From 9cf9c7382400f3c3ccf7d81a1281b639e3dadb5a Mon Sep 17 00:00:00 2001
From: Alex Moreo
Date: Wed, 15 Dec 2021 15:46:15 +0100
Subject: [PATCH] adding documentation for ensembles
---
docs/build/html/quapy.method.html | 49 ++++++++++++++++++++++++++++
docs/build/html/searchindex.js | 2 +-
quapy/method/meta.py | 53 +++++++++++++++++++++++++++++++
3 files changed, 103 insertions(+), 1 deletion(-)
diff --git a/docs/build/html/quapy.method.html b/docs/build/html/quapy.method.html
index b61573b..af5c33f 100644
--- a/docs/build/html/quapy.method.html
+++ b/docs/build/html/quapy.method.html
@@ -1394,6 +1394,11 @@ validation data, or as an integer, indicating that the misclassification rates s
quapy.method.meta.EACC(learner, param_grid=None, optim=None, param_mod_sel=None, **kwargs)
Implements an ensemble of quapy.method.aggregative.ACC
quantifiers, as used by
Pérez-Gállego et al., 2019.
+Equivalent to:
+>>> ensembleFactory(learner, ACC, param_grid, optim, param_mod_sel, **kwargs)
+
+
+See ensembleFactory()
for further details.
- Parameters
@@ -1416,6 +1421,11 @@ validation data, or as an integer, indicating that the misclassification rates s
quapy.method.meta.ECC(learner, param_grid=None, optim=None, param_mod_sel=None, **kwargs)
Implements an ensemble of quapy.method.aggregative.CC
quantifiers, as used by
Pérez-Gállego et al., 2019.
+Equivalent to:
+>>> ensembleFactory(learner, CC, param_grid, optim, param_mod_sel, **kwargs)
+
+
+See ensembleFactory()
for further details.
- Parameters
+Example to instantiate an Ensemble
based on quapy.method.aggregative.PACC
+in which the base members are optimized for quapy.error.mae()
via
+quapy.model_selection.GridSearchQ
. The ensemble follows the policy Accuracy based
+on quapy.error.mae()
(the same measure being optimized),
+meaning that a static selection of members of the ensemble is made based on their performance
+in terms of this error.
+>>> param_grid = {
+>>> 'C': np.logspace(-3,3,7),
+>>> 'class_weight': ['balanced', None]
+>>> }
+>>> param_mod_sel = {
+>>> 'sample_size': 500,
+>>> 'protocol': 'app'
+>>> }
+>>> common={
+>>> 'max_sample_size': 1000,
+>>> 'n_jobs': -1,
+>>> 'param_grid': param_grid,
+>>> 'param_mod_sel': param_mod_sel,
+>>> }
+>>>
+>>> ensembleFactory(LogisticRegression(), PACC, optim='mae', policy='mae', **common)
+
+
- Parameters
diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js
index 7492118..bf08407 100644
--- a/docs/build/html/searchindex.js
+++ b/docs/build/html/searchindex.js
@@ -1 +1 @@
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diff --git a/quapy/method/meta.py b/quapy/method/meta.py
index 7f7cba8..3504301 100644
--- a/quapy/method/meta.py
+++ b/quapy/method/meta.py
@@ -379,6 +379,29 @@ def ensembleFactory(learner, base_quantifier_class, param_grid=None, optim=None,
(instead of quantification-oriented), then the optimization will be carried out via sklearn's
`GridSearchCV `_.
+ Example to instantiate an :class:`Ensemble` based on :class:`quapy.method.aggregative.PACC`
+ in which the base members are optimized for :meth:`quapy.error.mae` via
+ :class:`quapy.model_selection.GridSearchQ`. The ensemble follows the policy `Accuracy` based
+ on :meth:`quapy.error.mae` (the same measure being optimized),
+ meaning that a static selection of members of the ensemble is made based on their performance
+ in terms of this error.
+
+ >>> param_grid = {
+ >>> 'C': np.logspace(-3,3,7),
+ >>> 'class_weight': ['balanced', None]
+ >>> }
+ >>> param_mod_sel = {
+ >>> 'sample_size': 500,
+ >>> 'protocol': 'app'
+ >>> }
+ >>> common={
+ >>> 'max_sample_size': 1000,
+ >>> 'n_jobs': -1,
+ >>> 'param_grid': param_grid,
+ >>> 'param_mod_sel': param_mod_sel,
+ >>> }
+ >>>
+ >>> ensembleFactory(LogisticRegression(), PACC, optim='mae', policy='mae', **common)
:param learner: sklearn's Estimator that generates a classifier
:param base_quantifier_class: a class of quantifiers
@@ -403,6 +426,12 @@ def ECC(learner, param_grid=None, optim=None, param_mod_sel=None, **kwargs):
Implements an ensemble of :class:`quapy.method.aggregative.CC` quantifiers, as used by
`Pérez-Gállego et al., 2019 `_.
+ Equivalent to:
+
+ >>> ensembleFactory(learner, CC, param_grid, optim, param_mod_sel, **kwargs)
+
+ See :meth:`ensembleFactory` for further details.
+
:param learner: sklearn's Estimator that generates a classifier
:param param_grid: a dictionary with the grid of parameters to optimize for
:param optim: a valid quantification or classification error, or a string name of it
@@ -420,6 +449,12 @@ def EACC(learner, param_grid=None, optim=None, param_mod_sel=None, **kwargs):
Implements an ensemble of :class:`quapy.method.aggregative.ACC` quantifiers, as used by
`Pérez-Gállego et al., 2019 `_.
+ Equivalent to:
+
+ >>> ensembleFactory(learner, ACC, param_grid, optim, param_mod_sel, **kwargs)
+
+ See :meth:`ensembleFactory` for further details.
+
:param learner: sklearn's Estimator that generates a classifier
:param param_grid: a dictionary with the grid of parameters to optimize for
:param optim: a valid quantification or classification error, or a string name of it
@@ -436,6 +471,12 @@ def EPACC(learner, param_grid=None, optim=None, param_mod_sel=None, **kwargs):
"""
Implements an ensemble of :class:`quapy.method.aggregative.PACC` quantifiers.
+ Equivalent to:
+
+ >>> ensembleFactory(learner, PACC, param_grid, optim, param_mod_sel, **kwargs)
+
+ See :meth:`ensembleFactory` for further details.
+
:param learner: sklearn's Estimator that generates a classifier
:param param_grid: a dictionary with the grid of parameters to optimize for
:param optim: a valid quantification or classification error, or a string name of it
@@ -453,6 +494,12 @@ def EHDy(learner, param_grid=None, optim=None, param_mod_sel=None, **kwargs):
Implements an ensemble of :class:`quapy.method.aggregative.HDy` quantifiers, as used by
`Pérez-Gállego et al., 2019 `_.
+ Equivalent to:
+
+ >>> ensembleFactory(learner, HDy, param_grid, optim, param_mod_sel, **kwargs)
+
+ See :meth:`ensembleFactory` for further details.
+
:param learner: sklearn's Estimator that generates a classifier
:param param_grid: a dictionary with the grid of parameters to optimize for
:param optim: a valid quantification or classification error, or a string name of it
@@ -469,6 +516,12 @@ def EEMQ(learner, param_grid=None, optim=None, param_mod_sel=None, **kwargs):
"""
Implements an ensemble of :class:`quapy.method.aggregative.EMQ` quantifiers.
+ Equivalent to:
+
+ >>> ensembleFactory(learner, EMQ, param_grid, optim, param_mod_sel, **kwargs)
+
+ See :meth:`ensembleFactory` for further details.
+
:param learner: sklearn's Estimator that generates a classifier
:param param_grid: a dictionary with the grid of parameters to optimize for
:param optim: a valid quantification or classification error, or a string name of it