GitHub Actions build openturns/otsvm 6735919320

otsvm/master/_downloads/2421e198bfdefccbcbfa5427d1acaa31/plot_example1.ipynb

@@ -22,7 +22,7 @@
       },
       "outputs": [],
       "source": [
-        "import openturns as ot\nimport otsvm\n\n# create a function, here we create the Sobol function\ndimension = 3\nmeanTh = 1.0\na = ot.Point(dimension)\ninputVariables = ot.Description(dimension)\nformula = \"1.0\"\ncovTh = 1.0\nfor i in range(dimension):\n    a[i] = 0.5 * i\n    covTh = covTh * (1.0 + 1.0 / (3.0 * (1.0 + a[i]) ** 2))\n    inputVariables[i] = \"xi\" + str(i)\n    formula += (\n        \" * ((abs(4.0 * xi\"\n        + str(i)\n        + \" -2.0) + \"\n        + str(a[i])\n        + \") / (1.0 + \"\n        + str(a[i])\n        + \"))\"\n    )\ncovTh = covTh - 1.0\nmodel = ot.SymbolicFunction(inputVariables, ot.Description(1, formula))\n\n# create the input distribution\not.RandomGenerator.SetSeed(0)\nmarginals = ot.DistributionCollection(dimension)\nfor i in range(dimension):\n    marginals[i] = ot.Uniform(0.0, 1.0)\ndistribution = ot.ComposedDistribution(marginals)\n\n# create lists of kernel parameters and tradeoff factors\ntradeoff = [0.01, 0.1, 1, 10, 100, 1000]\nkernel = [0.001, 0.01, 0.1, 1, 10, 100]\n\n# first example : create the problem with an input and output samples:\n# first, we create samples\ndataIn = distribution.getSample(250)\ndataOut = model(dataIn)\n# second, we create our svm regression object, we must select the third parameter\n# in an enumerate in the list { NormalRBF, Linear, Sigmoid, Polynomial }\nalgo = otsvm.LibSVMRegression(dataIn, dataOut, otsvm.LibSVM.NormalRbf)\n# third, we set kernel parameter and tradeoff factor\nalgo.setTradeoffFactor(tradeoff)\nalgo.setKernelParameter(kernel)\n# Perform the algorithm\nalgo.run()\n# Stream out the results\nresult = algo.getResult()\n# get the residual error\nresidual = result.getResiduals()\n# get the relative error\nrelativeError = result.getRelativeErrors()\nprint(f\"residual={residual} error={relativeError}\")\n\n# second example : create the problem with an experiment plane:\n# first, we create the plane\nmyExperiment = ot.MonteCarloExperiment(distribution, 250)\ndataIn = myExperiment.generate()\ndataOut = model(dataIn)\n# second, we create our svm regression object, the first parameter is the\n# function\nalgo2 = otsvm.LibSVMRegression(dataIn, dataOut, otsvm.LibSVM.Linear)\n# third, we set kernel parameter and tradeoff factor\nalgo2.setTradeoffFactor(tradeoff)\nalgo2.setKernelParameter(kernel)\n# Perform the algorithm\nalgo2.run()\n# Stream out the results\nresult = algo2.getResult()\n# get the residual error\nresidual = result.getResiduals()\n# get the relative error\nrelativeError = result.getRelativeErrors()\nprint(f\"residual={residual} error={relativeError}\")\n\n# third example is here to present you the SVMResourceMap class.\n# Users can fix others parameters like the degree and the constant of the\n# Polynomial Kernel,the cacheSize, the number of folds or the epsilon\n# first, we create samples\ndataIn = distribution.getSample(250)\ndataOut = model(dataIn)\n# second, we create our svm regression object\n# here, we select the Polynomial Kernel but by default his degree is 3. We want a\n# degree of 2\not.ResourceMap.Set(\"LibSVM-DegreePolynomialKernel\", \"2\")\n# now the degree of the Polynomial kernel is 2\nalgo = otsvm.LibSVMRegression(dataIn, dataOut, otsvm.LibSVM.Polynomial)\n# third, we set kernel parameter and tradeoff factor\nalgo.setTradeoffFactor(tradeoff)\nalgo.setKernelParameter(kernel)\n# Perform the algorithm\n#algo.run()\n## Stream out the results\n#result = algo.getResult()\n#print(result)\n## get the residual error\n#residual = result.getResiduals()\n## get the relative error\n#relativeError = result.getRelativeErrors()"
+        "import openturns as ot\nimport otsvm\n\n# create a function, here we create the Sobol function\ndimension = 3\nmeanTh = 1.0\na = ot.Point(dimension)\ninputVariables = ot.Description(dimension)\nformula = \"1.0\"\ncovTh = 1.0\nfor i in range(dimension):\n    a[i] = 0.5 * i\n    covTh = covTh * (1.0 + 1.0 / (3.0 * (1.0 + a[i]) ** 2))\n    inputVariables[i] = \"xi\" + str(i)\n    formula += (\n        \" * ((abs(4.0 * xi\"\n        + str(i)\n        + \" -2.0) + \"\n        + str(a[i])\n        + \") / (1.0 + \"\n        + str(a[i])\n        + \"))\"\n    )\ncovTh = covTh - 1.0\nmodel = ot.SymbolicFunction(inputVariables, ot.Description(1, formula))\n\n# create the input distribution\not.RandomGenerator.SetSeed(0)\nmarginals = ot.DistributionCollection(dimension)\nfor i in range(dimension):\n    marginals[i] = ot.Uniform(0.0, 1.0)\ndistribution = ot.ComposedDistribution(marginals)\n\n# create lists of kernel parameters and tradeoff factors\ntradeoff = [0.01, 0.1, 1, 10, 100, 1000]\nkernel = [0.001, 0.01, 0.1, 1, 10, 100]\n\n# first example : create the problem with an input and output samples:\n# first, we create samples\ndataIn = distribution.getSample(250)\ndataOut = model(dataIn)\n# second, we create our svm regression object, we must select the third parameter\n# in an enumerate in the list { NormalRBF, Linear, Sigmoid, Polynomial }\nalgo = otsvm.LibSVMRegression(dataIn, dataOut, otsvm.LibSVM.NormalRbf)\n# third, we set kernel parameter and tradeoff factor\nalgo.setTradeoffFactor(tradeoff)\nalgo.setKernelParameter(kernel)\n# Perform the algorithm\nalgo.run()\n# Stream out the results\nresult = algo.getResult()\n# get the residual error\nresidual = result.getResiduals()\n# get the relative error\nrelativeError = result.getRelativeErrors()\nprint(f\"residual={residual} error={relativeError}\")\n\n# second example : create the problem with an experiment plane:\n# first, we create the plane\nmyExperiment = ot.MonteCarloExperiment(distribution, 250)\ndataIn = myExperiment.generate()\ndataOut = model(dataIn)\n# second, we create our svm regression object, the first parameter is the\n# function\nalgo2 = otsvm.LibSVMRegression(dataIn, dataOut, otsvm.LibSVM.Linear)\n# third, we set kernel parameter and tradeoff factor\nalgo2.setTradeoffFactor(tradeoff)\nalgo2.setKernelParameter(kernel)\n# Perform the algorithm\nalgo2.run()\n# Stream out the results\nresult = algo2.getResult()\n# get the residual error\nresidual = result.getResiduals()\n# get the relative error\nrelativeError = result.getRelativeErrors()\nprint(f\"residual={residual} error={relativeError}\")\n\n# third example is here to present you the SVMResourceMap class.\n# Users can fix others parameters like the degree and the constant of the\n# Polynomial Kernel,the cacheSize, the number of folds or the epsilon\n# first, we create samples\ndataIn = distribution.getSample(250)\ndataOut = model(dataIn)\n# second, we create our svm regression object\n# here, we select the Polynomial Kernel but by default his degree is 3. We want a\n# degree of 2\not.ResourceMap.Set(\"LibSVM-DegreePolynomialKernel\", \"2\")\n# now the degree of the Polynomial kernel is 2\nalgo = otsvm.LibSVMRegression(dataIn, dataOut, otsvm.LibSVM.Polynomial)\n# third, we set kernel parameter and tradeoff factor\nalgo.setTradeoffFactor(tradeoff)\nalgo.setKernelParameter(kernel)\n# Perform the algorithm\n# algo.run()\n## Stream out the results\n# result = algo.getResult()\n# print(result)\n## get the residual error\n# residual = result.getResiduals()\n## get the relative error\n# relativeError = result.getRelativeErrors()"
       ]
     }
   ],

otsvm/master/_downloads/a8b175cd29751dc0229ceb76947711bf/plot_example2.py

@@ -5,7 +5,7 @@
 
 # %%
 # The objective of this Use Case is to create a SVM Classification algorithm in order to build a metamodel that separates data in 2 classes.
-# 
+#
 # otsvm enables to:
 #
 # - set lists of tradeoff factors and kernel parameter with the methods setTradeoffFactor, setKernelParameter.

otsvm/master/_downloads/ce615eeeaf183d728a05b647cb915a04/plot_example1.py

@@ -115,11 +115,11 @@
 algo.setTradeoffFactor(tradeoff)
 algo.setKernelParameter(kernel)
 # Perform the algorithm
-#algo.run()
+# algo.run()
 ## Stream out the results
-#result = algo.getResult()
-#print(result)
+# result = algo.getResult()
+# print(result)
 ## get the residual error
-#residual = result.getResiduals()
+# residual = result.getResiduals()
 ## get the relative error
-#relativeError = result.getRelativeErrors()
+# relativeError = result.getRelativeErrors()

otsvm/master/_sources/auto_examples/plot_example1.rst.txt

@@ -137,14 +137,14 @@ Usually, the algorithm always converges, but this can take a long while, mostly
     algo.setTradeoffFactor(tradeoff)
     algo.setKernelParameter(kernel)
     # Perform the algorithm
-    #algo.run()
+    # algo.run()
     ## Stream out the results
-    #result = algo.getResult()
-    #print(result)
+    # result = algo.getResult()
+    # print(result)
     ## get the residual error
-    #residual = result.getResiduals()
+    # residual = result.getResiduals()
     ## get the relative error
-    #relativeError = result.getRelativeErrors()
+    # relativeError = result.getRelativeErrors()
 
 
 
@@ -162,7 +162,7 @@ Usually, the algorithm always converges, but this can take a long while, mostly
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 4.452 seconds)
+   **Total running time of the script:** (0 minutes 6.811 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_example1.py:

otsvm/master/_sources/auto_examples/plot_example2.rst.txt

@@ -90,7 +90,7 @@ otsvm enables to:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.962 seconds)
+   **Total running time of the script:** (0 minutes 2.432 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_example2.py:

otsvm/master/_sources/auto_examples/sg_execution_times.rst.txt

@@ -6,10 +6,10 @@
 
 Computation times
 =================
-**00:06.414** total execution time for **auto_examples** files:
+**00:09.242** total execution time for **auto_examples** files:
 
 +-----------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_plot_example1.py` (``plot_example1.py``) | 00:04.452 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_plot_example1.py` (``plot_example1.py``) | 00:06.811 | 0.0 MB |
 +-----------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_plot_example2.py` (``plot_example2.py``) | 00:01.962 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_plot_example2.py` (``plot_example2.py``) | 00:02.432 | 0.0 MB |
 +-----------------------------------------------------------------------+-----------+--------+

otsvm/master/auto_examples/plot_example1.html

@@ -162,21 +162,21 @@ <h3>Navigation</h3>
 <span class="n">algo</span><span class="o">.</span><span class="n">setTradeoffFactor</span><span class="p">(</span><span class="n">tradeoff</span><span class="p">)</span>
 <span class="n">algo</span><span class="o">.</span><span class="n">setKernelParameter</span><span class="p">(</span><span class="n">kernel</span><span class="p">)</span>
 <span class="c1"># Perform the algorithm</span>
-<span class="c1">#algo.run()</span>
+<span class="c1"># algo.run()</span>
 <span class="c1">## Stream out the results</span>
-<span class="c1">#result = algo.getResult()</span>
-<span class="c1">#print(result)</span>
+<span class="c1"># result = algo.getResult()</span>
+<span class="c1"># print(result)</span>
 <span class="c1">## get the residual error</span>
-<span class="c1">#residual = result.getResiduals()</span>
+<span class="c1"># residual = result.getResiduals()</span>
 <span class="c1">## get the relative error</span>
-<span class="c1">#relativeError = result.getRelativeErrors()</span>
+<span class="c1"># relativeError = result.getRelativeErrors()</span>
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>residual=[0.0043475] error=[0.00793795]
 residual=[0.0515409] error=[1.04162]
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 4.452 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 6.811 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-example1-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/ce615eeeaf183d728a05b647cb915a04/plot_example1.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_example1.py</span></code></a></p>

otsvm/master/auto_examples/plot_example2.html

@@ -104,7 +104,7 @@ <h3>Navigation</h3>
 Accuracy(p.c.)= 100.0
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.962 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 2.432 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-example2-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/a8b175cd29751dc0229ceb76947711bf/plot_example2.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_example2.py</span></code></a></p>

otsvm/master/auto_examples/sg_execution_times.html

@@ -40,15 +40,15 @@ <h3>Navigation</h3>
 
   <section id="computation-times">
 <span id="sphx-glr-auto-examples-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Link to this heading">¶</a></h1>
-<p><strong>00:06.414</strong> total execution time for <strong>auto_examples</strong> files:</p>
+<p><strong>00:09.242</strong> total execution time for <strong>auto_examples</strong> files:</p>
 <table class="docutils align-default">
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="plot_example1.html#sphx-glr-auto-examples-plot-example1-py"><span class="std std-ref">Regression</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_example1.py</span></code>)</p></td>
-<td><p>00:04.452</p></td>
+<td><p>00:06.811</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="plot_example2.html#sphx-glr-auto-examples-plot-example2-py"><span class="std std-ref">Classification</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_example2.py</span></code>)</p></td>
-<td><p>00:01.962</p></td>
+<td><p>00:02.432</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
 </tbody>