Documentation contains the README's quickstart instructions and doc-related CI is prepared
This commit is contained in:
Mirko Bunse
parent
c408deacae
commit
e1f99eb201
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@ -4,7 +4,7 @@ on:
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pull_request:
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push:
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branches:
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- main
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- master
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- devel
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jobs:
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@ -31,3 +31,37 @@ jobs:
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python -m pip install -e .[bayes,composable,tests]
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- name: Test with unittest
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run: python -m unittest
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# build and push documentation to gh-pages (only if pushed to the master branch)
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docs:
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name: Documentation
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runs-on: ubuntu-latest
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if: github.ref == 'refs/heads/master'
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steps:
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- uses: actions/checkout@v1
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- name: Build documentation
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uses: ammaraskar/sphinx-action@master
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with:
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pre-build-command: |
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python -m pip install --upgrade pip setuptools wheel
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python -m pip install -e .[docs]
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mkdir -p docs/source/wiki/wiki_examples/selected_plots
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cp docs/source/wiki_editable/wiki_examples/selected_plots/* docs/source/wiki/wiki_examples/selected_plots/
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find docs/source/wiki_editable -name '*.md' -exec sh -c 'pandoc -f markdown -t rst "$$1" -o "docs/source/wiki/$$(basename "$$1" .md).rst"' _ {} \;
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sphinx-apidoc --force --output-dir docs/source quapy
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docs-folder: "docs/"
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- name: Publish documentation
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run: |
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git clone ${{ github.server_url }}/${{ github.repository }}.git --branch gh-pages --single-branch __gh-pages/
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cp -r docs/build/html/* __gh-pages/
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cd __gh-pages/
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git config --local user.email "action@github.com"
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git config --local user.name "GitHub Action"
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git add .
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git commit -am "Documentation based on ${{ github.sha }}" || true
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- name: Push changes
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uses: ad-m/github-push-action@master
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with:
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branch: gh-pages
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directory: __gh-pages/
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github_token: ${{ secrets.GITHUB_TOKEN }}
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|
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@ -116,4 +116,4 @@ are provided:
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## Acknowledgments:
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<img src="SoBigData.png" alt="SoBigData++" width="250"/>
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<img src="docs/source/SoBigData.png" alt="SoBigData++" width="250"/>
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|
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@ -0,0 +1,2 @@
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build/
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source/wiki/
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@ -1,58 +0,0 @@
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.. QuaPy: A Python-based open-source framework for quantification documentation master file, created by
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sphinx-quickstart on Wed Feb 7 16:26:46 2024.
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You can adapt this file completely to your liking, but it should at least
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contain the root `toctree` directive.
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Welcome to QuaPy's documentation!
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==========================================================================================
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QuaPy is a Python-based open-source framework for quantification.
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This document contains the API of the modules included in QuaPy.
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Installation
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------------
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`pip install quapy`
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GitHub
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------------
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QuaPy is hosted in GitHub at `https://github.com/HLT-ISTI/QuaPy <https://github.com/HLT-ISTI/QuaPy>`_
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Wiki Documents
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------------
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In this section you can find useful information concerning different aspects of QuaPy, with examples:
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.. toctree::
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:maxdepth: 1
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wiki/Datasets
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wiki/Evaluation
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wiki/ExplicitLossMinimization
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wiki/Methods
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wiki/Model-Selection
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wiki/Plotting
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wiki/Protocols
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.. toctree::
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:maxdepth: 2
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:caption: Contents:
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Contents
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--------
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.. toctree::
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modules
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Indices and tables
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==================
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* :ref:`genindex`
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* :ref:`modindex`
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* :ref:`search`
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|
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@ -1,7 +0,0 @@
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quapy
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=====
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.. toctree::
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:maxdepth: 4
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quapy
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|
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@ -1,45 +0,0 @@
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quapy.classification package
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============================
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Submodules
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----------
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|
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quapy.classification.calibration module
|
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---------------------------------------
|
||||
|
||||
.. automodule:: quapy.classification.calibration
|
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:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
quapy.classification.methods module
|
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-----------------------------------
|
||||
|
||||
.. automodule:: quapy.classification.methods
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:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
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quapy.classification.neural module
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----------------------------------
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|
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.. automodule:: quapy.classification.neural
|
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:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
quapy.classification.svmperf module
|
||||
-----------------------------------
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||||
|
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.. automodule:: quapy.classification.svmperf
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||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Module contents
|
||||
---------------
|
||||
|
||||
.. automodule:: quapy.classification
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:members:
|
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:undoc-members:
|
||||
:show-inheritance:
|
||||
|
|
@ -1,46 +0,0 @@
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|||
quapy.data package
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||||
==================
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||||
|
||||
Submodules
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||||
----------
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||||
|
||||
quapy.data.base module
|
||||
----------------------
|
||||
|
||||
.. automodule:: quapy.data.base
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
quapy.data.datasets module
|
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--------------------------
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||||
|
||||
.. automodule:: quapy.data.datasets
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
|
||||
quapy.data.preprocessing module
|
||||
-------------------------------
|
||||
|
||||
.. automodule:: quapy.data.preprocessing
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
quapy.data.reader module
|
||||
------------------------
|
||||
|
||||
.. automodule:: quapy.data.reader
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Module contents
|
||||
---------------
|
||||
|
||||
.. automodule:: quapy.data
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
|
@ -1,69 +0,0 @@
|
|||
quapy.method package
|
||||
====================
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
quapy.method.aggregative module
|
||||
-------------------------------
|
||||
|
||||
.. automodule:: quapy.method.aggregative
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
.. automodule:: quapy.method._kdey
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
.. automodule:: quapy.method._neural
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
.. automodule:: quapy.method._threshold_optim
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
|
||||
quapy.method.base module
|
||||
------------------------
|
||||
|
||||
.. automodule:: quapy.method.base
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
quapy.method.meta module
|
||||
------------------------
|
||||
|
||||
.. automodule:: quapy.method.meta
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
quapy.method.non\_aggregative module
|
||||
------------------------------------
|
||||
|
||||
.. automodule:: quapy.method.non_aggregative
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
quapy.method.composable module
|
||||
------------------------
|
||||
|
||||
.. automodule:: quapy.method.composable
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Module contents
|
||||
---------------
|
||||
|
||||
.. automodule:: quapy.method
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
|
@ -1,80 +0,0 @@
|
|||
quapy package
|
||||
=============
|
||||
|
||||
Subpackages
|
||||
-----------
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 4
|
||||
|
||||
quapy.classification
|
||||
quapy.data
|
||||
quapy.method
|
||||
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
quapy.error module
|
||||
------------------
|
||||
|
||||
.. automodule:: quapy.error
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
quapy.evaluation module
|
||||
-----------------------
|
||||
|
||||
.. automodule:: quapy.evaluation
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
quapy.functional module
|
||||
-----------------------
|
||||
|
||||
.. automodule:: quapy.functional
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
quapy.model\_selection module
|
||||
-----------------------------
|
||||
|
||||
.. automodule:: quapy.model_selection
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
quapy.plot module
|
||||
-----------------
|
||||
|
||||
.. automodule:: quapy.plot
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
quapy.protocol module
|
||||
---------------------
|
||||
|
||||
.. automodule:: quapy.protocol
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
quapy.util module
|
||||
-----------------
|
||||
|
||||
.. automodule:: quapy.util
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Module contents
|
||||
---------------
|
||||
|
||||
.. automodule:: quapy
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
|
@ -1,900 +0,0 @@
|
|||
/*
|
||||
* basic.css
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||||
* ~~~~~~~~~
|
||||
*
|
||||
* Sphinx stylesheet -- basic theme.
|
||||
*
|
||||
* :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
|
||||
* :license: BSD, see LICENSE for details.
|
||||
*
|
||||
*/
|
||||
|
||||
/* -- main layout ----------------------------------------------------------- */
|
||||
|
||||
div.clearer {
|
||||
clear: both;
|
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}
|
||||
|
||||
div.section::after {
|
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display: block;
|
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content: '';
|
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clear: left;
|
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}
|
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|
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/* -- relbar ---------------------------------------------------------------- */
|
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|
||||
div.related {
|
||||
width: 100%;
|
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font-size: 90%;
|
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}
|
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|
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div.related h3 {
|
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display: none;
|
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}
|
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|
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div.related ul {
|
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margin: 0;
|
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padding: 0 0 0 10px;
|
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list-style: none;
|
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}
|
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|
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div.related li {
|
||||
display: inline;
|
||||
}
|
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|
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div.related li.right {
|
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float: right;
|
||||
margin-right: 5px;
|
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}
|
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|
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/* -- sidebar --------------------------------------------------------------- */
|
||||
|
||||
div.sphinxsidebarwrapper {
|
||||
padding: 10px 5px 0 10px;
|
||||
}
|
||||
|
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div.sphinxsidebar {
|
||||
float: left;
|
||||
width: 230px;
|
||||
margin-left: -100%;
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font-size: 90%;
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||||
word-wrap: break-word;
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overflow-wrap : break-word;
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}
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|
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div.sphinxsidebar ul {
|
||||
list-style: none;
|
||||
}
|
||||
|
||||
div.sphinxsidebar ul ul,
|
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div.sphinxsidebar ul.want-points {
|
||||
margin-left: 20px;
|
||||
list-style: square;
|
||||
}
|
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|
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div.sphinxsidebar ul ul {
|
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margin-top: 0;
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margin-bottom: 0;
|
||||
}
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|
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div.sphinxsidebar form {
|
||||
margin-top: 10px;
|
||||
}
|
||||
|
||||
div.sphinxsidebar input {
|
||||
border: 1px solid #98dbcc;
|
||||
font-family: sans-serif;
|
||||
font-size: 1em;
|
||||
}
|
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|
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div.sphinxsidebar #searchbox form.search {
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
div.sphinxsidebar #searchbox input[type="text"] {
|
||||
float: left;
|
||||
width: 80%;
|
||||
padding: 0.25em;
|
||||
box-sizing: border-box;
|
||||
}
|
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|
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div.sphinxsidebar #searchbox input[type="submit"] {
|
||||
float: left;
|
||||
width: 20%;
|
||||
border-left: none;
|
||||
padding: 0.25em;
|
||||
box-sizing: border-box;
|
||||
}
|
||||
|
||||
|
||||
img {
|
||||
border: 0;
|
||||
max-width: 100%;
|
||||
}
|
||||
|
||||
/* -- search page ----------------------------------------------------------- */
|
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|
||||
ul.search {
|
||||
margin: 10px 0 0 20px;
|
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padding: 0;
|
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}
|
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|
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ul.search li {
|
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padding: 5px 0 5px 20px;
|
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background-image: url(file.png);
|
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background-repeat: no-repeat;
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background-position: 0 7px;
|
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}
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ul.search li a {
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font-weight: bold;
|
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}
|
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|
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ul.search li p.context {
|
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color: #888;
|
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margin: 2px 0 0 30px;
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text-align: left;
|
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}
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|
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ul.keywordmatches li.goodmatch a {
|
||||
font-weight: bold;
|
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}
|
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|
||||
/* -- index page ------------------------------------------------------------ */
|
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|
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table.contentstable {
|
||||
width: 90%;
|
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margin-left: auto;
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margin-right: auto;
|
||||
}
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|
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table.contentstable p.biglink {
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||||
line-height: 150%;
|
||||
}
|
||||
|
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a.biglink {
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||||
font-size: 1.3em;
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}
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span.linkdescr {
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||||
font-style: italic;
|
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padding-top: 5px;
|
||||
font-size: 90%;
|
||||
}
|
||||
|
||||
/* -- general index --------------------------------------------------------- */
|
||||
|
||||
table.indextable {
|
||||
width: 100%;
|
||||
}
|
||||
|
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table.indextable td {
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||||
text-align: left;
|
||||
vertical-align: top;
|
||||
}
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|
||||
table.indextable ul {
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||||
margin-top: 0;
|
||||
margin-bottom: 0;
|
||||
list-style-type: none;
|
||||
}
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||||
|
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table.indextable > tbody > tr > td > ul {
|
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padding-left: 0em;
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||||
}
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|
||||
table.indextable tr.pcap {
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||||
height: 10px;
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}
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table.indextable tr.cap {
|
||||
margin-top: 10px;
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||||
background-color: #f2f2f2;
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||||
}
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||||
|
||||
img.toggler {
|
||||
margin-right: 3px;
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||||
margin-top: 3px;
|
||||
cursor: pointer;
|
||||
}
|
||||
|
||||
div.modindex-jumpbox {
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||||
border-top: 1px solid #ddd;
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border-bottom: 1px solid #ddd;
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||||
margin: 1em 0 1em 0;
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||||
padding: 0.4em;
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||||
}
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||||
|
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div.genindex-jumpbox {
|
||||
border-top: 1px solid #ddd;
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||||
border-bottom: 1px solid #ddd;
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||||
margin: 1em 0 1em 0;
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padding: 0.4em;
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}
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/* -- domain module index --------------------------------------------------- */
|
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|
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table.modindextable td {
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||||
padding: 2px;
|
||||
border-collapse: collapse;
|
||||
}
|
||||
|
||||
/* -- general body styles --------------------------------------------------- */
|
||||
|
||||
div.body {
|
||||
min-width: 360px;
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||||
max-width: 800px;
|
||||
}
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||||
|
||||
div.body p, div.body dd, div.body li, div.body blockquote {
|
||||
-moz-hyphens: auto;
|
||||
-ms-hyphens: auto;
|
||||
-webkit-hyphens: auto;
|
||||
hyphens: auto;
|
||||
}
|
||||
|
||||
a.headerlink {
|
||||
visibility: hidden;
|
||||
}
|
||||
|
||||
h1:hover > a.headerlink,
|
||||
h2:hover > a.headerlink,
|
||||
h3:hover > a.headerlink,
|
||||
h4:hover > a.headerlink,
|
||||
h5:hover > a.headerlink,
|
||||
h6:hover > a.headerlink,
|
||||
dt:hover > a.headerlink,
|
||||
caption:hover > a.headerlink,
|
||||
p.caption:hover > a.headerlink,
|
||||
div.code-block-caption:hover > a.headerlink {
|
||||
visibility: visible;
|
||||
}
|
||||
|
||||
div.body p.caption {
|
||||
text-align: inherit;
|
||||
}
|
||||
|
||||
div.body td {
|
||||
text-align: left;
|
||||
}
|
||||
|
||||
.first {
|
||||
margin-top: 0 !important;
|
||||
}
|
||||
|
||||
p.rubric {
|
||||
margin-top: 30px;
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
img.align-left, figure.align-left, .figure.align-left, object.align-left {
|
||||
clear: left;
|
||||
float: left;
|
||||
margin-right: 1em;
|
||||
}
|
||||
|
||||
img.align-right, figure.align-right, .figure.align-right, object.align-right {
|
||||
clear: right;
|
||||
float: right;
|
||||
margin-left: 1em;
|
||||
}
|
||||
|
||||
img.align-center, figure.align-center, .figure.align-center, object.align-center {
|
||||
display: block;
|
||||
margin-left: auto;
|
||||
margin-right: auto;
|
||||
}
|
||||
|
||||
img.align-default, figure.align-default, .figure.align-default {
|
||||
display: block;
|
||||
margin-left: auto;
|
||||
margin-right: auto;
|
||||
}
|
||||
|
||||
.align-left {
|
||||
text-align: left;
|
||||
}
|
||||
|
||||
.align-center {
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
.align-default {
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
.align-right {
|
||||
text-align: right;
|
||||
}
|
||||
|
||||
/* -- sidebars -------------------------------------------------------------- */
|
||||
|
||||
div.sidebar,
|
||||
aside.sidebar {
|
||||
margin: 0 0 0.5em 1em;
|
||||
border: 1px solid #ddb;
|
||||
padding: 7px;
|
||||
background-color: #ffe;
|
||||
width: 40%;
|
||||
float: right;
|
||||
clear: right;
|
||||
overflow-x: auto;
|
||||
}
|
||||
|
||||
p.sidebar-title {
|
||||
font-weight: bold;
|
||||
}
|
||||
nav.contents,
|
||||
aside.topic,
|
||||
div.admonition, div.topic, blockquote {
|
||||
clear: left;
|
||||
}
|
||||
|
||||
/* -- topics ---------------------------------------------------------------- */
|
||||
nav.contents,
|
||||
aside.topic,
|
||||
div.topic {
|
||||
border: 1px solid #ccc;
|
||||
padding: 7px;
|
||||
margin: 10px 0 10px 0;
|
||||
}
|
||||
|
||||
p.topic-title {
|
||||
font-size: 1.1em;
|
||||
font-weight: bold;
|
||||
margin-top: 10px;
|
||||
}
|
||||
|
||||
/* -- admonitions ----------------------------------------------------------- */
|
||||
|
||||
div.admonition {
|
||||
margin-top: 10px;
|
||||
margin-bottom: 10px;
|
||||
padding: 7px;
|
||||
}
|
||||
|
||||
div.admonition dt {
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
p.admonition-title {
|
||||
margin: 0px 10px 5px 0px;
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
div.body p.centered {
|
||||
text-align: center;
|
||||
margin-top: 25px;
|
||||
}
|
||||
|
||||
/* -- content of sidebars/topics/admonitions -------------------------------- */
|
||||
|
||||
div.sidebar > :last-child,
|
||||
aside.sidebar > :last-child,
|
||||
nav.contents > :last-child,
|
||||
aside.topic > :last-child,
|
||||
div.topic > :last-child,
|
||||
div.admonition > :last-child {
|
||||
margin-bottom: 0;
|
||||
}
|
||||
|
||||
div.sidebar::after,
|
||||
aside.sidebar::after,
|
||||
nav.contents::after,
|
||||
aside.topic::after,
|
||||
div.topic::after,
|
||||
div.admonition::after,
|
||||
blockquote::after {
|
||||
display: block;
|
||||
content: '';
|
||||
clear: both;
|
||||
}
|
||||
|
||||
/* -- tables ---------------------------------------------------------------- */
|
||||
|
||||
table.docutils {
|
||||
margin-top: 10px;
|
||||
margin-bottom: 10px;
|
||||
border: 0;
|
||||
border-collapse: collapse;
|
||||
}
|
||||
|
||||
table.align-center {
|
||||
margin-left: auto;
|
||||
margin-right: auto;
|
||||
}
|
||||
|
||||
table.align-default {
|
||||
margin-left: auto;
|
||||
margin-right: auto;
|
||||
}
|
||||
|
||||
table caption span.caption-number {
|
||||
font-style: italic;
|
||||
}
|
||||
|
||||
table caption span.caption-text {
|
||||
}
|
||||
|
||||
table.docutils td, table.docutils th {
|
||||
padding: 1px 8px 1px 5px;
|
||||
border-top: 0;
|
||||
border-left: 0;
|
||||
border-right: 0;
|
||||
border-bottom: 1px solid #aaa;
|
||||
}
|
||||
|
||||
th {
|
||||
text-align: left;
|
||||
padding-right: 5px;
|
||||
}
|
||||
|
||||
table.citation {
|
||||
border-left: solid 1px gray;
|
||||
margin-left: 1px;
|
||||
}
|
||||
|
||||
table.citation td {
|
||||
border-bottom: none;
|
||||
}
|
||||
|
||||
th > :first-child,
|
||||
td > :first-child {
|
||||
margin-top: 0px;
|
||||
}
|
||||
|
||||
th > :last-child,
|
||||
td > :last-child {
|
||||
margin-bottom: 0px;
|
||||
}
|
||||
|
||||
/* -- figures --------------------------------------------------------------- */
|
||||
|
||||
div.figure, figure {
|
||||
margin: 0.5em;
|
||||
padding: 0.5em;
|
||||
}
|
||||
|
||||
div.figure p.caption, figcaption {
|
||||
padding: 0.3em;
|
||||
}
|
||||
|
||||
div.figure p.caption span.caption-number,
|
||||
figcaption span.caption-number {
|
||||
font-style: italic;
|
||||
}
|
||||
|
||||
div.figure p.caption span.caption-text,
|
||||
figcaption span.caption-text {
|
||||
}
|
||||
|
||||
/* -- field list styles ----------------------------------------------------- */
|
||||
|
||||
table.field-list td, table.field-list th {
|
||||
border: 0 !important;
|
||||
}
|
||||
|
||||
.field-list ul {
|
||||
margin: 0;
|
||||
padding-left: 1em;
|
||||
}
|
||||
|
||||
.field-list p {
|
||||
margin: 0;
|
||||
}
|
||||
|
||||
.field-name {
|
||||
-moz-hyphens: manual;
|
||||
-ms-hyphens: manual;
|
||||
-webkit-hyphens: manual;
|
||||
hyphens: manual;
|
||||
}
|
||||
|
||||
/* -- hlist styles ---------------------------------------------------------- */
|
||||
|
||||
table.hlist {
|
||||
margin: 1em 0;
|
||||
}
|
||||
|
||||
table.hlist td {
|
||||
vertical-align: top;
|
||||
}
|
||||
|
||||
/* -- object description styles --------------------------------------------- */
|
||||
|
||||
.sig {
|
||||
font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace;
|
||||
}
|
||||
|
||||
.sig-name, code.descname {
|
||||
background-color: transparent;
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
.sig-name {
|
||||
font-size: 1.1em;
|
||||
}
|
||||
|
||||
code.descname {
|
||||
font-size: 1.2em;
|
||||
}
|
||||
|
||||
.sig-prename, code.descclassname {
|
||||
background-color: transparent;
|
||||
}
|
||||
|
||||
.optional {
|
||||
font-size: 1.3em;
|
||||
}
|
||||
|
||||
.sig-paren {
|
||||
font-size: larger;
|
||||
}
|
||||
|
||||
.sig-param.n {
|
||||
font-style: italic;
|
||||
}
|
||||
|
||||
/* C++ specific styling */
|
||||
|
||||
.sig-inline.c-texpr,
|
||||
.sig-inline.cpp-texpr {
|
||||
font-family: unset;
|
||||
}
|
||||
|
||||
.sig.c .k, .sig.c .kt,
|
||||
.sig.cpp .k, .sig.cpp .kt {
|
||||
color: #0033B3;
|
||||
}
|
||||
|
||||
.sig.c .m,
|
||||
.sig.cpp .m {
|
||||
color: #1750EB;
|
||||
}
|
||||
|
||||
.sig.c .s, .sig.c .sc,
|
||||
.sig.cpp .s, .sig.cpp .sc {
|
||||
color: #067D17;
|
||||
}
|
||||
|
||||
|
||||
/* -- other body styles ----------------------------------------------------- */
|
||||
|
||||
ol.arabic {
|
||||
list-style: decimal;
|
||||
}
|
||||
|
||||
ol.loweralpha {
|
||||
list-style: lower-alpha;
|
||||
}
|
||||
|
||||
ol.upperalpha {
|
||||
list-style: upper-alpha;
|
||||
}
|
||||
|
||||
ol.lowerroman {
|
||||
list-style: lower-roman;
|
||||
}
|
||||
|
||||
ol.upperroman {
|
||||
list-style: upper-roman;
|
||||
}
|
||||
|
||||
:not(li) > ol > li:first-child > :first-child,
|
||||
:not(li) > ul > li:first-child > :first-child {
|
||||
margin-top: 0px;
|
||||
}
|
||||
|
||||
:not(li) > ol > li:last-child > :last-child,
|
||||
:not(li) > ul > li:last-child > :last-child {
|
||||
margin-bottom: 0px;
|
||||
}
|
||||
|
||||
ol.simple ol p,
|
||||
ol.simple ul p,
|
||||
ul.simple ol p,
|
||||
ul.simple ul p {
|
||||
margin-top: 0;
|
||||
}
|
||||
|
||||
ol.simple > li:not(:first-child) > p,
|
||||
ul.simple > li:not(:first-child) > p {
|
||||
margin-top: 0;
|
||||
}
|
||||
|
||||
ol.simple p,
|
||||
ul.simple p {
|
||||
margin-bottom: 0;
|
||||
}
|
||||
aside.footnote > span,
|
||||
div.citation > span {
|
||||
float: left;
|
||||
}
|
||||
aside.footnote > span:last-of-type,
|
||||
div.citation > span:last-of-type {
|
||||
padding-right: 0.5em;
|
||||
}
|
||||
aside.footnote > p {
|
||||
margin-left: 2em;
|
||||
}
|
||||
div.citation > p {
|
||||
margin-left: 4em;
|
||||
}
|
||||
aside.footnote > p:last-of-type,
|
||||
div.citation > p:last-of-type {
|
||||
margin-bottom: 0em;
|
||||
}
|
||||
aside.footnote > p:last-of-type:after,
|
||||
div.citation > p:last-of-type:after {
|
||||
content: "";
|
||||
clear: both;
|
||||
}
|
||||
|
||||
dl.field-list {
|
||||
display: grid;
|
||||
grid-template-columns: fit-content(30%) auto;
|
||||
}
|
||||
|
||||
dl.field-list > dt {
|
||||
font-weight: bold;
|
||||
word-break: break-word;
|
||||
padding-left: 0.5em;
|
||||
padding-right: 5px;
|
||||
}
|
||||
|
||||
dl.field-list > dd {
|
||||
padding-left: 0.5em;
|
||||
margin-top: 0em;
|
||||
margin-left: 0em;
|
||||
margin-bottom: 0em;
|
||||
}
|
||||
|
||||
dl {
|
||||
margin-bottom: 15px;
|
||||
}
|
||||
|
||||
dd > :first-child {
|
||||
margin-top: 0px;
|
||||
}
|
||||
|
||||
dd ul, dd table {
|
||||
margin-bottom: 10px;
|
||||
}
|
||||
|
||||
dd {
|
||||
margin-top: 3px;
|
||||
margin-bottom: 10px;
|
||||
margin-left: 30px;
|
||||
}
|
||||
|
||||
dl > dd:last-child,
|
||||
dl > dd:last-child > :last-child {
|
||||
margin-bottom: 0;
|
||||
}
|
||||
|
||||
dt:target, span.highlighted {
|
||||
background-color: #fbe54e;
|
||||
}
|
||||
|
||||
rect.highlighted {
|
||||
fill: #fbe54e;
|
||||
}
|
||||
|
||||
dl.glossary dt {
|
||||
font-weight: bold;
|
||||
font-size: 1.1em;
|
||||
}
|
||||
|
||||
.versionmodified {
|
||||
font-style: italic;
|
||||
}
|
||||
|
||||
.system-message {
|
||||
background-color: #fda;
|
||||
padding: 5px;
|
||||
border: 3px solid red;
|
||||
}
|
||||
|
||||
.footnote:target {
|
||||
background-color: #ffa;
|
||||
}
|
||||
|
||||
.line-block {
|
||||
display: block;
|
||||
margin-top: 1em;
|
||||
margin-bottom: 1em;
|
||||
}
|
||||
|
||||
.line-block .line-block {
|
||||
margin-top: 0;
|
||||
margin-bottom: 0;
|
||||
margin-left: 1.5em;
|
||||
}
|
||||
|
||||
.guilabel, .menuselection {
|
||||
font-family: sans-serif;
|
||||
}
|
||||
|
||||
.accelerator {
|
||||
text-decoration: underline;
|
||||
}
|
||||
|
||||
.classifier {
|
||||
font-style: oblique;
|
||||
}
|
||||
|
||||
.classifier:before {
|
||||
font-style: normal;
|
||||
margin: 0 0.5em;
|
||||
content: ":";
|
||||
display: inline-block;
|
||||
}
|
||||
|
||||
abbr, acronym {
|
||||
border-bottom: dotted 1px;
|
||||
cursor: help;
|
||||
}
|
||||
|
||||
/* -- code displays --------------------------------------------------------- */
|
||||
|
||||
pre {
|
||||
overflow: auto;
|
||||
overflow-y: hidden; /* fixes display issues on Chrome browsers */
|
||||
}
|
||||
|
||||
pre, div[class*="highlight-"] {
|
||||
clear: both;
|
||||
}
|
||||
|
||||
span.pre {
|
||||
-moz-hyphens: none;
|
||||
-ms-hyphens: none;
|
||||
-webkit-hyphens: none;
|
||||
hyphens: none;
|
||||
white-space: nowrap;
|
||||
}
|
||||
|
||||
div[class*="highlight-"] {
|
||||
margin: 1em 0;
|
||||
}
|
||||
|
||||
td.linenos pre {
|
||||
border: 0;
|
||||
background-color: transparent;
|
||||
color: #aaa;
|
||||
}
|
||||
|
||||
table.highlighttable {
|
||||
display: block;
|
||||
}
|
||||
|
||||
table.highlighttable tbody {
|
||||
display: block;
|
||||
}
|
||||
|
||||
table.highlighttable tr {
|
||||
display: flex;
|
||||
}
|
||||
|
||||
table.highlighttable td {
|
||||
margin: 0;
|
||||
padding: 0;
|
||||
}
|
||||
|
||||
table.highlighttable td.linenos {
|
||||
padding-right: 0.5em;
|
||||
}
|
||||
|
||||
table.highlighttable td.code {
|
||||
flex: 1;
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.highlight .hll {
|
||||
display: block;
|
||||
}
|
||||
|
||||
div.highlight pre,
|
||||
table.highlighttable pre {
|
||||
margin: 0;
|
||||
}
|
||||
|
||||
div.code-block-caption + div {
|
||||
margin-top: 0;
|
||||
}
|
||||
|
||||
div.code-block-caption {
|
||||
margin-top: 1em;
|
||||
padding: 2px 5px;
|
||||
font-size: small;
|
||||
}
|
||||
|
||||
div.code-block-caption code {
|
||||
background-color: transparent;
|
||||
}
|
||||
|
||||
table.highlighttable td.linenos,
|
||||
span.linenos,
|
||||
div.highlight span.gp { /* gp: Generic.Prompt */
|
||||
user-select: none;
|
||||
-webkit-user-select: text; /* Safari fallback only */
|
||||
-webkit-user-select: none; /* Chrome/Safari */
|
||||
-moz-user-select: none; /* Firefox */
|
||||
-ms-user-select: none; /* IE10+ */
|
||||
}
|
||||
|
||||
div.code-block-caption span.caption-number {
|
||||
padding: 0.1em 0.3em;
|
||||
font-style: italic;
|
||||
}
|
||||
|
||||
div.code-block-caption span.caption-text {
|
||||
}
|
||||
|
||||
div.literal-block-wrapper {
|
||||
margin: 1em 0;
|
||||
}
|
||||
|
||||
code.xref, a code {
|
||||
background-color: transparent;
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
h1 code, h2 code, h3 code, h4 code, h5 code, h6 code {
|
||||
background-color: transparent;
|
||||
}
|
||||
|
||||
.viewcode-link {
|
||||
float: right;
|
||||
}
|
||||
|
||||
.viewcode-back {
|
||||
float: right;
|
||||
font-family: sans-serif;
|
||||
}
|
||||
|
||||
div.viewcode-block:target {
|
||||
margin: -1px -10px;
|
||||
padding: 0 10px;
|
||||
}
|
||||
|
||||
/* -- math display ---------------------------------------------------------- */
|
||||
|
||||
img.math {
|
||||
vertical-align: middle;
|
||||
}
|
||||
|
||||
div.body div.math p {
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
span.eqno {
|
||||
float: right;
|
||||
}
|
||||
|
||||
span.eqno a.headerlink {
|
||||
position: absolute;
|
||||
z-index: 1;
|
||||
}
|
||||
|
||||
div.math:hover a.headerlink {
|
||||
visibility: visible;
|
||||
}
|
||||
|
||||
/* -- printout stylesheet --------------------------------------------------- */
|
||||
|
||||
@media print {
|
||||
div.document,
|
||||
div.documentwrapper,
|
||||
div.bodywrapper {
|
||||
margin: 0 !important;
|
||||
width: 100%;
|
||||
}
|
||||
|
||||
div.sphinxsidebar,
|
||||
div.related,
|
||||
div.footer,
|
||||
#top-link {
|
||||
display: none;
|
||||
}
|
||||
}
|
||||
|
|
@ -1,156 +0,0 @@
|
|||
/*
|
||||
* doctools.js
|
||||
* ~~~~~~~~~~~
|
||||
*
|
||||
* Base JavaScript utilities for all Sphinx HTML documentation.
|
||||
*
|
||||
* :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
|
||||
* :license: BSD, see LICENSE for details.
|
||||
*
|
||||
*/
|
||||
"use strict";
|
||||
|
||||
const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([
|
||||
"TEXTAREA",
|
||||
"INPUT",
|
||||
"SELECT",
|
||||
"BUTTON",
|
||||
]);
|
||||
|
||||
const _ready = (callback) => {
|
||||
if (document.readyState !== "loading") {
|
||||
callback();
|
||||
} else {
|
||||
document.addEventListener("DOMContentLoaded", callback);
|
||||
}
|
||||
};
|
||||
|
||||
/**
|
||||
* Small JavaScript module for the documentation.
|
||||
*/
|
||||
const Documentation = {
|
||||
init: () => {
|
||||
Documentation.initDomainIndexTable();
|
||||
Documentation.initOnKeyListeners();
|
||||
},
|
||||
|
||||
/**
|
||||
* i18n support
|
||||
*/
|
||||
TRANSLATIONS: {},
|
||||
PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
|
||||
LOCALE: "unknown",
|
||||
|
||||
// gettext and ngettext don't access this so that the functions
|
||||
// can safely bound to a different name (_ = Documentation.gettext)
|
||||
gettext: (string) => {
|
||||
const translated = Documentation.TRANSLATIONS[string];
|
||||
switch (typeof translated) {
|
||||
case "undefined":
|
||||
return string; // no translation
|
||||
case "string":
|
||||
return translated; // translation exists
|
||||
default:
|
||||
return translated[0]; // (singular, plural) translation tuple exists
|
||||
}
|
||||
},
|
||||
|
||||
ngettext: (singular, plural, n) => {
|
||||
const translated = Documentation.TRANSLATIONS[singular];
|
||||
if (typeof translated !== "undefined")
|
||||
return translated[Documentation.PLURAL_EXPR(n)];
|
||||
return n === 1 ? singular : plural;
|
||||
},
|
||||
|
||||
addTranslations: (catalog) => {
|
||||
Object.assign(Documentation.TRANSLATIONS, catalog.messages);
|
||||
Documentation.PLURAL_EXPR = new Function(
|
||||
"n",
|
||||
`return (${catalog.plural_expr})`
|
||||
);
|
||||
Documentation.LOCALE = catalog.locale;
|
||||
},
|
||||
|
||||
/**
|
||||
* helper function to focus on search bar
|
||||
*/
|
||||
focusSearchBar: () => {
|
||||
document.querySelectorAll("input[name=q]")[0]?.focus();
|
||||
},
|
||||
|
||||
/**
|
||||
* Initialise the domain index toggle buttons
|
||||
*/
|
||||
initDomainIndexTable: () => {
|
||||
const toggler = (el) => {
|
||||
const idNumber = el.id.substr(7);
|
||||
const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`);
|
||||
if (el.src.substr(-9) === "minus.png") {
|
||||
el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`;
|
||||
toggledRows.forEach((el) => (el.style.display = "none"));
|
||||
} else {
|
||||
el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`;
|
||||
toggledRows.forEach((el) => (el.style.display = ""));
|
||||
}
|
||||
};
|
||||
|
||||
const togglerElements = document.querySelectorAll("img.toggler");
|
||||
togglerElements.forEach((el) =>
|
||||
el.addEventListener("click", (event) => toggler(event.currentTarget))
|
||||
);
|
||||
togglerElements.forEach((el) => (el.style.display = ""));
|
||||
if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler);
|
||||
},
|
||||
|
||||
initOnKeyListeners: () => {
|
||||
// only install a listener if it is really needed
|
||||
if (
|
||||
!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
|
||||
!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
|
||||
)
|
||||
return;
|
||||
|
||||
document.addEventListener("keydown", (event) => {
|
||||
// bail for input elements
|
||||
if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
|
||||
// bail with special keys
|
||||
if (event.altKey || event.ctrlKey || event.metaKey) return;
|
||||
|
||||
if (!event.shiftKey) {
|
||||
switch (event.key) {
|
||||
case "ArrowLeft":
|
||||
if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
|
||||
|
||||
const prevLink = document.querySelector('link[rel="prev"]');
|
||||
if (prevLink && prevLink.href) {
|
||||
window.location.href = prevLink.href;
|
||||
event.preventDefault();
|
||||
}
|
||||
break;
|
||||
case "ArrowRight":
|
||||
if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
|
||||
|
||||
const nextLink = document.querySelector('link[rel="next"]');
|
||||
if (nextLink && nextLink.href) {
|
||||
window.location.href = nextLink.href;
|
||||
event.preventDefault();
|
||||
}
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
// some keyboard layouts may need Shift to get /
|
||||
switch (event.key) {
|
||||
case "/":
|
||||
if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
|
||||
Documentation.focusSearchBar();
|
||||
event.preventDefault();
|
||||
}
|
||||
});
|
||||
},
|
||||
};
|
||||
|
||||
// quick alias for translations
|
||||
const _ = Documentation.gettext;
|
||||
|
||||
_ready(Documentation.init);
|
||||
|
|
@ -1,14 +0,0 @@
|
|||
var DOCUMENTATION_OPTIONS = {
|
||||
URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
|
||||
VERSION: '0.1.9',
|
||||
LANGUAGE: 'en',
|
||||
COLLAPSE_INDEX: false,
|
||||
BUILDER: 'html',
|
||||
FILE_SUFFIX: '.html',
|
||||
LINK_SUFFIX: '.html',
|
||||
HAS_SOURCE: true,
|
||||
SOURCELINK_SUFFIX: '.txt',
|
||||
NAVIGATION_WITH_KEYS: false,
|
||||
SHOW_SEARCH_SUMMARY: true,
|
||||
ENABLE_SEARCH_SHORTCUTS: true,
|
||||
};
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 286 B |
File diff suppressed because one or more lines are too long
|
|
@ -1,199 +0,0 @@
|
|||
/*
|
||||
* language_data.js
|
||||
* ~~~~~~~~~~~~~~~~
|
||||
*
|
||||
* This script contains the language-specific data used by searchtools.js,
|
||||
* namely the list of stopwords, stemmer, scorer and splitter.
|
||||
*
|
||||
* :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
|
||||
* :license: BSD, see LICENSE for details.
|
||||
*
|
||||
*/
|
||||
|
||||
var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
|
||||
|
||||
|
||||
/* Non-minified version is copied as a separate JS file, is available */
|
||||
|
||||
/**
|
||||
* Porter Stemmer
|
||||
*/
|
||||
var Stemmer = function() {
|
||||
|
||||
var step2list = {
|
||||
ational: 'ate',
|
||||
tional: 'tion',
|
||||
enci: 'ence',
|
||||
anci: 'ance',
|
||||
izer: 'ize',
|
||||
bli: 'ble',
|
||||
alli: 'al',
|
||||
entli: 'ent',
|
||||
eli: 'e',
|
||||
ousli: 'ous',
|
||||
ization: 'ize',
|
||||
ation: 'ate',
|
||||
ator: 'ate',
|
||||
alism: 'al',
|
||||
iveness: 'ive',
|
||||
fulness: 'ful',
|
||||
ousness: 'ous',
|
||||
aliti: 'al',
|
||||
iviti: 'ive',
|
||||
biliti: 'ble',
|
||||
logi: 'log'
|
||||
};
|
||||
|
||||
var step3list = {
|
||||
icate: 'ic',
|
||||
ative: '',
|
||||
alize: 'al',
|
||||
iciti: 'ic',
|
||||
ical: 'ic',
|
||||
ful: '',
|
||||
ness: ''
|
||||
};
|
||||
|
||||
var c = "[^aeiou]"; // consonant
|
||||
var v = "[aeiouy]"; // vowel
|
||||
var C = c + "[^aeiouy]*"; // consonant sequence
|
||||
var V = v + "[aeiou]*"; // vowel sequence
|
||||
|
||||
var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0
|
||||
var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1
|
||||
var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1
|
||||
var s_v = "^(" + C + ")?" + v; // vowel in stem
|
||||
|
||||
this.stemWord = function (w) {
|
||||
var stem;
|
||||
var suffix;
|
||||
var firstch;
|
||||
var origword = w;
|
||||
|
||||
if (w.length < 3)
|
||||
return w;
|
||||
|
||||
var re;
|
||||
var re2;
|
||||
var re3;
|
||||
var re4;
|
||||
|
||||
firstch = w.substr(0,1);
|
||||
if (firstch == "y")
|
||||
w = firstch.toUpperCase() + w.substr(1);
|
||||
|
||||
// Step 1a
|
||||
re = /^(.+?)(ss|i)es$/;
|
||||
re2 = /^(.+?)([^s])s$/;
|
||||
|
||||
if (re.test(w))
|
||||
w = w.replace(re,"$1$2");
|
||||
else if (re2.test(w))
|
||||
w = w.replace(re2,"$1$2");
|
||||
|
||||
// Step 1b
|
||||
re = /^(.+?)eed$/;
|
||||
re2 = /^(.+?)(ed|ing)$/;
|
||||
if (re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
re = new RegExp(mgr0);
|
||||
if (re.test(fp[1])) {
|
||||
re = /.$/;
|
||||
w = w.replace(re,"");
|
||||
}
|
||||
}
|
||||
else if (re2.test(w)) {
|
||||
var fp = re2.exec(w);
|
||||
stem = fp[1];
|
||||
re2 = new RegExp(s_v);
|
||||
if (re2.test(stem)) {
|
||||
w = stem;
|
||||
re2 = /(at|bl|iz)$/;
|
||||
re3 = new RegExp("([^aeiouylsz])\\1$");
|
||||
re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
|
||||
if (re2.test(w))
|
||||
w = w + "e";
|
||||
else if (re3.test(w)) {
|
||||
re = /.$/;
|
||||
w = w.replace(re,"");
|
||||
}
|
||||
else if (re4.test(w))
|
||||
w = w + "e";
|
||||
}
|
||||
}
|
||||
|
||||
// Step 1c
|
||||
re = /^(.+?)y$/;
|
||||
if (re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
re = new RegExp(s_v);
|
||||
if (re.test(stem))
|
||||
w = stem + "i";
|
||||
}
|
||||
|
||||
// Step 2
|
||||
re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
|
||||
if (re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
suffix = fp[2];
|
||||
re = new RegExp(mgr0);
|
||||
if (re.test(stem))
|
||||
w = stem + step2list[suffix];
|
||||
}
|
||||
|
||||
// Step 3
|
||||
re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
|
||||
if (re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
suffix = fp[2];
|
||||
re = new RegExp(mgr0);
|
||||
if (re.test(stem))
|
||||
w = stem + step3list[suffix];
|
||||
}
|
||||
|
||||
// Step 4
|
||||
re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
|
||||
re2 = /^(.+?)(s|t)(ion)$/;
|
||||
if (re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
re = new RegExp(mgr1);
|
||||
if (re.test(stem))
|
||||
w = stem;
|
||||
}
|
||||
else if (re2.test(w)) {
|
||||
var fp = re2.exec(w);
|
||||
stem = fp[1] + fp[2];
|
||||
re2 = new RegExp(mgr1);
|
||||
if (re2.test(stem))
|
||||
w = stem;
|
||||
}
|
||||
|
||||
// Step 5
|
||||
re = /^(.+?)e$/;
|
||||
if (re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
re = new RegExp(mgr1);
|
||||
re2 = new RegExp(meq1);
|
||||
re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
|
||||
if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
|
||||
w = stem;
|
||||
}
|
||||
re = /ll$/;
|
||||
re2 = new RegExp(mgr1);
|
||||
if (re.test(w) && re2.test(w)) {
|
||||
re = /.$/;
|
||||
w = w.replace(re,"");
|
||||
}
|
||||
|
||||
// and turn initial Y back to y
|
||||
if (firstch == "y")
|
||||
w = firstch.toLowerCase() + w.substr(1);
|
||||
return w;
|
||||
}
|
||||
}
|
||||
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 90 B |
Binary file not shown.
|
Before Width: | Height: | Size: 90 B |
|
|
@ -1,74 +0,0 @@
|
|||
pre { line-height: 125%; }
|
||||
td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
|
||||
span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
|
||||
td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
|
||||
span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
|
||||
.highlight .hll { background-color: #ffffcc }
|
||||
.highlight { background: #f8f8f8; }
|
||||
.highlight .c { color: #3D7B7B; font-style: italic } /* Comment */
|
||||
.highlight .err { border: 1px solid #FF0000 } /* Error */
|
||||
.highlight .k { color: #008000; font-weight: bold } /* Keyword */
|
||||
.highlight .o { color: #666666 } /* Operator */
|
||||
.highlight .ch { color: #3D7B7B; font-style: italic } /* Comment.Hashbang */
|
||||
.highlight .cm { color: #3D7B7B; font-style: italic } /* Comment.Multiline */
|
||||
.highlight .cp { color: #9C6500 } /* Comment.Preproc */
|
||||
.highlight .cpf { color: #3D7B7B; font-style: italic } /* Comment.PreprocFile */
|
||||
.highlight .c1 { color: #3D7B7B; font-style: italic } /* Comment.Single */
|
||||
.highlight .cs { color: #3D7B7B; font-style: italic } /* Comment.Special */
|
||||
.highlight .gd { color: #A00000 } /* Generic.Deleted */
|
||||
.highlight .ge { font-style: italic } /* Generic.Emph */
|
||||
.highlight .gr { color: #E40000 } /* Generic.Error */
|
||||
.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
|
||||
.highlight .gi { color: #008400 } /* Generic.Inserted */
|
||||
.highlight .go { color: #717171 } /* Generic.Output */
|
||||
.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
|
||||
.highlight .gs { font-weight: bold } /* Generic.Strong */
|
||||
.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
|
||||
.highlight .gt { color: #0044DD } /* Generic.Traceback */
|
||||
.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
|
||||
.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
|
||||
.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
|
||||
.highlight .kp { color: #008000 } /* Keyword.Pseudo */
|
||||
.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
|
||||
.highlight .kt { color: #B00040 } /* Keyword.Type */
|
||||
.highlight .m { color: #666666 } /* Literal.Number */
|
||||
.highlight .s { color: #BA2121 } /* Literal.String */
|
||||
.highlight .na { color: #687822 } /* Name.Attribute */
|
||||
.highlight .nb { color: #008000 } /* Name.Builtin */
|
||||
.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
|
||||
.highlight .no { color: #880000 } /* Name.Constant */
|
||||
.highlight .nd { color: #AA22FF } /* Name.Decorator */
|
||||
.highlight .ni { color: #717171; font-weight: bold } /* Name.Entity */
|
||||
.highlight .ne { color: #CB3F38; font-weight: bold } /* Name.Exception */
|
||||
.highlight .nf { color: #0000FF } /* Name.Function */
|
||||
.highlight .nl { color: #767600 } /* Name.Label */
|
||||
.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
|
||||
.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
|
||||
.highlight .nv { color: #19177C } /* Name.Variable */
|
||||
.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
|
||||
.highlight .w { color: #bbbbbb } /* Text.Whitespace */
|
||||
.highlight .mb { color: #666666 } /* Literal.Number.Bin */
|
||||
.highlight .mf { color: #666666 } /* Literal.Number.Float */
|
||||
.highlight .mh { color: #666666 } /* Literal.Number.Hex */
|
||||
.highlight .mi { color: #666666 } /* Literal.Number.Integer */
|
||||
.highlight .mo { color: #666666 } /* Literal.Number.Oct */
|
||||
.highlight .sa { color: #BA2121 } /* Literal.String.Affix */
|
||||
.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
|
||||
.highlight .sc { color: #BA2121 } /* Literal.String.Char */
|
||||
.highlight .dl { color: #BA2121 } /* Literal.String.Delimiter */
|
||||
.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
|
||||
.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
|
||||
.highlight .se { color: #AA5D1F; font-weight: bold } /* Literal.String.Escape */
|
||||
.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
|
||||
.highlight .si { color: #A45A77; font-weight: bold } /* Literal.String.Interpol */
|
||||
.highlight .sx { color: #008000 } /* Literal.String.Other */
|
||||
.highlight .sr { color: #A45A77 } /* Literal.String.Regex */
|
||||
.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
|
||||
.highlight .ss { color: #19177C } /* Literal.String.Symbol */
|
||||
.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
|
||||
.highlight .fm { color: #0000FF } /* Name.Function.Magic */
|
||||
.highlight .vc { color: #19177C } /* Name.Variable.Class */
|
||||
.highlight .vg { color: #19177C } /* Name.Variable.Global */
|
||||
.highlight .vi { color: #19177C } /* Name.Variable.Instance */
|
||||
.highlight .vm { color: #19177C } /* Name.Variable.Magic */
|
||||
.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
|
||||
|
|
@ -1,566 +0,0 @@
|
|||
/*
|
||||
* searchtools.js
|
||||
* ~~~~~~~~~~~~~~~~
|
||||
*
|
||||
* Sphinx JavaScript utilities for the full-text search.
|
||||
*
|
||||
* :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
|
||||
* :license: BSD, see LICENSE for details.
|
||||
*
|
||||
*/
|
||||
"use strict";
|
||||
|
||||
/**
|
||||
* Simple result scoring code.
|
||||
*/
|
||||
if (typeof Scorer === "undefined") {
|
||||
var Scorer = {
|
||||
// Implement the following function to further tweak the score for each result
|
||||
// The function takes a result array [docname, title, anchor, descr, score, filename]
|
||||
// and returns the new score.
|
||||
/*
|
||||
score: result => {
|
||||
const [docname, title, anchor, descr, score, filename] = result
|
||||
return score
|
||||
},
|
||||
*/
|
||||
|
||||
// query matches the full name of an object
|
||||
objNameMatch: 11,
|
||||
// or matches in the last dotted part of the object name
|
||||
objPartialMatch: 6,
|
||||
// Additive scores depending on the priority of the object
|
||||
objPrio: {
|
||||
0: 15, // used to be importantResults
|
||||
1: 5, // used to be objectResults
|
||||
2: -5, // used to be unimportantResults
|
||||
},
|
||||
// Used when the priority is not in the mapping.
|
||||
objPrioDefault: 0,
|
||||
|
||||
// query found in title
|
||||
title: 15,
|
||||
partialTitle: 7,
|
||||
// query found in terms
|
||||
term: 5,
|
||||
partialTerm: 2,
|
||||
};
|
||||
}
|
||||
|
||||
const _removeChildren = (element) => {
|
||||
while (element && element.lastChild) element.removeChild(element.lastChild);
|
||||
};
|
||||
|
||||
/**
|
||||
* See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
|
||||
*/
|
||||
const _escapeRegExp = (string) =>
|
||||
string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
|
||||
|
||||
const _displayItem = (item, searchTerms) => {
|
||||
const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
|
||||
const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT;
|
||||
const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
|
||||
const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
|
||||
const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
|
||||
|
||||
const [docName, title, anchor, descr, score, _filename] = item;
|
||||
|
||||
let listItem = document.createElement("li");
|
||||
let requestUrl;
|
||||
let linkUrl;
|
||||
if (docBuilder === "dirhtml") {
|
||||
// dirhtml builder
|
||||
let dirname = docName + "/";
|
||||
if (dirname.match(/\/index\/$/))
|
||||
dirname = dirname.substring(0, dirname.length - 6);
|
||||
else if (dirname === "index/") dirname = "";
|
||||
requestUrl = docUrlRoot + dirname;
|
||||
linkUrl = requestUrl;
|
||||
} else {
|
||||
// normal html builders
|
||||
requestUrl = docUrlRoot + docName + docFileSuffix;
|
||||
linkUrl = docName + docLinkSuffix;
|
||||
}
|
||||
let linkEl = listItem.appendChild(document.createElement("a"));
|
||||
linkEl.href = linkUrl + anchor;
|
||||
linkEl.dataset.score = score;
|
||||
linkEl.innerHTML = title;
|
||||
if (descr)
|
||||
listItem.appendChild(document.createElement("span")).innerHTML =
|
||||
" (" + descr + ")";
|
||||
else if (showSearchSummary)
|
||||
fetch(requestUrl)
|
||||
.then((responseData) => responseData.text())
|
||||
.then((data) => {
|
||||
if (data)
|
||||
listItem.appendChild(
|
||||
Search.makeSearchSummary(data, searchTerms)
|
||||
);
|
||||
});
|
||||
Search.output.appendChild(listItem);
|
||||
};
|
||||
const _finishSearch = (resultCount) => {
|
||||
Search.stopPulse();
|
||||
Search.title.innerText = _("Search Results");
|
||||
if (!resultCount)
|
||||
Search.status.innerText = Documentation.gettext(
|
||||
"Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
|
||||
);
|
||||
else
|
||||
Search.status.innerText = _(
|
||||
`Search finished, found ${resultCount} page(s) matching the search query.`
|
||||
);
|
||||
};
|
||||
const _displayNextItem = (
|
||||
results,
|
||||
resultCount,
|
||||
searchTerms
|
||||
) => {
|
||||
// results left, load the summary and display it
|
||||
// this is intended to be dynamic (don't sub resultsCount)
|
||||
if (results.length) {
|
||||
_displayItem(results.pop(), searchTerms);
|
||||
setTimeout(
|
||||
() => _displayNextItem(results, resultCount, searchTerms),
|
||||
5
|
||||
);
|
||||
}
|
||||
// search finished, update title and status message
|
||||
else _finishSearch(resultCount);
|
||||
};
|
||||
|
||||
/**
|
||||
* Default splitQuery function. Can be overridden in ``sphinx.search`` with a
|
||||
* custom function per language.
|
||||
*
|
||||
* The regular expression works by splitting the string on consecutive characters
|
||||
* that are not Unicode letters, numbers, underscores, or emoji characters.
|
||||
* This is the same as ``\W+`` in Python, preserving the surrogate pair area.
|
||||
*/
|
||||
if (typeof splitQuery === "undefined") {
|
||||
var splitQuery = (query) => query
|
||||
.split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
|
||||
.filter(term => term) // remove remaining empty strings
|
||||
}
|
||||
|
||||
/**
|
||||
* Search Module
|
||||
*/
|
||||
const Search = {
|
||||
_index: null,
|
||||
_queued_query: null,
|
||||
_pulse_status: -1,
|
||||
|
||||
htmlToText: (htmlString) => {
|
||||
const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
|
||||
htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() });
|
||||
const docContent = htmlElement.querySelector('[role="main"]');
|
||||
if (docContent !== undefined) return docContent.textContent;
|
||||
console.warn(
|
||||
"Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template."
|
||||
);
|
||||
return "";
|
||||
},
|
||||
|
||||
init: () => {
|
||||
const query = new URLSearchParams(window.location.search).get("q");
|
||||
document
|
||||
.querySelectorAll('input[name="q"]')
|
||||
.forEach((el) => (el.value = query));
|
||||
if (query) Search.performSearch(query);
|
||||
},
|
||||
|
||||
loadIndex: (url) =>
|
||||
(document.body.appendChild(document.createElement("script")).src = url),
|
||||
|
||||
setIndex: (index) => {
|
||||
Search._index = index;
|
||||
if (Search._queued_query !== null) {
|
||||
const query = Search._queued_query;
|
||||
Search._queued_query = null;
|
||||
Search.query(query);
|
||||
}
|
||||
},
|
||||
|
||||
hasIndex: () => Search._index !== null,
|
||||
|
||||
deferQuery: (query) => (Search._queued_query = query),
|
||||
|
||||
stopPulse: () => (Search._pulse_status = -1),
|
||||
|
||||
startPulse: () => {
|
||||
if (Search._pulse_status >= 0) return;
|
||||
|
||||
const pulse = () => {
|
||||
Search._pulse_status = (Search._pulse_status + 1) % 4;
|
||||
Search.dots.innerText = ".".repeat(Search._pulse_status);
|
||||
if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
|
||||
};
|
||||
pulse();
|
||||
},
|
||||
|
||||
/**
|
||||
* perform a search for something (or wait until index is loaded)
|
||||
*/
|
||||
performSearch: (query) => {
|
||||
// create the required interface elements
|
||||
const searchText = document.createElement("h2");
|
||||
searchText.textContent = _("Searching");
|
||||
const searchSummary = document.createElement("p");
|
||||
searchSummary.classList.add("search-summary");
|
||||
searchSummary.innerText = "";
|
||||
const searchList = document.createElement("ul");
|
||||
searchList.classList.add("search");
|
||||
|
||||
const out = document.getElementById("search-results");
|
||||
Search.title = out.appendChild(searchText);
|
||||
Search.dots = Search.title.appendChild(document.createElement("span"));
|
||||
Search.status = out.appendChild(searchSummary);
|
||||
Search.output = out.appendChild(searchList);
|
||||
|
||||
const searchProgress = document.getElementById("search-progress");
|
||||
// Some themes don't use the search progress node
|
||||
if (searchProgress) {
|
||||
searchProgress.innerText = _("Preparing search...");
|
||||
}
|
||||
Search.startPulse();
|
||||
|
||||
// index already loaded, the browser was quick!
|
||||
if (Search.hasIndex()) Search.query(query);
|
||||
else Search.deferQuery(query);
|
||||
},
|
||||
|
||||
/**
|
||||
* execute search (requires search index to be loaded)
|
||||
*/
|
||||
query: (query) => {
|
||||
const filenames = Search._index.filenames;
|
||||
const docNames = Search._index.docnames;
|
||||
const titles = Search._index.titles;
|
||||
const allTitles = Search._index.alltitles;
|
||||
const indexEntries = Search._index.indexentries;
|
||||
|
||||
// stem the search terms and add them to the correct list
|
||||
const stemmer = new Stemmer();
|
||||
const searchTerms = new Set();
|
||||
const excludedTerms = new Set();
|
||||
const highlightTerms = new Set();
|
||||
const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
|
||||
splitQuery(query.trim()).forEach((queryTerm) => {
|
||||
const queryTermLower = queryTerm.toLowerCase();
|
||||
|
||||
// maybe skip this "word"
|
||||
// stopwords array is from language_data.js
|
||||
if (
|
||||
stopwords.indexOf(queryTermLower) !== -1 ||
|
||||
queryTerm.match(/^\d+$/)
|
||||
)
|
||||
return;
|
||||
|
||||
// stem the word
|
||||
let word = stemmer.stemWord(queryTermLower);
|
||||
// select the correct list
|
||||
if (word[0] === "-") excludedTerms.add(word.substr(1));
|
||||
else {
|
||||
searchTerms.add(word);
|
||||
highlightTerms.add(queryTermLower);
|
||||
}
|
||||
});
|
||||
|
||||
if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js
|
||||
localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
|
||||
}
|
||||
|
||||
// console.debug("SEARCH: searching for:");
|
||||
// console.info("required: ", [...searchTerms]);
|
||||
// console.info("excluded: ", [...excludedTerms]);
|
||||
|
||||
// array of [docname, title, anchor, descr, score, filename]
|
||||
let results = [];
|
||||
_removeChildren(document.getElementById("search-progress"));
|
||||
|
||||
const queryLower = query.toLowerCase();
|
||||
for (const [title, foundTitles] of Object.entries(allTitles)) {
|
||||
if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) {
|
||||
for (const [file, id] of foundTitles) {
|
||||
let score = Math.round(100 * queryLower.length / title.length)
|
||||
results.push([
|
||||
docNames[file],
|
||||
titles[file] !== title ? `${titles[file]} > ${title}` : title,
|
||||
id !== null ? "#" + id : "",
|
||||
null,
|
||||
score,
|
||||
filenames[file],
|
||||
]);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// search for explicit entries in index directives
|
||||
for (const [entry, foundEntries] of Object.entries(indexEntries)) {
|
||||
if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
|
||||
for (const [file, id] of foundEntries) {
|
||||
let score = Math.round(100 * queryLower.length / entry.length)
|
||||
results.push([
|
||||
docNames[file],
|
||||
titles[file],
|
||||
id ? "#" + id : "",
|
||||
null,
|
||||
score,
|
||||
filenames[file],
|
||||
]);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// lookup as object
|
||||
objectTerms.forEach((term) =>
|
||||
results.push(...Search.performObjectSearch(term, objectTerms))
|
||||
);
|
||||
|
||||
// lookup as search terms in fulltext
|
||||
results.push(...Search.performTermsSearch(searchTerms, excludedTerms));
|
||||
|
||||
// let the scorer override scores with a custom scoring function
|
||||
if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item)));
|
||||
|
||||
// now sort the results by score (in opposite order of appearance, since the
|
||||
// display function below uses pop() to retrieve items) and then
|
||||
// alphabetically
|
||||
results.sort((a, b) => {
|
||||
const leftScore = a[4];
|
||||
const rightScore = b[4];
|
||||
if (leftScore === rightScore) {
|
||||
// same score: sort alphabetically
|
||||
const leftTitle = a[1].toLowerCase();
|
||||
const rightTitle = b[1].toLowerCase();
|
||||
if (leftTitle === rightTitle) return 0;
|
||||
return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
|
||||
}
|
||||
return leftScore > rightScore ? 1 : -1;
|
||||
});
|
||||
|
||||
// remove duplicate search results
|
||||
// note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
|
||||
let seen = new Set();
|
||||
results = results.reverse().reduce((acc, result) => {
|
||||
let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
|
||||
if (!seen.has(resultStr)) {
|
||||
acc.push(result);
|
||||
seen.add(resultStr);
|
||||
}
|
||||
return acc;
|
||||
}, []);
|
||||
|
||||
results = results.reverse();
|
||||
|
||||
// for debugging
|
||||
//Search.lastresults = results.slice(); // a copy
|
||||
// console.info("search results:", Search.lastresults);
|
||||
|
||||
// print the results
|
||||
_displayNextItem(results, results.length, searchTerms);
|
||||
},
|
||||
|
||||
/**
|
||||
* search for object names
|
||||
*/
|
||||
performObjectSearch: (object, objectTerms) => {
|
||||
const filenames = Search._index.filenames;
|
||||
const docNames = Search._index.docnames;
|
||||
const objects = Search._index.objects;
|
||||
const objNames = Search._index.objnames;
|
||||
const titles = Search._index.titles;
|
||||
|
||||
const results = [];
|
||||
|
||||
const objectSearchCallback = (prefix, match) => {
|
||||
const name = match[4]
|
||||
const fullname = (prefix ? prefix + "." : "") + name;
|
||||
const fullnameLower = fullname.toLowerCase();
|
||||
if (fullnameLower.indexOf(object) < 0) return;
|
||||
|
||||
let score = 0;
|
||||
const parts = fullnameLower.split(".");
|
||||
|
||||
// check for different match types: exact matches of full name or
|
||||
// "last name" (i.e. last dotted part)
|
||||
if (fullnameLower === object || parts.slice(-1)[0] === object)
|
||||
score += Scorer.objNameMatch;
|
||||
else if (parts.slice(-1)[0].indexOf(object) > -1)
|
||||
score += Scorer.objPartialMatch; // matches in last name
|
||||
|
||||
const objName = objNames[match[1]][2];
|
||||
const title = titles[match[0]];
|
||||
|
||||
// If more than one term searched for, we require other words to be
|
||||
// found in the name/title/description
|
||||
const otherTerms = new Set(objectTerms);
|
||||
otherTerms.delete(object);
|
||||
if (otherTerms.size > 0) {
|
||||
const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
|
||||
if (
|
||||
[...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
|
||||
)
|
||||
return;
|
||||
}
|
||||
|
||||
let anchor = match[3];
|
||||
if (anchor === "") anchor = fullname;
|
||||
else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
|
||||
|
||||
const descr = objName + _(", in ") + title;
|
||||
|
||||
// add custom score for some objects according to scorer
|
||||
if (Scorer.objPrio.hasOwnProperty(match[2]))
|
||||
score += Scorer.objPrio[match[2]];
|
||||
else score += Scorer.objPrioDefault;
|
||||
|
||||
results.push([
|
||||
docNames[match[0]],
|
||||
fullname,
|
||||
"#" + anchor,
|
||||
descr,
|
||||
score,
|
||||
filenames[match[0]],
|
||||
]);
|
||||
};
|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
{ files: terms[word], score: Scorer.term },
|
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{ files: titleTerms[word], score: Scorer.title },
|
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];
|
||||
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|
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|
||||
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|
||||
Object.keys(terms).forEach((term) => {
|
||||
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|
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|
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|
||||
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|
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
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||||
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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||||
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|
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|
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|
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|
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|
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|
||||
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|
||||
|
||||
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|
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|
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|
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// check if all requirements are matched
|
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|
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// as search terms with length < 3 are discarded
|
||||
const filteredTermCount = [...searchTerms].filter(
|
||||
(term) => term.length > 2
|
||||
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|
||||
if (
|
||||
wordList.length !== searchTerms.size &&
|
||||
wordList.length !== filteredTermCount
|
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)
|
||||
continue;
|
||||
|
||||
// ensure that none of the excluded terms is in the search result
|
||||
if (
|
||||
[...excludedTerms].some(
|
||||
(term) =>
|
||||
terms[term] === file ||
|
||||
titleTerms[term] === file ||
|
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(terms[term] || []).includes(file) ||
|
||||
(titleTerms[term] || []).includes(file)
|
||||
)
|
||||
)
|
||||
break;
|
||||
|
||||
// select one (max) score for the file.
|
||||
const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
|
||||
// add result to the result list
|
||||
results.push([
|
||||
docNames[file],
|
||||
titles[file],
|
||||
"",
|
||||
null,
|
||||
score,
|
||||
filenames[file],
|
||||
]);
|
||||
}
|
||||
return results;
|
||||
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|
||||
|
||||
/**
|
||||
* helper function to return a node containing the
|
||||
* search summary for a given text. keywords is a list
|
||||
* of stemmed words.
|
||||
*/
|
||||
makeSearchSummary: (htmlText, keywords) => {
|
||||
const text = Search.htmlToText(htmlText);
|
||||
if (text === "") return null;
|
||||
|
||||
const textLower = text.toLowerCase();
|
||||
const actualStartPosition = [...keywords]
|
||||
.map((k) => textLower.indexOf(k.toLowerCase()))
|
||||
.filter((i) => i > -1)
|
||||
.slice(-1)[0];
|
||||
const startWithContext = Math.max(actualStartPosition - 120, 0);
|
||||
|
||||
const top = startWithContext === 0 ? "" : "...";
|
||||
const tail = startWithContext + 240 < text.length ? "..." : "";
|
||||
|
||||
let summary = document.createElement("p");
|
||||
summary.classList.add("context");
|
||||
summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
|
||||
|
||||
return summary;
|
||||
},
|
||||
};
|
||||
|
||||
_ready(Search.init);
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -1,826 +0,0 @@
|
|||
<!DOCTYPE html>
|
||||
<html class="writer-html5" lang="en">
|
||||
<head>
|
||||
<meta charset="utf-8" /><meta name="generator" content="Docutils 0.19: https://docutils.sourceforge.io/" />
|
||||
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>Welcome to QuaPy’s documentation! — QuaPy: A Python-based open-source framework for quantification 0.1.9 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="_static/pygments.css" />
|
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<link rel="stylesheet" type="text/css" href="_static/css/theme.css" />
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|
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<!--[if lt IE 9]>
|
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<script src="_static/js/html5shiv.min.js"></script>
|
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<![endif]-->
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<script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
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|
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<link rel="next" title="Datasets" href="wiki/Datasets.html" />
|
||||
</head>
|
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|
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<body class="wy-body-for-nav">
|
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<div class="wy-grid-for-nav">
|
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<nav data-toggle="wy-nav-shift" class="wy-nav-side">
|
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<div class="wy-side-scroll">
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<div class="wy-side-nav-search" >
|
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QuaPy: A Python-based open-source framework for quantification
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<input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
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<input type="hidden" name="check_keywords" value="yes" />
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<input type="hidden" name="area" value="default" />
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</form>
|
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</div>
|
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</div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
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<ul>
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<li class="toctree-l1"><a class="reference internal" href="wiki/Evaluation.html">Evaluation</a></li>
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<li class="toctree-l1"><a class="reference internal" href="wiki/ExplicitLossMinimization.html">Explicit Loss Minimization</a></li>
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<li class="toctree-l1"><a class="reference internal" href="wiki/Methods.html">Quantification Methods</a></li>
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<li class="toctree-l1"><a class="reference internal" href="wiki/Model-Selection.html">Model Selection</a></li>
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<li class="toctree-l1"><a class="reference internal" href="wiki/Plotting.html">Plotting</a></li>
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<li class="toctree-l1"><a class="reference internal" href="wiki/Protocols.html">Protocols</a></li>
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<li class="toctree-l1"><a class="reference internal" href="modules.html">quapy</a></li>
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<section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
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<li><a href="#" class="icon icon-home" aria-label="Home"></a></li>
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<a href="_sources/index.rst.txt" rel="nofollow"> View page source</a>
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<div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
|
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<div itemprop="articleBody">
|
||||
|
||||
<section id="welcome-to-quapy-s-documentation">
|
||||
<h1>Welcome to QuaPy’s documentation!<a class="headerlink" href="#welcome-to-quapy-s-documentation" title="Permalink to this heading"></a></h1>
|
||||
<p>QuaPy is a Python-based open-source framework for quantification.</p>
|
||||
<p>This document contains the API of the modules included in QuaPy.</p>
|
||||
<section id="installation">
|
||||
<h2>Installation<a class="headerlink" href="#installation" title="Permalink to this heading"></a></h2>
|
||||
<p><cite>pip install quapy</cite></p>
|
||||
</section>
|
||||
<section id="github">
|
||||
<h2>GitHub<a class="headerlink" href="#github" title="Permalink to this heading"></a></h2>
|
||||
<p>QuaPy is hosted in GitHub at <a class="reference external" href="https://github.com/HLT-ISTI/QuaPy">https://github.com/HLT-ISTI/QuaPy</a></p>
|
||||
</section>
|
||||
<section id="wiki-documents">
|
||||
<h2>Wiki Documents<a class="headerlink" href="#wiki-documents" title="Permalink to this heading"></a></h2>
|
||||
<p>In this section you can find useful information concerning different aspects of QuaPy, with examples:</p>
|
||||
<div class="toctree-wrapper compound">
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="wiki/Datasets.html">Datasets</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="wiki/Evaluation.html">Evaluation</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="wiki/ExplicitLossMinimization.html">Explicit Loss Minimization</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="wiki/Methods.html">Quantification Methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="wiki/Model-Selection.html">Model Selection</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="wiki/Plotting.html">Plotting</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="wiki/Protocols.html">Protocols</a></li>
|
||||
</ul>
|
||||
</div>
|
||||
<div class="toctree-wrapper compound">
|
||||
</div>
|
||||
</section>
|
||||
<section id="contents">
|
||||
<h2>Contents<a class="headerlink" href="#contents" title="Permalink to this heading"></a></h2>
|
||||
<div class="toctree-wrapper compound">
|
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<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="modules.html">quapy</a><ul>
|
||||
<li class="toctree-l2"><a class="reference internal" href="quapy.html">quapy package</a><ul>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#subpackages">Subpackages</a><ul>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.classification.html">quapy.classification package</a><ul>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.classification.html#submodules">Submodules</a></li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.classification.html#module-quapy.classification.calibration">quapy.classification.calibration module</a><ul>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.classification.html#quapy.classification.calibration.BCTSCalibration"><code class="docutils literal notranslate"><span class="pre">BCTSCalibration</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.classification.html#quapy.classification.calibration.NBVSCalibration"><code class="docutils literal notranslate"><span class="pre">NBVSCalibration</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifier"><code class="docutils literal notranslate"><span class="pre">RecalibratedProbabilisticClassifier</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase"><code class="docutils literal notranslate"><span class="pre">RecalibratedProbabilisticClassifierBase</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.classes_"><code class="docutils literal notranslate"><span class="pre">RecalibratedProbabilisticClassifierBase.classes_</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit"><code class="docutils literal notranslate"><span class="pre">RecalibratedProbabilisticClassifierBase.fit()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit_cv"><code class="docutils literal notranslate"><span class="pre">RecalibratedProbabilisticClassifierBase.fit_cv()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit_tr_val"><code class="docutils literal notranslate"><span class="pre">RecalibratedProbabilisticClassifierBase.fit_tr_val()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.predict"><code class="docutils literal notranslate"><span class="pre">RecalibratedProbabilisticClassifierBase.predict()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.predict_proba"><code class="docutils literal notranslate"><span class="pre">RecalibratedProbabilisticClassifierBase.predict_proba()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.classification.html#quapy.classification.calibration.TSCalibration"><code class="docutils literal notranslate"><span class="pre">TSCalibration</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.classification.html#quapy.classification.calibration.VSCalibration"><code class="docutils literal notranslate"><span class="pre">VSCalibration</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.classification.html#module-quapy.classification.methods">quapy.classification.methods module</a><ul>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression"><code class="docutils literal notranslate"><span class="pre">LowRankLogisticRegression</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.fit"><code class="docutils literal notranslate"><span class="pre">LowRankLogisticRegression.fit()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.get_params"><code class="docutils literal notranslate"><span class="pre">LowRankLogisticRegression.get_params()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.predict"><code class="docutils literal notranslate"><span class="pre">LowRankLogisticRegression.predict()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.predict_proba"><code class="docutils literal notranslate"><span class="pre">LowRankLogisticRegression.predict_proba()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.set_params"><code class="docutils literal notranslate"><span class="pre">LowRankLogisticRegression.set_params()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.transform"><code class="docutils literal notranslate"><span class="pre">LowRankLogisticRegression.transform()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.classification.html#module-quapy.classification.neural">quapy.classification.neural module</a><ul>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.CNNnet"><code class="docutils literal notranslate"><span class="pre">CNNnet</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.CNNnet.document_embedding"><code class="docutils literal notranslate"><span class="pre">CNNnet.document_embedding()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.CNNnet.get_params"><code class="docutils literal notranslate"><span class="pre">CNNnet.get_params()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.CNNnet.training"><code class="docutils literal notranslate"><span class="pre">CNNnet.training</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.CNNnet.vocabulary_size"><code class="docutils literal notranslate"><span class="pre">CNNnet.vocabulary_size</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.LSTMnet"><code class="docutils literal notranslate"><span class="pre">LSTMnet</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.LSTMnet.document_embedding"><code class="docutils literal notranslate"><span class="pre">LSTMnet.document_embedding()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.LSTMnet.get_params"><code class="docutils literal notranslate"><span class="pre">LSTMnet.get_params()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.LSTMnet.training"><code class="docutils literal notranslate"><span class="pre">LSTMnet.training</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.LSTMnet.vocabulary_size"><code class="docutils literal notranslate"><span class="pre">LSTMnet.vocabulary_size</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer"><code class="docutils literal notranslate"><span class="pre">NeuralClassifierTrainer</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.device"><code class="docutils literal notranslate"><span class="pre">NeuralClassifierTrainer.device</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.fit"><code class="docutils literal notranslate"><span class="pre">NeuralClassifierTrainer.fit()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.get_params"><code class="docutils literal notranslate"><span class="pre">NeuralClassifierTrainer.get_params()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.predict"><code class="docutils literal notranslate"><span class="pre">NeuralClassifierTrainer.predict()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.predict_proba"><code class="docutils literal notranslate"><span class="pre">NeuralClassifierTrainer.predict_proba()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.reset_net_params"><code class="docutils literal notranslate"><span class="pre">NeuralClassifierTrainer.reset_net_params()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.set_params"><code class="docutils literal notranslate"><span class="pre">NeuralClassifierTrainer.set_params()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.transform"><code class="docutils literal notranslate"><span class="pre">NeuralClassifierTrainer.transform()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.TextClassifierNet"><code class="docutils literal notranslate"><span class="pre">TextClassifierNet</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.TextClassifierNet.dimensions"><code class="docutils literal notranslate"><span class="pre">TextClassifierNet.dimensions()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.TextClassifierNet.document_embedding"><code class="docutils literal notranslate"><span class="pre">TextClassifierNet.document_embedding()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.TextClassifierNet.forward"><code class="docutils literal notranslate"><span class="pre">TextClassifierNet.forward()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.TextClassifierNet.get_params"><code class="docutils literal notranslate"><span class="pre">TextClassifierNet.get_params()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.TextClassifierNet.predict_proba"><code class="docutils literal notranslate"><span class="pre">TextClassifierNet.predict_proba()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.TextClassifierNet.training"><code class="docutils literal notranslate"><span class="pre">TextClassifierNet.training</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.TextClassifierNet.vocabulary_size"><code class="docutils literal notranslate"><span class="pre">TextClassifierNet.vocabulary_size</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.TextClassifierNet.xavier_uniform"><code class="docutils literal notranslate"><span class="pre">TextClassifierNet.xavier_uniform()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.TorchDataset"><code class="docutils literal notranslate"><span class="pre">TorchDataset</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.TorchDataset.asDataloader"><code class="docutils literal notranslate"><span class="pre">TorchDataset.asDataloader()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.classification.html#module-quapy.classification.svmperf">quapy.classification.svmperf module</a><ul>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.classification.html#quapy.classification.svmperf.SVMperf"><code class="docutils literal notranslate"><span class="pre">SVMperf</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.svmperf.SVMperf.decision_function"><code class="docutils literal notranslate"><span class="pre">SVMperf.decision_function()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.svmperf.SVMperf.fit"><code class="docutils literal notranslate"><span class="pre">SVMperf.fit()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.svmperf.SVMperf.predict"><code class="docutils literal notranslate"><span class="pre">SVMperf.predict()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.svmperf.SVMperf.valid_losses"><code class="docutils literal notranslate"><span class="pre">SVMperf.valid_losses</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.classification.html#module-quapy.classification">Module contents</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.data.html">quapy.data package</a><ul>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.data.html#submodules">Submodules</a></li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.data.html#module-quapy.data.base">quapy.data.base module</a><ul>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.data.html#quapy.data.base.Dataset"><code class="docutils literal notranslate"><span class="pre">Dataset</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.data.html#quapy.data.base.Dataset.SplitStratified"><code class="docutils literal notranslate"><span class="pre">Dataset.SplitStratified()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.data.html#quapy.data.base.Dataset.binary"><code class="docutils literal notranslate"><span class="pre">Dataset.binary</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.data.html#quapy.data.base.Dataset.classes_"><code class="docutils literal notranslate"><span class="pre">Dataset.classes_</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.data.html#quapy.data.base.Dataset.kFCV"><code class="docutils literal notranslate"><span class="pre">Dataset.kFCV()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.data.html#quapy.data.base.Dataset.load"><code class="docutils literal notranslate"><span class="pre">Dataset.load()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.data.html#quapy.data.base.Dataset.n_classes"><code class="docutils literal notranslate"><span class="pre">Dataset.n_classes</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.data.html#quapy.data.base.Dataset.reduce"><code class="docutils literal notranslate"><span class="pre">Dataset.reduce()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.data.html#quapy.data.base.Dataset.stats"><code class="docutils literal notranslate"><span class="pre">Dataset.stats()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.data.html#quapy.data.base.Dataset.train_test"><code class="docutils literal notranslate"><span class="pre">Dataset.train_test</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.data.html#quapy.data.base.Dataset.vocabulary_size"><code class="docutils literal notranslate"><span class="pre">Dataset.vocabulary_size</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection"><code class="docutils literal notranslate"><span class="pre">LabelledCollection</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection.X"><code class="docutils literal notranslate"><span class="pre">LabelledCollection.X</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection.Xp"><code class="docutils literal notranslate"><span class="pre">LabelledCollection.Xp</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection.Xy"><code class="docutils literal notranslate"><span class="pre">LabelledCollection.Xy</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection.binary"><code class="docutils literal notranslate"><span class="pre">LabelledCollection.binary</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection.counts"><code class="docutils literal notranslate"><span class="pre">LabelledCollection.counts()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection.join"><code class="docutils literal notranslate"><span class="pre">LabelledCollection.join()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection.kFCV"><code class="docutils literal notranslate"><span class="pre">LabelledCollection.kFCV()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection.load"><code class="docutils literal notranslate"><span class="pre">LabelledCollection.load()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection.n_classes"><code class="docutils literal notranslate"><span class="pre">LabelledCollection.n_classes</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection.p"><code class="docutils literal notranslate"><span class="pre">LabelledCollection.p</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection.prevalence"><code class="docutils literal notranslate"><span class="pre">LabelledCollection.prevalence()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection.sampling"><code class="docutils literal notranslate"><span class="pre">LabelledCollection.sampling()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection.sampling_from_index"><code class="docutils literal notranslate"><span class="pre">LabelledCollection.sampling_from_index()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection.sampling_index"><code class="docutils literal notranslate"><span class="pre">LabelledCollection.sampling_index()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection.split_random"><code class="docutils literal notranslate"><span class="pre">LabelledCollection.split_random()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection.split_stratified"><code class="docutils literal notranslate"><span class="pre">LabelledCollection.split_stratified()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection.stats"><code class="docutils literal notranslate"><span class="pre">LabelledCollection.stats()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection.uniform_sampling"><code class="docutils literal notranslate"><span class="pre">LabelledCollection.uniform_sampling()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection.uniform_sampling_index"><code class="docutils literal notranslate"><span class="pre">LabelledCollection.uniform_sampling_index()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection.y"><code class="docutils literal notranslate"><span class="pre">LabelledCollection.y</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.data.html#module-quapy.data.datasets">quapy.data.datasets module</a><ul>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.data.html#quapy.data.datasets.fetch_IFCB"><code class="docutils literal notranslate"><span class="pre">fetch_IFCB()</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.data.html#quapy.data.datasets.fetch_UCIBinaryDataset"><code class="docutils literal notranslate"><span class="pre">fetch_UCIBinaryDataset()</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.data.html#quapy.data.datasets.fetch_UCIBinaryLabelledCollection"><code class="docutils literal notranslate"><span class="pre">fetch_UCIBinaryLabelledCollection()</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.data.html#quapy.data.datasets.fetch_UCIMulticlassDataset"><code class="docutils literal notranslate"><span class="pre">fetch_UCIMulticlassDataset()</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.data.html#quapy.data.datasets.fetch_UCIMulticlassLabelledCollection"><code class="docutils literal notranslate"><span class="pre">fetch_UCIMulticlassLabelledCollection()</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.data.html#quapy.data.datasets.fetch_lequa2022"><code class="docutils literal notranslate"><span class="pre">fetch_lequa2022()</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.data.html#quapy.data.datasets.fetch_reviews"><code class="docutils literal notranslate"><span class="pre">fetch_reviews()</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.data.html#quapy.data.datasets.fetch_twitter"><code class="docutils literal notranslate"><span class="pre">fetch_twitter()</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.data.html#quapy.data.datasets.warn"><code class="docutils literal notranslate"><span class="pre">warn()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.data.html#module-quapy.data.preprocessing">quapy.data.preprocessing module</a><ul>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.data.html#quapy.data.preprocessing.IndexTransformer"><code class="docutils literal notranslate"><span class="pre">IndexTransformer</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.data.html#quapy.data.preprocessing.IndexTransformer.add_word"><code class="docutils literal notranslate"><span class="pre">IndexTransformer.add_word()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.data.html#quapy.data.preprocessing.IndexTransformer.fit"><code class="docutils literal notranslate"><span class="pre">IndexTransformer.fit()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.data.html#quapy.data.preprocessing.IndexTransformer.fit_transform"><code class="docutils literal notranslate"><span class="pre">IndexTransformer.fit_transform()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.data.html#quapy.data.preprocessing.IndexTransformer.transform"><code class="docutils literal notranslate"><span class="pre">IndexTransformer.transform()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.data.html#quapy.data.preprocessing.IndexTransformer.vocabulary_size"><code class="docutils literal notranslate"><span class="pre">IndexTransformer.vocabulary_size()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.data.html#quapy.data.preprocessing.index"><code class="docutils literal notranslate"><span class="pre">index()</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.data.html#quapy.data.preprocessing.reduce_columns"><code class="docutils literal notranslate"><span class="pre">reduce_columns()</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.data.html#quapy.data.preprocessing.standardize"><code class="docutils literal notranslate"><span class="pre">standardize()</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.data.html#quapy.data.preprocessing.text2tfidf"><code class="docutils literal notranslate"><span class="pre">text2tfidf()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.data.html#module-quapy.data.reader">quapy.data.reader module</a><ul>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.data.html#quapy.data.reader.binarize"><code class="docutils literal notranslate"><span class="pre">binarize()</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.data.html#quapy.data.reader.from_csv"><code class="docutils literal notranslate"><span class="pre">from_csv()</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.data.html#quapy.data.reader.from_sparse"><code class="docutils literal notranslate"><span class="pre">from_sparse()</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.data.html#quapy.data.reader.from_text"><code class="docutils literal notranslate"><span class="pre">from_text()</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.data.html#quapy.data.reader.reindex_labels"><code class="docutils literal notranslate"><span class="pre">reindex_labels()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.data.html#module-quapy.data">Module contents</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.method.html">quapy.method package</a><ul>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.method.html#submodules">Submodules</a></li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.method.html#module-quapy.method.aggregative">quapy.method.aggregative module</a><ul>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.ACC"><code class="docutils literal notranslate"><span class="pre">ACC</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.ACC.METHODS"><code class="docutils literal notranslate"><span class="pre">ACC.METHODS</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.ACC.NORMALIZATIONS"><code class="docutils literal notranslate"><span class="pre">ACC.NORMALIZATIONS</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.ACC.SOLVERS"><code class="docutils literal notranslate"><span class="pre">ACC.SOLVERS</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.ACC.aggregate"><code class="docutils literal notranslate"><span class="pre">ACC.aggregate()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.ACC.aggregation_fit"><code class="docutils literal notranslate"><span class="pre">ACC.aggregation_fit()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.ACC.getPteCondEstim"><code class="docutils literal notranslate"><span class="pre">ACC.getPteCondEstim()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.ACC.newInvariantRatioEstimation"><code class="docutils literal notranslate"><span class="pre">ACC.newInvariantRatioEstimation()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.AdjustedClassifyAndCount"><code class="docutils literal notranslate"><span class="pre">AdjustedClassifyAndCount</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.AggregativeCrispQuantifier"><code class="docutils literal notranslate"><span class="pre">AggregativeCrispQuantifier</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.AggregativeMedianEstimator"><code class="docutils literal notranslate"><span class="pre">AggregativeMedianEstimator</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.AggregativeMedianEstimator.fit"><code class="docutils literal notranslate"><span class="pre">AggregativeMedianEstimator.fit()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.AggregativeMedianEstimator.get_params"><code class="docutils literal notranslate"><span class="pre">AggregativeMedianEstimator.get_params()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.AggregativeMedianEstimator.quantify"><code class="docutils literal notranslate"><span class="pre">AggregativeMedianEstimator.quantify()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.AggregativeMedianEstimator.set_params"><code class="docutils literal notranslate"><span class="pre">AggregativeMedianEstimator.set_params()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.AggregativeQuantifier"><code class="docutils literal notranslate"><span class="pre">AggregativeQuantifier</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.aggregate"><code class="docutils literal notranslate"><span class="pre">AggregativeQuantifier.aggregate()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.aggregation_fit"><code class="docutils literal notranslate"><span class="pre">AggregativeQuantifier.aggregation_fit()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.classes_"><code class="docutils literal notranslate"><span class="pre">AggregativeQuantifier.classes_</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.classifier"><code class="docutils literal notranslate"><span class="pre">AggregativeQuantifier.classifier</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.classifier_fit_predict"><code class="docutils literal notranslate"><span class="pre">AggregativeQuantifier.classifier_fit_predict()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.classify"><code class="docutils literal notranslate"><span class="pre">AggregativeQuantifier.classify()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.fit"><code class="docutils literal notranslate"><span class="pre">AggregativeQuantifier.fit()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.quantify"><code class="docutils literal notranslate"><span class="pre">AggregativeQuantifier.quantify()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.val_split"><code class="docutils literal notranslate"><span class="pre">AggregativeQuantifier.val_split</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.val_split_"><code class="docutils literal notranslate"><span class="pre">AggregativeQuantifier.val_split_</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.AggregativeSoftQuantifier"><code class="docutils literal notranslate"><span class="pre">AggregativeSoftQuantifier</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.BayesianCC"><code class="docutils literal notranslate"><span class="pre">BayesianCC</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.BayesianCC.aggregate"><code class="docutils literal notranslate"><span class="pre">BayesianCC.aggregate()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.BayesianCC.aggregation_fit"><code class="docutils literal notranslate"><span class="pre">BayesianCC.aggregation_fit()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.BayesianCC.get_conditional_probability_samples"><code class="docutils literal notranslate"><span class="pre">BayesianCC.get_conditional_probability_samples()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.BayesianCC.get_prevalence_samples"><code class="docutils literal notranslate"><span class="pre">BayesianCC.get_prevalence_samples()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.BayesianCC.sample_from_posterior"><code class="docutils literal notranslate"><span class="pre">BayesianCC.sample_from_posterior()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.BinaryAggregativeQuantifier"><code class="docutils literal notranslate"><span class="pre">BinaryAggregativeQuantifier</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.BinaryAggregativeQuantifier.fit"><code class="docutils literal notranslate"><span class="pre">BinaryAggregativeQuantifier.fit()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.BinaryAggregativeQuantifier.neg_label"><code class="docutils literal notranslate"><span class="pre">BinaryAggregativeQuantifier.neg_label</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.BinaryAggregativeQuantifier.pos_label"><code class="docutils literal notranslate"><span class="pre">BinaryAggregativeQuantifier.pos_label</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.CC"><code class="docutils literal notranslate"><span class="pre">CC</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.CC.aggregate"><code class="docutils literal notranslate"><span class="pre">CC.aggregate()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.CC.aggregation_fit"><code class="docutils literal notranslate"><span class="pre">CC.aggregation_fit()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.ClassifyAndCount"><code class="docutils literal notranslate"><span class="pre">ClassifyAndCount</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.DMy"><code class="docutils literal notranslate"><span class="pre">DMy</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.DMy.aggregate"><code class="docutils literal notranslate"><span class="pre">DMy.aggregate()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.DMy.aggregation_fit"><code class="docutils literal notranslate"><span class="pre">DMy.aggregation_fit()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.DistributionMatchingY"><code class="docutils literal notranslate"><span class="pre">DistributionMatchingY</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.DyS"><code class="docutils literal notranslate"><span class="pre">DyS</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.DyS.aggregate"><code class="docutils literal notranslate"><span class="pre">DyS.aggregate()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.DyS.aggregation_fit"><code class="docutils literal notranslate"><span class="pre">DyS.aggregation_fit()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.EMQ"><code class="docutils literal notranslate"><span class="pre">EMQ</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.EMQ.EM"><code class="docutils literal notranslate"><span class="pre">EMQ.EM()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.EMQ.EMQ_BCTS"><code class="docutils literal notranslate"><span class="pre">EMQ.EMQ_BCTS()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.EMQ.EPSILON"><code class="docutils literal notranslate"><span class="pre">EMQ.EPSILON</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.EMQ.MAX_ITER"><code class="docutils literal notranslate"><span class="pre">EMQ.MAX_ITER</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.EMQ.aggregate"><code class="docutils literal notranslate"><span class="pre">EMQ.aggregate()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.EMQ.aggregation_fit"><code class="docutils literal notranslate"><span class="pre">EMQ.aggregation_fit()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.EMQ.classify"><code class="docutils literal notranslate"><span class="pre">EMQ.classify()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.EMQ.predict_proba"><code class="docutils literal notranslate"><span class="pre">EMQ.predict_proba()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.ExpectationMaximizationQuantifier"><code class="docutils literal notranslate"><span class="pre">ExpectationMaximizationQuantifier</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.HDy"><code class="docutils literal notranslate"><span class="pre">HDy</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.HDy.aggregate"><code class="docutils literal notranslate"><span class="pre">HDy.aggregate()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.HDy.aggregation_fit"><code class="docutils literal notranslate"><span class="pre">HDy.aggregation_fit()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.HellingerDistanceY"><code class="docutils literal notranslate"><span class="pre">HellingerDistanceY</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.OneVsAllAggregative"><code class="docutils literal notranslate"><span class="pre">OneVsAllAggregative</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.OneVsAllAggregative.aggregate"><code class="docutils literal notranslate"><span class="pre">OneVsAllAggregative.aggregate()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.OneVsAllAggregative.classify"><code class="docutils literal notranslate"><span class="pre">OneVsAllAggregative.classify()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.PACC"><code class="docutils literal notranslate"><span class="pre">PACC</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.PACC.aggregate"><code class="docutils literal notranslate"><span class="pre">PACC.aggregate()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.PACC.aggregation_fit"><code class="docutils literal notranslate"><span class="pre">PACC.aggregation_fit()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.PACC.getPteCondEstim"><code class="docutils literal notranslate"><span class="pre">PACC.getPteCondEstim()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.PCC"><code class="docutils literal notranslate"><span class="pre">PCC</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.PCC.aggregate"><code class="docutils literal notranslate"><span class="pre">PCC.aggregate()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.PCC.aggregation_fit"><code class="docutils literal notranslate"><span class="pre">PCC.aggregation_fit()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.ProbabilisticAdjustedClassifyAndCount"><code class="docutils literal notranslate"><span class="pre">ProbabilisticAdjustedClassifyAndCount</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.ProbabilisticClassifyAndCount"><code class="docutils literal notranslate"><span class="pre">ProbabilisticClassifyAndCount</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.SLD"><code class="docutils literal notranslate"><span class="pre">SLD</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.SMM"><code class="docutils literal notranslate"><span class="pre">SMM</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.SMM.aggregate"><code class="docutils literal notranslate"><span class="pre">SMM.aggregate()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.SMM.aggregation_fit"><code class="docutils literal notranslate"><span class="pre">SMM.aggregation_fit()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.newELM"><code class="docutils literal notranslate"><span class="pre">newELM()</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.newSVMAE"><code class="docutils literal notranslate"><span class="pre">newSVMAE()</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.newSVMKLD"><code class="docutils literal notranslate"><span class="pre">newSVMKLD()</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.newSVMQ"><code class="docutils literal notranslate"><span class="pre">newSVMQ()</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.newSVMRAE"><code class="docutils literal notranslate"><span class="pre">newSVMRAE()</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method._kdey.KDEBase"><code class="docutils literal notranslate"><span class="pre">KDEBase</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method._kdey.KDEBase.BANDWIDTH_METHOD"><code class="docutils literal notranslate"><span class="pre">KDEBase.BANDWIDTH_METHOD</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method._kdey.KDEBase.get_kde_function"><code class="docutils literal notranslate"><span class="pre">KDEBase.get_kde_function()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method._kdey.KDEBase.get_mixture_components"><code class="docutils literal notranslate"><span class="pre">KDEBase.get_mixture_components()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method._kdey.KDEBase.pdf"><code class="docutils literal notranslate"><span class="pre">KDEBase.pdf()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method._kdey.KDEyCS"><code class="docutils literal notranslate"><span class="pre">KDEyCS</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method._kdey.KDEyCS.aggregate"><code class="docutils literal notranslate"><span class="pre">KDEyCS.aggregate()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method._kdey.KDEyCS.aggregation_fit"><code class="docutils literal notranslate"><span class="pre">KDEyCS.aggregation_fit()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method._kdey.KDEyCS.gram_matrix_mix_sum"><code class="docutils literal notranslate"><span class="pre">KDEyCS.gram_matrix_mix_sum()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method._kdey.KDEyHD"><code class="docutils literal notranslate"><span class="pre">KDEyHD</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method._kdey.KDEyHD.aggregate"><code class="docutils literal notranslate"><span class="pre">KDEyHD.aggregate()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method._kdey.KDEyHD.aggregation_fit"><code class="docutils literal notranslate"><span class="pre">KDEyHD.aggregation_fit()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method._kdey.KDEyML"><code class="docutils literal notranslate"><span class="pre">KDEyML</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method._kdey.KDEyML.aggregate"><code class="docutils literal notranslate"><span class="pre">KDEyML.aggregate()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method._kdey.KDEyML.aggregation_fit"><code class="docutils literal notranslate"><span class="pre">KDEyML.aggregation_fit()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method._neural.QuaNetModule"><code class="docutils literal notranslate"><span class="pre">QuaNetModule</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method._neural.QuaNetModule.device"><code class="docutils literal notranslate"><span class="pre">QuaNetModule.device</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method._neural.QuaNetModule.forward"><code class="docutils literal notranslate"><span class="pre">QuaNetModule.forward()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method._neural.QuaNetModule.training"><code class="docutils literal notranslate"><span class="pre">QuaNetModule.training</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method._neural.QuaNetTrainer"><code class="docutils literal notranslate"><span class="pre">QuaNetTrainer</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method._neural.QuaNetTrainer.classes_"><code class="docutils literal notranslate"><span class="pre">QuaNetTrainer.classes_</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method._neural.QuaNetTrainer.clean_checkpoint"><code class="docutils literal notranslate"><span class="pre">QuaNetTrainer.clean_checkpoint()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method._neural.QuaNetTrainer.clean_checkpoint_dir"><code class="docutils literal notranslate"><span class="pre">QuaNetTrainer.clean_checkpoint_dir()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method._neural.QuaNetTrainer.fit"><code class="docutils literal notranslate"><span class="pre">QuaNetTrainer.fit()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method._neural.QuaNetTrainer.get_params"><code class="docutils literal notranslate"><span class="pre">QuaNetTrainer.get_params()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method._neural.QuaNetTrainer.quantify"><code class="docutils literal notranslate"><span class="pre">QuaNetTrainer.quantify()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method._neural.QuaNetTrainer.set_params"><code class="docutils literal notranslate"><span class="pre">QuaNetTrainer.set_params()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method._neural.mae_loss"><code class="docutils literal notranslate"><span class="pre">mae_loss()</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method._threshold_optim.MAX"><code class="docutils literal notranslate"><span class="pre">MAX</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method._threshold_optim.MAX.condition"><code class="docutils literal notranslate"><span class="pre">MAX.condition()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method._threshold_optim.MS"><code class="docutils literal notranslate"><span class="pre">MS</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method._threshold_optim.MS.aggregate"><code class="docutils literal notranslate"><span class="pre">MS.aggregate()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method._threshold_optim.MS.aggregation_fit"><code class="docutils literal notranslate"><span class="pre">MS.aggregation_fit()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method._threshold_optim.MS.condition"><code class="docutils literal notranslate"><span class="pre">MS.condition()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method._threshold_optim.MS2"><code class="docutils literal notranslate"><span class="pre">MS2</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method._threshold_optim.MS2.discard"><code class="docutils literal notranslate"><span class="pre">MS2.discard()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method._threshold_optim.T50"><code class="docutils literal notranslate"><span class="pre">T50</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method._threshold_optim.T50.condition"><code class="docutils literal notranslate"><span class="pre">T50.condition()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization"><code class="docutils literal notranslate"><span class="pre">ThresholdOptimization</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization.aggregate"><code class="docutils literal notranslate"><span class="pre">ThresholdOptimization.aggregate()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization.aggregate_with_threshold"><code class="docutils literal notranslate"><span class="pre">ThresholdOptimization.aggregate_with_threshold()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization.aggregation_fit"><code class="docutils literal notranslate"><span class="pre">ThresholdOptimization.aggregation_fit()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization.condition"><code class="docutils literal notranslate"><span class="pre">ThresholdOptimization.condition()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization.discard"><code class="docutils literal notranslate"><span class="pre">ThresholdOptimization.discard()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method._threshold_optim.X"><code class="docutils literal notranslate"><span class="pre">X</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method._threshold_optim.X.condition"><code class="docutils literal notranslate"><span class="pre">X.condition()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.method.html#module-quapy.method.base">quapy.method.base module</a><ul>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.base.BaseQuantifier"><code class="docutils literal notranslate"><span class="pre">BaseQuantifier</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.base.BaseQuantifier.fit"><code class="docutils literal notranslate"><span class="pre">BaseQuantifier.fit()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.base.BaseQuantifier.quantify"><code class="docutils literal notranslate"><span class="pre">BaseQuantifier.quantify()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.base.BinaryQuantifier"><code class="docutils literal notranslate"><span class="pre">BinaryQuantifier</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.base.OneVsAll"><code class="docutils literal notranslate"><span class="pre">OneVsAll</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.base.OneVsAllGeneric"><code class="docutils literal notranslate"><span class="pre">OneVsAllGeneric</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.base.OneVsAllGeneric.classes_"><code class="docutils literal notranslate"><span class="pre">OneVsAllGeneric.classes_</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.base.OneVsAllGeneric.fit"><code class="docutils literal notranslate"><span class="pre">OneVsAllGeneric.fit()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.base.OneVsAllGeneric.quantify"><code class="docutils literal notranslate"><span class="pre">OneVsAllGeneric.quantify()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.base.newOneVsAll"><code class="docutils literal notranslate"><span class="pre">newOneVsAll()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.method.html#module-quapy.method.meta">quapy.method.meta module</a><ul>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.meta.EACC"><code class="docutils literal notranslate"><span class="pre">EACC()</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.meta.ECC"><code class="docutils literal notranslate"><span class="pre">ECC()</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.meta.EEMQ"><code class="docutils literal notranslate"><span class="pre">EEMQ()</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.meta.EHDy"><code class="docutils literal notranslate"><span class="pre">EHDy()</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.meta.EPACC"><code class="docutils literal notranslate"><span class="pre">EPACC()</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.meta.Ensemble"><code class="docutils literal notranslate"><span class="pre">Ensemble</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.meta.Ensemble.VALID_POLICIES"><code class="docutils literal notranslate"><span class="pre">Ensemble.VALID_POLICIES</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.meta.Ensemble.aggregative"><code class="docutils literal notranslate"><span class="pre">Ensemble.aggregative</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.meta.Ensemble.fit"><code class="docutils literal notranslate"><span class="pre">Ensemble.fit()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.meta.Ensemble.get_params"><code class="docutils literal notranslate"><span class="pre">Ensemble.get_params()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.meta.Ensemble.probabilistic"><code class="docutils literal notranslate"><span class="pre">Ensemble.probabilistic</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.meta.Ensemble.quantify"><code class="docutils literal notranslate"><span class="pre">Ensemble.quantify()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.meta.Ensemble.set_params"><code class="docutils literal notranslate"><span class="pre">Ensemble.set_params()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.meta.MedianEstimator"><code class="docutils literal notranslate"><span class="pre">MedianEstimator</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.meta.MedianEstimator.fit"><code class="docutils literal notranslate"><span class="pre">MedianEstimator.fit()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.meta.MedianEstimator.get_params"><code class="docutils literal notranslate"><span class="pre">MedianEstimator.get_params()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.meta.MedianEstimator.quantify"><code class="docutils literal notranslate"><span class="pre">MedianEstimator.quantify()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.meta.MedianEstimator.set_params"><code class="docutils literal notranslate"><span class="pre">MedianEstimator.set_params()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.meta.MedianEstimator2"><code class="docutils literal notranslate"><span class="pre">MedianEstimator2</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.meta.MedianEstimator2.fit"><code class="docutils literal notranslate"><span class="pre">MedianEstimator2.fit()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.meta.MedianEstimator2.get_params"><code class="docutils literal notranslate"><span class="pre">MedianEstimator2.get_params()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.meta.MedianEstimator2.quantify"><code class="docutils literal notranslate"><span class="pre">MedianEstimator2.quantify()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.meta.MedianEstimator2.set_params"><code class="docutils literal notranslate"><span class="pre">MedianEstimator2.set_params()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.meta.ensembleFactory"><code class="docutils literal notranslate"><span class="pre">ensembleFactory()</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.meta.get_probability_distribution"><code class="docutils literal notranslate"><span class="pre">get_probability_distribution()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.method.html#module-quapy.method.non_aggregative">quapy.method.non_aggregative module</a><ul>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.non_aggregative.DMx"><code class="docutils literal notranslate"><span class="pre">DMx</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.non_aggregative.DMx.HDx"><code class="docutils literal notranslate"><span class="pre">DMx.HDx()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.non_aggregative.DMx.fit"><code class="docutils literal notranslate"><span class="pre">DMx.fit()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.non_aggregative.DMx.quantify"><code class="docutils literal notranslate"><span class="pre">DMx.quantify()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.non_aggregative.DistributionMatchingX"><code class="docutils literal notranslate"><span class="pre">DistributionMatchingX</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation"><code class="docutils literal notranslate"><span class="pre">MaximumLikelihoodPrevalenceEstimation</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.fit"><code class="docutils literal notranslate"><span class="pre">MaximumLikelihoodPrevalenceEstimation.fit()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.quantify"><code class="docutils literal notranslate"><span class="pre">MaximumLikelihoodPrevalenceEstimation.quantify()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.non_aggregative.ReadMe"><code class="docutils literal notranslate"><span class="pre">ReadMe</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.non_aggregative.ReadMe.fit"><code class="docutils literal notranslate"><span class="pre">ReadMe.fit()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.non_aggregative.ReadMe.quantify"><code class="docutils literal notranslate"><span class="pre">ReadMe.quantify()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.non_aggregative.ReadMe.std_constrained_linear_ls"><code class="docutils literal notranslate"><span class="pre">ReadMe.std_constrained_linear_ls()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.method.html#quapy-method-composable-module">quapy.method.composable module</a><ul>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.composable.BlobelLoss"><code class="docutils literal notranslate"><span class="pre">BlobelLoss</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.composable.CVClassifier"><code class="docutils literal notranslate"><span class="pre">CVClassifier</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.composable.CVClassifier.fit"><code class="docutils literal notranslate"><span class="pre">CVClassifier.fit()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.composable.CVClassifier.predict"><code class="docutils literal notranslate"><span class="pre">CVClassifier.predict()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.composable.CVClassifier.predict_proba"><code class="docutils literal notranslate"><span class="pre">CVClassifier.predict_proba()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.composable.ClassTransformer"><code class="docutils literal notranslate"><span class="pre">ClassTransformer</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.composable.ClassTransformer.fit_transform"><code class="docutils literal notranslate"><span class="pre">ClassTransformer.fit_transform()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.composable.ClassTransformer.transform"><code class="docutils literal notranslate"><span class="pre">ClassTransformer.transform()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.composable.CombinedLoss"><code class="docutils literal notranslate"><span class="pre">CombinedLoss</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.composable.ComposableQuantifier"><code class="docutils literal notranslate"><span class="pre">ComposableQuantifier()</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.composable.DistanceTransformer"><code class="docutils literal notranslate"><span class="pre">DistanceTransformer</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.composable.DistanceTransformer.fit_transform"><code class="docutils literal notranslate"><span class="pre">DistanceTransformer.fit_transform()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.composable.DistanceTransformer.transform"><code class="docutils literal notranslate"><span class="pre">DistanceTransformer.transform()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.composable.EnergyKernelTransformer"><code class="docutils literal notranslate"><span class="pre">EnergyKernelTransformer</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.composable.EnergyKernelTransformer.fit_transform"><code class="docutils literal notranslate"><span class="pre">EnergyKernelTransformer.fit_transform()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.composable.EnergyKernelTransformer.transform"><code class="docutils literal notranslate"><span class="pre">EnergyKernelTransformer.transform()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.composable.EnergyLoss"><code class="docutils literal notranslate"><span class="pre">EnergyLoss</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.composable.GaussianKernelTransformer"><code class="docutils literal notranslate"><span class="pre">GaussianKernelTransformer</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.composable.GaussianKernelTransformer.fit_transform"><code class="docutils literal notranslate"><span class="pre">GaussianKernelTransformer.fit_transform()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.composable.GaussianKernelTransformer.transform"><code class="docutils literal notranslate"><span class="pre">GaussianKernelTransformer.transform()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.composable.GaussianRFFKernelTransformer"><code class="docutils literal notranslate"><span class="pre">GaussianRFFKernelTransformer</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.composable.GaussianRFFKernelTransformer.fit_transform"><code class="docutils literal notranslate"><span class="pre">GaussianRFFKernelTransformer.fit_transform()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.composable.GaussianRFFKernelTransformer.transform"><code class="docutils literal notranslate"><span class="pre">GaussianRFFKernelTransformer.transform()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.composable.HellingerSurrogateLoss"><code class="docutils literal notranslate"><span class="pre">HellingerSurrogateLoss</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.composable.HistogramTransformer"><code class="docutils literal notranslate"><span class="pre">HistogramTransformer</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.composable.HistogramTransformer.fit_transform"><code class="docutils literal notranslate"><span class="pre">HistogramTransformer.fit_transform()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.composable.HistogramTransformer.transform"><code class="docutils literal notranslate"><span class="pre">HistogramTransformer.transform()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.composable.KernelTransformer"><code class="docutils literal notranslate"><span class="pre">KernelTransformer</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.composable.KernelTransformer.fit_transform"><code class="docutils literal notranslate"><span class="pre">KernelTransformer.fit_transform()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.composable.KernelTransformer.transform"><code class="docutils literal notranslate"><span class="pre">KernelTransformer.transform()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.composable.LaplacianKernelTransformer"><code class="docutils literal notranslate"><span class="pre">LaplacianKernelTransformer</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.composable.LaplacianKernelTransformer.kernel"><code class="docutils literal notranslate"><span class="pre">LaplacianKernelTransformer.kernel</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.composable.LeastSquaresLoss"><code class="docutils literal notranslate"><span class="pre">LeastSquaresLoss</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.composable.TikhonovRegularization"><code class="docutils literal notranslate"><span class="pre">TikhonovRegularization</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.composable.TikhonovRegularized"><code class="docutils literal notranslate"><span class="pre">TikhonovRegularized()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.method.html#module-quapy.method">Module contents</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#submodules">Submodules</a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#module-quapy.error">quapy.error module</a><ul>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.error.absolute_error"><code class="docutils literal notranslate"><span class="pre">absolute_error()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.error.acc_error"><code class="docutils literal notranslate"><span class="pre">acc_error()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.error.acce"><code class="docutils literal notranslate"><span class="pre">acce()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.error.ae"><code class="docutils literal notranslate"><span class="pre">ae()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.error.f1_error"><code class="docutils literal notranslate"><span class="pre">f1_error()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.error.f1e"><code class="docutils literal notranslate"><span class="pre">f1e()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.error.from_name"><code class="docutils literal notranslate"><span class="pre">from_name()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.error.kld"><code class="docutils literal notranslate"><span class="pre">kld()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.error.mae"><code class="docutils literal notranslate"><span class="pre">mae()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.error.mean_absolute_error"><code class="docutils literal notranslate"><span class="pre">mean_absolute_error()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.error.mean_normalized_absolute_error"><code class="docutils literal notranslate"><span class="pre">mean_normalized_absolute_error()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.error.mean_normalized_relative_absolute_error"><code class="docutils literal notranslate"><span class="pre">mean_normalized_relative_absolute_error()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.error.mean_relative_absolute_error"><code class="docutils literal notranslate"><span class="pre">mean_relative_absolute_error()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.error.mkld"><code class="docutils literal notranslate"><span class="pre">mkld()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.error.mnae"><code class="docutils literal notranslate"><span class="pre">mnae()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.error.mnkld"><code class="docutils literal notranslate"><span class="pre">mnkld()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.error.mnrae"><code class="docutils literal notranslate"><span class="pre">mnrae()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.error.mrae"><code class="docutils literal notranslate"><span class="pre">mrae()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.error.mse"><code class="docutils literal notranslate"><span class="pre">mse()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.error.nae"><code class="docutils literal notranslate"><span class="pre">nae()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.error.nkld"><code class="docutils literal notranslate"><span class="pre">nkld()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.error.normalized_absolute_error"><code class="docutils literal notranslate"><span class="pre">normalized_absolute_error()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.error.normalized_relative_absolute_error"><code class="docutils literal notranslate"><span class="pre">normalized_relative_absolute_error()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.error.nrae"><code class="docutils literal notranslate"><span class="pre">nrae()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.error.rae"><code class="docutils literal notranslate"><span class="pre">rae()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.error.relative_absolute_error"><code class="docutils literal notranslate"><span class="pre">relative_absolute_error()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.error.se"><code class="docutils literal notranslate"><span class="pre">se()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.error.smooth"><code class="docutils literal notranslate"><span class="pre">smooth()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#module-quapy.evaluation">quapy.evaluation module</a><ul>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.evaluation.evaluate"><code class="docutils literal notranslate"><span class="pre">evaluate()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.evaluation.evaluate_on_samples"><code class="docutils literal notranslate"><span class="pre">evaluate_on_samples()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.evaluation.evaluation_report"><code class="docutils literal notranslate"><span class="pre">evaluation_report()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.evaluation.prediction"><code class="docutils literal notranslate"><span class="pre">prediction()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#module-quapy.functional">quapy.functional module</a><ul>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.HellingerDistance"><code class="docutils literal notranslate"><span class="pre">HellingerDistance()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.TopsoeDistance"><code class="docutils literal notranslate"><span class="pre">TopsoeDistance()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.argmin_prevalence"><code class="docutils literal notranslate"><span class="pre">argmin_prevalence()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.as_binary_prevalence"><code class="docutils literal notranslate"><span class="pre">as_binary_prevalence()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.check_prevalence_vector"><code class="docutils literal notranslate"><span class="pre">check_prevalence_vector()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.clip"><code class="docutils literal notranslate"><span class="pre">clip()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.condsoftmax"><code class="docutils literal notranslate"><span class="pre">condsoftmax()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.counts_from_labels"><code class="docutils literal notranslate"><span class="pre">counts_from_labels()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.get_divergence"><code class="docutils literal notranslate"><span class="pre">get_divergence()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.get_nprevpoints_approximation"><code class="docutils literal notranslate"><span class="pre">get_nprevpoints_approximation()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.l1_norm"><code class="docutils literal notranslate"><span class="pre">l1_norm()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.linear_search"><code class="docutils literal notranslate"><span class="pre">linear_search()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.normalize_prevalence"><code class="docutils literal notranslate"><span class="pre">normalize_prevalence()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.num_prevalence_combinations"><code class="docutils literal notranslate"><span class="pre">num_prevalence_combinations()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.optim_minimize"><code class="docutils literal notranslate"><span class="pre">optim_minimize()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.prevalence_from_labels"><code class="docutils literal notranslate"><span class="pre">prevalence_from_labels()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.prevalence_from_probabilities"><code class="docutils literal notranslate"><span class="pre">prevalence_from_probabilities()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.prevalence_linspace"><code class="docutils literal notranslate"><span class="pre">prevalence_linspace()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.projection_simplex_sort"><code class="docutils literal notranslate"><span class="pre">projection_simplex_sort()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.softmax"><code class="docutils literal notranslate"><span class="pre">softmax()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.solve_adjustment"><code class="docutils literal notranslate"><span class="pre">solve_adjustment()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.solve_adjustment_binary"><code class="docutils literal notranslate"><span class="pre">solve_adjustment_binary()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.strprev"><code class="docutils literal notranslate"><span class="pre">strprev()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.ternary_search"><code class="docutils literal notranslate"><span class="pre">ternary_search()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.uniform_prevalence"><code class="docutils literal notranslate"><span class="pre">uniform_prevalence()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.uniform_prevalence_sampling"><code class="docutils literal notranslate"><span class="pre">uniform_prevalence_sampling()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.uniform_simplex_sampling"><code class="docutils literal notranslate"><span class="pre">uniform_simplex_sampling()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#module-quapy.model_selection">quapy.model_selection module</a><ul>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.model_selection.ConfigStatus"><code class="docutils literal notranslate"><span class="pre">ConfigStatus</span></code></a><ul>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.html#quapy.model_selection.ConfigStatus.failed"><code class="docutils literal notranslate"><span class="pre">ConfigStatus.failed()</span></code></a></li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.html#quapy.model_selection.ConfigStatus.success"><code class="docutils literal notranslate"><span class="pre">ConfigStatus.success()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.model_selection.GridSearchQ"><code class="docutils literal notranslate"><span class="pre">GridSearchQ</span></code></a><ul>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.html#quapy.model_selection.GridSearchQ.best_model"><code class="docutils literal notranslate"><span class="pre">GridSearchQ.best_model()</span></code></a></li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.html#quapy.model_selection.GridSearchQ.fit"><code class="docutils literal notranslate"><span class="pre">GridSearchQ.fit()</span></code></a></li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.html#quapy.model_selection.GridSearchQ.get_params"><code class="docutils literal notranslate"><span class="pre">GridSearchQ.get_params()</span></code></a></li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.html#quapy.model_selection.GridSearchQ.quantify"><code class="docutils literal notranslate"><span class="pre">GridSearchQ.quantify()</span></code></a></li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.html#quapy.model_selection.GridSearchQ.set_params"><code class="docutils literal notranslate"><span class="pre">GridSearchQ.set_params()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.model_selection.Status"><code class="docutils literal notranslate"><span class="pre">Status</span></code></a><ul>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.html#quapy.model_selection.Status.ERROR"><code class="docutils literal notranslate"><span class="pre">Status.ERROR</span></code></a></li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.html#quapy.model_selection.Status.INVALID"><code class="docutils literal notranslate"><span class="pre">Status.INVALID</span></code></a></li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.html#quapy.model_selection.Status.SUCCESS"><code class="docutils literal notranslate"><span class="pre">Status.SUCCESS</span></code></a></li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.html#quapy.model_selection.Status.TIMEOUT"><code class="docutils literal notranslate"><span class="pre">Status.TIMEOUT</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.model_selection.cross_val_predict"><code class="docutils literal notranslate"><span class="pre">cross_val_predict()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.model_selection.expand_grid"><code class="docutils literal notranslate"><span class="pre">expand_grid()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.model_selection.group_params"><code class="docutils literal notranslate"><span class="pre">group_params()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#module-quapy.plot">quapy.plot module</a><ul>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.plot.binary_bias_bins"><code class="docutils literal notranslate"><span class="pre">binary_bias_bins()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.plot.binary_bias_global"><code class="docutils literal notranslate"><span class="pre">binary_bias_global()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.plot.binary_diagonal"><code class="docutils literal notranslate"><span class="pre">binary_diagonal()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.plot.brokenbar_supremacy_by_drift"><code class="docutils literal notranslate"><span class="pre">brokenbar_supremacy_by_drift()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.plot.error_by_drift"><code class="docutils literal notranslate"><span class="pre">error_by_drift()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#module-quapy.protocol">quapy.protocol module</a><ul>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.protocol.APP"><code class="docutils literal notranslate"><span class="pre">APP</span></code></a><ul>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.html#quapy.protocol.APP.prevalence_grid"><code class="docutils literal notranslate"><span class="pre">APP.prevalence_grid()</span></code></a></li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.html#quapy.protocol.APP.sample"><code class="docutils literal notranslate"><span class="pre">APP.sample()</span></code></a></li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.html#quapy.protocol.APP.samples_parameters"><code class="docutils literal notranslate"><span class="pre">APP.samples_parameters()</span></code></a></li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.html#quapy.protocol.APP.total"><code class="docutils literal notranslate"><span class="pre">APP.total()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.protocol.AbstractProtocol"><code class="docutils literal notranslate"><span class="pre">AbstractProtocol</span></code></a><ul>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.html#quapy.protocol.AbstractProtocol.total"><code class="docutils literal notranslate"><span class="pre">AbstractProtocol.total()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.protocol.AbstractStochasticSeededProtocol"><code class="docutils literal notranslate"><span class="pre">AbstractStochasticSeededProtocol</span></code></a><ul>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.html#quapy.protocol.AbstractStochasticSeededProtocol.collator"><code class="docutils literal notranslate"><span class="pre">AbstractStochasticSeededProtocol.collator()</span></code></a></li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.html#quapy.protocol.AbstractStochasticSeededProtocol.random_state"><code class="docutils literal notranslate"><span class="pre">AbstractStochasticSeededProtocol.random_state</span></code></a></li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.html#quapy.protocol.AbstractStochasticSeededProtocol.sample"><code class="docutils literal notranslate"><span class="pre">AbstractStochasticSeededProtocol.sample()</span></code></a></li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.html#quapy.protocol.AbstractStochasticSeededProtocol.samples_parameters"><code class="docutils literal notranslate"><span class="pre">AbstractStochasticSeededProtocol.samples_parameters()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.protocol.ArtificialPrevalenceProtocol"><code class="docutils literal notranslate"><span class="pre">ArtificialPrevalenceProtocol</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.protocol.DomainMixer"><code class="docutils literal notranslate"><span class="pre">DomainMixer</span></code></a><ul>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.html#quapy.protocol.DomainMixer.sample"><code class="docutils literal notranslate"><span class="pre">DomainMixer.sample()</span></code></a></li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.html#quapy.protocol.DomainMixer.samples_parameters"><code class="docutils literal notranslate"><span class="pre">DomainMixer.samples_parameters()</span></code></a></li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.html#quapy.protocol.DomainMixer.total"><code class="docutils literal notranslate"><span class="pre">DomainMixer.total()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.protocol.IterateProtocol"><code class="docutils literal notranslate"><span class="pre">IterateProtocol</span></code></a><ul>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.html#quapy.protocol.IterateProtocol.total"><code class="docutils literal notranslate"><span class="pre">IterateProtocol.total()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.protocol.NPP"><code class="docutils literal notranslate"><span class="pre">NPP</span></code></a><ul>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.html#quapy.protocol.NPP.sample"><code class="docutils literal notranslate"><span class="pre">NPP.sample()</span></code></a></li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.html#quapy.protocol.NPP.samples_parameters"><code class="docutils literal notranslate"><span class="pre">NPP.samples_parameters()</span></code></a></li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.html#quapy.protocol.NPP.total"><code class="docutils literal notranslate"><span class="pre">NPP.total()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.protocol.NaturalPrevalenceProtocol"><code class="docutils literal notranslate"><span class="pre">NaturalPrevalenceProtocol</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.protocol.OnLabelledCollectionProtocol"><code class="docutils literal notranslate"><span class="pre">OnLabelledCollectionProtocol</span></code></a><ul>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.html#quapy.protocol.OnLabelledCollectionProtocol.RETURN_TYPES"><code class="docutils literal notranslate"><span class="pre">OnLabelledCollectionProtocol.RETURN_TYPES</span></code></a></li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.html#quapy.protocol.OnLabelledCollectionProtocol.get_collator"><code class="docutils literal notranslate"><span class="pre">OnLabelledCollectionProtocol.get_collator()</span></code></a></li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.html#quapy.protocol.OnLabelledCollectionProtocol.get_labelled_collection"><code class="docutils literal notranslate"><span class="pre">OnLabelledCollectionProtocol.get_labelled_collection()</span></code></a></li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.html#quapy.protocol.OnLabelledCollectionProtocol.on_preclassified_instances"><code class="docutils literal notranslate"><span class="pre">OnLabelledCollectionProtocol.on_preclassified_instances()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.protocol.UPP"><code class="docutils literal notranslate"><span class="pre">UPP</span></code></a><ul>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.html#quapy.protocol.UPP.sample"><code class="docutils literal notranslate"><span class="pre">UPP.sample()</span></code></a></li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.html#quapy.protocol.UPP.samples_parameters"><code class="docutils literal notranslate"><span class="pre">UPP.samples_parameters()</span></code></a></li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.html#quapy.protocol.UPP.total"><code class="docutils literal notranslate"><span class="pre">UPP.total()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.protocol.UniformPrevalenceProtocol"><code class="docutils literal notranslate"><span class="pre">UniformPrevalenceProtocol</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#module-quapy.util">quapy.util module</a><ul>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.util.EarlyStop"><code class="docutils literal notranslate"><span class="pre">EarlyStop</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.util.create_if_not_exist"><code class="docutils literal notranslate"><span class="pre">create_if_not_exist()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.util.create_parent_dir"><code class="docutils literal notranslate"><span class="pre">create_parent_dir()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.util.download_file"><code class="docutils literal notranslate"><span class="pre">download_file()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.util.download_file_if_not_exists"><code class="docutils literal notranslate"><span class="pre">download_file_if_not_exists()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.util.get_quapy_home"><code class="docutils literal notranslate"><span class="pre">get_quapy_home()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.util.load_report"><code class="docutils literal notranslate"><span class="pre">load_report()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.util.map_parallel"><code class="docutils literal notranslate"><span class="pre">map_parallel()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.util.parallel"><code class="docutils literal notranslate"><span class="pre">parallel()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.util.parallel_unpack"><code class="docutils literal notranslate"><span class="pre">parallel_unpack()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.util.pickled_resource"><code class="docutils literal notranslate"><span class="pre">pickled_resource()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.util.save_text_file"><code class="docutils literal notranslate"><span class="pre">save_text_file()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.util.temp_seed"><code class="docutils literal notranslate"><span class="pre">temp_seed()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.util.timeout"><code class="docutils literal notranslate"><span class="pre">timeout()</span></code></a></li>
|
||||
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|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.method.html#submodules">Submodules</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.method.html#module-quapy.method.aggregative">quapy.method.aggregative module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.method.html#module-quapy.method.base">quapy.method.base module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.method.html#module-quapy.method.meta">quapy.method.meta module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.method.html#module-quapy.method.non_aggregative">quapy.method.non_aggregative module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.method.html#quapy-method-composable-module">quapy.method.composable module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.method.html#module-quapy.method">Module contents</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="quapy.html#submodules">Submodules</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="quapy.html#module-quapy.error">quapy.error module</a><ul>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.error.absolute_error"><code class="docutils literal notranslate"><span class="pre">absolute_error()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.error.acc_error"><code class="docutils literal notranslate"><span class="pre">acc_error()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.error.acce"><code class="docutils literal notranslate"><span class="pre">acce()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.error.ae"><code class="docutils literal notranslate"><span class="pre">ae()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.error.f1_error"><code class="docutils literal notranslate"><span class="pre">f1_error()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.error.f1e"><code class="docutils literal notranslate"><span class="pre">f1e()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.error.from_name"><code class="docutils literal notranslate"><span class="pre">from_name()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.error.kld"><code class="docutils literal notranslate"><span class="pre">kld()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.error.mae"><code class="docutils literal notranslate"><span class="pre">mae()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.error.mean_absolute_error"><code class="docutils literal notranslate"><span class="pre">mean_absolute_error()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.error.mean_normalized_absolute_error"><code class="docutils literal notranslate"><span class="pre">mean_normalized_absolute_error()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.error.mean_normalized_relative_absolute_error"><code class="docutils literal notranslate"><span class="pre">mean_normalized_relative_absolute_error()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.error.mean_relative_absolute_error"><code class="docutils literal notranslate"><span class="pre">mean_relative_absolute_error()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.error.mkld"><code class="docutils literal notranslate"><span class="pre">mkld()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.error.mnae"><code class="docutils literal notranslate"><span class="pre">mnae()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.error.mnkld"><code class="docutils literal notranslate"><span class="pre">mnkld()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.error.mnrae"><code class="docutils literal notranslate"><span class="pre">mnrae()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.error.mrae"><code class="docutils literal notranslate"><span class="pre">mrae()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.error.mse"><code class="docutils literal notranslate"><span class="pre">mse()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.error.nae"><code class="docutils literal notranslate"><span class="pre">nae()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.error.nkld"><code class="docutils literal notranslate"><span class="pre">nkld()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.error.normalized_absolute_error"><code class="docutils literal notranslate"><span class="pre">normalized_absolute_error()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.error.normalized_relative_absolute_error"><code class="docutils literal notranslate"><span class="pre">normalized_relative_absolute_error()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.error.nrae"><code class="docutils literal notranslate"><span class="pre">nrae()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.error.rae"><code class="docutils literal notranslate"><span class="pre">rae()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.error.relative_absolute_error"><code class="docutils literal notranslate"><span class="pre">relative_absolute_error()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.error.se"><code class="docutils literal notranslate"><span class="pre">se()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.error.smooth"><code class="docutils literal notranslate"><span class="pre">smooth()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="quapy.html#module-quapy.evaluation">quapy.evaluation module</a><ul>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.evaluation.evaluate"><code class="docutils literal notranslate"><span class="pre">evaluate()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.evaluation.evaluate_on_samples"><code class="docutils literal notranslate"><span class="pre">evaluate_on_samples()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.evaluation.evaluation_report"><code class="docutils literal notranslate"><span class="pre">evaluation_report()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.evaluation.prediction"><code class="docutils literal notranslate"><span class="pre">prediction()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="quapy.html#module-quapy.functional">quapy.functional module</a><ul>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.HellingerDistance"><code class="docutils literal notranslate"><span class="pre">HellingerDistance()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.TopsoeDistance"><code class="docutils literal notranslate"><span class="pre">TopsoeDistance()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.argmin_prevalence"><code class="docutils literal notranslate"><span class="pre">argmin_prevalence()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.as_binary_prevalence"><code class="docutils literal notranslate"><span class="pre">as_binary_prevalence()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.check_prevalence_vector"><code class="docutils literal notranslate"><span class="pre">check_prevalence_vector()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.clip"><code class="docutils literal notranslate"><span class="pre">clip()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.condsoftmax"><code class="docutils literal notranslate"><span class="pre">condsoftmax()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.counts_from_labels"><code class="docutils literal notranslate"><span class="pre">counts_from_labels()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.get_divergence"><code class="docutils literal notranslate"><span class="pre">get_divergence()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.get_nprevpoints_approximation"><code class="docutils literal notranslate"><span class="pre">get_nprevpoints_approximation()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.l1_norm"><code class="docutils literal notranslate"><span class="pre">l1_norm()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.linear_search"><code class="docutils literal notranslate"><span class="pre">linear_search()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.normalize_prevalence"><code class="docutils literal notranslate"><span class="pre">normalize_prevalence()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.num_prevalence_combinations"><code class="docutils literal notranslate"><span class="pre">num_prevalence_combinations()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.optim_minimize"><code class="docutils literal notranslate"><span class="pre">optim_minimize()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.prevalence_from_labels"><code class="docutils literal notranslate"><span class="pre">prevalence_from_labels()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.prevalence_from_probabilities"><code class="docutils literal notranslate"><span class="pre">prevalence_from_probabilities()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.prevalence_linspace"><code class="docutils literal notranslate"><span class="pre">prevalence_linspace()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.projection_simplex_sort"><code class="docutils literal notranslate"><span class="pre">projection_simplex_sort()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.softmax"><code class="docutils literal notranslate"><span class="pre">softmax()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.solve_adjustment"><code class="docutils literal notranslate"><span class="pre">solve_adjustment()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.solve_adjustment_binary"><code class="docutils literal notranslate"><span class="pre">solve_adjustment_binary()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.strprev"><code class="docutils literal notranslate"><span class="pre">strprev()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.ternary_search"><code class="docutils literal notranslate"><span class="pre">ternary_search()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.uniform_prevalence"><code class="docutils literal notranslate"><span class="pre">uniform_prevalence()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.uniform_prevalence_sampling"><code class="docutils literal notranslate"><span class="pre">uniform_prevalence_sampling()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.uniform_simplex_sampling"><code class="docutils literal notranslate"><span class="pre">uniform_simplex_sampling()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="quapy.html#module-quapy.model_selection">quapy.model_selection module</a><ul>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.model_selection.ConfigStatus"><code class="docutils literal notranslate"><span class="pre">ConfigStatus</span></code></a><ul>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.model_selection.ConfigStatus.failed"><code class="docutils literal notranslate"><span class="pre">ConfigStatus.failed()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.model_selection.ConfigStatus.success"><code class="docutils literal notranslate"><span class="pre">ConfigStatus.success()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.model_selection.GridSearchQ"><code class="docutils literal notranslate"><span class="pre">GridSearchQ</span></code></a><ul>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.model_selection.GridSearchQ.best_model"><code class="docutils literal notranslate"><span class="pre">GridSearchQ.best_model()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.model_selection.GridSearchQ.fit"><code class="docutils literal notranslate"><span class="pre">GridSearchQ.fit()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.model_selection.GridSearchQ.get_params"><code class="docutils literal notranslate"><span class="pre">GridSearchQ.get_params()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.model_selection.GridSearchQ.quantify"><code class="docutils literal notranslate"><span class="pre">GridSearchQ.quantify()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.model_selection.GridSearchQ.set_params"><code class="docutils literal notranslate"><span class="pre">GridSearchQ.set_params()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.model_selection.Status"><code class="docutils literal notranslate"><span class="pre">Status</span></code></a><ul>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.model_selection.Status.ERROR"><code class="docutils literal notranslate"><span class="pre">Status.ERROR</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.model_selection.Status.INVALID"><code class="docutils literal notranslate"><span class="pre">Status.INVALID</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.model_selection.Status.SUCCESS"><code class="docutils literal notranslate"><span class="pre">Status.SUCCESS</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.model_selection.Status.TIMEOUT"><code class="docutils literal notranslate"><span class="pre">Status.TIMEOUT</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.model_selection.cross_val_predict"><code class="docutils literal notranslate"><span class="pre">cross_val_predict()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.model_selection.expand_grid"><code class="docutils literal notranslate"><span class="pre">expand_grid()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.model_selection.group_params"><code class="docutils literal notranslate"><span class="pre">group_params()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="quapy.html#module-quapy.plot">quapy.plot module</a><ul>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.plot.binary_bias_bins"><code class="docutils literal notranslate"><span class="pre">binary_bias_bins()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.plot.binary_bias_global"><code class="docutils literal notranslate"><span class="pre">binary_bias_global()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.plot.binary_diagonal"><code class="docutils literal notranslate"><span class="pre">binary_diagonal()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.plot.brokenbar_supremacy_by_drift"><code class="docutils literal notranslate"><span class="pre">brokenbar_supremacy_by_drift()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.plot.error_by_drift"><code class="docutils literal notranslate"><span class="pre">error_by_drift()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="quapy.html#module-quapy.protocol">quapy.protocol module</a><ul>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.protocol.APP"><code class="docutils literal notranslate"><span class="pre">APP</span></code></a><ul>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.protocol.APP.prevalence_grid"><code class="docutils literal notranslate"><span class="pre">APP.prevalence_grid()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.protocol.APP.sample"><code class="docutils literal notranslate"><span class="pre">APP.sample()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.protocol.APP.samples_parameters"><code class="docutils literal notranslate"><span class="pre">APP.samples_parameters()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.protocol.APP.total"><code class="docutils literal notranslate"><span class="pre">APP.total()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.protocol.AbstractProtocol"><code class="docutils literal notranslate"><span class="pre">AbstractProtocol</span></code></a><ul>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.protocol.AbstractProtocol.total"><code class="docutils literal notranslate"><span class="pre">AbstractProtocol.total()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.protocol.AbstractStochasticSeededProtocol"><code class="docutils literal notranslate"><span class="pre">AbstractStochasticSeededProtocol</span></code></a><ul>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.protocol.AbstractStochasticSeededProtocol.collator"><code class="docutils literal notranslate"><span class="pre">AbstractStochasticSeededProtocol.collator()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.protocol.AbstractStochasticSeededProtocol.random_state"><code class="docutils literal notranslate"><span class="pre">AbstractStochasticSeededProtocol.random_state</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.protocol.AbstractStochasticSeededProtocol.sample"><code class="docutils literal notranslate"><span class="pre">AbstractStochasticSeededProtocol.sample()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.protocol.AbstractStochasticSeededProtocol.samples_parameters"><code class="docutils literal notranslate"><span class="pre">AbstractStochasticSeededProtocol.samples_parameters()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.protocol.ArtificialPrevalenceProtocol"><code class="docutils literal notranslate"><span class="pre">ArtificialPrevalenceProtocol</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.protocol.DomainMixer"><code class="docutils literal notranslate"><span class="pre">DomainMixer</span></code></a><ul>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.protocol.DomainMixer.sample"><code class="docutils literal notranslate"><span class="pre">DomainMixer.sample()</span></code></a></li>
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||||
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||||
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||||
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|
||||
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|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.protocol.IterateProtocol.total"><code class="docutils literal notranslate"><span class="pre">IterateProtocol.total()</span></code></a></li>
|
||||
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|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.protocol.NPP"><code class="docutils literal notranslate"><span class="pre">NPP</span></code></a><ul>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.protocol.NPP.sample"><code class="docutils literal notranslate"><span class="pre">NPP.sample()</span></code></a></li>
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||||
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||||
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||||
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<section id="quapy-classification-package">
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<h1>quapy.classification package<a class="headerlink" href="#quapy-classification-package" title="Permalink to this heading"></a></h1>
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<section id="submodules">
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<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this heading"></a></h2>
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</section>
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<section id="module-quapy.classification.calibration">
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<span id="quapy-classification-calibration-module"></span><h2>quapy.classification.calibration module<a class="headerlink" href="#module-quapy.classification.calibration" title="Permalink to this heading"></a></h2>
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.classification.calibration.BCTSCalibration">
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.calibration.</span></span><span class="sig-name descname"><span class="pre">BCTSCalibration</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/calibration.html#BCTSCalibration"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.calibration.BCTSCalibration" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase" title="quapy.classification.calibration.RecalibratedProbabilisticClassifierBase"><code class="xref py py-class docutils literal notranslate"><span class="pre">RecalibratedProbabilisticClassifierBase</span></code></a></p>
|
||||
<p>Applies the Bias-Corrected Temperature Scaling (BCTS) calibration method from <cite>abstention.calibration</cite>, as defined in
|
||||
<a class="reference external" href="http://proceedings.mlr.press/v119/alexandari20a.html">Alexandari et al. paper</a>:</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><ul class="simple">
|
||||
<li><p><strong>classifier</strong> – a scikit-learn probabilistic classifier</p></li>
|
||||
<li><p><strong>val_split</strong> – indicate an integer k for performing kFCV to obtain the posterior prevalences, or a float p
|
||||
in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the
|
||||
training instances (the rest is used for training). In any case, the classifier is retrained in the whole
|
||||
training set afterwards. Default value is 5.</p></li>
|
||||
<li><p><strong>n_jobs</strong> – indicate the number of parallel workers (only when val_split is an integer)</p></li>
|
||||
<li><p><strong>verbose</strong> – whether or not to display information in the standard output</p></li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.classification.calibration.NBVSCalibration">
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.calibration.</span></span><span class="sig-name descname"><span class="pre">NBVSCalibration</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/calibration.html#NBVSCalibration"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.calibration.NBVSCalibration" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase" title="quapy.classification.calibration.RecalibratedProbabilisticClassifierBase"><code class="xref py py-class docutils literal notranslate"><span class="pre">RecalibratedProbabilisticClassifierBase</span></code></a></p>
|
||||
<p>Applies the No-Bias Vector Scaling (NBVS) calibration method from <cite>abstention.calibration</cite>, as defined in
|
||||
<a class="reference external" href="http://proceedings.mlr.press/v119/alexandari20a.html">Alexandari et al. paper</a>:</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><ul class="simple">
|
||||
<li><p><strong>classifier</strong> – a scikit-learn probabilistic classifier</p></li>
|
||||
<li><p><strong>val_split</strong> – indicate an integer k for performing kFCV to obtain the posterior prevalences, or a float p
|
||||
in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the
|
||||
training instances (the rest is used for training). In any case, the classifier is retrained in the whole
|
||||
training set afterwards. Default value is 5.</p></li>
|
||||
<li><p><strong>n_jobs</strong> – indicate the number of parallel workers (only when val_split is an integer)</p></li>
|
||||
<li><p><strong>verbose</strong> – whether or not to display information in the standard output</p></li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.classification.calibration.RecalibratedProbabilisticClassifier">
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.calibration.</span></span><span class="sig-name descname"><span class="pre">RecalibratedProbabilisticClassifier</span></span><a class="reference internal" href="_modules/quapy/classification/calibration.html#RecalibratedProbabilisticClassifier"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifier" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
|
||||
<p>Abstract class for (re)calibration method from <cite>abstention.calibration</cite>, as defined in
|
||||
<a class="reference external" href="http://proceedings.mlr.press/v119/alexandari20a.html">Alexandari, A., Kundaje, A., & Shrikumar, A. (2020, November). Maximum likelihood with bias-corrected calibration
|
||||
is hard-to-beat at label shift adaptation. In International Conference on Machine Learning (pp. 222-232). PMLR.</a>:</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.classification.calibration.RecalibratedProbabilisticClassifierBase">
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.calibration.</span></span><span class="sig-name descname"><span class="pre">RecalibratedProbabilisticClassifierBase</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">calibrator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/calibration.html#RecalibratedProbabilisticClassifierBase"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">BaseEstimator</span></code>, <a class="reference internal" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifier" title="quapy.classification.calibration.RecalibratedProbabilisticClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">RecalibratedProbabilisticClassifier</span></code></a></p>
|
||||
<p>Applies a (re)calibration method from <cite>abstention.calibration</cite>, as defined in
|
||||
<a class="reference external" href="http://proceedings.mlr.press/v119/alexandari20a.html">Alexandari et al. paper</a>.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><ul class="simple">
|
||||
<li><p><strong>classifier</strong> – a scikit-learn probabilistic classifier</p></li>
|
||||
<li><p><strong>calibrator</strong> – the calibration object (an instance of abstention.calibration.CalibratorFactory)</p></li>
|
||||
<li><p><strong>val_split</strong> – indicate an integer k for performing kFCV to obtain the posterior probabilities, or a float p
|
||||
in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the
|
||||
training instances (the rest is used for training). In any case, the classifier is retrained in the whole
|
||||
training set afterwards. Default value is 5.</p></li>
|
||||
<li><p><strong>n_jobs</strong> – indicate the number of parallel workers (only when val_split is an integer); default=None</p></li>
|
||||
<li><p><strong>verbose</strong> – whether or not to display information in the standard output</p></li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.classes_">
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">classes_</span></span><a class="headerlink" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.classes_" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Returns the classes on which the classifier has been trained on</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><p>array-like of shape <cite>(n_classes)</cite></p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit">
|
||||
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/calibration.html#RecalibratedProbabilisticClassifierBase.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Fits the calibration for the probabilistic classifier.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><ul class="simple">
|
||||
<li><p><strong>X</strong> – array-like of shape <cite>(n_samples, n_features)</cite> with the data instances</p></li>
|
||||
<li><p><strong>y</strong> – array-like of shape <cite>(n_samples,)</cite> with the class labels</p></li>
|
||||
</ul>
|
||||
</dd>
|
||||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||||
<dd class="field-even"><p>self</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit_cv">
|
||||
<span class="sig-name descname"><span class="pre">fit_cv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/calibration.html#RecalibratedProbabilisticClassifierBase.fit_cv"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit_cv" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Fits the calibration in a cross-validation manner, i.e., it generates posterior probabilities for all
|
||||
training instances via cross-validation, and then retrains the classifier on all training instances.
|
||||
The posterior probabilities thus generated are used for calibrating the outputs of the classifier.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><ul class="simple">
|
||||
<li><p><strong>X</strong> – array-like of shape <cite>(n_samples, n_features)</cite> with the data instances</p></li>
|
||||
<li><p><strong>y</strong> – array-like of shape <cite>(n_samples,)</cite> with the class labels</p></li>
|
||||
</ul>
|
||||
</dd>
|
||||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||||
<dd class="field-even"><p>self</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit_tr_val">
|
||||
<span class="sig-name descname"><span class="pre">fit_tr_val</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/calibration.html#RecalibratedProbabilisticClassifierBase.fit_tr_val"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit_tr_val" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Fits the calibration in a train/val-split manner, i.e.t, it partitions the training instances into a
|
||||
training and a validation set, and then uses the training samples to learn classifier which is then used
|
||||
to generate posterior probabilities for the held-out validation data. These posteriors are used to calibrate
|
||||
the classifier. The classifier is not retrained on the whole dataset.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><ul class="simple">
|
||||
<li><p><strong>X</strong> – array-like of shape <cite>(n_samples, n_features)</cite> with the data instances</p></li>
|
||||
<li><p><strong>y</strong> – array-like of shape <cite>(n_samples,)</cite> with the class labels</p></li>
|
||||
</ul>
|
||||
</dd>
|
||||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||||
<dd class="field-even"><p>self</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.predict">
|
||||
<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/calibration.html#RecalibratedProbabilisticClassifierBase.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.predict" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Predicts class labels for the data instances in <cite>X</cite></p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><p><strong>X</strong> – array-like of shape <cite>(n_samples, n_features)</cite> with the data instances</p>
|
||||
</dd>
|
||||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||||
<dd class="field-even"><p>array-like of shape <cite>(n_samples,)</cite> with the class label predictions</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.predict_proba">
|
||||
<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/calibration.html#RecalibratedProbabilisticClassifierBase.predict_proba"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.predict_proba" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Generates posterior probabilities for the data instances in <cite>X</cite></p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><p><strong>X</strong> – array-like of shape <cite>(n_samples, n_features)</cite> with the data instances</p>
|
||||
</dd>
|
||||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||||
<dd class="field-even"><p>array-like of shape <cite>(n_samples, n_classes)</cite> with posterior probabilities</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.classification.calibration.TSCalibration">
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.calibration.</span></span><span class="sig-name descname"><span class="pre">TSCalibration</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/calibration.html#TSCalibration"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.calibration.TSCalibration" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase" title="quapy.classification.calibration.RecalibratedProbabilisticClassifierBase"><code class="xref py py-class docutils literal notranslate"><span class="pre">RecalibratedProbabilisticClassifierBase</span></code></a></p>
|
||||
<p>Applies the Temperature Scaling (TS) calibration method from <cite>abstention.calibration</cite>, as defined in
|
||||
<a class="reference external" href="http://proceedings.mlr.press/v119/alexandari20a.html">Alexandari et al. paper</a>:</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><ul class="simple">
|
||||
<li><p><strong>classifier</strong> – a scikit-learn probabilistic classifier</p></li>
|
||||
<li><p><strong>val_split</strong> – indicate an integer k for performing kFCV to obtain the posterior prevalences, or a float p
|
||||
in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the
|
||||
training instances (the rest is used for training). In any case, the classifier is retrained in the whole
|
||||
training set afterwards. Default value is 5.</p></li>
|
||||
<li><p><strong>n_jobs</strong> – indicate the number of parallel workers (only when val_split is an integer)</p></li>
|
||||
<li><p><strong>verbose</strong> – whether or not to display information in the standard output</p></li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.classification.calibration.VSCalibration">
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.calibration.</span></span><span class="sig-name descname"><span class="pre">VSCalibration</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/calibration.html#VSCalibration"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.calibration.VSCalibration" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase" title="quapy.classification.calibration.RecalibratedProbabilisticClassifierBase"><code class="xref py py-class docutils literal notranslate"><span class="pre">RecalibratedProbabilisticClassifierBase</span></code></a></p>
|
||||
<p>Applies the Vector Scaling (VS) calibration method from <cite>abstention.calibration</cite>, as defined in
|
||||
<a class="reference external" href="http://proceedings.mlr.press/v119/alexandari20a.html">Alexandari et al. paper</a>:</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><ul class="simple">
|
||||
<li><p><strong>classifier</strong> – a scikit-learn probabilistic classifier</p></li>
|
||||
<li><p><strong>val_split</strong> – indicate an integer k for performing kFCV to obtain the posterior prevalences, or a float p
|
||||
in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the
|
||||
training instances (the rest is used for training). In any case, the classifier is retrained in the whole
|
||||
training set afterwards. Default value is 5.</p></li>
|
||||
<li><p><strong>n_jobs</strong> – indicate the number of parallel workers (only when val_split is an integer)</p></li>
|
||||
<li><p><strong>verbose</strong> – whether or not to display information in the standard output</p></li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
</section>
|
||||
<section id="module-quapy.classification.methods">
|
||||
<span id="quapy-classification-methods-module"></span><h2>quapy.classification.methods module<a class="headerlink" href="#module-quapy.classification.methods" title="Permalink to this heading"></a></h2>
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.classification.methods.LowRankLogisticRegression">
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.methods.</span></span><span class="sig-name descname"><span class="pre">LowRankLogisticRegression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/methods.html#LowRankLogisticRegression"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.methods.LowRankLogisticRegression" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">BaseEstimator</span></code></p>
|
||||
<p>An example of a classification method (i.e., an object that implements <cite>fit</cite>, <cite>predict</cite>, and <cite>predict_proba</cite>)
|
||||
that also generates embedded inputs (i.e., that implements <cite>transform</cite>), as those required for
|
||||
<code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.neural.QuaNet</span></code>. This is a mock method to allow for easily instantiating
|
||||
<code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.neural.QuaNet</span></code> on array-like real-valued instances.
|
||||
The transformation consists of applying <code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.decomposition.TruncatedSVD</span></code>
|
||||
while classification is performed using <code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.linear_model.LogisticRegression</span></code> on the low-rank space.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><ul class="simple">
|
||||
<li><p><strong>n_components</strong> – the number of principal components to retain</p></li>
|
||||
<li><p><strong>kwargs</strong> – parameters for the
|
||||
<a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html">Logistic Regression</a> classifier</p></li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.methods.LowRankLogisticRegression.fit">
|
||||
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/methods.html#LowRankLogisticRegression.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.methods.LowRankLogisticRegression.fit" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Fit the model according to the given training data. The fit consists of
|
||||
fitting <cite>TruncatedSVD</cite> and then <cite>LogisticRegression</cite> on the low-rank representation.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><ul class="simple">
|
||||
<li><p><strong>X</strong> – array-like of shape <cite>(n_samples, n_features)</cite> with the instances</p></li>
|
||||
<li><p><strong>y</strong> – array-like of shape <cite>(n_samples, n_classes)</cite> with the class labels</p></li>
|
||||
</ul>
|
||||
</dd>
|
||||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||||
<dd class="field-even"><p><cite>self</cite></p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.methods.LowRankLogisticRegression.get_params">
|
||||
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/methods.html#LowRankLogisticRegression.get_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.methods.LowRankLogisticRegression.get_params" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Get hyper-parameters for this estimator.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><p>a dictionary with parameter names mapped to their values</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.methods.LowRankLogisticRegression.predict">
|
||||
<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/methods.html#LowRankLogisticRegression.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.methods.LowRankLogisticRegression.predict" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Predicts labels for the instances <cite>X</cite> embedded into the low-rank space.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><p><strong>X</strong> – array-like of shape <cite>(n_samples, n_features)</cite> instances to classify</p>
|
||||
</dd>
|
||||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||||
<dd class="field-even"><p>a <cite>numpy</cite> array of length <cite>n</cite> containing the label predictions, where <cite>n</cite> is the number of
|
||||
instances in <cite>X</cite></p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.methods.LowRankLogisticRegression.predict_proba">
|
||||
<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/methods.html#LowRankLogisticRegression.predict_proba"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.methods.LowRankLogisticRegression.predict_proba" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Predicts posterior probabilities for the instances <cite>X</cite> embedded into the low-rank space.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><p><strong>X</strong> – array-like of shape <cite>(n_samples, n_features)</cite> instances to classify</p>
|
||||
</dd>
|
||||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||||
<dd class="field-even"><p>array-like of shape <cite>(n_samples, n_classes)</cite> with the posterior probabilities</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.methods.LowRankLogisticRegression.set_params">
|
||||
<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/methods.html#LowRankLogisticRegression.set_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.methods.LowRankLogisticRegression.set_params" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Set the parameters of this estimator.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><p><strong>parameters</strong> – a <cite>**kwargs</cite> dictionary with the estimator parameters for
|
||||
<a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html">Logistic Regression</a>
|
||||
and eventually also <cite>n_components</cite> for <cite>TruncatedSVD</cite></p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.methods.LowRankLogisticRegression.transform">
|
||||
<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/methods.html#LowRankLogisticRegression.transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.methods.LowRankLogisticRegression.transform" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Returns the low-rank approximation of <cite>X</cite> with <cite>n_components</cite> dimensions, or <cite>X</cite> unaltered if
|
||||
<cite>n_components</cite> >= <cite>X.shape[1]</cite>.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><p><strong>X</strong> – array-like of shape <cite>(n_samples, n_features)</cite> instances to embed</p>
|
||||
</dd>
|
||||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||||
<dd class="field-even"><p>array-like of shape <cite>(n_samples, n_components)</cite> with the embedded instances</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
</section>
|
||||
<section id="module-quapy.classification.neural">
|
||||
<span id="quapy-classification-neural-module"></span><h2>quapy.classification.neural module<a class="headerlink" href="#module-quapy.classification.neural" title="Permalink to this heading"></a></h2>
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.CNNnet">
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.neural.</span></span><span class="sig-name descname"><span class="pre">CNNnet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">vocabulary_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">embedding_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">256</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">repr_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_heights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[3,</span> <span class="pre">5,</span> <span class="pre">7]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#CNNnet"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.CNNnet" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#quapy.classification.neural.TextClassifierNet" title="quapy.classification.neural.TextClassifierNet"><code class="xref py py-class docutils literal notranslate"><span class="pre">TextClassifierNet</span></code></a></p>
|
||||
<p>An implementation of <a class="reference internal" href="#quapy.classification.neural.TextClassifierNet" title="quapy.classification.neural.TextClassifierNet"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.classification.neural.TextClassifierNet</span></code></a> based on
|
||||
Convolutional Neural Networks.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><ul class="simple">
|
||||
<li><p><strong>vocabulary_size</strong> – the size of the vocabulary</p></li>
|
||||
<li><p><strong>n_classes</strong> – number of target classes</p></li>
|
||||
<li><p><strong>embedding_size</strong> – the dimensionality of the word embeddings space (default 100)</p></li>
|
||||
<li><p><strong>hidden_size</strong> – the dimensionality of the hidden space (default 256)</p></li>
|
||||
<li><p><strong>repr_size</strong> – the dimensionality of the document embeddings space (default 100)</p></li>
|
||||
<li><p><strong>kernel_heights</strong> – list of kernel lengths (default [3,5,7]), i.e., the number of
|
||||
consecutive tokens that each kernel covers</p></li>
|
||||
<li><p><strong>stride</strong> – convolutional stride (default 1)</p></li>
|
||||
<li><p><strong>stride</strong> – convolutional pad (default 0)</p></li>
|
||||
<li><p><strong>drop_p</strong> – drop probability for dropout (default 0.5)</p></li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.CNNnet.document_embedding">
|
||||
<span class="sig-name descname"><span class="pre">document_embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#CNNnet.document_embedding"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.CNNnet.document_embedding" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Embeds documents (i.e., performs the forward pass up to the
|
||||
next-to-last layer).</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><p><strong>input</strong> – a batch of instances, typically generated by a torch’s <cite>DataLoader</cite>
|
||||
instance (see <a class="reference internal" href="#quapy.classification.neural.TorchDataset" title="quapy.classification.neural.TorchDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.classification.neural.TorchDataset</span></code></a>)</p>
|
||||
</dd>
|
||||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||||
<dd class="field-even"><p>a torch tensor of shape <cite>(n_samples, n_dimensions)</cite>, where
|
||||
<cite>n_samples</cite> is the number of documents, and <cite>n_dimensions</cite> is the
|
||||
dimensionality of the embedding</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.CNNnet.get_params">
|
||||
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#CNNnet.get_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.CNNnet.get_params" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Get hyper-parameters for this estimator</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><p>a dictionary with parameter names mapped to their values</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py attribute">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.CNNnet.training">
|
||||
<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#quapy.classification.neural.CNNnet.training" title="Permalink to this definition"></a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.CNNnet.vocabulary_size">
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">vocabulary_size</span></span><a class="headerlink" href="#quapy.classification.neural.CNNnet.vocabulary_size" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Return the size of the vocabulary</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><p>integer</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.LSTMnet">
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.neural.</span></span><span class="sig-name descname"><span class="pre">LSTMnet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">vocabulary_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">embedding_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">256</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">repr_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lstm_class_nlayers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#LSTMnet"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.LSTMnet" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#quapy.classification.neural.TextClassifierNet" title="quapy.classification.neural.TextClassifierNet"><code class="xref py py-class docutils literal notranslate"><span class="pre">TextClassifierNet</span></code></a></p>
|
||||
<p>An implementation of <a class="reference internal" href="#quapy.classification.neural.TextClassifierNet" title="quapy.classification.neural.TextClassifierNet"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.classification.neural.TextClassifierNet</span></code></a> based on
|
||||
Long Short Term Memory networks.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><ul class="simple">
|
||||
<li><p><strong>vocabulary_size</strong> – the size of the vocabulary</p></li>
|
||||
<li><p><strong>n_classes</strong> – number of target classes</p></li>
|
||||
<li><p><strong>embedding_size</strong> – the dimensionality of the word embeddings space (default 100)</p></li>
|
||||
<li><p><strong>hidden_size</strong> – the dimensionality of the hidden space (default 256)</p></li>
|
||||
<li><p><strong>repr_size</strong> – the dimensionality of the document embeddings space (default 100)</p></li>
|
||||
<li><p><strong>lstm_class_nlayers</strong> – number of LSTM layers (default 1)</p></li>
|
||||
<li><p><strong>drop_p</strong> – drop probability for dropout (default 0.5)</p></li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.LSTMnet.document_embedding">
|
||||
<span class="sig-name descname"><span class="pre">document_embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#LSTMnet.document_embedding"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.LSTMnet.document_embedding" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Embeds documents (i.e., performs the forward pass up to the
|
||||
next-to-last layer).</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><p><strong>x</strong> – a batch of instances, typically generated by a torch’s <cite>DataLoader</cite>
|
||||
instance (see <a class="reference internal" href="#quapy.classification.neural.TorchDataset" title="quapy.classification.neural.TorchDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.classification.neural.TorchDataset</span></code></a>)</p>
|
||||
</dd>
|
||||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||||
<dd class="field-even"><p>a torch tensor of shape <cite>(n_samples, n_dimensions)</cite>, where
|
||||
<cite>n_samples</cite> is the number of documents, and <cite>n_dimensions</cite> is the
|
||||
dimensionality of the embedding</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.LSTMnet.get_params">
|
||||
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#LSTMnet.get_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.LSTMnet.get_params" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Get hyper-parameters for this estimator</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><p>a dictionary with parameter names mapped to their values</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py attribute">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.LSTMnet.training">
|
||||
<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#quapy.classification.neural.LSTMnet.training" title="Permalink to this definition"></a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.LSTMnet.vocabulary_size">
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">vocabulary_size</span></span><a class="headerlink" href="#quapy.classification.neural.LSTMnet.vocabulary_size" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Return the size of the vocabulary</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><p>integer</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer">
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.neural.</span></span><span class="sig-name descname"><span class="pre">NeuralClassifierTrainer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">net</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.classification.neural.TextClassifierNet" title="quapy.classification.neural.TextClassifierNet"><span class="pre">TextClassifierNet</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">patience</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epochs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">64</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size_test</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">512</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">checkpointpath</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'../checkpoint/classifier_net.dat'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#NeuralClassifierTrainer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
|
||||
<p>Trains a neural network for text classification.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><ul class="simple">
|
||||
<li><p><strong>net</strong> – an instance of <cite>TextClassifierNet</cite> implementing the forward pass</p></li>
|
||||
<li><p><strong>lr</strong> – learning rate (default 1e-3)</p></li>
|
||||
<li><p><strong>weight_decay</strong> – weight decay (default 0)</p></li>
|
||||
<li><p><strong>patience</strong> – number of epochs that do not show any improvement in validation
|
||||
to wait before applying early stop (default 10)</p></li>
|
||||
<li><p><strong>epochs</strong> – maximum number of training epochs (default 200)</p></li>
|
||||
<li><p><strong>batch_size</strong> – batch size for training (default 64)</p></li>
|
||||
<li><p><strong>batch_size_test</strong> – batch size for test (default 512)</p></li>
|
||||
<li><p><strong>padding_length</strong> – maximum number of tokens to consider in a document (default 300)</p></li>
|
||||
<li><p><strong>device</strong> – specify ‘cpu’ (default) or ‘cuda’ for enabling gpu</p></li>
|
||||
<li><p><strong>checkpointpath</strong> – where to store the parameters of the best model found so far
|
||||
according to the evaluation in the held-out validation split (default ‘../checkpoint/classifier_net.dat’)</p></li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer.device">
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">device</span></span><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.device" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Gets the device in which the network is allocated</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><p>device</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer.fit">
|
||||
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#NeuralClassifierTrainer.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.fit" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Fits the model according to the given training data.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><ul class="simple">
|
||||
<li><p><strong>instances</strong> – list of lists of indexed tokens</p></li>
|
||||
<li><p><strong>labels</strong> – array-like of shape <cite>(n_samples, n_classes)</cite> with the class labels</p></li>
|
||||
<li><p><strong>val_split</strong> – proportion of training documents to be taken as the validation set (default 0.3)</p></li>
|
||||
</ul>
|
||||
</dd>
|
||||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||||
<dd class="field-even"><p></p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer.get_params">
|
||||
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#NeuralClassifierTrainer.get_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.get_params" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Get hyper-parameters for this estimator</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><p>a dictionary with parameter names mapped to their values</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer.predict">
|
||||
<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#NeuralClassifierTrainer.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.predict" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Predicts labels for the instances</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><p><strong>instances</strong> – list of lists of indexed tokens</p>
|
||||
</dd>
|
||||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||||
<dd class="field-even"><p>a <cite>numpy</cite> array of length <cite>n</cite> containing the label predictions, where <cite>n</cite> is the number of
|
||||
instances in <cite>X</cite></p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer.predict_proba">
|
||||
<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#NeuralClassifierTrainer.predict_proba"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.predict_proba" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Predicts posterior probabilities for the instances</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><p><strong>X</strong> – array-like of shape <cite>(n_samples, n_features)</cite> instances to classify</p>
|
||||
</dd>
|
||||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||||
<dd class="field-even"><p>array-like of shape <cite>(n_samples, n_classes)</cite> with the posterior probabilities</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer.reset_net_params">
|
||||
<span class="sig-name descname"><span class="pre">reset_net_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">vocab_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#NeuralClassifierTrainer.reset_net_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.reset_net_params" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Reinitialize the network parameters</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><ul class="simple">
|
||||
<li><p><strong>vocab_size</strong> – the size of the vocabulary</p></li>
|
||||
<li><p><strong>n_classes</strong> – the number of target classes</p></li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer.set_params">
|
||||
<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#NeuralClassifierTrainer.set_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.set_params" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Set the parameters of this trainer and the learner it is training.
|
||||
In this current version, parameter names for the trainer and learner should
|
||||
be disjoint.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><p><strong>params</strong> – a <cite>**kwargs</cite> dictionary with the parameters</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer.transform">
|
||||
<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#NeuralClassifierTrainer.transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.transform" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Returns the embeddings of the instances</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><p><strong>instances</strong> – list of lists of indexed tokens</p>
|
||||
</dd>
|
||||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||||
<dd class="field-even"><p>array-like of shape <cite>(n_samples, embed_size)</cite> with the embedded instances,
|
||||
where <cite>embed_size</cite> is defined by the classification network</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet">
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.neural.</span></span><span class="sig-name descname"><span class="pre">TextClassifierNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#TextClassifierNet"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></p>
|
||||
<p>Abstract Text classifier (<cite>torch.nn.Module</cite>)</p>
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet.dimensions">
|
||||
<span class="sig-name descname"><span class="pre">dimensions</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#TextClassifierNet.dimensions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet.dimensions" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Gets the number of dimensions of the embedding space</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><p>integer</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet.document_embedding">
|
||||
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">document_embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#TextClassifierNet.document_embedding"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet.document_embedding" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Embeds documents (i.e., performs the forward pass up to the
|
||||
next-to-last layer).</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><p><strong>x</strong> – a batch of instances, typically generated by a torch’s <cite>DataLoader</cite>
|
||||
instance (see <a class="reference internal" href="#quapy.classification.neural.TorchDataset" title="quapy.classification.neural.TorchDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.classification.neural.TorchDataset</span></code></a>)</p>
|
||||
</dd>
|
||||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||||
<dd class="field-even"><p>a torch tensor of shape <cite>(n_samples, n_dimensions)</cite>, where
|
||||
<cite>n_samples</cite> is the number of documents, and <cite>n_dimensions</cite> is the
|
||||
dimensionality of the embedding</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet.forward">
|
||||
<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#TextClassifierNet.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet.forward" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Performs the forward pass.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><p><strong>x</strong> – a batch of instances, typically generated by a torch’s <cite>DataLoader</cite>
|
||||
instance (see <a class="reference internal" href="#quapy.classification.neural.TorchDataset" title="quapy.classification.neural.TorchDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.classification.neural.TorchDataset</span></code></a>)</p>
|
||||
</dd>
|
||||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||||
<dd class="field-even"><p>a tensor of shape <cite>(n_instances, n_classes)</cite> with the decision scores
|
||||
for each of the instances and classes</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet.get_params">
|
||||
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#TextClassifierNet.get_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet.get_params" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Get hyper-parameters for this estimator</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><p>a dictionary with parameter names mapped to their values</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet.predict_proba">
|
||||
<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#TextClassifierNet.predict_proba"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet.predict_proba" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Predicts posterior probabilities for the instances in <cite>x</cite></p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><p><strong>x</strong> – a torch tensor of indexed tokens with shape <cite>(n_instances, pad_length)</cite>
|
||||
where <cite>n_instances</cite> is the number of instances in the batch, and <cite>pad_length</cite>
|
||||
is length of the pad in the batch</p>
|
||||
</dd>
|
||||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||||
<dd class="field-even"><p>array-like of shape <cite>(n_samples, n_classes)</cite> with the posterior probabilities</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py attribute">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet.training">
|
||||
<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet.training" title="Permalink to this definition"></a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet.vocabulary_size">
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">vocabulary_size</span></span><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet.vocabulary_size" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Return the size of the vocabulary</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><p>integer</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet.xavier_uniform">
|
||||
<span class="sig-name descname"><span class="pre">xavier_uniform</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#TextClassifierNet.xavier_uniform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet.xavier_uniform" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Performs Xavier initialization of the network parameters</p>
|
||||
</dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.TorchDataset">
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.neural.</span></span><span class="sig-name descname"><span class="pre">TorchDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#TorchDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.TorchDataset" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Dataset</span></code></p>
|
||||
<p>Transforms labelled instances into a Torch’s <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.utils.data.DataLoader</span></code> object</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><ul class="simple">
|
||||
<li><p><strong>instances</strong> – list of lists of indexed tokens</p></li>
|
||||
<li><p><strong>labels</strong> – array-like of shape <cite>(n_samples, n_classes)</cite> with the class labels</p></li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.TorchDataset.asDataloader">
|
||||
<span class="sig-name descname"><span class="pre">asDataloader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pad_length</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#TorchDataset.asDataloader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.TorchDataset.asDataloader" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Converts the labelled collection into a Torch DataLoader with dynamic padding for
|
||||
the batch</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><ul class="simple">
|
||||
<li><p><strong>batch_size</strong> – batch size</p></li>
|
||||
<li><p><strong>shuffle</strong> – whether or not to shuffle instances</p></li>
|
||||
<li><p><strong>pad_length</strong> – the maximum length for the list of tokens (dynamic padding is
|
||||
applied, meaning that if the longest document in the batch is shorter than
|
||||
<cite>pad_length</cite>, then the batch is padded up to its length, and not to <cite>pad_length</cite>.</p></li>
|
||||
<li><p><strong>device</strong> – whether to allocate tensors in cpu or in cuda</p></li>
|
||||
</ul>
|
||||
</dd>
|
||||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||||
<dd class="field-even"><p>a <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.utils.data.DataLoader</span></code> object</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
</section>
|
||||
<section id="module-quapy.classification.svmperf">
|
||||
<span id="quapy-classification-svmperf-module"></span><h2>quapy.classification.svmperf module<a class="headerlink" href="#module-quapy.classification.svmperf" title="Permalink to this heading"></a></h2>
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.classification.svmperf.SVMperf">
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.svmperf.</span></span><span class="sig-name descname"><span class="pre">SVMperf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">svmperf_base</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">C</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'01'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">host_folder</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/svmperf.html#SVMperf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.svmperf.SVMperf" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">BaseEstimator</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">ClassifierMixin</span></code></p>
|
||||
<p>A wrapper for the <a class="reference external" href="https://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html">SVM-perf package</a> by Thorsten Joachims.
|
||||
When using losses for quantification, the source code has to be patched. See
|
||||
the <a class="reference external" href="https://hlt-isti.github.io/QuaPy/build/html/Installation.html#svm-perf-with-quantification-oriented-losses">installation documentation</a>
|
||||
for further details.</p>
|
||||
<p class="rubric">References</p>
|
||||
<ul class="simple">
|
||||
<li><p><a class="reference external" href="https://dl.acm.org/doi/abs/10.1145/2700406?casa_token=8D2fHsGCVn0AAAAA:ZfThYOvrzWxMGfZYlQW_y8Cagg-o_l6X_PcF09mdETQ4Tu7jK98mxFbGSXp9ZSO14JkUIYuDGFG0">Esuli et al.2015</a></p></li>
|
||||
<li><p><a class="reference external" href="https://www.sciencedirect.com/science/article/abs/pii/S003132031400291X">Barranquero et al.2015</a></p></li>
|
||||
</ul>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><ul class="simple">
|
||||
<li><p><strong>svmperf_base</strong> – path to directory containing the binary files <cite>svm_perf_learn</cite> and <cite>svm_perf_classify</cite></p></li>
|
||||
<li><p><strong>C</strong> – trade-off between training error and margin (default 0.01)</p></li>
|
||||
<li><p><strong>verbose</strong> – set to True to print svm-perf std outputs</p></li>
|
||||
<li><p><strong>loss</strong> – the loss to optimize for. Available losses are “01”, “f1”, “kld”, “nkld”, “q”, “qacc”, “qf1”, “qgm”, “mae”, “mrae”.</p></li>
|
||||
<li><p><strong>host_folder</strong> – directory where to store the trained model; set to None (default) for using a tmp directory
|
||||
(temporal directories are automatically deleted)</p></li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.svmperf.SVMperf.decision_function">
|
||||
<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/svmperf.html#SVMperf.decision_function"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.svmperf.SVMperf.decision_function" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Evaluate the decision function for the samples in <cite>X</cite>.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><ul class="simple">
|
||||
<li><p><strong>X</strong> – array-like of shape <cite>(n_samples, n_features)</cite> containing the instances to classify</p></li>
|
||||
<li><p><strong>y</strong> – unused</p></li>
|
||||
</ul>
|
||||
</dd>
|
||||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||||
<dd class="field-even"><p>array-like of shape <cite>(n_samples,)</cite> containing the decision scores of the instances</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.svmperf.SVMperf.fit">
|
||||
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/svmperf.html#SVMperf.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.svmperf.SVMperf.fit" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Trains the SVM for the multivariate performance loss</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><ul class="simple">
|
||||
<li><p><strong>X</strong> – training instances</p></li>
|
||||
<li><p><strong>y</strong> – a binary vector of labels</p></li>
|
||||
</ul>
|
||||
</dd>
|
||||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||||
<dd class="field-even"><p><cite>self</cite></p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.svmperf.SVMperf.predict">
|
||||
<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/svmperf.html#SVMperf.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.svmperf.SVMperf.predict" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Predicts labels for the instances <cite>X</cite></p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><p><strong>X</strong> – array-like of shape <cite>(n_samples, n_features)</cite> instances to classify</p>
|
||||
</dd>
|
||||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||||
<dd class="field-even"><p>a <cite>numpy</cite> array of length <cite>n</cite> containing the label predictions, where <cite>n</cite> is the number of
|
||||
instances in <cite>X</cite></p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py attribute">
|
||||
<dt class="sig sig-object py" id="quapy.classification.svmperf.SVMperf.valid_losses">
|
||||
<span class="sig-name descname"><span class="pre">valid_losses</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{'01':</span> <span class="pre">0,</span> <span class="pre">'f1':</span> <span class="pre">1,</span> <span class="pre">'kld':</span> <span class="pre">12,</span> <span class="pre">'mae':</span> <span class="pre">26,</span> <span class="pre">'mrae':</span> <span class="pre">27,</span> <span class="pre">'nkld':</span> <span class="pre">13,</span> <span class="pre">'q':</span> <span class="pre">22,</span> <span class="pre">'qacc':</span> <span class="pre">23,</span> <span class="pre">'qf1':</span> <span class="pre">24,</span> <span class="pre">'qgm':</span> <span class="pre">25}</span></em><a class="headerlink" href="#quapy.classification.svmperf.SVMperf.valid_losses" title="Permalink to this definition"></a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
</section>
|
||||
<section id="module-quapy.classification">
|
||||
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-quapy.classification" title="Permalink to this heading"></a></h2>
|
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|||
You can adapt this file completely to your liking, but it should at least
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||||
contain the root `toctree` directive.
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||||
Welcome to QuaPy's documentation!
|
||||
.. toctree::
|
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:hidden:
|
||||
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||||
self
|
||||
|
||||
Quickstart
|
||||
==========================================================================================
|
||||
|
||||
QuaPy is a Python-based open-source framework for quantification.
|
||||
QuaPy is an open source framework for quantification (a.k.a. supervised prevalence estimation, or learning to quantify) written in Python.
|
||||
|
||||
This document contains the API of the modules included in QuaPy.
|
||||
QuaPy is based on the concept of "data sample", and provides implementations of the most important aspects of the quantification workflow, such as (baseline and advanced) quantification methods, quantification-oriented model selection mechanisms, evaluation measures, and evaluations protocols used for evaluating quantification methods. QuaPy also makes available commonly used datasets, and offers visualization tools for facilitating the analysis and interpretation of the experimental results.
|
||||
|
||||
QuaPy is hosted on GitHub at `<https://github.com/HLT-ISTI/QuaPy>`_
|
||||
|
||||
Installation
|
||||
------------
|
||||
|
||||
`pip install quapy`
|
||||
.. code-block:: none
|
||||
|
||||
GitHub
|
||||
pip install quapy
|
||||
|
||||
Citing QuaPy
|
||||
------------
|
||||
|
||||
QuaPy is hosted in GitHub at `https://github.com/HLT-ISTI/QuaPy <https://github.com/HLT-ISTI/QuaPy>`_
|
||||
If you find QuaPy useful (and we hope you will), please consider citing the original paper in your research.
|
||||
|
||||
.. code-block:: none
|
||||
|
||||
Wiki Documents
|
||||
------------
|
||||
@inproceedings{moreo2021quapy,
|
||||
title={QuaPy: a python-based framework for quantification},
|
||||
author={Moreo, Alejandro and Esuli, Andrea and Sebastiani, Fabrizio},
|
||||
booktitle={Proceedings of the 30th ACM International Conference on Information \& Knowledge Management},
|
||||
pages={4534--4543},
|
||||
year={2021}
|
||||
}
|
||||
|
||||
In this section you can find useful information concerning different aspects of QuaPy, with examples:
|
||||
Usage
|
||||
-----
|
||||
|
||||
The following script fetches a dataset of tweets, trains, applies, and evaluates a quantifier based on the *Adjusted Classify & Count* quantification method, using, as the evaluation measure, the *Mean Absolute Error* (MAE) between the predicted and the true class prevalence values of the test set::
|
||||
|
||||
import quapy as qp
|
||||
from sklearn.linear_model import LogisticRegression
|
||||
|
||||
dataset = qp.datasets.fetch_twitter('semeval16')
|
||||
|
||||
# create an "Adjusted Classify & Count" quantifier
|
||||
model = qp.method.aggregative.ACC(LogisticRegression())
|
||||
model.fit(dataset.training)
|
||||
|
||||
estim_prevalence = model.quantify(dataset.test.instances)
|
||||
true_prevalence = dataset.test.prevalence()
|
||||
|
||||
error = qp.error.mae(true_prevalence, estim_prevalence)
|
||||
|
||||
print(f'Mean Absolute Error (MAE)={error:.3f}')
|
||||
|
||||
Quantification is useful in scenarios characterized by prior probability shift. In other words, we would be little interested in estimating the class prevalence values of the test set if we could assume the IID assumption to hold, as this prevalence would be roughly equivalent to the class prevalence of the training set. For this reason, any quantification model should be tested across many samples, even ones characterized by class prevalence values different or very different from those found in the training set. QuaPy implements sampling procedures and evaluation protocols that automate this workflow. See the `Manuals`_ for detailed examples.
|
||||
|
||||
Manuals
|
||||
-------
|
||||
|
||||
The following manuals illustrate several aspects of QuaPy through examples:
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
|
|
@ -37,22 +78,38 @@ In this section you can find useful information concerning different aspects of
|
|||
wiki/Plotting
|
||||
wiki/Protocols
|
||||
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: Contents:
|
||||
:hidden:
|
||||
|
||||
Contents
|
||||
List of Modules <modules>
|
||||
|
||||
Features
|
||||
--------
|
||||
|
||||
.. toctree::
|
||||
* Implementation of many popular quantification methods (Classify-&-Count and its variants, Expectation Maximization, quantification methods based on structured output learning, HDy, QuaNet, quantification ensembles, among others).
|
||||
* Versatile functionality for performing evaluation based on sampling generation protocols (e.g., APP, NPP, etc.).
|
||||
* Implementation of most commonly used evaluation metrics (e.g., AE, RAE, NAE, NRAE, SE, KLD, NKLD, etc.).
|
||||
* Datasets frequently used in quantification (textual and numeric), including:
|
||||
|
||||
modules
|
||||
* 32 UCI Machine Learning binary datasets.
|
||||
* 5 UCI Machine Learning multiclass datasets (new in v0.1.8!).
|
||||
* 11 Twitter quantification-by-sentiment datasets.
|
||||
* 3 product reviews quantification-by-sentiment datasets.
|
||||
* 4 tasks from LeQua competition (new in v0.1.7!)
|
||||
* IFCB dataset of plankton water samples (new in v0.1.8!).
|
||||
|
||||
* Native support for binary and single-label multiclass quantification scenarios.
|
||||
* Model selection functionality that minimizes quantification-oriented loss functions.
|
||||
* Visualization tools for analysing the experimental results.
|
||||
|
||||
Indices and tables
|
||||
==================
|
||||
Contributing
|
||||
------------
|
||||
|
||||
* :ref:`genindex`
|
||||
* :ref:`modindex`
|
||||
* :ref:`search`
|
||||
In case you want to contribute improvements to quapy, please generate pull request to the "devel" branch.
|
||||
|
||||
Acknowledgments
|
||||
---------------
|
||||
|
||||
.. image:: SoBigData.png
|
||||
:width: 250px
|
||||
:alt: SoBigData++
|
||||
|
|
|
|||
Loading…
Reference in New Issue