = 0.0782 , 95.0 % confidence interval :[0.0711,0.0854] + Number of function calls = 8192 , Pf = 0.0781 , 95.0 % confidence interval :[0.0710,0.0852] + Number of function calls = 8192 , Pf = 0.0770 , 95.0 % confidence interval :[0.0700,0.0841] + Number of function calls = 8192 , Pf = 0.0768 , 95.0 % confidence interval :[0.0697,0.0838] + Number of function calls = 8192 , Pf = 0.0745 , 95.0 % confidence interval :[0.0675,0.0815] + Number of function calls = 8192 , Pf = 0.0780 , 95.0 % confidence interval :[0.0709,0.0851] + Number of function calls = 8192 , Pf = 0.0797 , 95.0 % confidence interval :[0.0725,0.0869] Number of function calls = 8192 , Pf = 0.0797 , 95.0 % confidence interval :[0.0725,0.0869] - Number of function calls = 8192 , Pf = 0.0785 , 95.0 % confidence interval :[0.0713,0.0856] - Number of function calls = 8192 , Pf = 0.0809 , 95.0 % confidence interval :[0.0737,0.0882] - Number of function calls = 8192 , Pf = 0.0785 , 95.0 % confidence interval :[0.0714,0.0856] - Number of function calls = 8192 , Pf = 0.0764 , 95.0 % confidence interval :[0.0694,0.0834] - Number of function calls = 8192 , Pf = 0.0833 , 95.0 % confidence interval :[0.0759,0.0906] - Number of function calls = 8192 , Pf = 0.0803 , 95.0 % confidence interval :[0.0731,0.0875] - Number of function calls = 8192 , Pf = 0.0789 , 95.0 % confidence interval :[0.0717,0.0860] - Number of function calls = 8192 , Pf = 0.0859 , 95.0 % confidence interval :[0.0785,0.0934] - Number of function calls = 16384 , Pf = 0.0776 , 95.0 % confidence interval :[0.0726,0.0827] - Number of function calls = 16384 , Pf = 0.0801 , 95.0 % confidence interval :[0.0750,0.0852] - Number of function calls = 16384 , Pf = 0.0792 , 95.0 % confidence interval :[0.0741,0.0842] - Number of function calls = 16384 , Pf = 0.0768 , 95.0 % confidence interval :[0.0718,0.0818] - Number of function calls = 16384 , Pf = 0.0770 , 95.0 % confidence interval :[0.0720,0.0820] - Number of function calls = 16384 , Pf = 0.0778 , 95.0 % confidence interval :[0.0728,0.0829] - Number of function calls = 16384 , Pf = 0.0804 , 95.0 % confidence interval :[0.0753,0.0855] - Number of function calls = 16384 , Pf = 0.0817 , 95.0 % confidence interval :[0.0765,0.0868] - Number of function calls = 16384 , Pf = 0.0779 , 95.0 % confidence interval :[0.0728,0.0829] - Number of function calls = 16384 , Pf = 0.0802 , 95.0 % confidence interval :[0.0751,0.0853] - Number of function calls = 32768 , Pf = 0.0785 , 95.0 % confidence interval :[0.0749,0.0821] - Number of function calls = 32768 , Pf = 0.0782 , 95.0 % confidence interval :[0.0746,0.0818] - Number of function calls = 32768 , Pf = 0.0803 , 95.0 % confidence interval :[0.0767,0.0839] - Number of function calls = 32768 , Pf = 0.0782 , 95.0 % confidence interval :[0.0747,0.0818] + Number of function calls = 8192 , Pf = 0.0760 , 95.0 % confidence interval :[0.0690,0.0831] + Number of function calls = 8192 , Pf = 0.0818 , 95.0 % confidence interval :[0.0745,0.0891] + Number of function calls = 16384 , Pf = 0.0825 , 95.0 % confidence interval :[0.0773,0.0876] + Number of function calls = 16384 , Pf = 0.0812 , 95.0 % confidence interval :[0.0761,0.0863] + Number of function calls = 16384 , Pf = 0.0760 , 95.0 % confidence interval :[0.0710,0.0810] + Number of function calls = 16384 , Pf = 0.0790 , 95.0 % confidence interval :[0.0740,0.0841] + Number of function calls = 16384 , Pf = 0.0793 , 95.0 % confidence interval :[0.0743,0.0844] + Number of function calls = 16384 , Pf = 0.0806 , 95.0 % confidence interval :[0.0755,0.0857] + Number of function calls = 16384 , Pf = 0.0785 , 95.0 % confidence interval :[0.0735,0.0835] + Number of function calls = 16384 , Pf = 0.0779 , 95.0 % confidence interval :[0.0729,0.0830] + Number of function calls = 16384 , Pf = 0.0786 , 95.0 % confidence interval :[0.0736,0.0837] + Number of function calls = 16384 , Pf = 0.0750 , 95.0 % confidence interval :[0.0701,0.0799] + Number of function calls = 32768 , Pf = 0.0772 , 95.0 % confidence interval :[0.0737,0.0808] + Number of function calls = 32768 , Pf = 0.0766 , 95.0 % confidence interval :[0.0731,0.0801] + Number of function calls = 32768 , Pf = 0.0796 , 95.0 % confidence interval :[0.0760,0.0832] Number of function calls = 32768 , Pf = 0.0802 , 95.0 % confidence interval :[0.0766,0.0838] - Number of function calls = 32768 , Pf = 0.0775 , 95.0 % confidence interval :[0.0740,0.0811] - Number of function calls = 32768 , Pf = 0.0794 , 95.0 % confidence interval :[0.0758,0.0830] - Number of function calls = 32768 , Pf = 0.0794 , 95.0 % confidence interval :[0.0758,0.0830] - Number of function calls = 32768 , Pf = 0.0799 , 95.0 % confidence interval :[0.0763,0.0835] - Number of function calls = 32768 , Pf = 0.0789 , 95.0 % confidence interval :[0.0753,0.0825] - Number of function calls = 65536 , Pf = 0.0781 , 95.0 % confidence interval :[0.0756,0.0806] - Number of function calls = 65536 , Pf = 0.0777 , 95.0 % confidence interval :[0.0752,0.0802] - Number of function calls = 65536 , Pf = 0.0804 , 95.0 % confidence interval :[0.0779,0.0830] - Number of function calls = 65536 , Pf = 0.0778 , 95.0 % confidence interval :[0.0753,0.0803] + Number of function calls = 32768 , Pf = 0.0796 , 95.0 % confidence interval :[0.0760,0.0831] + Number of function calls = 32768 , Pf = 0.0772 , 95.0 % confidence interval :[0.0737,0.0807] + Number of function calls = 32768 , Pf = 0.0786 , 95.0 % confidence interval :[0.0750,0.0822] + Number of function calls = 32768 , Pf = 0.0778 , 95.0 % confidence interval :[0.0743,0.0814] + Number of function calls = 32768 , Pf = 0.0810 , 95.0 % confidence interval :[0.0774,0.0846] + Number of function calls = 32768 , Pf = 0.0808 , 95.0 % confidence interval :[0.0772,0.0844] + Number of function calls = 65536 , Pf = 0.0790 , 95.0 % confidence interval :[0.0765,0.0815] + Number of function calls = 65536 , Pf = 0.0774 , 95.0 % confidence interval :[0.0749,0.0799] Number of function calls = 65536 , Pf = 0.0786 , 95.0 % confidence interval :[0.0761,0.0812] - Number of function calls = 65536 , Pf = 0.0779 , 95.0 % confidence interval :[0.0754,0.0804] - Number of function calls = 65536 , Pf = 0.0818 , 95.0 % confidence interval :[0.0792,0.0844] - Number of function calls = 65536 , Pf = 0.0793 , 95.0 % confidence interval :[0.0768,0.0819] - Number of function calls = 65536 , Pf = 0.0786 , 95.0 % confidence interval :[0.0761,0.0811] - Number of function calls = 65536 , Pf = 0.0814 , 95.0 % confidence interval :[0.0788,0.0839] + Number of function calls = 65536 , Pf = 0.0781 , 95.0 % confidence interval :[0.0756,0.0807] + Number of function calls = 65536 , Pf = 0.0793 , 95.0 % confidence interval :[0.0767,0.0818] + Number of function calls = 65536 , Pf = 0.0793 , 95.0 % confidence interval :[0.0768,0.0818] + Number of function calls = 65536 , Pf = 0.0806 , 95.0 % confidence interval :[0.0781,0.0832] + Number of function calls = 65536 , Pf = 0.0784 , 95.0 % confidence interval :[0.0759,0.0810] + Number of function calls = 65536 , Pf = 0.0783 , 95.0 % confidence interval :[0.0758,0.0808] + Number of function calls = 65536 , Pf = 0.0789 , 95.0 % confidence interval :[0.0763,0.0814] @@ -352,7 +352,7 @@ where :math:`n` is the sample size. .. code-block:: none - Elapsed = 1.75 (s) + Elapsed = 1.74 (s) @@ -414,7 +414,7 @@ where :math:`n` is the sample size. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 2.174 seconds) + **Total running time of the script:** (0 minutes 2.177 seconds) .. _sphx_glr_download_auto_examples_plot_convergence_reliability_mc.py: diff --git a/otbenchmark/master/_sources/auto_examples/plot_crosscut_distribution_2d.rst b/otbenchmark/master/_sources/auto_examples/plot_crosscut_distribution_2d.rst index 0fe72ae277f..7638d14386d 100644 --- a/otbenchmark/master/_sources/auto_examples/plot_crosscut_distribution_2d.rst +++ b/otbenchmark/master/_sources/auto_examples/plot_crosscut_distribution_2d.rst @@ -152,7 +152,7 @@ Plot cross-cuts of the distribution .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 3.997 seconds) + **Total running time of the script:** (0 minutes 2.066 seconds) .. _sphx_glr_download_auto_examples_plot_crosscut_distribution_2d.py: diff --git a/otbenchmark/master/_sources/auto_examples/plot_crosscut_distribution_3d.rst b/otbenchmark/master/_sources/auto_examples/plot_crosscut_distribution_3d.rst index d8967bce4a5..358c323007a 100644 --- a/otbenchmark/master/_sources/auto_examples/plot_crosscut_distribution_3d.rst +++ b/otbenchmark/master/_sources/auto_examples/plot_crosscut_distribution_3d.rst @@ -146,7 +146,7 @@ Print the iso-values of the distribution .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 8.366 seconds) + **Total running time of the script:** (0 minutes 7.724 seconds) .. _sphx_glr_download_auto_examples_plot_crosscut_distribution_3d.py: diff --git a/otbenchmark/master/_sources/auto_examples/plot_methodFactory.rst b/otbenchmark/master/_sources/auto_examples/plot_methodFactory.rst index 440a6975aa9..0b51e0c6633 100644 --- a/otbenchmark/master/_sources/auto_examples/plot_methodFactory.rst +++ b/otbenchmark/master/_sources/auto_examples/plot_methodFactory.rst @@ -331,7 +331,7 @@ Create a LHS algorithm .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 4.439 seconds) + **Total running time of the script:** (0 minutes 7.465 seconds) .. _sphx_glr_download_auto_examples_plot_methodFactory.py: diff --git a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/index.rst b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/index.rst index 51535d15c69..1cc1b1cd3ef 100644 --- a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/index.rst +++ b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/index.rst @@ -30,6 +30,193 @@ Sensitivity examples +.. raw:: html + +
+ +.. only:: html + + .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_flood_sensitivity_thumb.png + :alt: + + :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_flood_sensitivity.py` + +.. raw:: html + +
Benchmark the Flooding test function
+
+ + +.. raw:: html + +
+ +.. only:: html + + .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_borgonovo_sensitivity_thumb.png + :alt: + + :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_borgonovo_sensitivity.py` + +.. raw:: html + +
Benchmark the Borgonovo test function
+
+ + +.. raw:: html + +
+ +.. only:: html + + .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_ishigami_sensitivity_thumb.png + :alt: + + :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_ishigami_sensitivity.py` + +.. raw:: html + +
Benchmark the Ishigami test function
+
+ + +.. raw:: html + +
+ +.. only:: html + + .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_dirichlet_sensitivity_thumb.png + :alt: + + :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_dirichlet_sensitivity.py` + +.. raw:: html + +
Benchmark the Dirichlet test function
+
+ + +.. raw:: html + +
+ +.. only:: html + + .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_borehole_sensitivity_thumb.png + :alt: + + :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_borehole_sensitivity.py` + +.. raw:: html + +
Benchmark the Borehole test function
+
+ + +.. raw:: html + +
+ +.. only:: html + + .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_nloscillator_sensitivity_thumb.png + :alt: + + :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_nloscillator_sensitivity.py` + +.. raw:: html + +
Benchmark the NLOscillator test function
+
+ + +.. raw:: html + +
+ +.. only:: html + + .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_gaussian_sum_thumb.png + :alt: + + :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_gaussian_sum.py` + +.. raw:: html + +
Benchmark the gaussian sum test function
+
+ + +.. raw:: html + +
+ +.. only:: html + + .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_gaussian_product_sensitivity_thumb.png + :alt: + + :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_gaussian_product_sensitivity.py` + +.. raw:: html + +
Benchmark the gaussian product test function
+
+ + +.. raw:: html + +
+ +.. only:: html + + .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_morris_sensitivity_thumb.png + :alt: + + :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_morris_sensitivity.py` + +.. raw:: html + +
Benchmark the Morris test function
+
+ + +.. raw:: html + +
+ +.. only:: html + + .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_oakleyohagan_sensitivity_thumb.png + :alt: + + :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_oakleyohagan_sensitivity.py` + +.. raw:: html + +
Benchmark the Oakley-O'Hagan test function
+
+ + +.. raw:: html + +
+ +.. only:: html + + .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_gsobol_sensitivity_thumb.png + :alt: + + :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_gsobol_sensitivity.py` + +.. raw:: html + +
Benchmark the G-Sobol test function
+
+ + .. raw:: html
@@ -47,6 +234,23 @@ Sensitivity examples
+.. raw:: html + +
+ +.. only:: html + + .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_benchmark_sensitivity_methods_thumb.png + :alt: + + :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_benchmark_sensitivity_methods.py` + +.. raw:: html + +
Benchmark sensitivity analysis methods
+
+ + .. thumbnail-parent-div-close .. raw:: html @@ -58,5 +262,17 @@ Sensitivity examples :hidden: /auto_examples/sensitivity_problems/plot_print_problems + /auto_examples/sensitivity_problems/plot_flood_sensitivity + /auto_examples/sensitivity_problems/plot_borgonovo_sensitivity + /auto_examples/sensitivity_problems/plot_ishigami_sensitivity + /auto_examples/sensitivity_problems/plot_dirichlet_sensitivity + /auto_examples/sensitivity_problems/plot_borehole_sensitivity + /auto_examples/sensitivity_problems/plot_nloscillator_sensitivity + /auto_examples/sensitivity_problems/plot_gaussian_sum + /auto_examples/sensitivity_problems/plot_gaussian_product_sensitivity + /auto_examples/sensitivity_problems/plot_morris_sensitivity + /auto_examples/sensitivity_problems/plot_oakleyohagan_sensitivity + /auto_examples/sensitivity_problems/plot_gsobol_sensitivity /auto_examples/sensitivity_problems/plot_convergence_ishigami + /auto_examples/sensitivity_problems/plot_benchmark_sensitivity_methods diff --git a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_benchmark_sensitivity_methods.rst b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_benchmark_sensitivity_methods.rst new file mode 100644 index 00000000000..e7958717c8a --- /dev/null +++ b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_benchmark_sensitivity_methods.rst @@ -0,0 +1,576 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "auto_examples/sensitivity_problems/plot_benchmark_sensitivity_methods.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + :ref:`Go to the end ` + to download the full example code. + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_auto_examples_sensitivity_problems_plot_benchmark_sensitivity_methods.py: + + +Benchmark sensitivity analysis methods +====================================== + +.. GENERATED FROM PYTHON SOURCE LINES 7-10 + +.. code-block:: Python + + import openturns as ot + import otbenchmark as otb + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 11-17 + +When we estimate Sobol' indices, we may encounter the following warning messages: +``` +WRN - The estimated first order Sobol index (2) is greater than its total order index ... +WRN - The estimated total order Sobol index (2) is lesser than first order index ... +``` +Lots of these messages are printed in the current Notebook. This is why we disable them with: + +.. GENERATED FROM PYTHON SOURCE LINES 17-19 + +.. code-block:: Python + + ot.Log.Show(ot.Log.NONE) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 20-21 + +Use Borgonovo problem + +.. GENERATED FROM PYTHON SOURCE LINES 21-25 + +.. code-block:: Python + + problem = otb.BorgonovoSensitivity() + distribution = problem.getInputDistribution() + model = problem.getFunction() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 26-27 + +Exact first and total order + +.. GENERATED FROM PYTHON SOURCE LINES 27-30 + +.. code-block:: Python + + exact_first_order = problem.getFirstOrderIndices() + exact_total_order = problem.getTotalOrderIndices() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 31-33 + +Saltelli estimator with Monte-Carlo sample +------------------------------------------ + +.. GENERATED FROM PYTHON SOURCE LINES 35-37 + +.. code-block:: Python + + sample_size = 10000 + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 38-41 + +.. code-block:: Python + + inputDesign = ot.SobolIndicesExperiment(distribution, sample_size).generate() + outputDesign = model(inputDesign) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 42-43 + +Compute first order indices using the Saltelli estimator + +.. GENERATED FROM PYTHON SOURCE LINES 43-49 + +.. code-block:: Python + + sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm( + inputDesign, outputDesign, sample_size + ) + computed_first_order = sensitivityAnalysis.getFirstOrderIndices() + computed_total_order = sensitivityAnalysis.getTotalOrderIndices() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 50-51 + +Compare with exact results + +.. GENERATED FROM PYTHON SOURCE LINES 51-59 + +.. code-block:: Python + + print("Sample size : ", sample_size) + # First order + print("Computed first order = ", computed_first_order) + print("Exact first order = ", exact_first_order) + # Total order + print("Computed total order = ", computed_total_order) + print("Exact total order = ", exact_total_order) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + Sample size : 10000 + Computed first order = [0.18553,0.173199,0.637105] + Exact first order = [0.157895,0.157895,0.631579] + Computed total order = [0.203344,0.210474,0.620406] + Exact total order = [0.210526,0.210526,0.631579] + + + + +.. GENERATED FROM PYTHON SOURCE LINES 60-62 + +Saltelli estimator with Quasi Monte-Carlo sample +------------------------------------------------ + +.. GENERATED FROM PYTHON SOURCE LINES 64-66 + +.. code-block:: Python + + sample_size = 500 + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 67-72 + +.. code-block:: Python + + dimension = distribution.getDimension() + sequence = ot.SobolSequence(dimension) + restart = True + experiment = ot.LowDiscrepancyExperiment(sequence, distribution, sample_size, restart) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 73-76 + +.. code-block:: Python + + inputDesign = ot.SobolIndicesExperiment(experiment).generate() + outputDesign = model(inputDesign) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 77-78 + +Compute first order indices using the Saltelli estimator + +.. GENERATED FROM PYTHON SOURCE LINES 78-84 + +.. code-block:: Python + + sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm( + inputDesign, outputDesign, sample_size + ) + first_order = sensitivityAnalysis.getFirstOrderIndices() + total_order = sensitivityAnalysis.getTotalOrderIndices() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 85-86 + +Compare with exact results + +.. GENERATED FROM PYTHON SOURCE LINES 86-94 + +.. code-block:: Python + + print("Sample size : ", sample_size) + # First order + print("Computed first order = ", computed_first_order) + print("Exact first order = ", exact_first_order) + # Total order + print("Computed total order = ", computed_total_order) + print("Exact total order = ", exact_total_order) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + Sample size : 500 + Computed first order = [0.18553,0.173199,0.637105] + Exact first order = [0.157895,0.157895,0.631579] + Computed total order = [0.203344,0.210474,0.620406] + Exact total order = [0.210526,0.210526,0.631579] + + + + +.. GENERATED FROM PYTHON SOURCE LINES 95-97 + +Loop over the estimators +------------------------ + +.. GENERATED FROM PYTHON SOURCE LINES 99-105 + +.. code-block:: Python + + print("Available estimators:") + estimators_list = otb.SensitivityBenchmarkMetaAlgorithm.GetEstimators() + for sobolAlgorithm in estimators_list: + name = sobolAlgorithm.getClassName() + print(" - ", name) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + Available estimators: + - SaltelliSensitivityAlgorithm + - MartinezSensitivityAlgorithm + - JansenSensitivityAlgorithm + - MauntzKucherenkoSensitivityAlgorithm + + + + +.. GENERATED FROM PYTHON SOURCE LINES 106-108 + +.. code-block:: Python + + metaSAAlgorithm = otb.SensitivityBenchmarkMetaAlgorithm(problem) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 109-120 + +.. code-block:: Python + + print("Monte-Carlo sampling") + for sobolAlgorithm in estimators_list: + ( + computed_first_order, + computed_total_order, + ) = metaSAAlgorithm.runSamplingEstimator(sample_size) + name = sobolAlgorithm.getClassName() + print(name) + print(" S = ", computed_first_order) + print(" T = ", computed_total_order) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + Monte-Carlo sampling + SaltelliSensitivityAlgorithm + S = [0.199997,0.180685,0.664071] + T = [0.223721,0.228732,0.6502] + MartinezSensitivityAlgorithm + S = [0.191903,0.173622,0.638601] + T = [0.2242,0.207898,0.591462] + JansenSensitivityAlgorithm + S = [0.0952801,0.125079,0.615803] + T = [0.253686,0.237742,0.592254] + MauntzKucherenkoSensitivityAlgorithm + S = [0.205369,0.314775,0.698245] + T = [0.220371,0.172107,0.567821] + + + + +.. GENERATED FROM PYTHON SOURCE LINES 121-134 + +.. code-block:: Python + + print("Quasi Monte-Carlo sampling") + for estimator in ["Saltelli", "Martinez", "Jansen", "MauntzKucherenko"]: + ( + computed_first_order, + computed_total_order, + ) = metaSAAlgorithm.runSamplingEstimator( + sample_size, estimator=estimator, sampling_method="QMC" + ) + name = sobolAlgorithm.getClassName() + print(name) + print(" S = ", computed_first_order) + print(" T = ", computed_total_order) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + Quasi Monte-Carlo sampling + MauntzKucherenkoSensitivityAlgorithm + S = [0.147144,0.151326,0.624455] + T = [0.212622,0.209087,0.643622] + MauntzKucherenkoSensitivityAlgorithm + S = [0.147601,0.150976,0.629051] + T = [0.210037,0.210799,0.640943] + MauntzKucherenkoSensitivityAlgorithm + S = [0.151099,0.149846,0.632098] + T = [0.20915,0.21105,0.635553] + MauntzKucherenkoSensitivityAlgorithm + S = [0.159027,0.163209,0.636338] + T = [0.212622,0.209087,0.643622] + + + + +.. GENERATED FROM PYTHON SOURCE LINES 135-149 + +.. code-block:: Python + + print("Polynomial chaos") + sample_size = 500 + ( + computed_first_order, + computed_total_order, + ) = metaSAAlgorithm.runPolynomialChaosEstimator( + sample_size_train=sample_size, + sample_size_test=2, + total_degree=5, + hyperbolic_quasinorm=0.5, + ) + print(" S = ", computed_first_order) + print(" T = ", computed_total_order) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + Polynomial chaos + S = [0.157895,0.157895,0.631579] + T = [0.210526,0.210526,0.631579] + + + + +.. GENERATED FROM PYTHON SOURCE LINES 150-152 + +Define the metric +----------------- + +.. GENERATED FROM PYTHON SOURCE LINES 154-164 + +We consider the following accuracy metrics: +* the vector or log relative errors for a given index (first order or total order), +* the mean log relative error, as the mean of the LRE vector (first order or total order), +* the average mean log relative error, as the mean of the first and total order mean log relative error. + +Larger LRE values are prefered. + +The first order (resp. total order) mean LRE represents the mean number of digits for all components +of the first order indices (resp. total order indices). +The average mean LRE represents the mean LRE for both first and total order indices. + +.. GENERATED FROM PYTHON SOURCE LINES 166-176 + +.. code-block:: Python + + S_LRE = ot.Point(dimension) + T_LRE = ot.Point(dimension) + for i in range(dimension): + S_LRE[i] = otb.ComputeLogRelativeError( + computed_first_order[i], exact_first_order[i] + ) + T_LRE[i] = otb.ComputeLogRelativeError( + computed_total_order[i], exact_total_order[i] + ) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 177-180 + +.. code-block:: Python + + print("LRE S = ", S_LRE) + print("LRE T = ", T_LRE) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + LRE S = [14.7136,14.5509,15.153] + LRE T = [15.0349,14.8008,15.153] + + + + +.. GENERATED FROM PYTHON SOURCE LINES 181-188 + +.. code-block:: Python + + mean_LRE_S = sum(S_LRE) / dimension + mean_LRE_T = sum(T_LRE) / dimension + mean_LRE = (mean_LRE_S + mean_LRE_T) / 2.0 + print("Mean LRE S = %.2f" % (mean_LRE_S)) + print("Mean LRE T = %.2f" % (mean_LRE_T)) + print("Mean LRE = %.2f" % (mean_LRE)) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + Mean LRE S = 14.81 + Mean LRE T = 15.00 + Mean LRE = 14.90 + + + + +.. GENERATED FROM PYTHON SOURCE LINES 189-190 + +The digit per point ratio measure the number of digits relatively to the sample size. A greater value is prefered. + +.. GENERATED FROM PYTHON SOURCE LINES 190-192 + +.. code-block:: Python + + digit_per_point_ratio = mean_LRE / sample_size + print("Digit / point = %.3e" % (digit_per_point_ratio)) + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + Digit / point = 2.980e-02 + + + + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** (0 minutes 0.034 seconds) + + +.. _sphx_glr_download_auto_examples_sensitivity_problems_plot_benchmark_sensitivity_methods.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: plot_benchmark_sensitivity_methods.ipynb ` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: plot_benchmark_sensitivity_methods.py ` + + .. container:: sphx-glr-download sphx-glr-download-zip + + :download:`Download zipped: plot_benchmark_sensitivity_methods.zip ` diff --git a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_borehole_sensitivity.rst b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_borehole_sensitivity.rst new file mode 100644 index 00000000000..60250c68e5f --- /dev/null +++ b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_borehole_sensitivity.rst @@ -0,0 +1,340 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "auto_examples/sensitivity_problems/plot_borehole_sensitivity.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + :ref:`Go to the end ` + to download the full example code. + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_auto_examples_sensitivity_problems_plot_borehole_sensitivity.py: + + +Benchmark the Borehole test function +==================================== + +.. GENERATED FROM PYTHON SOURCE LINES 7-11 + +.. code-block:: Python + + import openturns as ot + import otbenchmark as otb + import openturns.viewer as otv + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 12-15 + +.. code-block:: Python + + problem = otb.BoreholeSensitivity() + print(problem) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + name = Borehole + distribution = ComposedDistribution(Normal(mu = 0.1, sigma = 0.0161812), LogNormal(muLog = 7.71, sigmaLog = 1.0056, gamma = 0), Uniform(a = 63070, b = 115600), Uniform(a = 990, b = 1110), Uniform(a = 63.1, b = 116), Uniform(a = 700, b = 820), Uniform(a = 1120, b = 1680), Uniform(a = 9855, b = 12045), IndependentCopula(dimension = 8)) + function = [rw,r,Tu,Hu,Tl,Hl,L,Kw]->[(2*pi_*Tu*(Hu-Hl))/(ln(r/rw)*(1+(2*L*Tu)/(ln(r/rw)*rw^2*Kw)+Tu/Tl))] + firstOrderIndices = [0.66,0,0,0.09,0,0.09,0.09,0.02] + totalOrderIndices = [0.69,0,0,0.11,0,0.11,0.1,0.02] + + + + +.. GENERATED FROM PYTHON SOURCE LINES 16-19 + +.. code-block:: Python + + distribution = problem.getInputDistribution() + model = problem.getFunction() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 20-21 + +Exact first and total order + +.. GENERATED FROM PYTHON SOURCE LINES 21-24 + +.. code-block:: Python + + exact_first_order = problem.getFirstOrderIndices() + exact_first_order + + + + + + +.. raw:: html + +
+ class=Point name=Unnamed dimension=8 values=[0.66,0,0,0.09,0,0.09,0.09,0.02] +
+
+
+ +.. GENERATED FROM PYTHON SOURCE LINES 25-28 + +.. code-block:: Python + + exact_total_order = problem.getTotalOrderIndices() + exact_total_order + + + + + + +.. raw:: html + +
+ class=Point name=Unnamed dimension=8 values=[0.69,0,0,0.11,0,0.11,0.1,0.02] +
+
+
+ +.. GENERATED FROM PYTHON SOURCE LINES 29-31 + +Plot the function +----------------- + +.. GENERATED FROM PYTHON SOURCE LINES 33-34 + +Create X/Y data + +.. GENERATED FROM PYTHON SOURCE LINES 34-39 + +.. code-block:: Python + + ot.RandomGenerator.SetSeed(0) + size = 200 + inputDesign = ot.MonteCarloExperiment(distribution, size).generate() + outputDesign = model(inputDesign) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 40-48 + +.. code-block:: Python + + dimension = distribution.getDimension() + full_sample = ot.Sample(size, 1 + dimension) + full_sample[:, range(dimension)] = inputDesign + full_sample[:, dimension] = outputDesign + full_description = list(inputDesign.getDescription()) + full_description.append(outputDesign.getDescription()[0]) + full_sample.setDescription(full_description) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 49-58 + +.. code-block:: Python + + marginal_distribution = ot.ComposedDistribution( + [ + ot.KernelSmoothing().build(full_sample.getMarginal(i)) + for i in range(1 + dimension) + ] + ) + clouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution) + _ = otv.View(clouds, figure_kw={"figsize": (10.0, 10.0)}) + + + + +.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_borehole_sensitivity_001.png + :alt: plot borehole sensitivity + :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_borehole_sensitivity_001.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 59-62 + +.. code-block:: Python + + output_distribution = ot.KernelSmoothing().build(outputDesign) + _ = otv.View(output_distribution.drawPDF()) + + + + +.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_borehole_sensitivity_002.png + :alt: plot borehole sensitivity + :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_borehole_sensitivity_002.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 63-65 + +Perform sensitivity analysis +---------------------------- + +.. GENERATED FROM PYTHON SOURCE LINES 67-68 + +Create X/Y data + +.. GENERATED FROM PYTHON SOURCE LINES 68-73 + +.. code-block:: Python + + ot.RandomGenerator.SetSeed(0) + size = 10000 + inputDesign = ot.SobolIndicesExperiment(distribution, size).generate() + outputDesign = model(inputDesign) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 74-75 + +Compute first order indices using the Saltelli estimator + +.. GENERATED FROM PYTHON SOURCE LINES 75-79 + +.. code-block:: Python + + sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size) + computed_first_order = sensitivityAnalysis.getFirstOrderIndices() + computed_total_order = sensitivityAnalysis.getTotalOrderIndices() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 80-81 + +Compare with exact results + +.. GENERATED FROM PYTHON SOURCE LINES 81-91 + +.. code-block:: Python + + print("Sample size : ", size) + # First order + # Compute absolute error (the LRE cannot be computed, + # because S can be zero) + print("Computed first order = ", computed_first_order) + print("Exact first order = ", exact_first_order) + # Total order + print("Computed total order = ", computed_total_order) + print("Exact total order = ", exact_total_order) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + Sample size : 10000 + Computed first order = [0.673759,0.00700637,0.00702324,0.0955299,0.00698057,0.106366,0.0916606,0.0283122] + Exact first order = [0.66,0,0,0.09,0,0.09,0.09,0.02] + Computed total order = [0.699598,-5.69378e-05,-3.20159e-08,0.0973057,9.79799e-05,0.103265,0.095226,0.0247122] + Exact total order = [0.69,0,0,0.11,0,0.11,0.1,0.02] + + + + +.. GENERATED FROM PYTHON SOURCE LINES 92-94 + +.. code-block:: Python + + _ = otv.View(sensitivityAnalysis.draw()) + + + + +.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_borehole_sensitivity_003.png + :alt: Sobol' indices - SaltelliSensitivityAlgorithm + :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_borehole_sensitivity_003.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 95-96 + +.. code-block:: Python + + otv.View.ShowAll() + + + + + + + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** (0 minutes 3.696 seconds) + + +.. _sphx_glr_download_auto_examples_sensitivity_problems_plot_borehole_sensitivity.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: plot_borehole_sensitivity.ipynb ` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: plot_borehole_sensitivity.py ` + + .. container:: sphx-glr-download sphx-glr-download-zip + + :download:`Download zipped: plot_borehole_sensitivity.zip ` diff --git a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_borgonovo_sensitivity.rst b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_borgonovo_sensitivity.rst new file mode 100644 index 00000000000..b356b8c58bb --- /dev/null +++ b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_borgonovo_sensitivity.rst @@ -0,0 +1,340 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "auto_examples/sensitivity_problems/plot_borgonovo_sensitivity.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + :ref:`Go to the end ` + to download the full example code. + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_auto_examples_sensitivity_problems_plot_borgonovo_sensitivity.py: + + +Benchmark the Borgonovo test function +===================================== + +.. GENERATED FROM PYTHON SOURCE LINES 7-11 + +.. code-block:: Python + + import openturns as ot + import otbenchmark as otb + import openturns.viewer as otv + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 12-15 + +.. code-block:: Python + + problem = otb.BorgonovoSensitivity() + print(problem) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + name = Borgonovo + distribution = ComposedDistribution(Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), IndependentCopula(dimension = 3)) + function = [x1,x2,x3]->[x1 * x2 + x3] + firstOrderIndices = [0.157895,0.157895,0.631579] + totalOrderIndices = [0.210526,0.210526,0.631579] + + + + +.. GENERATED FROM PYTHON SOURCE LINES 16-19 + +.. code-block:: Python + + distribution = problem.getInputDistribution() + model = problem.getFunction() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 20-21 + +Exact first and total order + +.. GENERATED FROM PYTHON SOURCE LINES 21-24 + +.. code-block:: Python + + exact_first_order = problem.getFirstOrderIndices() + exact_first_order + + + + + + +.. raw:: html + +
+ class=Point name=Unnamed dimension=3 values=[0.157895,0.157895,0.631579] +
+
+
+ +.. GENERATED FROM PYTHON SOURCE LINES 25-28 + +.. code-block:: Python + + exact_total_order = problem.getTotalOrderIndices() + exact_total_order + + + + + + +.. raw:: html + +
+ class=Point name=Unnamed dimension=3 values=[0.210526,0.210526,0.631579] +
+
+
+ +.. GENERATED FROM PYTHON SOURCE LINES 29-31 + +Plot the function +----------------- + +.. GENERATED FROM PYTHON SOURCE LINES 33-34 + +Create X/Y data + +.. GENERATED FROM PYTHON SOURCE LINES 34-39 + +.. code-block:: Python + + ot.RandomGenerator.SetSeed(0) + size = 200 + inputDesign = ot.MonteCarloExperiment(distribution, size).generate() + outputDesign = model(inputDesign) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 40-48 + +.. code-block:: Python + + dimension = distribution.getDimension() + full_sample = ot.Sample(size, 1 + dimension) + full_sample[:, range(dimension)] = inputDesign + full_sample[:, dimension] = outputDesign + full_description = list(inputDesign.getDescription()) + full_description.append(outputDesign.getDescription()[0]) + full_sample.setDescription(full_description) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 49-58 + +.. code-block:: Python + + marginal_distribution = ot.ComposedDistribution( + [ + ot.KernelSmoothing().build(full_sample.getMarginal(i)) + for i in range(1 + dimension) + ] + ) + clouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution) + _ = otv.View(clouds, figure_kw={"figsize": (6.0, 6.0)}) + + + + +.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_borgonovo_sensitivity_001.png + :alt: plot borgonovo sensitivity + :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_borgonovo_sensitivity_001.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 59-62 + +.. code-block:: Python + + output_distribution = ot.KernelSmoothing().build(outputDesign) + _ = otv.View(output_distribution.drawPDF()) + + + + +.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_borgonovo_sensitivity_002.png + :alt: plot borgonovo sensitivity + :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_borgonovo_sensitivity_002.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 63-65 + +Perform sensitivity analysis +---------------------------- + +.. GENERATED FROM PYTHON SOURCE LINES 67-68 + +Create X/Y data + +.. GENERATED FROM PYTHON SOURCE LINES 68-73 + +.. code-block:: Python + + ot.RandomGenerator.SetSeed(0) + size = 10000 + inputDesign = ot.SobolIndicesExperiment(distribution, size).generate() + outputDesign = model(inputDesign) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 74-75 + +Compute first order indices using the Saltelli estimator + +.. GENERATED FROM PYTHON SOURCE LINES 75-79 + +.. code-block:: Python + + sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size) + computed_first_order = sensitivityAnalysis.getFirstOrderIndices() + computed_total_order = sensitivityAnalysis.getTotalOrderIndices() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 80-81 + +Compare with exact results + +.. GENERATED FROM PYTHON SOURCE LINES 81-91 + +.. code-block:: Python + + print("Sample size : ", size) + # First order + # Compute absolute error (the LRE cannot be computed, + # because S can be zero) + print("Computed first order = ", computed_first_order) + print("Exact first order = ", exact_first_order) + # Total order + print("Computed total order = ", computed_total_order) + print("Exact total order = ", exact_total_order) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + Sample size : 10000 + Computed first order = [0.166136,0.157381,0.623798] + Exact first order = [0.157895,0.157895,0.631579] + Computed total order = [0.207054,0.2159,0.623765] + Exact total order = [0.210526,0.210526,0.631579] + + + + +.. GENERATED FROM PYTHON SOURCE LINES 92-94 + +.. code-block:: Python + + _ = otv.View(sensitivityAnalysis.draw()) + + + + +.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_borgonovo_sensitivity_003.png + :alt: Sobol' indices - SaltelliSensitivityAlgorithm + :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_borgonovo_sensitivity_003.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 95-96 + +.. code-block:: Python + + otv.View.ShowAll() + + + + + + + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** (0 minutes 1.145 seconds) + + +.. _sphx_glr_download_auto_examples_sensitivity_problems_plot_borgonovo_sensitivity.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: plot_borgonovo_sensitivity.ipynb ` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: plot_borgonovo_sensitivity.py ` + + .. container:: sphx-glr-download sphx-glr-download-zip + + :download:`Download zipped: plot_borgonovo_sensitivity.zip ` diff --git a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_convergence_ishigami.rst b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_convergence_ishigami.rst index bf50767a2b0..ceea9c95a27 100644 --- a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_convergence_ishigami.rst +++ b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_convergence_ishigami.rst @@ -463,7 +463,7 @@ Use polynomial chaos. Elapsed = 0.1 (s), Sample size = 160 Elapsed = 0.6 (s), Sample size = 320 Elapsed = 3.1 (s), Sample size = 640 - Elapsed = 18.60 (s) + Elapsed = 18.70 (s) @@ -483,7 +483,7 @@ Use polynomial chaos. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 44.837 seconds) + **Total running time of the script:** (0 minutes 45.054 seconds) .. _sphx_glr_download_auto_examples_sensitivity_problems_plot_convergence_ishigami.py: diff --git a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_dirichlet_sensitivity.rst b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_dirichlet_sensitivity.rst new file mode 100644 index 00000000000..e108acad3cb --- /dev/null +++ b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_dirichlet_sensitivity.rst @@ -0,0 +1,340 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "auto_examples/sensitivity_problems/plot_dirichlet_sensitivity.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + :ref:`Go to the end ` + to download the full example code. + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_auto_examples_sensitivity_problems_plot_dirichlet_sensitivity.py: + + +Benchmark the Dirichlet test function +===================================== + +.. GENERATED FROM PYTHON SOURCE LINES 7-11 + +.. code-block:: Python + + import openturns as ot + import otbenchmark as otb + import openturns.viewer as otv + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 12-15 + +.. code-block:: Python + + problem = otb.DirichletSensitivity() + print(problem) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + name = Dirichlet + distribution = ComposedDistribution(Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), IndependentCopula(dimension = 3)) + function = class=PythonEvaluation name=DirichletFunction + firstOrderIndices = [0.525547,0.233577,0.131387] + totalOrderIndices = [0.620438,0.310219,0.182482] + + + + +.. GENERATED FROM PYTHON SOURCE LINES 16-19 + +.. code-block:: Python + + distribution = problem.getInputDistribution() + model = problem.getFunction() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 20-21 + +Exact first and total order + +.. GENERATED FROM PYTHON SOURCE LINES 21-24 + +.. code-block:: Python + + exact_first_order = problem.getFirstOrderIndices() + exact_first_order + + + + + + +.. raw:: html + +
+ class=Point name=Unnamed dimension=3 values=[0.525547,0.233577,0.131387] +
+
+
+ +.. GENERATED FROM PYTHON SOURCE LINES 25-28 + +.. code-block:: Python + + exact_total_order = problem.getTotalOrderIndices() + exact_total_order + + + + + + +.. raw:: html + +
+ class=Point name=Unnamed dimension=3 values=[0.620438,0.310219,0.182482] +
+
+
+ +.. GENERATED FROM PYTHON SOURCE LINES 29-31 + +Plot the function +----------------- + +.. GENERATED FROM PYTHON SOURCE LINES 33-34 + +Create X/Y data + +.. GENERATED FROM PYTHON SOURCE LINES 34-39 + +.. code-block:: Python + + ot.RandomGenerator.SetSeed(0) + size = 200 + inputDesign = ot.MonteCarloExperiment(distribution, size).generate() + outputDesign = model(inputDesign) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 40-48 + +.. code-block:: Python + + dimension = distribution.getDimension() + full_sample = ot.Sample(size, 1 + dimension) + full_sample[:, range(dimension)] = inputDesign + full_sample[:, dimension] = outputDesign + full_description = list(inputDesign.getDescription()) + full_description.append(outputDesign.getDescription()[0]) + full_sample.setDescription(full_description) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 49-58 + +.. code-block:: Python + + marginal_distribution = ot.ComposedDistribution( + [ + ot.KernelSmoothing().build(full_sample.getMarginal(i)) + for i in range(1 + dimension) + ] + ) + clouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution) + _ = otv.View(clouds, figure_kw={"figsize": (6.0, 6.0)}) + + + + +.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_dirichlet_sensitivity_001.png + :alt: plot dirichlet sensitivity + :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_dirichlet_sensitivity_001.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 59-62 + +.. code-block:: Python + + output_distribution = ot.KernelSmoothing().build(outputDesign) + _ = otv.View(output_distribution.drawPDF()) + + + + +.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_dirichlet_sensitivity_002.png + :alt: plot dirichlet sensitivity + :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_dirichlet_sensitivity_002.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 63-65 + +Perform sensitivity analysis +---------------------------- + +.. GENERATED FROM PYTHON SOURCE LINES 67-68 + +Create X/Y data + +.. GENERATED FROM PYTHON SOURCE LINES 68-73 + +.. code-block:: Python + + ot.RandomGenerator.SetSeed(0) + size = 10000 + inputDesign = ot.SobolIndicesExperiment(distribution, size).generate() + outputDesign = model(inputDesign) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 74-75 + +Compute first order indices using the Saltelli estimator + +.. GENERATED FROM PYTHON SOURCE LINES 75-79 + +.. code-block:: Python + + sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size) + computed_first_order = sensitivityAnalysis.getFirstOrderIndices() + computed_total_order = sensitivityAnalysis.getTotalOrderIndices() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 80-81 + +Compare with exact results + +.. GENERATED FROM PYTHON SOURCE LINES 81-91 + +.. code-block:: Python + + print("Sample size : ", size) + # First order + # Compute absolute error (the LRE cannot be computed, + # because S can be zero) + print("Computed first order = ", computed_first_order) + print("Exact first order = ", exact_first_order) + # Total order + print("Computed total order = ", computed_total_order) + print("Exact total order = ", exact_total_order) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + Sample size : 10000 + Computed first order = [0.5332,0.240662,0.130402] + Exact first order = [0.525547,0.233577,0.131387] + Computed total order = [0.616112,0.301247,0.180961] + Exact total order = [0.620438,0.310219,0.182482] + + + + +.. GENERATED FROM PYTHON SOURCE LINES 92-94 + +.. code-block:: Python + + _ = otv.View(sensitivityAnalysis.draw()) + + + + +.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_dirichlet_sensitivity_003.png + :alt: Sobol' indices - SaltelliSensitivityAlgorithm + :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_dirichlet_sensitivity_003.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 95-96 + +.. code-block:: Python + + otv.View.ShowAll() + + + + + + + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** (0 minutes 2.273 seconds) + + +.. _sphx_glr_download_auto_examples_sensitivity_problems_plot_dirichlet_sensitivity.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: plot_dirichlet_sensitivity.ipynb ` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: plot_dirichlet_sensitivity.py ` + + .. container:: sphx-glr-download sphx-glr-download-zip + + :download:`Download zipped: plot_dirichlet_sensitivity.zip ` diff --git a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_flood_sensitivity.rst b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_flood_sensitivity.rst new file mode 100644 index 00000000000..91a7016915e --- /dev/null +++ b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_flood_sensitivity.rst @@ -0,0 +1,350 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "auto_examples/sensitivity_problems/plot_flood_sensitivity.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + :ref:`Go to the end ` + to download the full example code. + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_auto_examples_sensitivity_problems_plot_flood_sensitivity.py: + + +Benchmark the Flooding test function +==================================== + +.. GENERATED FROM PYTHON SOURCE LINES 7-11 + +.. code-block:: Python + + import openturns as ot + import otbenchmark as otb + import openturns.viewer as otv + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 12-14 + +.. code-block:: Python + + problem = otb.FloodingSensitivity() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 15-17 + +.. code-block:: Python + + print(problem) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + name = Flooding + distribution = ComposedDistribution(TruncatedDistribution(Gumbel(beta = 558, gamma = 1013), bounds = [0, (19000.8) +inf[), TruncatedDistribution(Normal(mu = 30, sigma = 7.5), bounds = [0, (87.3797) +inf[), Uniform(a = 49, b = 51), Uniform(a = 54, b = 56), Uniform(a = 7, b = 9), Triangular(a = 55, m = 55.5, b = 56), Triangular(a = 4990, m = 5000, b = 5010), Triangular(a = 295, m = 300, b = 305), IndependentCopula(dimension = 8)) + function = [Q,Ks,Zv,Zm,Hd,Zb,L,B]->[(Q / (Ks * B * sqrt((Zm - Zv) / L)))^(3.0 / 5.0) + Zv - Zb - Hd] + firstOrderIndices = [0.38,0.13,0.25,0,0.19,0.02,0,0] + totalOrderIndices = [0.4,0.15,0.25,0.01,0.19,0.02,0,0] + + + + +.. GENERATED FROM PYTHON SOURCE LINES 18-21 + +.. code-block:: Python + + distribution = problem.getInputDistribution() + model = problem.getFunction() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 22-23 + +Exact first and total order + +.. GENERATED FROM PYTHON SOURCE LINES 23-26 + +.. code-block:: Python + + exact_first_order = problem.getFirstOrderIndices() + exact_first_order + + + + + + +.. raw:: html + +
+ class=Point name=Unnamed dimension=8 values=[0.38,0.13,0.25,0,0.19,0.02,0,0] +
+
+
+ +.. GENERATED FROM PYTHON SOURCE LINES 27-30 + +.. code-block:: Python + + exact_total_order = problem.getTotalOrderIndices() + exact_total_order + + + + + + +.. raw:: html + +
+ class=Point name=Unnamed dimension=8 values=[0.4,0.15,0.25,0.01,0.19,0.02,0,0] +
+
+
+ +.. GENERATED FROM PYTHON SOURCE LINES 31-33 + +Plot the function +----------------- + +.. GENERATED FROM PYTHON SOURCE LINES 35-36 + +Create X/Y data + +.. GENERATED FROM PYTHON SOURCE LINES 36-41 + +.. code-block:: Python + + ot.RandomGenerator.SetSeed(0) + size = 200 + inputDesign = ot.MonteCarloExperiment(distribution, size).generate() + outputDesign = model(inputDesign) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 42-50 + +.. code-block:: Python + + dimension = distribution.getDimension() + full_sample = ot.Sample(size, 1 + dimension) + full_sample[:, range(dimension)] = inputDesign + full_sample[:, dimension] = outputDesign + full_description = list(inputDesign.getDescription()) + full_description.append(outputDesign.getDescription()[0]) + full_sample.setDescription(full_description) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 51-60 + +.. code-block:: Python + + marginal_distribution = ot.ComposedDistribution( + [ + ot.KernelSmoothing().build(full_sample.getMarginal(i)) + for i in range(1 + dimension) + ] + ) + clouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution) + _ = otv.View(clouds, figure_kw={"figsize": (10.0, 10.0)}) + + + + +.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_flood_sensitivity_001.png + :alt: plot flood sensitivity + :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_flood_sensitivity_001.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 61-64 + +.. code-block:: Python + + output_distribution = ot.KernelSmoothing().build(outputDesign) + _ = otv.View(output_distribution.drawPDF()) + + + + +.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_flood_sensitivity_002.png + :alt: plot flood sensitivity + :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_flood_sensitivity_002.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 65-67 + +Perform sensitivity analysis +---------------------------- + +.. GENERATED FROM PYTHON SOURCE LINES 69-70 + +Create X/Y data + +.. GENERATED FROM PYTHON SOURCE LINES 70-75 + +.. code-block:: Python + + ot.RandomGenerator.SetSeed(0) + size = 10000 + inputDesign = ot.SobolIndicesExperiment(distribution, size).generate() + outputDesign = model(inputDesign) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 76-77 + +Compute first order indices using the Saltelli estimator + +.. GENERATED FROM PYTHON SOURCE LINES 77-81 + +.. code-block:: Python + + sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size) + computed_first_order = sensitivityAnalysis.getFirstOrderIndices() + computed_total_order = sensitivityAnalysis.getTotalOrderIndices() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 82-83 + +Compare with exact results + +.. GENERATED FROM PYTHON SOURCE LINES 83-91 + +.. code-block:: Python + + print("Sample size : ", size) + # First order + print("Computed first order = ", computed_first_order) + print("Exact first order = ", exact_first_order) + # Total order + print("Computed total order = ", computed_total_order) + print("Exact total order = ", exact_total_order) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + Sample size : 10000 + Computed first order = [0.371175,0.132331,0.26067,0.0122818,0.191486,0.0325675,0.00737938,0.00742294] + Exact first order = [0.38,0.13,0.25,0,0.19,0.02,0,0] + Computed total order = [0.413371,0.145399,0.24342,0.00473238,0.181701,0.0226346,5.5996e-06,0.000250305] + Exact total order = [0.4,0.15,0.25,0.01,0.19,0.02,0,0] + + + + +.. GENERATED FROM PYTHON SOURCE LINES 92-94 + +.. code-block:: Python + + _ = otv.View(sensitivityAnalysis.draw()) + + + + +.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_flood_sensitivity_003.png + :alt: Sobol' indices - SaltelliSensitivityAlgorithm + :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_flood_sensitivity_003.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 95-96 + +.. code-block:: Python + + otv.View.ShowAll() + + + + + + + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** (0 minutes 2.451 seconds) + + +.. _sphx_glr_download_auto_examples_sensitivity_problems_plot_flood_sensitivity.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: plot_flood_sensitivity.ipynb ` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: plot_flood_sensitivity.py ` + + .. container:: sphx-glr-download sphx-glr-download-zip + + :download:`Download zipped: plot_flood_sensitivity.zip ` diff --git a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_gaussian_product_sensitivity.rst b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_gaussian_product_sensitivity.rst new file mode 100644 index 00000000000..00565812625 --- /dev/null +++ b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_gaussian_product_sensitivity.rst @@ -0,0 +1,352 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "auto_examples/sensitivity_problems/plot_gaussian_product_sensitivity.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + :ref:`Go to the end ` + to download the full example code. + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_auto_examples_sensitivity_problems_plot_gaussian_product_sensitivity.py: + + +Benchmark the gaussian product test function +============================================ + +.. GENERATED FROM PYTHON SOURCE LINES 7-11 + +.. code-block:: Python + + import openturns as ot + import otbenchmark as otb + import openturns.viewer as otv + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 12-14 + +.. code-block:: Python + + problem = otb.GaussianProductSensitivity() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 15-17 + +.. code-block:: Python + + print(problem) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + name = GaussianProduct + distribution = ComposedDistribution(Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), IndependentCopula(dimension = 2)) + function = class=PythonEvaluation name=OpenTURNSPythonFunction + firstOrderIndices = [0,0] + totalOrderIndices = [1,1] + + + + +.. GENERATED FROM PYTHON SOURCE LINES 18-21 + +.. code-block:: Python + + distribution = problem.getInputDistribution() + model = problem.getFunction() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 22-23 + +Exact first and total order + +.. GENERATED FROM PYTHON SOURCE LINES 23-26 + +.. code-block:: Python + + exact_first_order = problem.getFirstOrderIndices() + print(exact_first_order) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + [0,0] + + + + +.. GENERATED FROM PYTHON SOURCE LINES 27-30 + +.. code-block:: Python + + exact_total_order = problem.getTotalOrderIndices() + print(exact_total_order) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + [1,1] + + + + +.. GENERATED FROM PYTHON SOURCE LINES 31-33 + +Plot the function +----------------- + +.. GENERATED FROM PYTHON SOURCE LINES 35-36 + +Create X/Y data + +.. GENERATED FROM PYTHON SOURCE LINES 36-41 + +.. code-block:: Python + + ot.RandomGenerator.SetSeed(0) + size = 200 + inputDesign = ot.MonteCarloExperiment(distribution, size).generate() + outputDesign = model(inputDesign) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 42-50 + +.. code-block:: Python + + dimension = distribution.getDimension() + full_sample = ot.Sample(size, 1 + dimension) + full_sample[:, range(dimension)] = inputDesign + full_sample[:, dimension] = outputDesign + full_description = list(inputDesign.getDescription()) + full_description.append(outputDesign.getDescription()[0]) + full_sample.setDescription(full_description) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 51-60 + +.. code-block:: Python + + marginal_distribution = ot.ComposedDistribution( + [ + ot.KernelSmoothing().build(full_sample.getMarginal(i)) + for i in range(1 + dimension) + ] + ) + clouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution) + view = otv.View(clouds, figure_kw={"figsize": (6.0, 6.0)}) + + + + +.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_gaussian_product_sensitivity_001.png + :alt: plot gaussian product sensitivity + :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_gaussian_product_sensitivity_001.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 61-64 + +.. code-block:: Python + + output_distribution = ot.KernelSmoothing().build(outputDesign) + _ = otv.View(output_distribution.drawPDF()) + + + + +.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_gaussian_product_sensitivity_002.png + :alt: plot gaussian product sensitivity + :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_gaussian_product_sensitivity_002.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 65-67 + +Perform sensitivity analysis +---------------------------- + +.. GENERATED FROM PYTHON SOURCE LINES 69-70 + +Create X/Y data + +.. GENERATED FROM PYTHON SOURCE LINES 70-75 + +.. code-block:: Python + + ot.RandomGenerator.SetSeed(0) + size = 10000 + inputDesign = ot.SobolIndicesExperiment(distribution, size).generate() + outputDesign = model(inputDesign) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 76-77 + +Compute first order indices using the Saltelli estimator + +.. GENERATED FROM PYTHON SOURCE LINES 77-81 + +.. code-block:: Python + + sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size) + computed_first_order = sensitivityAnalysis.getFirstOrderIndices() + computed_total_order = sensitivityAnalysis.getTotalOrderIndices() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 82-83 + +Compare with exact results + +.. GENERATED FROM PYTHON SOURCE LINES 83-93 + +.. code-block:: Python + + print("Sample size : ", size) + # First order + # Compute absolute error (the LRE cannot be computed, + # because S can be zero) + print("Computed first order = ", computed_first_order) + print("Exact first order = ", exact_first_order) + # Total order + print("Computed total order = ", computed_total_order) + print("Exact total order = ", exact_total_order) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + Sample size : 10000 + Computed first order = [0.00444004,-0.030873] + Exact first order = [0,0] + Computed total order = [1.02181,0.996161] + Exact total order = [1,1] + + + + +.. GENERATED FROM PYTHON SOURCE LINES 94-96 + +.. code-block:: Python + + _ = otv.View(sensitivityAnalysis.draw()) + + + + +.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_gaussian_product_sensitivity_003.png + :alt: Sobol' indices - SaltelliSensitivityAlgorithm + :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_gaussian_product_sensitivity_003.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 97-98 + +.. code-block:: Python + + otv.View.ShowAll() + + + + + + + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** (0 minutes 1.658 seconds) + + +.. _sphx_glr_download_auto_examples_sensitivity_problems_plot_gaussian_product_sensitivity.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: plot_gaussian_product_sensitivity.ipynb ` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: plot_gaussian_product_sensitivity.py ` + + .. container:: sphx-glr-download sphx-glr-download-zip + + :download:`Download zipped: plot_gaussian_product_sensitivity.zip ` diff --git a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_gaussian_sum.rst b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_gaussian_sum.rst new file mode 100644 index 00000000000..80defe138fd --- /dev/null +++ b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_gaussian_sum.rst @@ -0,0 +1,352 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "auto_examples/sensitivity_problems/plot_gaussian_sum.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + :ref:`Go to the end ` + to download the full example code. + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_auto_examples_sensitivity_problems_plot_gaussian_sum.py: + + +Benchmark the gaussian sum test function +======================================== + +.. GENERATED FROM PYTHON SOURCE LINES 7-11 + +.. code-block:: Python + + import openturns as ot + import otbenchmark as otb + import openturns.viewer as otv + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 12-14 + +.. code-block:: Python + + problem = otb.GaussianSumSensitivity() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 15-17 + +.. code-block:: Python + + print(problem) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + name = GaussianSum + distribution = ComposedDistribution(Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), IndependentCopula(dimension = 2)) + function = class=PythonEvaluation name=OpenTURNSPythonFunction + firstOrderIndices = [0.5,0.5] + totalOrderIndices = [0.5,0.5] + + + + +.. GENERATED FROM PYTHON SOURCE LINES 18-21 + +.. code-block:: Python + + distribution = problem.getInputDistribution() + model = problem.getFunction() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 22-23 + +Exact first and total order + +.. GENERATED FROM PYTHON SOURCE LINES 23-26 + +.. code-block:: Python + + exact_first_order = problem.getFirstOrderIndices() + print(exact_first_order) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + [0.5,0.5] + + + + +.. GENERATED FROM PYTHON SOURCE LINES 27-30 + +.. code-block:: Python + + exact_total_order = problem.getTotalOrderIndices() + print(exact_total_order) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + [0.5,0.5] + + + + +.. GENERATED FROM PYTHON SOURCE LINES 31-33 + +Plot the function +----------------- + +.. GENERATED FROM PYTHON SOURCE LINES 35-36 + +Create X/Y data + +.. GENERATED FROM PYTHON SOURCE LINES 36-41 + +.. code-block:: Python + + ot.RandomGenerator.SetSeed(0) + size = 200 + inputDesign = ot.MonteCarloExperiment(distribution, size).generate() + outputDesign = model(inputDesign) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 42-50 + +.. code-block:: Python + + dimension = distribution.getDimension() + full_sample = ot.Sample(size, 1 + dimension) + full_sample[:, range(dimension)] = inputDesign + full_sample[:, dimension] = outputDesign + full_description = list(inputDesign.getDescription()) + full_description.append(outputDesign.getDescription()[0]) + full_sample.setDescription(full_description) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 51-60 + +.. code-block:: Python + + marginal_distribution = ot.ComposedDistribution( + [ + ot.KernelSmoothing().build(full_sample.getMarginal(i)) + for i in range(1 + dimension) + ] + ) + clouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution) + view = otv.View(clouds, figure_kw={"figsize": (6.0, 6.0)}) + + + + +.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_gaussian_sum_001.png + :alt: plot gaussian sum + :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_gaussian_sum_001.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 61-64 + +.. code-block:: Python + + output_distribution = ot.KernelSmoothing().build(outputDesign) + _ = otv.View(output_distribution.drawPDF()) + + + + +.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_gaussian_sum_002.png + :alt: plot gaussian sum + :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_gaussian_sum_002.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 65-67 + +Perform sensitivity analysis +---------------------------- + +.. GENERATED FROM PYTHON SOURCE LINES 69-70 + +Create X/Y data + +.. GENERATED FROM PYTHON SOURCE LINES 70-75 + +.. code-block:: Python + + ot.RandomGenerator.SetSeed(0) + size = 10000 + inputDesign = ot.SobolIndicesExperiment(distribution, size).generate() + outputDesign = model(inputDesign) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 76-77 + +Compute first order indices using the Saltelli estimator + +.. GENERATED FROM PYTHON SOURCE LINES 77-81 + +.. code-block:: Python + + sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size) + computed_first_order = sensitivityAnalysis.getFirstOrderIndices() + computed_total_order = sensitivityAnalysis.getTotalOrderIndices() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 82-83 + +Compare with exact results + +.. GENERATED FROM PYTHON SOURCE LINES 83-93 + +.. code-block:: Python + + print("Sample size : ", size) + # First order + # Compute absolute error (the LRE cannot be computed, + # because S can be zero) + print("Computed first order = ", computed_first_order) + print("Exact first order = ", exact_first_order) + # Total order + print("Computed total order = ", computed_total_order) + print("Exact total order = ", exact_total_order) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + Sample size : 10000 + Computed first order = [0.513373,0.504889] + Exact first order = [0.5,0.5] + Computed total order = [0.50317,0.494875] + Exact total order = [0.5,0.5] + + + + +.. GENERATED FROM PYTHON SOURCE LINES 94-96 + +.. code-block:: Python + + _ = otv.View(sensitivityAnalysis.draw()) + + + + +.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_gaussian_sum_003.png + :alt: Sobol' indices - SaltelliSensitivityAlgorithm + :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_gaussian_sum_003.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 97-98 + +.. code-block:: Python + + otv.View.ShowAll() + + + + + + + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** (0 minutes 1.770 seconds) + + +.. _sphx_glr_download_auto_examples_sensitivity_problems_plot_gaussian_sum.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: plot_gaussian_sum.ipynb ` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: plot_gaussian_sum.py ` + + .. container:: sphx-glr-download sphx-glr-download-zip + + :download:`Download zipped: plot_gaussian_sum.zip ` diff --git a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_gsobol_sensitivity.rst b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_gsobol_sensitivity.rst new file mode 100644 index 00000000000..eada954f850 --- /dev/null +++ b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_gsobol_sensitivity.rst @@ -0,0 +1,361 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "auto_examples/sensitivity_problems/plot_gsobol_sensitivity.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + :ref:`Go to the end ` + to download the full example code. + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_auto_examples_sensitivity_problems_plot_gsobol_sensitivity.py: + + +Benchmark the G-Sobol test function +=================================== + +.. GENERATED FROM PYTHON SOURCE LINES 7-11 + +.. code-block:: Python + + import openturns as ot + import otbenchmark as otb + import openturns.viewer as otv + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 12-15 + +.. code-block:: Python + + problem = otb.GSobolSensitivity() + print(problem) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + name = GSobol + distribution = ComposedDistribution(Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), IndependentCopula(dimension = 3)) + function = class=PythonEvaluation name=OpenTURNSPythonFunction + firstOrderIndices = [0.986712,0.00986712,9.86712e-05] + totalOrderIndices = [0.990034,0.0131566,0.000132] + + + + +.. GENERATED FROM PYTHON SOURCE LINES 16-19 + +.. code-block:: Python + + distribution = problem.getInputDistribution() + model = problem.getFunction() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 20-21 + +Exact first and total order + +.. GENERATED FROM PYTHON SOURCE LINES 21-24 + +.. code-block:: Python + + exact_first_order = problem.getFirstOrderIndices() + exact_first_order + + + + + + +.. raw:: html + +
+ class=Point name=Unnamed dimension=3 values=[0.986712,0.00986712,9.86712e-05] +
+
+
+ +.. GENERATED FROM PYTHON SOURCE LINES 25-28 + +.. code-block:: Python + + exact_total_order = problem.getTotalOrderIndices() + print(exact_total_order) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + [0.990034,0.0131566,0.000132] + + + + +.. GENERATED FROM PYTHON SOURCE LINES 29-31 + +Plot function +------------- + +.. GENERATED FROM PYTHON SOURCE LINES 33-34 + +Create X/Y data + +.. GENERATED FROM PYTHON SOURCE LINES 34-39 + +.. code-block:: Python + + ot.RandomGenerator.SetSeed(0) + size = 200 + inputDesign = ot.MonteCarloExperiment(distribution, size).generate() + outputDesign = model(inputDesign) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 40-48 + +.. code-block:: Python + + dimension = distribution.getDimension() + full_sample = ot.Sample(size, 1 + dimension) + full_sample[:, range(dimension)] = inputDesign + full_sample[:, dimension] = outputDesign + full_description = list(inputDesign.getDescription()) + full_description.append(outputDesign.getDescription()[0]) + full_sample.setDescription(full_description) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 49-59 + +.. code-block:: Python + + marginal_distribution = ot.ComposedDistribution( + [ + ot.KernelSmoothing().build(full_sample.getMarginal(i)) + for i in range(1 + dimension) + ] + ) + clouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution) + _ = otv.View(clouds, figure_kw={"figsize": (6.0, 6.0)}) + + + + + +.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_gsobol_sensitivity_001.png + :alt: plot gsobol sensitivity + :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_gsobol_sensitivity_001.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 60-61 + +Create X/Y data + +.. GENERATED FROM PYTHON SOURCE LINES 61-66 + +.. code-block:: Python + + ot.RandomGenerator.SetSeed(0) + size = 1000 + inputDesign = ot.MonteCarloExperiment(distribution, size).generate() + outputDesign = model(inputDesign) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 67-70 + +.. code-block:: Python + + output_distribution = ot.KernelSmoothing().build(outputDesign) + _ = otv.View(output_distribution.drawPDF()) + + + + +.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_gsobol_sensitivity_002.png + :alt: plot gsobol sensitivity + :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_gsobol_sensitivity_002.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 71-73 + +Perform SA +---------- + +.. GENERATED FROM PYTHON SOURCE LINES 75-76 + +Create X/Y data + +.. GENERATED FROM PYTHON SOURCE LINES 76-81 + +.. code-block:: Python + + ot.RandomGenerator.SetSeed(0) + size = 10000 + inputDesign = ot.SobolIndicesExperiment(distribution, size).generate() + outputDesign = model(inputDesign) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 82-83 + +Compute first order indices using the Saltelli estimator + +.. GENERATED FROM PYTHON SOURCE LINES 83-87 + +.. code-block:: Python + + sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size) + computed_first_order = sensitivityAnalysis.getFirstOrderIndices() + computed_total_order = sensitivityAnalysis.getTotalOrderIndices() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 88-89 + +Compare with exact results + +.. GENERATED FROM PYTHON SOURCE LINES 89-99 + +.. code-block:: Python + + print("Sample size : ", size) + # First order + # Compute absolute error (the LRE cannot be computed, + # because S can be zero) + print("Computed first order = ", computed_first_order) + print("Exact first order = ", exact_first_order) + # Total order + print("Computed total order = ", computed_total_order) + print("Exact total order = ", exact_total_order) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + Sample size : 10000 + Computed first order = [0.978665,0.021486,0.0124414] + Exact first order = [0.986712,0.00986712,9.86712e-05] + Computed total order = [0.979669,0.0104535,-4.12658e-05] + Exact total order = [0.990034,0.0131566,0.000132] + + + + +.. GENERATED FROM PYTHON SOURCE LINES 100-102 + +.. code-block:: Python + + _ = otv.View(sensitivityAnalysis.draw()) + + + + +.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_gsobol_sensitivity_003.png + :alt: Sobol' indices - SaltelliSensitivityAlgorithm + :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_gsobol_sensitivity_003.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 103-104 + +.. code-block:: Python + + otv.View.ShowAll() + + + + + + + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** (0 minutes 2.334 seconds) + + +.. _sphx_glr_download_auto_examples_sensitivity_problems_plot_gsobol_sensitivity.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: plot_gsobol_sensitivity.ipynb ` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: plot_gsobol_sensitivity.py ` + + .. container:: sphx-glr-download sphx-glr-download-zip + + :download:`Download zipped: plot_gsobol_sensitivity.zip ` diff --git a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_ishigami_sensitivity.rst b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_ishigami_sensitivity.rst new file mode 100644 index 00000000000..0ce0ecddc29 --- /dev/null +++ b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_ishigami_sensitivity.rst @@ -0,0 +1,352 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "auto_examples/sensitivity_problems/plot_ishigami_sensitivity.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + :ref:`Go to the end ` + to download the full example code. + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_auto_examples_sensitivity_problems_plot_ishigami_sensitivity.py: + + +Benchmark the Ishigami test function +==================================== + +.. GENERATED FROM PYTHON SOURCE LINES 7-11 + +.. code-block:: Python + + import openturns as ot + import otbenchmark as otb + import openturns.viewer as otv + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 12-14 + +.. code-block:: Python + + problem = otb.IshigamiSensitivity() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 15-17 + +.. code-block:: Python + + print(problem) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + name = Ishigami + distribution = ComposedDistribution(Uniform(a = -3.14159, b = 3.14159), Uniform(a = -3.14159, b = 3.14159), Uniform(a = -3.14159, b = 3.14159), IndependentCopula(dimension = 3)) + function = ParametricEvaluation([X1,X2,X3,a,b]->[sin(X1) + a * sin(X2)^2 + b * X3^4 * sin(X1)], parameters positions=[3,4], parameters=[a : 7, b : 0.1], input positions=[0,1,2]) + firstOrderIndices = [0.313905,0.442411,0] + totalOrderIndices = [0.557589,0.442411,0.243684] + + + + +.. GENERATED FROM PYTHON SOURCE LINES 18-21 + +.. code-block:: Python + + distribution = problem.getInputDistribution() + model = problem.getFunction() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 22-23 + +Exact first and total order + +.. GENERATED FROM PYTHON SOURCE LINES 23-26 + +.. code-block:: Python + + exact_first_order = problem.getFirstOrderIndices() + exact_first_order + + + + + + +.. raw:: html + +
+ class=Point name=Unnamed dimension=3 values=[0.313905,0.442411,0] +
+
+
+ +.. GENERATED FROM PYTHON SOURCE LINES 27-30 + +.. code-block:: Python + + exact_total_order = problem.getTotalOrderIndices() + exact_total_order + + + + + + +.. raw:: html + +
+ class=Point name=Unnamed dimension=3 values=[0.557589,0.442411,0.243684] +
+
+
+ +.. GENERATED FROM PYTHON SOURCE LINES 31-33 + +Plot the function +----------------- + +.. GENERATED FROM PYTHON SOURCE LINES 35-36 + +Create X/Y data + +.. GENERATED FROM PYTHON SOURCE LINES 36-41 + +.. code-block:: Python + + ot.RandomGenerator.SetSeed(0) + size = 200 + inputDesign = ot.MonteCarloExperiment(distribution, size).generate() + outputDesign = model(inputDesign) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 42-50 + +.. code-block:: Python + + dimension = distribution.getDimension() + full_sample = ot.Sample(size, 1 + dimension) + full_sample[:, range(dimension)] = inputDesign + full_sample[:, dimension] = outputDesign + full_description = list(inputDesign.getDescription()) + full_description.append(outputDesign.getDescription()[0]) + full_sample.setDescription(full_description) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 51-60 + +.. code-block:: Python + + marginal_distribution = ot.ComposedDistribution( + [ + ot.KernelSmoothing().build(full_sample.getMarginal(i)) + for i in range(1 + dimension) + ] + ) + clouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution) + _ = otv.View(clouds, figure_kw={"figsize": (6.0, 6.0)}) + + + + +.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_ishigami_sensitivity_001.png + :alt: plot ishigami sensitivity + :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_ishigami_sensitivity_001.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 61-64 + +.. code-block:: Python + + output_distribution = ot.KernelSmoothing().build(outputDesign) + _ = otv.View(output_distribution.drawPDF()) + + + + +.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_ishigami_sensitivity_002.png + :alt: plot ishigami sensitivity + :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_ishigami_sensitivity_002.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 65-67 + +Perform sensitivity analysis +---------------------------- + +.. GENERATED FROM PYTHON SOURCE LINES 69-70 + +Create X/Y data + +.. GENERATED FROM PYTHON SOURCE LINES 70-75 + +.. code-block:: Python + + ot.RandomGenerator.SetSeed(0) + size = 10000 + inputDesign = ot.SobolIndicesExperiment(distribution, size).generate() + outputDesign = model(inputDesign) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 76-77 + +Compute first order indices using the Saltelli estimator + +.. GENERATED FROM PYTHON SOURCE LINES 77-81 + +.. code-block:: Python + + sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size) + computed_first_order = sensitivityAnalysis.getFirstOrderIndices() + computed_total_order = sensitivityAnalysis.getTotalOrderIndices() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 82-83 + +Compare with exact results + +.. GENERATED FROM PYTHON SOURCE LINES 83-93 + +.. code-block:: Python + + print("Sample size : ", size) + # First order + # Compute absolute error (the LRE cannot be computed, + # because S can be zero) + print("Computed first order = ", computed_first_order) + print("Exact first order = ", exact_first_order) + # Total order + print("Computed total order = ", computed_total_order) + print("Exact total order = ", exact_total_order) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + Sample size : 10000 + Computed first order = [0.302745,0.460846,0.0066916] + Exact first order = [0.313905,0.442411,0] + Computed total order = [0.574996,0.427126,0.256689] + Exact total order = [0.557589,0.442411,0.243684] + + + + +.. GENERATED FROM PYTHON SOURCE LINES 94-96 + +.. code-block:: Python + + _ = otv.View(sensitivityAnalysis.draw()) + + + + +.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_ishigami_sensitivity_003.png + :alt: Sobol' indices - SaltelliSensitivityAlgorithm + :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_ishigami_sensitivity_003.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 97-98 + +.. code-block:: Python + + otv.View.ShowAll() + + + + + + + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** (0 minutes 1.187 seconds) + + +.. _sphx_glr_download_auto_examples_sensitivity_problems_plot_ishigami_sensitivity.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: plot_ishigami_sensitivity.ipynb ` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: plot_ishigami_sensitivity.py ` + + .. container:: sphx-glr-download sphx-glr-download-zip + + :download:`Download zipped: plot_ishigami_sensitivity.zip ` diff --git a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_morris_sensitivity.rst b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_morris_sensitivity.rst new file mode 100644 index 00000000000..3bc2afd5873 --- /dev/null +++ b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_morris_sensitivity.rst @@ -0,0 +1,330 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "auto_examples/sensitivity_problems/plot_morris_sensitivity.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + :ref:`Go to the end ` + to download the full example code. + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_auto_examples_sensitivity_problems_plot_morris_sensitivity.py: + + +Benchmark the Morris test function +================================== + +.. GENERATED FROM PYTHON SOURCE LINES 7-11 + +.. code-block:: Python + + import openturns as ot + import otbenchmark as otb + import openturns.viewer as otv + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 12-15 + +.. code-block:: Python + + problem = otb.MorrisSensitivity() + print(problem) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + name = Morris + distribution = ComposedDistribution(Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), IndependentCopula(dimension = 20)) + function = class=PythonEvaluation name=MorrisFunction + firstOrderIndices = [0.08,0.08,0.06,0.08,0.06,0.13,0.06,0.13,0.13,0.12,0,0,0,0,0,0,0,0,0,0]#20 + totalOrderIndices = [0.11,0.11,0.06,0.11,0.06,0.13,0.06,0.13,0.13,0.12,0,0,0,0,0,0,0,0,0,0]#20 + + + + +.. GENERATED FROM PYTHON SOURCE LINES 16-19 + +.. code-block:: Python + + distribution = problem.getInputDistribution() + model = problem.getFunction() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 20-21 + +Exact first and total order + +.. GENERATED FROM PYTHON SOURCE LINES 21-24 + +.. code-block:: Python + + exact_first_order = problem.getFirstOrderIndices() + exact_first_order + + + + + + +.. raw:: html + +
+ class=Point name=Unnamed dimension=20 values=[0.08,0.08,0.06,0.08,0.06,0.13,0.06,0.13,0.13,0.12,0,0,0,0,0,0,0,0,0,0] +
+
+
+ +.. GENERATED FROM PYTHON SOURCE LINES 25-28 + +.. code-block:: Python + + exact_total_order = problem.getTotalOrderIndices() + exact_total_order + + + + + + +.. raw:: html + +
+ class=Point name=Unnamed dimension=20 values=[0.11,0.11,0.06,0.11,0.06,0.13,0.06,0.13,0.13,0.12,0,0,0,0,0,0,0,0,0,0] +
+
+
+ +.. GENERATED FROM PYTHON SOURCE LINES 29-31 + +Plot the function +----------------- + +.. GENERATED FROM PYTHON SOURCE LINES 33-34 + +Create X/Y data + +.. GENERATED FROM PYTHON SOURCE LINES 34-39 + +.. code-block:: Python + + ot.RandomGenerator.SetSeed(0) + size = 200 + inputDesign = ot.MonteCarloExperiment(distribution, size).generate() + outputDesign = model(inputDesign) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 40-65 + +.. code-block:: Python + + dimension = distribution.getDimension() + nbcolumns = 4 + nbrows = int(dimension / nbcolumns) + grid = ot.GridLayout(nbrows, nbcolumns) + inputDescription = distribution.getDescription() + outputDescription = model.getOutputDescription()[0] + index = 0 + for i in range(nbrows): + for j in range(nbcolumns): + graph = ot.Graph( + "n=%d" % (size), inputDescription[index], outputDescription, True, "" + ) + sample = ot.Sample(size, 2) + sample[:, 0] = inputDesign[:, index] + sample[:, 1] = outputDesign[:, 0] + cloud = ot.Cloud(sample) + graph.add(cloud) + grid.setGraph(i, j, graph) + index += 1 + _ = otv.View(grid, figure_kw={"figsize": (10.0, 10.0)}) + + # % + output_distribution = ot.KernelSmoothing().build(outputDesign) + _ = otv.View(output_distribution.drawPDF()) + + + + +.. rst-class:: sphx-glr-horizontal + + + * + + .. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_morris_sensitivity_001.png + :alt: , n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200 + :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_morris_sensitivity_001.png + :class: sphx-glr-multi-img + + * + + .. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_morris_sensitivity_002.png + :alt: plot morris sensitivity + :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_morris_sensitivity_002.png + :class: sphx-glr-multi-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 66-68 + +Perform sensitivity analysis +---------------------------- + +.. GENERATED FROM PYTHON SOURCE LINES 70-71 + +Create X/Y data + +.. GENERATED FROM PYTHON SOURCE LINES 71-76 + +.. code-block:: Python + + ot.RandomGenerator.SetSeed(0) + size = 30 + inputDesign = ot.SobolIndicesExperiment(distribution, size).generate() + outputDesign = model(inputDesign) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 77-78 + +Compute first order indices using the Saltelli estimator + +.. GENERATED FROM PYTHON SOURCE LINES 78-82 + +.. code-block:: Python + + sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size) + computed_first_order = sensitivityAnalysis.getFirstOrderIndices() + computed_total_order = sensitivityAnalysis.getTotalOrderIndices() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 83-84 + +Compare with exact results + +.. GENERATED FROM PYTHON SOURCE LINES 84-94 + +.. code-block:: Python + + print("Sample size : ", size) + # First order + # Compute absolute error (the LRE cannot be computed, + # because S can be zero) + print("Computed first order = ", computed_first_order) + print("Exact first order = ", exact_first_order) + # Total order + print("Computed total order = ", computed_total_order) + print("Exact total order = ", exact_total_order) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + Sample size : 30 + Computed first order = [0.0607395,0.20637,0.267428,0.262258,0.314109,0.61827,0.294943,0.439378,0.246433,0.274413,0.221509,0.18807,0.218819,0.228214,0.208058,0.215507,0.23585,0.21719,0.237087,0.214426]#20 + Exact first order = [0.08,0.08,0.06,0.08,0.06,0.13,0.06,0.13,0.13,0.12,0,0,0,0,0,0,0,0,0,0]#20 + Computed total order = [0.0311589,0.22498,0.219259,0.078154,-0.0539979,-0.0392477,-0.0216198,0.20086,0.311105,0.0877451,-0.0160964,-0.000247103,-0.0276497,0.000406818,-0.0165498,-0.0183569,-0.0308313,-0.0264689,-0.00283312,-0.00660521]#20 + Exact total order = [0.11,0.11,0.06,0.11,0.06,0.13,0.06,0.13,0.13,0.12,0,0,0,0,0,0,0,0,0,0]#20 + + + + +.. GENERATED FROM PYTHON SOURCE LINES 95-97 + +.. code-block:: Python + + _ = otv.View(sensitivityAnalysis.draw()) + + + + +.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_morris_sensitivity_003.png + :alt: Sobol' indices - SaltelliSensitivityAlgorithm + :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_morris_sensitivity_003.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 98-99 + +.. code-block:: Python + + otv.View.ShowAll() + + + + + + + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** (0 minutes 16.944 seconds) + + +.. _sphx_glr_download_auto_examples_sensitivity_problems_plot_morris_sensitivity.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: plot_morris_sensitivity.ipynb ` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: plot_morris_sensitivity.py ` + + .. container:: sphx-glr-download sphx-glr-download-zip + + :download:`Download zipped: plot_morris_sensitivity.zip ` diff --git a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_nloscillator_sensitivity.rst b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_nloscillator_sensitivity.rst new file mode 100644 index 00000000000..a5b1eb10717 --- /dev/null +++ b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_nloscillator_sensitivity.rst @@ -0,0 +1,340 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "auto_examples/sensitivity_problems/plot_nloscillator_sensitivity.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + :ref:`Go to the end ` + to download the full example code. + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_auto_examples_sensitivity_problems_plot_nloscillator_sensitivity.py: + + +Benchmark the NLOscillator test function +======================================== + +.. GENERATED FROM PYTHON SOURCE LINES 7-11 + +.. code-block:: Python + + import openturns as ot + import otbenchmark as otb + import openturns.viewer as otv + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 12-15 + +.. code-block:: Python + + problem = otb.NLOscillatorSensitivity() + print(problem) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + name = N.L. Oscillator + distribution = ComposedDistribution(class=ParametrizedDistribution parameters=class=LogNormalMuSigma name=Unnamed mu=21.5 sigma=2.15 gamma=0 distribution=class=LogNormal name=LogNormal dimension=1 muLog=3.06308 sigmaLog=0.0997513 gamma=0, class=ParametrizedDistribution parameters=class=LogNormalMuSigma name=Unnamed mu=1.5 sigma=0.15 gamma=0 distribution=class=LogNormal name=LogNormal dimension=1 muLog=0.40049 sigmaLog=0.0997513 gamma=0, class=ParametrizedDistribution parameters=class=LogNormalMuSigma name=Unnamed mu=0.01 sigma=0.001 gamma=0 distribution=class=LogNormal name=LogNormal dimension=1 muLog=-4.61015 sigmaLog=0.0997513 gamma=0, class=ParametrizedDistribution parameters=class=LogNormalMuSigma name=Unnamed mu=1 sigma=0.2 gamma=0 distribution=class=LogNormal name=LogNormal dimension=1 muLog=-0.0196104 sigmaLog=0.198042 gamma=0, class=ParametrizedDistribution parameters=class=LogNormalMuSigma name=Unnamed mu=0.01 sigma=0.002 gamma=0 distribution=class=LogNormal name=LogNormal dimension=1 muLog=-4.62478 sigmaLog=0.198042 gamma=0, class=ParametrizedDistribution parameters=class=LogNormalMuSigma name=Unnamed mu=0.05 sigma=0.02 gamma=0 distribution=class=LogNormal name=LogNormal dimension=1 muLog=-3.06994 sigmaLog=0.385253 gamma=0, class=ParametrizedDistribution parameters=class=LogNormalMuSigma name=Unnamed mu=0.02 sigma=0.01 gamma=0 distribution=class=LogNormal name=LogNormal dimension=1 muLog=-4.02359 sigmaLog=0.472381 gamma=0, class=ParametrizedDistribution parameters=class=LogNormalMuSigma name=Unnamed mu=100 sigma=10 gamma=0 distribution=class=LogNormal name=LogNormal dimension=1 muLog=4.6002 sigmaLog=0.0997513 gamma=0, IndependentCopula(dimension = 8)) + function = class=PythonEvaluation name=OpenTURNSPythonFunction + firstOrderIndices = [0.4,0.03,0.09,0.18,0.12,0.05,0.05,0] + totalOrderIndices = [0.4,0.04,0.1,0.23,0.16,0.07,0.06,0.01] + + + + +.. GENERATED FROM PYTHON SOURCE LINES 16-19 + +.. code-block:: Python + + distribution = problem.getInputDistribution() + model = problem.getFunction() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 20-21 + +Exact first and total order + +.. GENERATED FROM PYTHON SOURCE LINES 21-24 + +.. code-block:: Python + + exact_first_order = problem.getFirstOrderIndices() + exact_first_order + + + + + + +.. raw:: html + +
+ class=Point name=Unnamed dimension=8 values=[0.4,0.03,0.09,0.18,0.12,0.05,0.05,0] +
+
+
+ +.. GENERATED FROM PYTHON SOURCE LINES 25-28 + +.. code-block:: Python + + exact_total_order = problem.getTotalOrderIndices() + exact_total_order + + + + + + +.. raw:: html + +
+ class=Point name=Unnamed dimension=8 values=[0.4,0.04,0.1,0.23,0.16,0.07,0.06,0.01] +
+
+
+ +.. GENERATED FROM PYTHON SOURCE LINES 29-31 + +Plot the function +----------------- + +.. GENERATED FROM PYTHON SOURCE LINES 33-34 + +Create X/Y data + +.. GENERATED FROM PYTHON SOURCE LINES 34-39 + +.. code-block:: Python + + ot.RandomGenerator.SetSeed(0) + size = 200 + inputDesign = ot.MonteCarloExperiment(distribution, size).generate() + outputDesign = model(inputDesign) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 40-48 + +.. code-block:: Python + + dimension = distribution.getDimension() + full_sample = ot.Sample(size, 1 + dimension) + full_sample[:, range(dimension)] = inputDesign + full_sample[:, dimension] = outputDesign + full_description = list(inputDesign.getDescription()) + full_description.append(outputDesign.getDescription()[0]) + full_sample.setDescription(full_description) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 49-58 + +.. code-block:: Python + + marginal_distribution = ot.ComposedDistribution( + [ + ot.KernelSmoothing().build(full_sample.getMarginal(i)) + for i in range(1 + dimension) + ] + ) + clouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution) + _ = otv.View(clouds, figure_kw={"figsize": (10.0, 10.0)}) + + + + +.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_nloscillator_sensitivity_001.png + :alt: plot nloscillator sensitivity + :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_nloscillator_sensitivity_001.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 59-62 + +.. code-block:: Python + + output_distribution = ot.KernelSmoothing().build(outputDesign) + _ = otv.View(output_distribution.drawPDF()) + + + + +.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_nloscillator_sensitivity_002.png + :alt: plot nloscillator sensitivity + :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_nloscillator_sensitivity_002.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 63-65 + +Perform sensitivity analysis +---------------------------- + +.. GENERATED FROM PYTHON SOURCE LINES 67-68 + +Create X/Y data + +.. GENERATED FROM PYTHON SOURCE LINES 68-73 + +.. code-block:: Python + + ot.RandomGenerator.SetSeed(0) + size = 10000 + inputDesign = ot.SobolIndicesExperiment(distribution, size).generate() + outputDesign = model(inputDesign) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 74-75 + +Compute first order indices using the Saltelli estimator + +.. GENERATED FROM PYTHON SOURCE LINES 75-79 + +.. code-block:: Python + + sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size) + computed_first_order = sensitivityAnalysis.getFirstOrderIndices() + computed_total_order = sensitivityAnalysis.getTotalOrderIndices() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 80-81 + +Compare with exact results + +.. GENERATED FROM PYTHON SOURCE LINES 81-91 + +.. code-block:: Python + + print("Sample size : ", size) + # First order + # Compute absolute error (the LRE cannot be computed, + # because S can be zero) + print("Computed first order = ", computed_first_order) + print("Exact first order = ", exact_first_order) + # Total order + print("Computed total order = ", computed_total_order) + print("Exact total order = ", exact_total_order) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + Sample size : 10000 + Computed first order = [0.374552,0.0347919,0.0853174,0.186246,0.120437,0.04356,0.0358369,0.00467826] + Exact first order = [0.4,0.03,0.09,0.18,0.12,0.05,0.05,0] + Computed total order = [0.375092,0.0778412,0.127126,0.279952,0.201389,0.0667096,0.0458004,0.00812321] + Exact total order = [0.4,0.04,0.1,0.23,0.16,0.07,0.06,0.01] + + + + +.. GENERATED FROM PYTHON SOURCE LINES 92-94 + +.. code-block:: Python + + _ = otv.View(sensitivityAnalysis.draw()) + + + + +.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_nloscillator_sensitivity_003.png + :alt: Sobol' indices - SaltelliSensitivityAlgorithm + :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_nloscillator_sensitivity_003.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 95-96 + +.. code-block:: Python + + otv.View.ShowAll() + + + + + + + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** (0 minutes 3.807 seconds) + + +.. _sphx_glr_download_auto_examples_sensitivity_problems_plot_nloscillator_sensitivity.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: plot_nloscillator_sensitivity.ipynb ` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: plot_nloscillator_sensitivity.py ` + + .. container:: sphx-glr-download sphx-glr-download-zip + + :download:`Download zipped: plot_nloscillator_sensitivity.zip ` diff --git a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_oakleyohagan_sensitivity.rst b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_oakleyohagan_sensitivity.rst new file mode 100644 index 00000000000..fde2ab72657 --- /dev/null +++ b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_oakleyohagan_sensitivity.rst @@ -0,0 +1,330 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "auto_examples/sensitivity_problems/plot_oakleyohagan_sensitivity.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + :ref:`Go to the end ` + to download the full example code. + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_auto_examples_sensitivity_problems_plot_oakleyohagan_sensitivity.py: + + +Benchmark the Oakley-O'Hagan test function +========================================== + +.. GENERATED FROM PYTHON SOURCE LINES 7-11 + +.. code-block:: Python + + import openturns as ot + import otbenchmark as otb + import openturns.viewer as otv + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 12-15 + +.. code-block:: Python + + problem = otb.OakleyOHaganSensitivity() + print(problem) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + name = Oakley-O'Hagan + distribution = ComposedDistribution(Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), IndependentCopula(dimension = 15)) + function = class=PythonEvaluation name=OpenTURNSPythonFunction + firstOrderIndices = [0,0,0,0,0,0.02,0.02,0.03,0.05,0.01,0.1,0.14,0.1,0.11,0.12]#15 + totalOrderIndices = [0.06,0.06,0.04,0.05,0.02,0.04,0.06,0.08,0.1,0.04,0.15,0.15,0.14,0.14,0.16]#15 + + + + +.. GENERATED FROM PYTHON SOURCE LINES 16-19 + +.. code-block:: Python + + distribution = problem.getInputDistribution() + model = problem.getFunction() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 20-21 + +Exact first and total order + +.. GENERATED FROM PYTHON SOURCE LINES 21-24 + +.. code-block:: Python + + exact_first_order = problem.getFirstOrderIndices() + print(exact_first_order) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + [0,0,0,0,0,0.02,0.02,0.03,0.05,0.01,0.1,0.14,0.1,0.11,0.12]#15 + + + + +.. GENERATED FROM PYTHON SOURCE LINES 25-31 + +.. code-block:: Python + + exact_total_order = problem.getTotalOrderIndices() + print(exact_total_order) + + + # ## Plot the function + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + [0.06,0.06,0.04,0.05,0.02,0.04,0.06,0.08,0.1,0.04,0.15,0.15,0.14,0.14,0.16]#15 + + + + +.. GENERATED FROM PYTHON SOURCE LINES 32-33 + +Create X/Y data + +.. GENERATED FROM PYTHON SOURCE LINES 33-38 + +.. code-block:: Python + + ot.RandomGenerator.SetSeed(0) + size = 200 + inputDesign = ot.MonteCarloExperiment(distribution, size).generate() + outputDesign = model(inputDesign) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 39-60 + +.. code-block:: Python + + dimension = distribution.getDimension() + nbcolumns = 5 + nbrows = int(dimension / nbcolumns) + grid = ot.GridLayout(nbrows, nbcolumns) + inputDescription = distribution.getDescription() + outputDescription = model.getOutputDescription()[0] + index = 0 + for i in range(nbrows): + for j in range(nbcolumns): + graph = ot.Graph( + "n=%d" % (size), inputDescription[index], outputDescription, True, "" + ) + sample = ot.Sample(size, 2) + sample[:, 0] = inputDesign[:, index] + sample[:, 1] = outputDesign[:, 0] + cloud = ot.Cloud(sample) + graph.add(cloud) + grid.setGraph(i, j, graph) + index += 1 + _ = otv.View(grid, figure_kw={"figsize": (10.0, 10.0)}) + + + + +.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_oakleyohagan_sensitivity_001.png + :alt: , n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200 + :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_oakleyohagan_sensitivity_001.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 61-64 + +.. code-block:: Python + + output_distribution = ot.KernelSmoothing().build(outputDesign) + _ = otv.View(output_distribution.drawPDF()) + + + + +.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_oakleyohagan_sensitivity_002.png + :alt: plot oakleyohagan sensitivity + :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_oakleyohagan_sensitivity_002.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 65-66 + +## Perform sensitivity analysis + +.. GENERATED FROM PYTHON SOURCE LINES 68-69 + +Create X/Y data + +.. GENERATED FROM PYTHON SOURCE LINES 69-74 + +.. code-block:: Python + + ot.RandomGenerator.SetSeed(0) + size = 1000 + inputDesign = ot.SobolIndicesExperiment(distribution, size).generate() + outputDesign = model(inputDesign) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 75-76 + +Compute first order indices using the Saltelli estimator + +.. GENERATED FROM PYTHON SOURCE LINES 76-80 + +.. code-block:: Python + + sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size) + computed_first_order = sensitivityAnalysis.getFirstOrderIndices() + computed_total_order = sensitivityAnalysis.getTotalOrderIndices() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 81-82 + +Compare with exact results + +.. GENERATED FROM PYTHON SOURCE LINES 82-92 + +.. code-block:: Python + + print("Sample size : ", size) + # First order + # Compute absolute error (the LRE cannot be computed, + # because S can be zero) + print("Computed first order = ", computed_first_order) + print("Exact first order = ", exact_first_order) + # Total order + print("Computed total order = ", computed_total_order) + print("Exact total order = ", exact_total_order) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + Sample size : 1000 + Computed first order = [0.00686154,0.0173649,0.0121877,-0.0153947,-0.0223964,0.0038293,0.0373239,0.014391,0.0568532,0.0057647,0.0867005,0.162502,0.0882588,0.0706715,0.108779]#15 + Exact first order = [0,0,0,0,0,0.02,0.02,0.03,0.05,0.01,0.1,0.14,0.1,0.11,0.12]#15 + Computed total order = [0.0565808,0.0625981,0.0461935,0.0758801,0.0356766,0.028928,0.058367,0.101891,0.087846,0.0501691,0.128598,0.146953,0.125396,0.149566,0.16276]#15 + Exact total order = [0.06,0.06,0.04,0.05,0.02,0.04,0.06,0.08,0.1,0.04,0.15,0.15,0.14,0.14,0.16]#15 + + + + +.. GENERATED FROM PYTHON SOURCE LINES 93-95 + +.. code-block:: Python + + _ = otv.View(sensitivityAnalysis.draw()) + + + + +.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_oakleyohagan_sensitivity_003.png + :alt: Sobol' indices - SaltelliSensitivityAlgorithm + :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_oakleyohagan_sensitivity_003.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 96-97 + +.. code-block:: Python + + otv.View.ShowAll() + + + + + + + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** (0 minutes 1.088 seconds) + + +.. _sphx_glr_download_auto_examples_sensitivity_problems_plot_oakleyohagan_sensitivity.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: plot_oakleyohagan_sensitivity.ipynb ` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: plot_oakleyohagan_sensitivity.py ` + + .. container:: sphx-glr-download sphx-glr-download-zip + + :download:`Download zipped: plot_oakleyohagan_sensitivity.zip ` diff --git a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_print_problems.rst b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_print_problems.rst index 0238c66b83c..1cf29e6d0cc 100644 --- a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_print_problems.rst +++ b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_print_problems.rst @@ -178,7 +178,7 @@ For each problem in the benchmark, print the problem name and the exact Sobol' i .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 0.165 seconds) + **Total running time of the script:** (0 minutes 0.252 seconds) .. _sphx_glr_download_auto_examples_sensitivity_problems_plot_print_problems.py: diff --git a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/sg_execution_times.rst b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/sg_execution_times.rst index 8723708d561..3608e6f635a 100644 --- a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/sg_execution_times.rst +++ b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/sg_execution_times.rst @@ -6,7 +6,7 @@ Computation times ================= -**00:45.002** total execution time for 2 files **from auto_examples/sensitivity_problems**: +**01:23.694** total execution time for 14 files **from auto_examples/sensitivity_problems**: .. container:: @@ -33,8 +33,44 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_convergence_ishigami.py` (``plot_convergence_ishigami.py``) - - 00:44.837 + - 00:45.054 + - 0.0 + * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_morris_sensitivity.py` (``plot_morris_sensitivity.py``) + - 00:16.944 + - 0.0 + * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_nloscillator_sensitivity.py` (``plot_nloscillator_sensitivity.py``) + - 00:03.807 + - 0.0 + * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_borehole_sensitivity.py` (``plot_borehole_sensitivity.py``) + - 00:03.696 + - 0.0 + * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_flood_sensitivity.py` (``plot_flood_sensitivity.py``) + - 00:02.451 + - 0.0 + * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_gsobol_sensitivity.py` (``plot_gsobol_sensitivity.py``) + - 00:02.334 + - 0.0 + * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_dirichlet_sensitivity.py` (``plot_dirichlet_sensitivity.py``) + - 00:02.273 + - 0.0 + * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_gaussian_sum.py` (``plot_gaussian_sum.py``) + - 00:01.770 + - 0.0 + * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_gaussian_product_sensitivity.py` (``plot_gaussian_product_sensitivity.py``) + - 00:01.658 + - 0.0 + * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_ishigami_sensitivity.py` (``plot_ishigami_sensitivity.py``) + - 00:01.187 + - 0.0 + * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_borgonovo_sensitivity.py` (``plot_borgonovo_sensitivity.py``) + - 00:01.145 + - 0.0 + * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_oakleyohagan_sensitivity.py` (``plot_oakleyohagan_sensitivity.py``) + - 00:01.088 - 0.0 * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_print_problems.py` (``plot_print_problems.py``) - - 00:00.165 + - 00:00.252 + - 0.0 + * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_benchmark_sensitivity_methods.py` (``plot_benchmark_sensitivity_methods.py``) + - 00:00.034 - 0.0 diff --git a/otbenchmark/master/_sources/auto_examples/sg_execution_times.rst b/otbenchmark/master/_sources/auto_examples/sg_execution_times.rst index 49da92f6963..f3616dc40f5 100644 --- a/otbenchmark/master/_sources/auto_examples/sg_execution_times.rst +++ b/otbenchmark/master/_sources/auto_examples/sg_execution_times.rst @@ -6,7 +6,7 @@ Computation times ================= -**00:24.333** total execution time for 6 files **from auto_examples**: +**00:23.239** total execution time for 6 files **from auto_examples**: .. container:: @@ -33,20 +33,20 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_examples_plot_crosscut_distribution_3d.py` (``plot_crosscut_distribution_3d.py``) - - 00:08.366 - - 0.0 - * - :ref:`sphx_glr_auto_examples_plot_ConditionalDistribution_Demo.py` (``plot_ConditionalDistribution_Demo.py``) - - 00:04.619 + - 00:07.724 - 0.0 * - :ref:`sphx_glr_auto_examples_plot_methodFactory.py` (``plot_methodFactory.py``) - - 00:04.439 + - 00:07.465 - 0.0 - * - :ref:`sphx_glr_auto_examples_plot_crosscut_distribution_2d.py` (``plot_crosscut_distribution_2d.py``) - - 00:03.997 + * - :ref:`sphx_glr_auto_examples_plot_ConditionalDistribution_Demo.py` (``plot_ConditionalDistribution_Demo.py``) + - 00:02.577 - 0.0 * - :ref:`sphx_glr_auto_examples_plot_convergence_reliability_mc.py` (``plot_convergence_reliability_mc.py``) - - 00:02.174 + - 00:02.177 + - 0.0 + * - :ref:`sphx_glr_auto_examples_plot_crosscut_distribution_2d.py` (``plot_crosscut_distribution_2d.py``) + - 00:02.066 - 0.0 * - :ref:`sphx_glr_auto_examples_plot_CrossCutFunction_Demo.py` (``plot_CrossCutFunction_Demo.py``) - - 00:00.738 + - 00:01.229 - 0.0 diff --git a/otbenchmark/master/_sources/index.rst b/otbenchmark/master/_sources/index.rst index 01b259514a6..9fc8066f0f0 100644 --- a/otbenchmark/master/_sources/index.rst +++ b/otbenchmark/master/_sources/index.rst @@ -10,14 +10,6 @@ User documentation user_manual/user_manual ../auto_examples/index.rst -Developer guide ---------------- - -.. toctree:: - :maxdepth: 1 - - developer_guide/developer_guide - Indices and tables ================== diff --git a/otbenchmark/master/_sources/sg_execution_times.rst b/otbenchmark/master/_sources/sg_execution_times.rst index d91e6aab675..2a0d646c170 100644 --- a/otbenchmark/master/_sources/sg_execution_times.rst +++ b/otbenchmark/master/_sources/sg_execution_times.rst @@ -6,7 +6,7 @@ Computation times ================= -**01:09.335** total execution time for 8 files **from all galleries**: +**01:46.932** total execution time for 20 files **from all galleries**: .. container:: @@ -33,26 +33,62 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_convergence_ishigami.py` (``examples/sensitivity_problems/plot_convergence_ishigami.py``) - - 00:44.837 + - 00:45.054 + - 0.0 + * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_morris_sensitivity.py` (``examples/sensitivity_problems/plot_morris_sensitivity.py``) + - 00:16.944 - 0.0 * - :ref:`sphx_glr_auto_examples_plot_crosscut_distribution_3d.py` (``examples/plot_crosscut_distribution_3d.py``) - - 00:08.366 + - 00:07.724 + - 0.0 + * - :ref:`sphx_glr_auto_examples_plot_methodFactory.py` (``examples/plot_methodFactory.py``) + - 00:07.465 + - 0.0 + * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_nloscillator_sensitivity.py` (``examples/sensitivity_problems/plot_nloscillator_sensitivity.py``) + - 00:03.807 + - 0.0 + * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_borehole_sensitivity.py` (``examples/sensitivity_problems/plot_borehole_sensitivity.py``) + - 00:03.696 - 0.0 * - :ref:`sphx_glr_auto_examples_plot_ConditionalDistribution_Demo.py` (``examples/plot_ConditionalDistribution_Demo.py``) - - 00:04.619 + - 00:02.577 - 0.0 - * - :ref:`sphx_glr_auto_examples_plot_methodFactory.py` (``examples/plot_methodFactory.py``) - - 00:04.439 + * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_flood_sensitivity.py` (``examples/sensitivity_problems/plot_flood_sensitivity.py``) + - 00:02.451 - 0.0 - * - :ref:`sphx_glr_auto_examples_plot_crosscut_distribution_2d.py` (``examples/plot_crosscut_distribution_2d.py``) - - 00:03.997 + * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_gsobol_sensitivity.py` (``examples/sensitivity_problems/plot_gsobol_sensitivity.py``) + - 00:02.334 + - 0.0 + * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_dirichlet_sensitivity.py` (``examples/sensitivity_problems/plot_dirichlet_sensitivity.py``) + - 00:02.273 - 0.0 * - :ref:`sphx_glr_auto_examples_plot_convergence_reliability_mc.py` (``examples/plot_convergence_reliability_mc.py``) - - 00:02.174 + - 00:02.177 + - 0.0 + * - :ref:`sphx_glr_auto_examples_plot_crosscut_distribution_2d.py` (``examples/plot_crosscut_distribution_2d.py``) + - 00:02.066 + - 0.0 + * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_gaussian_sum.py` (``examples/sensitivity_problems/plot_gaussian_sum.py``) + - 00:01.770 + - 0.0 + * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_gaussian_product_sensitivity.py` (``examples/sensitivity_problems/plot_gaussian_product_sensitivity.py``) + - 00:01.658 - 0.0 * - :ref:`sphx_glr_auto_examples_plot_CrossCutFunction_Demo.py` (``examples/plot_CrossCutFunction_Demo.py``) - - 00:00.738 + - 00:01.229 + - 0.0 + * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_ishigami_sensitivity.py` (``examples/sensitivity_problems/plot_ishigami_sensitivity.py``) + - 00:01.187 + - 0.0 + * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_borgonovo_sensitivity.py` (``examples/sensitivity_problems/plot_borgonovo_sensitivity.py``) + - 00:01.145 + - 0.0 + * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_oakleyohagan_sensitivity.py` (``examples/sensitivity_problems/plot_oakleyohagan_sensitivity.py``) + - 00:01.088 - 0.0 * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_print_problems.py` (``examples/sensitivity_problems/plot_print_problems.py``) - - 00:00.165 + - 00:00.252 + - 0.0 + * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_benchmark_sensitivity_methods.py` (``examples/sensitivity_problems/plot_benchmark_sensitivity_methods.py``) + - 00:00.034 - 0.0 diff --git a/otbenchmark/master/auto_examples/index.html b/otbenchmark/master/auto_examples/index.html index 6a833f3e79a..333a577beb1 100644 --- a/otbenchmark/master/auto_examples/index.html +++ b/otbenchmark/master/auto_examples/index.html @@ -75,9 +75,6 @@

Table of Contents

  • Sensitivity examples
    • - -
    @@ -143,9 +140,45 @@

    Sensitivity examples

    Print the sensitivity analysis problems

    Print the sensitivity analysis problems
    +
    +

    Benchmark the Flooding test function

    +
    Benchmark the Flooding test function
    +
    +

    Benchmark the Borgonovo test function

    +
    Benchmark the Borgonovo test function
    +
    +

    Benchmark the Ishigami test function

    +
    Benchmark the Ishigami test function
    +
    +

    Benchmark the Dirichlet test function

    +
    Benchmark the Dirichlet test function
    +
    +

    Benchmark the Borehole test function

    +
    Benchmark the Borehole test function
    +
    +

    Benchmark the NLOscillator test function

    +
    Benchmark the NLOscillator test function
    +
    +

    Benchmark the gaussian sum test function

    +
    Benchmark the gaussian sum test function
    +
    +

    Benchmark the gaussian product test function

    +
    Benchmark the gaussian product test function
    +
    +

    Benchmark the Morris test function

    +
    Benchmark the Morris test function
    +
    +

    Benchmark the Oakley-O’Hagan test function

    +
    Benchmark the Oakley-O'Hagan test function
    +
    +

    Benchmark the G-Sobol test function

    +
    Benchmark the G-Sobol test function

    Convergence of estimators on Ishigami

    Convergence of estimators on Ishigami
    +
    +

    Benchmark sensitivity analysis methods

    +
    Benchmark sensitivity analysis methods

    diff --git a/otbenchmark/master/auto_examples/plot_ConditionalDistribution_Demo.html b/otbenchmark/master/auto_examples/plot_ConditionalDistribution_Demo.html index 797d0477c2b..1237c052d02 100644 --- a/otbenchmark/master/auto_examples/plot_ConditionalDistribution_Demo.html +++ b/otbenchmark/master/auto_examples/plot_ConditionalDistribution_Demo.html @@ -73,9 +73,6 @@

    Table of Contents

  • Sensitivity examples
    • - -
    @@ -191,7 +188,7 @@

    Conditional distribution of a three dimensional mixture
    otv.View.ShowAll()
    -

    Total running time of the script: (0 minutes 4.619 seconds)

    +

    Total running time of the script: (0 minutes 2.577 seconds)

  • Sensitivity examples
    • - -
    @@ -81,8 +78,8 @@

    Previous topic

    This Page

    @@ -200,7 +197,7 @@

    Quick search

    latex_code = df_problems_list.to_latex()
    -

    Total running time of the script: (0 minutes 0.165 seconds)

    +

    Total running time of the script: (0 minutes 0.252 seconds)

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  • API Reference
  • Examples
  • Sensitivity examples
    • - -
    @@ -88,7 +85,7 @@

    Quick search

    Computation times

    -

    00:45.002 total execution time for 2 files from auto_examples/sensitivity_problems:

    +

    01:23.694 total execution time for 14 files from auto_examples/sensitivity_problems: