renesass: Updated sphinx-gallery

automl · Oct 20, 2021 · d35bd69 · d35bd69
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diff --git a/development/.doctrees/environment.pickle b/development/.doctrees/environment.pickle
diff --git a/development/.doctrees/pages/details/arguments.doctree b/development/.doctrees/pages/details/arguments.doctree
diff --git a/development/.doctrees/pages/examples/commandline/branin.doctree b/development/.doctrees/pages/examples/commandline/branin.doctree
diff --git a/development/.doctrees/pages/examples/commandline/restore_branin.doctree b/development/.doctrees/pages/examples/commandline/restore_branin.doctree
diff --git a/development/.doctrees/pages/examples/index.doctree b/development/.doctrees/pages/examples/index.doctree
diff --git a/development/.doctrees/pages/examples/python/gb_non_deterministic.doctree b/development/.doctrees/pages/examples/python/gb_non_deterministic.doctree
diff --git a/development/.doctrees/pages/examples/python/mlp_mf.doctree b/development/.doctrees/pages/examples/python/mlp_mf.doctree
diff --git a/development/.doctrees/pages/examples/python/sgd_instances.doctree b/development/.doctrees/pages/examples/python/sgd_instances.doctree
diff --git a/development/.doctrees/pages/examples/python/spear_mf_instances.doctree b/development/.doctrees/pages/examples/python/spear_mf_instances.doctree
diff --git a/development/.doctrees/pages/examples/python/svm_cv.doctree b/development/.doctrees/pages/examples/python/svm_cv.doctree
diff --git a/development/.doctrees/pages/examples/python/synthetic_function.doctree b/development/.doctrees/pages/examples/python/synthetic_function.doctree
diff --git a/development/_downloads/0713842f2ba18c946313bf17c241d792/restore_branin.ipynb b/development/_downloads/0713842f2ba18c946313bf17c241d792/restore_branin.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nRestore Branin\n^^^^^^^^^^^^^^\nThis file runs SMAC and then restores the branin run with an extended computation\nbudget. This will also work for SMAC runs that have crashed and are continued.\n"
+        "\n# Restore Branin\nThis file runs SMAC and then restores the branin run with an extended computation\nbudget. This will also work for SMAC runs that have crashed and are continued.\n"
       ]
     },
     {

diff --git a/...80b6d9485930909139a7/examples_jupyter.zip → ...03ae6a2ed4b246b8d31e/examples_jupyter.zip b/...80b6d9485930909139a7/examples_jupyter.zip → ...03ae6a2ed4b246b8d31e/examples_jupyter.zip
diff --git a/development/_downloads/1cae360cf7d7a0383b1909e2f21f8b34/branin.ipynb b/development/_downloads/1cae360cf7d7a0383b1909e2f21f8b34/branin.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nBranin\n^^^^^^\n\nThis file is a wrapper used by SMAC to optimize parameters on the branin function.\nTo run this example in the terminal, execute:\n\n.. code-block:: bash\n\n    cd examples/commandline\n    python ../../scripts/smac.py --scenario branin/scenario.txt\n\n\nInside the scenario, this file and also ``configspace.pcs`` is referenced and therefore used\nfor the optimization. A full call by SMAC looks like this:\n\n.. code-block:: bash\n\n    <algo>           <instance> <instance specific> <cutoff time>  <runlength> <seed> <parameters>\n    python branin.py 0          0                   9999999        0           12345  -x1 0 -x2 0\n\n\nSince SMAC processes results from the commandline, print-statements are\ncrucial. The format of the results must be the following to ensure correct usage:\n\n.. code-block:: bash\n\n    Result for SMAC: <STATUS>, <runtime>, <runlength>, <quality>, <seed>, <instance-specifics>\n"
+        "\n# Branin\n\nThis file is a wrapper used by SMAC to optimize parameters on the branin function.\nTo run this example in the terminal, execute:\n\n.. code-block:: bash\n\n    cd examples/commandline\n    python ../../scripts/smac.py --scenario branin/scenario.txt\n\n\nInside the scenario, this file and also ``configspace.pcs`` is referenced and therefore used\nfor the optimization. A full call by SMAC looks like this:\n\n.. code-block:: bash\n\n    <algo>           <instance> <instance specific> <cutoff time>  <runlength> <seed> <parameters>\n    python branin.py 0          0                   9999999        0           12345  -x1 0 -x2 0\n\n\nSince SMAC processes results from the commandline, print-statements are\ncrucial. The format of the results must be the following to ensure correct usage:\n\n.. code-block:: bash\n\n    Result for SMAC: <STATUS>, <runtime>, <runlength>, <quality>, <seed>, <instance-specifics>\n"
       ]
     },
     {

diff --git a/...cff86b75061670383e479/examples_python.zip → ...8a3486c50a2453adf6bb1/examples_python.zip b/...cff86b75061670383e479/examples_python.zip → ...8a3486c50a2453adf6bb1/examples_python.zip
diff --git a/development/_downloads/50ec7e341398627061b5a57f92b39aca/synthetic_function.ipynb b/development/_downloads/50ec7e341398627061b5a57f92b39aca/synthetic_function.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nSynthetic Function with few Hyperparameters\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nAn example of applying SMAC to optimize a synthetic function (2d rosenbrock function).\n\nWe use the SMAC4BB facade because it is designed for black-box function optimization.\nSMAC4BB uses a :term:`Gaussian Process<GP>` or a set of Gaussian Processes whose\nhyperparameters are integrated by Markov-Chain Monte-Carlo as its surrogate model.\nSMAC4BB works best on numerical hyperparameter configuration space and should not\nbe applied to the problems with large evaluation budgets (up to 1000 evaluations).\n"
+        "\n# Synthetic Function with few Hyperparameters\n\nAn example of applying SMAC to optimize a synthetic function (2d rosenbrock function).\n\nWe use the SMAC4BB facade because it is designed for black-box function optimization.\nSMAC4BB uses a :term:`Gaussian Process<GP>` or a set of Gaussian Processes whose\nhyperparameters are integrated by Markov-Chain Monte-Carlo as its surrogate model.\nSMAC4BB works best on numerical hyperparameter configuration space and should not\nbe applied to the problems with large evaluation budgets (up to 1000 evaluations).\n"
       ]
     },
     {

diff --git a/development/_downloads/5c8d4a0d90c70cdb3f32a34a80c7d161/svm_cv.ipynb b/development/_downloads/5c8d4a0d90c70cdb3f32a34a80c7d161/svm_cv.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nSVM with Cross-Validation\n^^^^^^^^^^^^^^^^^^^^^^^^^\n\nAn example to optimize a simple SVM on the IRIS-benchmark. SMAC4HPO is designed\nfor hyperparameter optimization (HPO) problems and uses an RF as its surrogate model.\nIt is able to scale to higher evaluation budgets and higher number of\ndimensions. Also, you can use mixed data types as well as conditional hyperparameters.\n\nSMAC4HPO by default only contains single fidelity approach. Therefore, only the configuration is\nprocessed by the :term:`TAE`.\n"
+        "\n# SVM with Cross-Validation\n\nAn example to optimize a simple SVM on the IRIS-benchmark. SMAC4HPO is designed\nfor hyperparameter optimization (HPO) problems and uses an RF as its surrogate model.\nIt is able to scale to higher evaluation budgets and higher number of\ndimensions. Also, you can use mixed data types as well as conditional hyperparameters.\n\nSMAC4HPO by default only contains single fidelity approach. Therefore, only the configuration is\nprocessed by the :term:`TAE`.\n"
       ]
     },
     {

diff --git a/development/_downloads/6d837ffad5410309d09486423e2e0d81/spear_mf_instances.ipynb b/development/_downloads/6d837ffad5410309d09486423e2e0d81/spear_mf_instances.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nSPEAR-QCP with Multi-Fidelity on Instances\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nWe optimize the SPEAR algorithm on QCP to demonstrate the powerful SMAC4AC facade. Algorithm and\ninstance definition is done inside scenario file.\n\nMoreover, we present you an alternative :term:`intensification<Intensification>` procedure \"Successive Halving\".\n"
+        "\n# SPEAR-QCP with Multi-Fidelity on Instances\n\nWe optimize the SPEAR algorithm on QCP to demonstrate the powerful SMAC4AC facade. Algorithm and\ninstance definition is done inside scenario file.\n\nMoreover, we present you an alternative :term:`intensification<Intensification>` procedure \"Successive Halving\".\n"
       ]
     },
     {

diff --git a/development/_downloads/89ad22a76b9027c57cc1d354304c8534/mlp_mf.ipynb b/development/_downloads/89ad22a76b9027c57cc1d354304c8534/mlp_mf.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nMLP with Multi-Fidelity\n^^^^^^^^^^^^^^^^^^^^^^^\n\nExample for optimizing a Multi-Layer Perceptron (MLP) using multiple budgets.\nSince we want to take advantage of Multi-Fidelity, the SMAC4MF facade is a good choice. By default,\nSMAC4MF internally runs with `hyperband <https://arxiv.org/abs/1603.06560>`_, which is a combination of an\naggressive racing mechanism and successive halving.\n\nMLP is a deep neural network, and therefore, we choose epochs as fidelity type. The digits dataset\nis chosen to optimize the average accuracy on 5-fold cross validation.\n"
+        "\n# MLP with Multi-Fidelity\n\nExample for optimizing a Multi-Layer Perceptron (MLP) using multiple budgets.\nSince we want to take advantage of Multi-Fidelity, the SMAC4MF facade is a good choice. By default,\nSMAC4MF internally runs with `hyperband <https://arxiv.org/abs/1603.06560>`_, which is a combination of an\naggressive racing mechanism and successive halving.\n\nMLP is a deep neural network, and therefore, we choose epochs as fidelity type. The digits dataset\nis chosen to optimize the average accuracy on 5-fold cross validation.\n"
       ]
     },
     {

diff --git a/development/_downloads/91231fe1280aab46461729959f06d30a/gb_non_deterministic.ipynb b/development/_downloads/91231fe1280aab46461729959f06d30a/gb_non_deterministic.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nNon-Deterministic Gradient-Boosting\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nWe optimize a GradientBoosting on an artificially created binary classification dataset.\nThe results are not deterministic so we need to evaluate each configuration\nmultiple times. To ensure fair comparison, SMAC will only sample from a fixed set of random seeds and apply them to\ncontrol the randomness of the function to be evaluated.\n\nTo evaluate undeterministic functions, we need to set \"deterministic\" as \"false\".\nAdditional to the configuration, the function should make use of the seed parameter as well.\n"
+        "\n# Non-Deterministic Gradient-Boosting\n\nWe optimize a GradientBoosting on an artificially created binary classification dataset.\nThe results are not deterministic so we need to evaluate each configuration\nmultiple times. To ensure fair comparison, SMAC will only sample from a fixed set of random seeds and apply them to\ncontrol the randomness of the function to be evaluated.\n\nTo evaluate undeterministic functions, we need to set \"deterministic\" as \"false\".\nAdditional to the configuration, the function should make use of the seed parameter as well.\n"
       ]
     },
     {

diff --git a/development/_downloads/acdd01ed4856661886713f2ef63d16f0/sgd_instances.ipynb b/development/_downloads/acdd01ed4856661886713f2ef63d16f0/sgd_instances.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nSGD on Instances\n^^^^^^^^^^^^^^^^\n\nExample for optimizing a Multi-Layer Perceptron (MLP) using multiple instances.\n\nAlternative to budgets, here we consider instances as a fidelity type. An instance represents a specific\nscenario/condition (e.g. different datasets, subsets, transformations) for the algorithm to run. SMAC then returns the\nalgorithm that had the best performance across all the instances. In this case, an instance is a binary dataset i.e.,\ndigit-2 vs digit-3.\n\nIf we use instance as our fidelity, we need to initialize scenario with argument instance. In this case the argument\nbudget is no longer required by the target function.\n"
+        "\n# SGD on Instances\n\nExample for optimizing a Multi-Layer Perceptron (MLP) using multiple instances.\n\nAlternative to budgets, here we consider instances as a fidelity type. An instance represents a specific\nscenario/condition (e.g. different datasets, subsets, transformations) for the algorithm to run. SMAC then returns the\nalgorithm that had the best performance across all the instances. In this case, an instance is a binary dataset i.e.,\ndigit-2 vs digit-3.\n\nIf we use instance as our fidelity, we need to initialize scenario with argument instance. In this case the argument\nbudget is no longer required by the target function.\n"
       ]
     },
     {

diff --git a/development/_images/sphx_glr_branin_thumb.png b/development/_images/sphx_glr_branin_thumb.png
diff --git a/development/_images/sphx_glr_gb_non_deterministic_thumb.png b/development/_images/sphx_glr_gb_non_deterministic_thumb.png
diff --git a/development/_images/sphx_glr_mlp_mf_thumb.png b/development/_images/sphx_glr_mlp_mf_thumb.png
diff --git a/development/_images/sphx_glr_restore_branin_thumb.png b/development/_images/sphx_glr_restore_branin_thumb.png
diff --git a/development/_images/sphx_glr_sgd_instances_thumb.png b/development/_images/sphx_glr_sgd_instances_thumb.png
diff --git a/development/_images/sphx_glr_spear_mf_instances_thumb.png b/development/_images/sphx_glr_spear_mf_instances_thumb.png
diff --git a/development/_images/sphx_glr_svm_cv_thumb.png b/development/_images/sphx_glr_svm_cv_thumb.png
diff --git a/development/_images/sphx_glr_synthetic_function_thumb.png b/development/_images/sphx_glr_synthetic_function_thumb.png
diff --git a/development/_sources/pages/examples/commandline/branin.rst.txt b/development/_sources/pages/examples/commandline/branin.rst.txt
@@ -1,7 +1,18 @@
-.. note::
-    :class: sphx-glr-download-link-note
 
-    Click :ref:`here <sphx_glr_download_pages_examples_commandline_branin.py>` to download the full example code
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "pages/examples/commandline/branin.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        Click :ref:`here <sphx_glr_download_pages_examples_commandline_branin.py>`
+        to download the full example code
+
 .. rst-class:: sphx-glr-example-title
 
 .. _sphx_glr_pages_examples_commandline_branin.py:
@@ -35,6 +46,7 @@ crucial. The format of the results must be the following to ensure correct usage
 
     Result for SMAC: <STATUS>, <runtime>, <runlength>, <quality>, <seed>, <instance-specifics>
 
+.. GENERATED FROM PYTHON SOURCE LINES 31-66
 
 .. code-block:: default
 
@@ -90,13 +102,13 @@ crucial. The format of the results must be the following to ensure correct usage
 
 
 
-  .. container:: sphx-glr-download
+  .. container:: sphx-glr-download sphx-glr-download-python
 
      :download:`Download Python source code: branin.py <branin.py>`
 
 
 
-  .. container:: sphx-glr-download
+  .. container:: sphx-glr-download sphx-glr-download-jupyter
 
      :download:`Download Jupyter notebook: branin.ipynb <branin.ipynb>`
 

diff --git a/development/_sources/pages/examples/commandline/restore_branin.rst.txt b/development/_sources/pages/examples/commandline/restore_branin.rst.txt
@@ -1,7 +1,18 @@
-.. note::
-    :class: sphx-glr-download-link-note
 
-    Click :ref:`here <sphx_glr_download_pages_examples_commandline_restore_branin.py>` to download the full example code
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "pages/examples/commandline/restore_branin.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        Click :ref:`here <sphx_glr_download_pages_examples_commandline_restore_branin.py>`
+        to download the full example code
+
 .. rst-class:: sphx-glr-example-title
 
 .. _sphx_glr_pages_examples_commandline_restore_branin.py:
@@ -12,6 +23,7 @@ Restore Branin
 This file runs SMAC and then restores the branin run with an extended computation
 budget. This will also work for SMAC runs that have crashed and are continued.
 
+.. GENERATED FROM PYTHON SOURCE LINES 8-92
 
 .. code-block:: default
 
@@ -116,13 +128,13 @@ budget. This will also work for SMAC runs that have crashed and are continued.
 
 
 
-  .. container:: sphx-glr-download
+  .. container:: sphx-glr-download sphx-glr-download-python
 
      :download:`Download Python source code: restore_branin.py <restore_branin.py>`
 
 
 
-  .. container:: sphx-glr-download
+  .. container:: sphx-glr-download sphx-glr-download-jupyter
 
      :download:`Download Jupyter notebook: restore_branin.ipynb <restore_branin.ipynb>`
 

diff --git a/development/_sources/pages/examples/index.rst.txt b/development/_sources/pages/examples/index.rst.txt
@@ -29,6 +29,7 @@ Commandline
 .. only:: html
 
  .. figure:: /pages/examples/commandline/images/thumb/sphx_glr_branin_thumb.png
+     :alt: Branin
 
      :ref:`sphx_glr_pages_examples_commandline_branin.py`
 
@@ -49,6 +50,7 @@ Commandline
 .. only:: html
 
  .. figure:: /pages/examples/commandline/images/thumb/sphx_glr_restore_branin_thumb.png
+     :alt: Restore Branin
 
      :ref:`sphx_glr_pages_examples_commandline_restore_branin.py`
 
@@ -80,6 +82,7 @@ Python
 .. only:: html
 
  .. figure:: /pages/examples/python/images/thumb/sphx_glr_spear_mf_instances_thumb.png
+     :alt: SPEAR-QCP with Multi-Fidelity on Instances
 
      :ref:`sphx_glr_pages_examples_python_spear_mf_instances.py`
 
@@ -100,6 +103,7 @@ Python
 .. only:: html
 
  .. figure:: /pages/examples/python/images/thumb/sphx_glr_synthetic_function_thumb.png
+     :alt: Synthetic Function with few Hyperparameters
 
      :ref:`sphx_glr_pages_examples_python_synthetic_function.py`
 
@@ -120,6 +124,7 @@ Python
 .. only:: html
 
  .. figure:: /pages/examples/python/images/thumb/sphx_glr_gb_non_deterministic_thumb.png
+     :alt: Non-Deterministic Gradient-Boosting
 
      :ref:`sphx_glr_pages_examples_python_gb_non_deterministic.py`
 
@@ -140,6 +145,7 @@ Python
 .. only:: html
 
  .. figure:: /pages/examples/python/images/thumb/sphx_glr_svm_cv_thumb.png
+     :alt: SVM with Cross-Validation
 
      :ref:`sphx_glr_pages_examples_python_svm_cv.py`
 
@@ -160,6 +166,7 @@ Python
 .. only:: html
 
  .. figure:: /pages/examples/python/images/thumb/sphx_glr_sgd_instances_thumb.png
+     :alt: SGD on Instances
 
      :ref:`sphx_glr_pages_examples_python_sgd_instances.py`
 
@@ -180,6 +187,7 @@ Python
 .. only:: html
 
  .. figure:: /pages/examples/python/images/thumb/sphx_glr_mlp_mf_thumb.png
+     :alt: MLP with Multi-Fidelity
 
      :ref:`sphx_glr_pages_examples_python_mlp_mf.py`
 
@@ -204,15 +212,15 @@ Python
     :class: sphx-glr-footer-gallery
 
 
-  .. container:: sphx-glr-download
+  .. container:: sphx-glr-download sphx-glr-download-python
 
-    :download:`Download all examples in Python source code: examples_python.zip <//home/runner/work/SMAC3/SMAC3/docs/pages/examples/examples_python.zip>`
+    :download:`Download all examples in Python source code: examples_python.zip </pages/examples/examples_python.zip>`
 
 
 
-  .. container:: sphx-glr-download
+  .. container:: sphx-glr-download sphx-glr-download-jupyter
 
-    :download:`Download all examples in Jupyter notebooks: examples_jupyter.zip <//home/runner/work/SMAC3/SMAC3/docs/pages/examples/examples_jupyter.zip>`
+    :download:`Download all examples in Jupyter notebooks: examples_jupyter.zip </pages/examples/examples_jupyter.zip>`
 
 
 .. only:: html

diff --git a/development/_sources/pages/examples/python/gb_non_deterministic.rst.txt b/development/_sources/pages/examples/python/gb_non_deterministic.rst.txt
@@ -1,7 +1,18 @@
-.. note::
-    :class: sphx-glr-download-link-note
 
-    Click :ref:`here <sphx_glr_download_pages_examples_python_gb_non_deterministic.py>` to download the full example code
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "pages/examples/python/gb_non_deterministic.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        Click :ref:`here <sphx_glr_download_pages_examples_python_gb_non_deterministic.py>`
+        to download the full example code
+
 .. rst-class:: sphx-glr-example-title
 
 .. _sphx_glr_pages_examples_python_gb_non_deterministic.py:
@@ -18,6 +29,7 @@ control the randomness of the function to be evaluated.
 To evaluate undeterministic functions, we need to set "deterministic" as "false".
 Additional to the configuration, the function should make use of the seed parameter as well.
 
+.. GENERATED FROM PYTHON SOURCE LINES 13-125
 
 .. code-block:: default
 
@@ -150,13 +162,13 @@ Additional to the configuration, the function should make use of the seed parame
 
 
 
-  .. container:: sphx-glr-download
+  .. container:: sphx-glr-download sphx-glr-download-python
 
      :download:`Download Python source code: gb_non_deterministic.py <gb_non_deterministic.py>`
 
 
 
-  .. container:: sphx-glr-download
+  .. container:: sphx-glr-download sphx-glr-download-jupyter
 
      :download:`Download Jupyter notebook: gb_non_deterministic.ipynb <gb_non_deterministic.ipynb>`
 

diff --git a/development/_sources/pages/examples/python/mlp_mf.rst.txt b/development/_sources/pages/examples/python/mlp_mf.rst.txt
@@ -1,7 +1,18 @@
-.. note::
-    :class: sphx-glr-download-link-note
 
-    Click :ref:`here <sphx_glr_download_pages_examples_python_mlp_mf.py>` to download the full example code
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "pages/examples/python/mlp_mf.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        Click :ref:`here <sphx_glr_download_pages_examples_python_mlp_mf.py>`
+        to download the full example code
+
 .. rst-class:: sphx-glr-example-title
 
 .. _sphx_glr_pages_examples_python_mlp_mf.py:
@@ -18,6 +29,7 @@ aggressive racing mechanism and successive halving.
 MLP is a deep neural network, and therefore, we choose epochs as fidelity type. The digits dataset
 is chosen to optimize the average accuracy on 5-fold cross validation.
 
+.. GENERATED FROM PYTHON SOURCE LINES 13-168
 
 .. code-block:: default
 
@@ -193,13 +205,13 @@ is chosen to optimize the average accuracy on 5-fold cross validation.
 
 
 
-  .. container:: sphx-glr-download
+  .. container:: sphx-glr-download sphx-glr-download-python
 
      :download:`Download Python source code: mlp_mf.py <mlp_mf.py>`
 
 
 
-  .. container:: sphx-glr-download
+  .. container:: sphx-glr-download sphx-glr-download-jupyter
 
      :download:`Download Jupyter notebook: mlp_mf.ipynb <mlp_mf.ipynb>`
 

diff --git a/development/_sources/pages/examples/python/sgd_instances.rst.txt b/development/_sources/pages/examples/python/sgd_instances.rst.txt
@@ -1,7 +1,18 @@
-.. note::
-    :class: sphx-glr-download-link-note
 
-    Click :ref:`here <sphx_glr_download_pages_examples_python_sgd_instances.py>` to download the full example code
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "pages/examples/python/sgd_instances.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        Click :ref:`here <sphx_glr_download_pages_examples_python_sgd_instances.py>`
+        to download the full example code
+
 .. rst-class:: sphx-glr-example-title
 
 .. _sphx_glr_pages_examples_python_sgd_instances.py:
@@ -20,6 +31,7 @@ digit-2 vs digit-3.
 If we use instance as our fidelity, we need to initialize scenario with argument instance. In this case the argument
 budget is no longer required by the target function.
 
+.. GENERATED FROM PYTHON SOURCE LINES 15-170
 
 .. code-block:: default
 
@@ -195,13 +207,13 @@ budget is no longer required by the target function.
 
 
 
-  .. container:: sphx-glr-download
+  .. container:: sphx-glr-download sphx-glr-download-python
 
      :download:`Download Python source code: sgd_instances.py <sgd_instances.py>`
 
 
 
-  .. container:: sphx-glr-download
+  .. container:: sphx-glr-download sphx-glr-download-jupyter
 
      :download:`Download Jupyter notebook: sgd_instances.ipynb <sgd_instances.ipynb>`