Merge branch 'scikit-learn:main' into submodulev3

Author: adam2392

doc/conf.py

@@ -68,6 +68,7 @@
 # Specify how to identify the prompt when copying code snippets
 copybutton_prompt_text = r">>> |\.\.\. "
 copybutton_prompt_is_regexp = True
+copybutton_exclude = "style"
 
 try:
     import jupyterlite_sphinx  # noqa: F401

doc/developers/advanced_installation.rst

@@ -229,10 +229,13 @@ console:
 For 64-bit Python, configure the build environment by running the following
 commands in ``cmd`` or an Anaconda Prompt (if you use Anaconda):
 
-    ::
+.. sphinx-prompt 1.3.0 (used in doc-min-dependencies CI task) does not support `batch` prompt type,
+.. so we work around by using a known prompt type and an explicit prompt text.
+..
+.. prompt:: bash C:\>
 
-      $ SET DISTUTILS_USE_SDK=1
-      $ "C:\Program Files (x86)\Microsoft Visual Studio\2019\BuildTools\VC\Auxiliary\Build\vcvarsall.bat" x64
+    SET DISTUTILS_USE_SDK=1
+    "C:\Program Files (x86)\Microsoft Visual Studio\2019\BuildTools\VC\Auxiliary\Build\vcvarsall.bat" x64
 
 Replace ``x64`` by ``x86`` to build for 32-bit Python.
 

doc/install.rst

@@ -71,32 +71,32 @@ Then run:
 
   <div class="highlight">
     <pre class="sk-expandable" data-packager="pip" data-os="linux" data-venv="no"
-    ><span class="prompt1">pip3 install -U scikit-learn</span></pre>
-    
+    ><span>pip3 install -U scikit-learn</span></pre>
+
     <pre class="sk-expandable" data-packager="pip" data-os="windows" data-venv="no"
-    ><span class="prompt1">pip install -U scikit-learn</span></pre>
-    
+    ><span>pip install -U scikit-learn</span></pre>
+
     <pre class="sk-expandable" data-packager="pip" data-os="mac" data-venv="no"
-    ><span class="prompt1">pip install -U scikit-learn</span></pre>
-    
+    ><span>pip install -U scikit-learn</span></pre>
+
     <pre class="sk-expandable" data-packager="pip" data-os="linux" data-venv=""
-    ><span class="prompt1">python3 -m venv sklearn-venv</span>
-  <span class="prompt1">source sklearn-venv/bin/activate</span>
-  <span class="prompt1">pip3 install -U scikit-learn</span></pre>
-    
+    ><span>python3 -m venv sklearn-venv</span>
+  <span>source sklearn-venv/bin/activate</span>
+  <span>pip3 install -U scikit-learn</span></pre>
+
     <pre class="sk-expandable" data-packager="pip" data-os="windows" data-venv=""
-    ><span class="prompt1">python -m venv sklearn-venv</span>
-  <span class="prompt1">sklearn-venv\Scripts\activate</span>
-  <span class="prompt1">pip install -U scikit-learn</span></pre>
-    
+    ><span>python -m venv sklearn-venv</span>
+  <span>sklearn-venv\Scripts\activate</span>
+  <span>pip install -U scikit-learn</span></pre>
+
     <pre class="sk-expandable" data-packager="pip" data-os="mac" data-venv=""
-    ><span class="prompt1">python -m venv sklearn-venv</span>
-  <span class="prompt1">source sklearn-venv/bin/activate</span>
-  <span class="prompt1">pip install -U scikit-learn</span></pre>
+    ><span>python -m venv sklearn-venv</span>
+  <span>source sklearn-venv/bin/activate</span>
+  <span>pip install -U scikit-learn</span></pre>
 
     <pre class="sk-expandable" data-packager="conda"
-    ><span class="prompt1">conda create -n sklearn-env -c conda-forge scikit-learn</span>
-  <span class="prompt1">conda activate sklearn-env</span></pre>
+    ><span>conda create -n sklearn-env -c conda-forge scikit-learn</span>
+  <span>conda activate sklearn-env</span></pre>
   </div>
 
 In order to check your installation you can use
@@ -105,29 +105,29 @@ In order to check your installation you can use
 
   <div class="highlight">
     <pre class="sk-expandable" data-packager="pip" data-os="linux" data-venv="no"
-    ><span class="prompt1">python3 -m pip show scikit-learn  # to see which version and where scikit-learn is installed</span>
-  <span class="prompt1">python3 -m pip freeze  # to see all packages installed in the active virtualenv</span>
-  <span class="prompt1">python3 -c "import sklearn; sklearn.show_versions()"</span></pre>
+    ><span>python3 -m pip show scikit-learn  # to see which version and where scikit-learn is installed</span>
+  <span>python3 -m pip freeze  # to see all packages installed in the active virtualenv</span>
+  <span>python3 -c "import sklearn; sklearn.show_versions()"</span></pre>
 
     <pre class="sk-expandable" data-packager="pip" data-os="windows" data-venv="no"
-    ><span class="prompt1">python -m pip show scikit-learn  # to see which version and where scikit-learn is installed</span>
-  <span class="prompt1">python -m pip freeze  # to see all packages installed in the active virtualenv</span>
-  <span class="prompt1">python -c "import sklearn; sklearn.show_versions()"</span></pre>
+    ><span>python -m pip show scikit-learn  # to see which version and where scikit-learn is installed</span>
+  <span>python -m pip freeze  # to see all packages installed in the active virtualenv</span>
+  <span>python -c "import sklearn; sklearn.show_versions()"</span></pre>
 
     <pre class="sk-expandable" data-packager="pip" data-os="mac" data-venv="no"
-    ><span class="prompt1">python -m pip show scikit-learn  # to see which version and where scikit-learn is installed</span>
-  <span class="prompt1">python -m pip freeze  # to see all packages installed in the active virtualenv</span>
-  <span class="prompt1">python -c "import sklearn; sklearn.show_versions()"</span></pre>
+    ><span>python -m pip show scikit-learn  # to see which version and where scikit-learn is installed</span>
+  <span>python -m pip freeze  # to see all packages installed in the active virtualenv</span>
+  <span>python -c "import sklearn; sklearn.show_versions()"</span></pre>
 
     <pre class="sk-expandable" data-packager="pip" data-venv=""
-    ><span class="prompt1">python -m pip show scikit-learn  # to see which version and where scikit-learn is installed</span>
-  <span class="prompt1">python -m pip freeze  # to see all packages installed in the active virtualenv</span>
-  <span class="prompt1">python -c "import sklearn; sklearn.show_versions()"</span></pre>
+    ><span>python -m pip show scikit-learn  # to see which version and where scikit-learn is installed</span>
+  <span>python -m pip freeze  # to see all packages installed in the active virtualenv</span>
+  <span>python -c "import sklearn; sklearn.show_versions()"</span></pre>
 
     <pre class="sk-expandable" data-packager="conda"
-    ><span class="prompt1">conda list scikit-learn  # to see which scikit-learn version is installed</span>
-  <span class="prompt1">conda list  # to see all packages installed in the active conda environment</span>
-  <span class="prompt1">python -c "import sklearn; sklearn.show_versions()"</span></pre>
+    ><span>conda list scikit-learn  # to see which scikit-learn version is installed</span>
+  <span>conda list  # to see all packages installed in the active conda environment</span>
+  <span>python -c "import sklearn; sklearn.show_versions()"</span></pre>
   </div>
 
 Note that in order to avoid potential conflicts with other packages it is
@@ -290,7 +290,7 @@ Note that those solvers are not enabled by default, please refer to the
 `scikit-learn-intelex <https://intel.github.io/scikit-learn-intelex/what-is-patching.html>`_
 documentation for more details on usage scenarios. Direct export example:
 
-.. prompt:: bash $
+.. prompt:: python >>>
 
   from sklearnex.neighbors import NearestNeighbors
 
@@ -340,6 +340,6 @@ using the ``regedit`` tool:
 
 #. Reinstall scikit-learn (ignoring the previous broken installation):
 
-.. prompt:: python $
+.. prompt:: bash $
 
     pip install --exists-action=i scikit-learn

doc/modules/array_api.rst

@@ -125,8 +125,8 @@ automatically skipped. Therefore it's important to run the tests with the
 
 .. prompt:: bash $
 
-    $ pip install array-api-compat  # and other libraries as needed
-    $ pytest -k "array_api" -v
+    pip install array-api-compat  # and other libraries as needed
+    pytest -k "array_api" -v
 
 Note on MPS device support
 --------------------------
@@ -143,7 +143,7 @@ To enable the MPS support in PyTorch, set the environment variable
 
 .. prompt:: bash $
 
-    $ PYTORCH_ENABLE_MPS_FALLBACK=1 pytest -k "array_api" -v
+    PYTORCH_ENABLE_MPS_FALLBACK=1 pytest -k "array_api" -v
 
 At the time of writing all scikit-learn tests should pass, however, the
 computational speed is not necessarily better than with the CPU device.

doc/modules/feature_selection.rst

@@ -201,31 +201,36 @@ alpha parameter, the fewer features selected.
 
 .. _compressive_sensing:
 
-.. topic:: **L1-recovery and compressive sensing**
-
-   For a good choice of alpha, the :ref:`lasso` can fully recover the
-   exact set of non-zero variables using only few observations, provided
-   certain specific conditions are met. In particular, the number of
-   samples should be "sufficiently large", or L1 models will perform at
-   random, where "sufficiently large" depends on the number of non-zero
-   coefficients, the logarithm of the number of features, the amount of
-   noise, the smallest absolute value of non-zero coefficients, and the
-   structure of the design matrix X. In addition, the design matrix must
-   display certain specific properties, such as not being too correlated.
-
-   There is no general rule to select an alpha parameter for recovery of
-   non-zero coefficients. It can by set by cross-validation
-   (:class:`~sklearn.linear_model.LassoCV` or
-   :class:`~sklearn.linear_model.LassoLarsCV`), though this may lead to
-   under-penalized models: including a small number of non-relevant variables
-   is not detrimental to prediction score. BIC
-   (:class:`~sklearn.linear_model.LassoLarsIC`) tends, on the opposite, to set
-   high values of alpha.
-
-   **Reference** Richard G. Baraniuk "Compressive Sensing", IEEE Signal
+|details-start|
+**L1-recovery and compressive sensing**
+|details-split|
+
+For a good choice of alpha, the :ref:`lasso` can fully recover the
+exact set of non-zero variables using only few observations, provided
+certain specific conditions are met. In particular, the number of
+samples should be "sufficiently large", or L1 models will perform at
+random, where "sufficiently large" depends on the number of non-zero
+coefficients, the logarithm of the number of features, the amount of
+noise, the smallest absolute value of non-zero coefficients, and the
+structure of the design matrix X. In addition, the design matrix must
+display certain specific properties, such as not being too correlated.
+
+There is no general rule to select an alpha parameter for recovery of
+non-zero coefficients. It can by set by cross-validation
+(:class:`~sklearn.linear_model.LassoCV` or
+:class:`~sklearn.linear_model.LassoLarsCV`), though this may lead to
+under-penalized models: including a small number of non-relevant variables
+is not detrimental to prediction score. BIC
+(:class:`~sklearn.linear_model.LassoLarsIC`) tends, on the opposite, to set
+high values of alpha.
+
+.. topic:: Reference
+
+   Richard G. Baraniuk "Compressive Sensing", IEEE Signal
    Processing Magazine [120] July 2007
    http://users.isr.ist.utl.pt/~aguiar/CS_notes.pdf
 
+|details-end|
 
 Tree-based feature selection
 ----------------------------
@@ -282,6 +287,10 @@ instead of starting with no features and greedily adding features, we start
 with *all* the features and greedily *remove* features from the set. The
 `direction` parameter controls whether forward or backward SFS is used.
 
+|details-start|
+**Detail on Sequential Feature Selection**
+|details-split|
+
 In general, forward and backward selection do not yield equivalent results.
 Also, one may be much faster than the other depending on the requested number
 of selected features: if we have 10 features and ask for 7 selected features,
@@ -299,16 +308,18 @@ cross-validation requires fitting `m * k` models, while
 :class:`~sklearn.feature_selection.SelectFromModel` always just does a single
 fit and requires no iterations.
 
-.. topic:: Examples
-
-    * :ref:`sphx_glr_auto_examples_feature_selection_plot_select_from_model_diabetes.py`
-
-.. topic:: References:
+.. topic:: Reference
 
    .. [sfs] Ferri et al, `Comparative study of techniques for
       large-scale feature selection
       <https://citeseerx.ist.psu.edu/doc_view/pid/5fedabbb3957bbb442802e012d829ee0629a01b6>`_.
 
+|details-end|
+
+.. topic:: Examples
+
+    * :ref:`sphx_glr_auto_examples_feature_selection_plot_select_from_model_diabetes.py`
+
 Feature selection as part of a pipeline
 =======================================