Build pdf docs
Add a new pdf-docs environment to enable PDF build. Change-Id: I02125d7f348ed956695f9b60ab605b2ffbddfd55
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@ -227,6 +227,17 @@ texinfo_documents = [
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'Miscellaneous'),
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'Miscellaneous'),
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]
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]
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# Disable usage of xindy https://bugzilla.redhat.com/show_bug.cgi?id=1643664
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latex_use_xindy = False
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latex_domain_indices = False
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latex_elements = {
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'makeindex': '',
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'printindex': '',
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'preamble': r'\setcounter{tocdepth}{3}',
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}
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# Documents to append as an appendix to all manuals.
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# Documents to append as an appendix to all manuals.
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#texinfo_appendices = []
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#texinfo_appendices = []
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@ -1,5 +1,6 @@
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pbr>=2.0.0,!=2.1.0 # Apache-2.0
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pbr>=2.0.0,!=2.1.0 # Apache-2.0
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sphinx>=1.6.2 # BSD
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sphinx!=1.6.6,!=1.6.7,>=1.6.5,<2.0.0;python_version=='2.7' # BSD
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sphinx!=1.6.6,!=1.6.7,>=1.6.5,!=2.1.0;python_version>='3.4' # BSD
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testtools>=1.4.0
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testtools>=1.4.0
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yasfb>=0.8.0
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yasfb>=0.8.0
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openstackdocstheme>=1.19.0 # Apache-2.0
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openstackdocstheme>=1.19.0 # Apache-2.0
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@ -124,63 +124,63 @@ That said, the following changes are going to be implemented:
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* In the watcher/common package:
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* In the watcher/common package:
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* ScoringEngine class defining an abstract base class for all Scoring
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ScoringEngine class defining an abstract base class for all Scoring
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Engine implementations. The abstract class will include the following
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Engine implementations. The abstract class will include the following
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abstract methods:
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abstract methods:
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:get_engine_id:
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:get_engine_id:
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Method will return a unique string identifier of the Scoring Engine.
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Method will return a unique string identifier of the Scoring Engine.
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This ID will be used by factory classes and `Strategies`_ wanting to
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This ID will be used by factory classes and `Strategies`_ wanting to
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use a specific Scoring Engine
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use a specific Scoring Engine
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:Input:
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:Input:
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none
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none
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:Result:
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:Result:
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unique string ID (must be unique across all Scoring Engines)
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unique string ID (must be unique across all Scoring Engines)
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:get_model_metadata:
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:get_model_metadata:
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Method will return a map with metadata information about the data
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Method will return a map with metadata information about the data
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model. This might include informations about used algorithm, labels
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model. This might include informations about used algorithm, labels
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for data returned by the score method (useful for interpreting the
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for data returned by the score method (useful for interpreting the
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results)
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results)
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:Input:
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:Input:
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none
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none
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:Result:
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:Result:
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dictionary with metadata, both keys and values as strings
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dictionary with metadata, both keys and values as strings
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For example, the metadata can contain the following information (real
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For example, the metadata can contain the following information (real
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world example):
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world example):
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* scoring engine is a classifier, which is based on the learning data
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* scoring engine is a classifier, which is based on the learning data
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with these column labels (last column is the result used for
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with these column labels (last column is the result used for
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learning): [MEM_USAGE, PROC_USAGE, PCI_USAGE, POWER_CONSUMPTION,
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learning): [MEM_USAGE, PROC_USAGE, PCI_USAGE, POWER_CONSUMPTION,
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CLASSIFICATION_ID]
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CLASSIFICATION_ID]
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* during the learning process, the machine learning decides that it
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* during the learning process, the machine learning decides that it
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actually only needs these columns to calculate the expected
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actually only needs these columns to calculate the expected
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CLASSIFICATION_ID: [MEM_USAGE, PROC_USAGE]
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CLASSIFICATION_ID: [MEM_USAGE, PROC_USAGE]
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* because the scoring result is a list of doubles, we need to know
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* because the scoring result is a list of doubles, we need to know
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what it means, e.g. 0.0 == CLASSIFICATION_ID_2, 1.0 ==
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what it means, e.g. 0.0 == CLASSIFICATION_ID_2, 1.0 ==
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CLASSIFICATION_ID_1, etc.
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CLASSIFICATION_ID_1, etc.
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* there is no guarantee of the order of the columns or even the
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* there is no guarantee of the order of the columns or even the
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existence of them in input/output list
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existence of them in input/output list
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* this information must be passed as metadata, so the user of the
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* this information must be passed as metadata, so the user of the
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scoring engine is able to "understand" the results
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scoring engine is able to "understand" the results
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* in addition, the metadata might provide some insights like what was
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* in addition, the metadata might provide some insights like what was
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the algorithm used for learning or how many training records were
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the algorithm used for learning or how many training records were
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used
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used
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:calculate_score:
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:calculate_score:
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Method responsible for performing the actual scoring, such as
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Method responsible for performing the actual scoring, such as
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classifying or predicting data
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classifying or predicting data
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:Input:
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:Input:
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list of float numbers (e.g. feature values)
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list of float numbers (e.g. feature values)
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:Result:
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:Result:
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list of float numbers (e.g. classified values, predicted results)
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list of float numbers (e.g. classified values, predicted results)
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* In the `Watcher Decision Engine`_:
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* In the `Watcher Decision Engine`_:
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15
tox.ini
15
tox.ini
@ -21,11 +21,26 @@ commands = {posargs}
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[testenv:docs]
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[testenv:docs]
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basepython = python3
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basepython = python3
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deps =
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-c{env:UPPER_CONSTRAINTS_FILE:https://releases.openstack.org/constraints/upper/master}
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-r{toxinidir}/requirements.txt
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setenv = PYTHONHASHSEED=0
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setenv = PYTHONHASHSEED=0
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commands =
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commands =
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find . -type f -name "*.pyc" -delete
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find . -type f -name "*.pyc" -delete
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python setup.py build_sphinx
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python setup.py build_sphinx
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[testenv:pdf-docs]
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basepython = python3
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envdir = {toxworkdir}/docs
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deps = {[testenv:docs]deps}
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whitelist_externals =
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rm
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make
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commands =
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rm -rf doc/build/pdf
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sphinx-build -W -b latex doc/source doc/build/pdf
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make -C doc/build/pdf
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[testenv:pep8]
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[testenv:pep8]
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basepython = python3
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basepython = python3
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deps =
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deps =
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