Merge "Build pdf docs"

doc/source/conf.py

@@ -227,6 +227,17 @@ texinfo_documents = [
    'Miscellaneous'),
 ]
 
+# Disable usage of xindy https://bugzilla.redhat.com/show_bug.cgi?id=1643664
+latex_use_xindy = False
+
+latex_domain_indices = False
+
+latex_elements = {
+    'makeindex': '',
+    'printindex': '',
+    'preamble': r'\setcounter{tocdepth}{3}',
+}
+
 # Documents to append as an appendix to all manuals.
 #texinfo_appendices = []
 

requirements.txt

@@ -1,5 +1,6 @@
 pbr>=2.0.0,!=2.1.0 # Apache-2.0
-sphinx>=1.6.2 # BSD
+sphinx!=1.6.6,!=1.6.7,>=1.6.5,<2.0.0;python_version=='2.7' # BSD
+sphinx!=1.6.6,!=1.6.7,>=1.6.5,!=2.1.0;python_version>='3.4' # BSD
 testtools>=1.4.0
 yasfb>=0.8.0
 openstackdocstheme>=1.19.0 # Apache-2.0

specs/newton/implemented/scoring-module.rst

@@ -124,63 +124,63 @@ That said, the following changes are going to be implemented:
 
 * In the watcher/common package:
 
-  * ScoringEngine class defining an abstract base class for all Scoring
-    Engine implementations. The abstract class will include the following
-    abstract methods:
+  ScoringEngine class defining an abstract base class for all Scoring
+  Engine implementations. The abstract class will include the following
+  abstract methods:
 
-    :get_engine_id:
-      Method will return a unique string identifier of the Scoring Engine.
-      This ID will be used by factory classes and `Strategies`_ wanting to
-      use a specific Scoring Engine
+  :get_engine_id:
+    Method will return a unique string identifier of the Scoring Engine.
+    This ID will be used by factory classes and `Strategies`_ wanting to
+    use a specific Scoring Engine
 
-      :Input:
-        none
+    :Input:
+      none
 
-      :Result:
-        unique string ID (must be unique across all Scoring Engines)
+    :Result:
+      unique string ID (must be unique across all Scoring Engines)
 
-    :get_model_metadata:
-      Method will return a map with metadata information about the data
-      model. This might include informations about used algorithm, labels
-      for data returned by the score method (useful for interpreting the
-      results)
+  :get_model_metadata:
+    Method will return a map with metadata information about the data
+    model. This might include informations about used algorithm, labels
+    for data returned by the score method (useful for interpreting the
+    results)
 
-      :Input:
-        none
+    :Input:
+      none
 
-      :Result:
-        dictionary with metadata, both keys and values as strings
+    :Result:
+      dictionary with metadata, both keys and values as strings
 
-        For example, the metadata can contain the following information (real
-        world example):
+      For example, the metadata can contain the following information (real
+      world example):
 
-        * scoring engine is a classifier, which is based on the learning data
-          with these column labels (last column is the result used for
-          learning): [MEM_USAGE, PROC_USAGE, PCI_USAGE, POWER_CONSUMPTION,
-          CLASSIFICATION_ID]
-        * during the learning process, the machine learning decides that it
-          actually only needs these columns to calculate the expected
-          CLASSIFICATION_ID: [MEM_USAGE, PROC_USAGE]
-        * because the scoring result is a list of doubles, we need to know
-          what it means, e.g. 0.0 == CLASSIFICATION_ID_2, 1.0 ==
-          CLASSIFICATION_ID_1, etc.
-        * there is no guarantee of the order of the columns or even the
-          existence of them in input/output list
-        * this information must be passed as metadata, so the user of the
-          scoring engine is able to "understand" the results
-        * in addition, the metadata might provide some insights like what was
-          the algorithm used for learning or how many training records were
-          used
+      * scoring engine is a classifier, which is based on the learning data
+        with these column labels (last column is the result used for
+        learning): [MEM_USAGE, PROC_USAGE, PCI_USAGE, POWER_CONSUMPTION,
+        CLASSIFICATION_ID]
+      * during the learning process, the machine learning decides that it
+        actually only needs these columns to calculate the expected
+        CLASSIFICATION_ID: [MEM_USAGE, PROC_USAGE]
+      * because the scoring result is a list of doubles, we need to know
+        what it means, e.g. 0.0 == CLASSIFICATION_ID_2, 1.0 ==
+        CLASSIFICATION_ID_1, etc.
+      * there is no guarantee of the order of the columns or even the
+        existence of them in input/output list
+      * this information must be passed as metadata, so the user of the
+        scoring engine is able to "understand" the results
+      * in addition, the metadata might provide some insights like what was
+        the algorithm used for learning or how many training records were
+        used
 
-    :calculate_score:
-      Method responsible for performing the actual scoring, such as
-      classifying or predicting data
+  :calculate_score:
+    Method responsible for performing the actual scoring, such as
+    classifying or predicting data
 
-      :Input:
-        list of float numbers (e.g. feature values)
+    :Input:
+      list of float numbers (e.g. feature values)
 
-      :Result:
-        list of float numbers (e.g. classified values, predicted results)
+    :Result:
+      list of float numbers (e.g. classified values, predicted results)
 
 * In the `Watcher Decision Engine`_:
 

tox.ini

@@ -21,11 +21,26 @@ commands = {posargs}
 
 [testenv:docs]
 basepython = python3
+deps =
+  -c{env:UPPER_CONSTRAINTS_FILE:https://releases.openstack.org/constraints/upper/master}
+  -r{toxinidir}/requirements.txt
 setenv = PYTHONHASHSEED=0
 commands =
     find . -type f -name "*.pyc" -delete
     python setup.py build_sphinx
 
+[testenv:pdf-docs]
+basepython = python3
+envdir = {toxworkdir}/docs
+deps = {[testenv:docs]deps}
+whitelist_externals =
+  rm
+  make
+commands =
+  rm -rf doc/build/pdf
+  sphinx-build -W -b latex doc/source doc/build/pdf
+  make -C doc/build/pdf
+
 [testenv:pep8]
 basepython = python3
 deps =