Fix some layout glitches and improve doc

doc/development.rst

@@ -1,14 +1,47 @@
 ===========
 Development
 ===========
-The library is still experimental and under heavy development.
+
+The library is still experimental and under heavy development. Checkout
+the `next
+milestone <https://github.com/scikit-optimize/scikit-optimize/milestone/6>`__
+for the plans for the next release or look at some `easy
+issues <https://github.com/scikit-optimize/scikit-optimize/issues?q=is%3Aissue+is%3Aopen+label%3AEasy>`__
+to get started contributing.
+
 The development version can be installed through:
 
 .. code-block:: bash
 
     git clone https://github.com/scikit-optimize/scikit-optimize.git
     cd scikit-optimize
-    pip install -r requirements.txt
-    python setup.py develop
+    pip install -e.
+
+Run all tests by executing ``pytest`` in the top level directory.
+
+To only run the subset of tests with short run time, you can use ``pytest -m 'fast_test'`` (``pytest -m 'slow_test'`` is also possible). To exclude all slow running tests try ``pytest -m 'not slow_test'``.
+
+This is implemented using pytest `attributes <https://docs.pytest.org/en/latest/mark.html>`__. If a tests runs longer than 1 second, it is marked as slow, else as fast.
+
+All contributors are welcome!
+
+
+Making a Release
+~~~~~~~~~~~~~~~~
+
+The release procedure is almost completely automated. By tagging a new release
+travis will build all required packages and push them to PyPI. To make a release
+create a new issue and work through the following checklist:
+
+* update the version tag in ``__init__.py``
+* update the version tag mentioned in the README
+* check if the dependencies in ``setup.py`` are valid or need unpinning
+* check that the ``CHANGELOG.md`` is up to date
+* did the last build of master succeed?
+* create a `new release <https://github.com/scikit-optimize/scikit-optimize/releases>`__
+* ping `conda-forge <https://github.com/conda-forge/scikit-optimize-feedstock>`__
 
-Run the tests by executing `pytest` in the top level directory.
+Before making a release we usually create a release candidate. If the next
+release is v0.X then the release candidate should be tagged v0.Xrc1 in
+``__init__.py``. Mark a release candidate as a "pre-release"
+on GitHub when you tag it.

doc/install.rst

@@ -22,3 +22,18 @@ The newest development version of scikit-optimize can be installed by:
 .. code-block:: bash
 
     $ pip install git+https://github.com/scikit-optimize/scikit-optimize.git
+
+Development version
+~~~~~~~~~~~~~~~~~~~
+
+The library is still experimental and under heavy development.
+The development version can be installed through:
+
+.. code-block:: bash
+
+    git clone https://github.com/scikit-optimize/scikit-optimize.git
+    cd scikit-optimize
+    pip install -r requirements.txt
+    python setup.py develop
+
+Run the tests by executing `pytest` in the top level directory.

doc/templates/index.html

@@ -105,6 +105,7 @@ <h4 class="sk-landing-call-header">News</h4>
         <li><strong>On-going development:</strong>
         <a href="whats_new.html"><strong>What's new</strong> (Changelog)</a>
         </li>
+        <li><strong>Jan 2020.</strong> scikit-optimize 0.7  (<a href="whats_new/v0.7.html">Changelog</a>).
         <li><strong>April 2018.</strong> scikit-optimize 0.6  (<a href="whats_new/v0.6.html">Changelog</a>).
         <li><strong>Mar 2018.</strong> scikit-optimize 0.5  (<a href="whats_new/v0.5.html">Changelog</a>).
         <li><strong>Aug 2017.</strong> scikit-optimize 0.4 (<a href="whats_new/v0.4.html">Changelog</a>).

examples/interruptible-optimization.py

@@ -20,12 +20,9 @@
 
 This is useful, for example,
 
-* if you don't know how long the process will take and cannot hog
-    computational resources forever
-* if there might be system failures due to shaky infrastructure
-    (or colleagues...)
-* if you want to adjust some parameters and continue with the already obtained
-    results
+* if you don't know how long the process will take and cannot hog computational resources forever
+* if there might be system failures due to shaky infrastructure (or colleagues...)
+* if you want to adjust some parameters and continue with the already obtained results
 
 """
 print(__doc__)
@@ -123,10 +120,11 @@ def obj_fun(x, noise_level=noise_level):
 # =================
 #
 # * **changes in search space:** You can use this technique to interrupt
-# the search, tune the search space and continue the optimization. Note
-# that the optimizers will complain if `x0` contains parameter values not
-# covered by the dimension definitions, so in many cases shrinking the
-# search space will not work without deleting the offending runs from
-# `x0` and `y0`.
+#   the search, tune the search space and continue the optimization. Note
+#   that the optimizers will complain if `x0` contains parameter values not
+#   covered by the dimension definitions, so in many cases shrinking the
+#   search space will not work without deleting the offending runs from
+#   `x0` and `y0`.
 # * see :ref:`sphx_glr_auto_examples_store-and-load-results.py`
+#
 # for more information on how the results get saved and possible caveats

examples/parallel-optimization.py

@@ -32,11 +32,9 @@
 interface is as follows:
 
 1. Initialize instance of the `Optimizer` class from skopt
-2. Obtain n points for evaluation in parallel by calling the `ask` method of
-    an optimizer instance with the `n_points` argument set to n > 0
+2. Obtain n points for evaluation in parallel by calling the `ask` method of an optimizer instance with the `n_points` argument set to n > 0
 3. Evaluate points
-4. Provide points and corresponding objectives using the `tell` method of
-    an optimizer instance
+4. Provide points and corresponding objectives using the `tell` method of an optimizer instance
 5. Continue from step 2 until eg maximum number of evaluations reached
 """
 
@@ -72,14 +70,14 @@
 
 #############################################################################
 # Note that if `n_points` is set to some integer > 0 for the `ask` method, the
-# result will be a list of points, even for `n_points`=1. If the argument is
+# result will be a list of points, even for `n_points` = 1. If the argument is
 # set to `None` (default value) then a single point (but not a list of points)
 # will be returned.
 #
 # The default "minimum constant liar" [1] parallelization strategy is used in
 # the example, which allows to obtain multiple points for evaluation with a
 # single call to the `ask` method with any surrogate or acquisition function.
-# Paralellization strategy can be set using the "strategy" argument of `ask`.
+# Parallelization strategy can be set using the "strategy" argument of `ask`.
 # For supported parallelization strategies see the documentation of
 # scikit-optimize.
 #