Merge branch 'pawel-czyz-additional-solvers-and-documentation' into devel
I have revised this PR (which was very nice, thanks). I have made some modifications including improvements in the normalization functions, documentation, and refactoring of qp.functional. I will leave this in devel until I find the time to "stress-test" the modifications. Thanks to Pawel Czyz for the nice contribution!
This commit is contained in:
commit
472e49047e
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TODO.txt
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TODO.txt
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@ -1,95 +0,0 @@
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ensembles seem to be broken; they have an internal model selection which takes the parameters, but since quapy now
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works with protocols it would need to know the validation set in order to pass something like
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"protocol: APP(val, etc.)"
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sample_size should not be mandatory when qp.environ['SAMPLE_SIZE'] has been specified
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clean all the cumbersome methods that have to be implemented for new quantifiers (e.g., n_classes_ prop, etc.)
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make truly parallel the GridSearchQ
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make more examples in the "examples" directory
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merge with master, because I had to fix some problems with QuaNet due to an issue notified via GitHub!
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added cross_val_predict in qp.model_selection (i.e., a cross_val_predict for quantification) --would be nice to have
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it parallelized
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check the OneVsAll module(s)
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check the set_params de neural.py, because the separation of estimator__<param> is not implemented; see also
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__check_params_colision
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HDy can be customized so that the number of bins is specified, instead of explored within the fit method
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Packaging:
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==========================================
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Document methods with paper references
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unit-tests
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clean wiki_examples!
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Refactor:
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==========================================
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Unify ThresholdOptimization methods, as an extension of PACC (and not ACC), the fit methods are almost identical and
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use a prob classifier (take into account that PACC uses pcc internally, whereas the threshold methods use cc
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instead). The fit method of ACC and PACC has a block for estimating the validation estimates that should be unified
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as well...
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Refactor protocols. APP and NPP related functionalities are duplicated in functional, LabelledCollection, and evaluation
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New features:
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||||
==========================================
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Add "measures for evaluating ordinal"?
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Add datasets for topic.
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Do we want to cover cross-lingual quantification natively in QuaPy, or does it make more sense as an application on top?
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Current issues:
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||||
==========================================
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Revise the class structure of quantification methods and the methods they inherit... There is some confusion regarding
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methods isbinary, isprobabilistic, and the like. The attribute "learner_" in aggregative quantifiers is also
|
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confusing, since there is a getter and a setter.
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Remove the "deep" in get_params. There is no real compatibility with scikit-learn as for now.
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SVMperf-based learners do not remove temp files in __del__?
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In binary quantification (hp, kindle, imdb) we used F1 in the minority class (which in kindle and hp happens to be the
|
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negative class). This is not covered in this new implementation, in which the binary case is not treated as such, but as
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an instance of single-label with 2 labels. Check
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Add automatic reindex of class labels in LabelledCollection (currently, class indexes should be ordered and with no gaps)
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OVR I believe is currently tied to aggregative methods. We should provide a general interface also for general quantifiers
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Currently, being "binary" only adds one checker; we should figure out how to impose the check to be automatically performed
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Add random seed management to support replicability (see temp_seed in util.py).
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GridSearchQ is not trully parallelized. It only parallelizes on the predictions.
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In the context of a quantifier (e.g., QuaNet or CC), the parameters of the learner should be prefixed with "estimator__",
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in QuaNet this is resolved with a __check_params_colision, but this should be improved. It might be cumbersome to
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impose the "estimator__" prefix for, e.g., quantifiers like CC though... This should be changed everywhere...
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QuaNet needs refactoring. The base quantifiers ACC and PACC receive val_data with instances already transformed. This
|
||||
issue is due to a bad design.
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Improvements:
|
||||
==========================================
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Explore the hyperparameter "number of bins" in HDy
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Rename EMQ to SLD ?
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Parallelize the kFCV in ACC and PACC?
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Parallelize model selection trainings
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We might want to think of (improving and) adding the class Tabular (it is defined and used on branch tweetsent). A more
|
||||
recent version is in the project ql4facct. This class is meant to generate latex tables from results (highligting
|
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best results, computing statistical tests, colouring cells, producing rankings, producing averages, etc.). Trying
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||||
to generate tables is typically a bad idea, but in this specific case we do have pretty good control of what an
|
||||
experiment looks like. (Do we want to abstract experimental results? this could be useful not only for tables but
|
||||
also for plots).
|
||||
Add proper logging system. Currently we use print
|
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It might be good to simplify the number of methods that have to be implemented for any new Quantifier. At the moment,
|
||||
there are many functions like get_params, set_params, and, specially, @property classes_, which are cumbersome to
|
||||
implement for quick experiments. A possible solution is to impose get_params and set_params only in cases in which
|
||||
the model extends some "ModelSelectable" interface only. The classes_ should have a default implementation.
|
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|
||||
Checks:
|
||||
==========================================
|
||||
How many times is the system of equations for ACC and PACC not solved? How many times is it clipped? Do they sum up
|
||||
to one always?
|
||||
Re-check how hyperparameters from the quantifier and hyperparameters from the classifier (in aggregative quantifiers)
|
||||
is handled. In scikit-learn the hyperparameters from a wrapper method are indicated directly whereas the hyperparams
|
||||
from the internal learner are prefixed with "estimator__". In QuaPy, combinations having to do with the classifier
|
||||
can be computed at the begining, and then in an internal loop the hyperparams of the quantifier can be explored,
|
||||
passing fit_learner=False.
|
||||
Re-check Ensembles. As for now, they are strongly tied to aggregative quantifiers.
|
||||
Re-think the environment variables. Maybe add new ones (like, for example, parameters for the plots)
|
||||
Do we want to wrap prevalences (currently simple np.ndarray) as a class? This might be convenient for some interfaces
|
||||
(e.g., for specifying artificial prevalences in samplings, for printing them -- currently supported through
|
||||
F.strprev(), etc.). This might however add some overload, and prevent/difficult post processing with numpy.
|
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Would be nice to get a better integration with sklearn.
|
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@ -0,0 +1,20 @@
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|||
# Minimal makefile for Sphinx documentation
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||||
#
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# You can set these variables from the command line, and also
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||||
# from the environment for the first two.
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||||
SPHINXOPTS ?=
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||||
SPHINXBUILD ?= sphinx-build
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||||
SOURCEDIR = source
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||||
BUILDDIR = build
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||||
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||||
# Put it first so that "make" without argument is like "make help".
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||||
help:
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||||
@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
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.PHONY: help Makefile
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||||
# Catch-all target: route all unknown targets to Sphinx using the new
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# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
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%: Makefile
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@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
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@ -4,7 +4,7 @@
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|||
*
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||||
* Sphinx stylesheet -- basic theme.
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||||
*
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||||
* :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
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||||
* :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
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||||
* :license: BSD, see LICENSE for details.
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*
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*/
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@ -237,10 +237,6 @@ a.headerlink {
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visibility: hidden;
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}
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a:visited {
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color: #551A8B;
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}
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h1:hover > a.headerlink,
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h2:hover > a.headerlink,
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h3:hover > a.headerlink,
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@ -328,7 +324,6 @@ aside.sidebar {
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p.sidebar-title {
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font-weight: bold;
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}
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nav.contents,
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aside.topic,
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div.admonition, div.topic, blockquote {
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@ -336,7 +331,6 @@ div.admonition, div.topic, blockquote {
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}
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/* -- topics ---------------------------------------------------------------- */
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nav.contents,
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aside.topic,
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div.topic {
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@ -612,7 +606,6 @@ ol.simple p,
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ul.simple p {
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margin-bottom: 0;
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}
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aside.footnote > span,
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div.citation > span {
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float: left;
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@ -674,16 +667,6 @@ dd {
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margin-left: 30px;
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}
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.sig dd {
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margin-top: 0px;
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margin-bottom: 0px;
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}
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.sig dl {
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margin-top: 0px;
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margin-bottom: 0px;
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||||
}
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dl > dd:last-child,
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dl > dd:last-child > :last-child {
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margin-bottom: 0;
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@ -752,14 +735,6 @@ abbr, acronym {
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cursor: help;
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}
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.translated {
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||||
background-color: rgba(207, 255, 207, 0.2)
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||||
}
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||||
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.untranslated {
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||||
background-color: rgba(255, 207, 207, 0.2)
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}
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||||
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||||
/* -- code displays --------------------------------------------------------- */
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||||
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pre {
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@ -4,7 +4,7 @@
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|||
*
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||||
* Base JavaScript utilities for all Sphinx HTML documentation.
|
||||
*
|
||||
* :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
|
||||
* :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
|
||||
* :license: BSD, see LICENSE for details.
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||||
*
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||||
*/
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||||
|
|
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|||
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@ -1,5 +1,6 @@
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|||
const DOCUMENTATION_OPTIONS = {
|
||||
VERSION: '0.1.8',
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||||
var DOCUMENTATION_OPTIONS = {
|
||||
URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
|
||||
VERSION: '0.1.9',
|
||||
LANGUAGE: 'en',
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||||
COLLAPSE_INDEX: false,
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||||
BUILDER: 'html',
|
||||
|
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|||
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@ -5,7 +5,7 @@
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|||
* This script contains the language-specific data used by searchtools.js,
|
||||
* namely the list of stopwords, stemmer, scorer and splitter.
|
||||
*
|
||||
* :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
|
||||
* :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
|
||||
* :license: BSD, see LICENSE for details.
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||||
*
|
||||
*/
|
||||
|
|
|
|||
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@ -4,7 +4,7 @@
|
|||
*
|
||||
* Sphinx JavaScript utilities for the full-text search.
|
||||
*
|
||||
* :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
|
||||
* :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
|
||||
* :license: BSD, see LICENSE for details.
|
||||
*
|
||||
*/
|
||||
|
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@ -57,12 +57,12 @@ const _removeChildren = (element) => {
|
|||
const _escapeRegExp = (string) =>
|
||||
string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
|
||||
|
||||
const _displayItem = (item, searchTerms, highlightTerms) => {
|
||||
const _displayItem = (item, searchTerms) => {
|
||||
const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
|
||||
const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT;
|
||||
const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
|
||||
const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
|
||||
const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
|
||||
const contentRoot = document.documentElement.dataset.content_root;
|
||||
|
||||
const [docName, title, anchor, descr, score, _filename] = item;
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||||
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@ -75,24 +75,20 @@ const _displayItem = (item, searchTerms, highlightTerms) => {
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|||
if (dirname.match(/\/index\/$/))
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||||
dirname = dirname.substring(0, dirname.length - 6);
|
||||
else if (dirname === "index/") dirname = "";
|
||||
requestUrl = contentRoot + dirname;
|
||||
requestUrl = docUrlRoot + dirname;
|
||||
linkUrl = requestUrl;
|
||||
} else {
|
||||
// normal html builders
|
||||
requestUrl = contentRoot + docName + docFileSuffix;
|
||||
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) {
|
||||
if (descr)
|
||||
listItem.appendChild(document.createElement("span")).innerHTML =
|
||||
" (" + descr + ")";
|
||||
// highlight search terms in the description
|
||||
if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js
|
||||
highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
|
||||
}
|
||||
else if (showSearchSummary)
|
||||
fetch(requestUrl)
|
||||
.then((responseData) => responseData.text())
|
||||
|
|
@ -101,9 +97,6 @@ const _displayItem = (item, searchTerms, highlightTerms) => {
|
|||
listItem.appendChild(
|
||||
Search.makeSearchSummary(data, searchTerms)
|
||||
);
|
||||
// highlight search terms in the summary
|
||||
if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js
|
||||
highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
|
||||
});
|
||||
Search.output.appendChild(listItem);
|
||||
};
|
||||
|
|
@ -122,15 +115,14 @@ const _finishSearch = (resultCount) => {
|
|||
const _displayNextItem = (
|
||||
results,
|
||||
resultCount,
|
||||
searchTerms,
|
||||
highlightTerms,
|
||||
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, highlightTerms);
|
||||
_displayItem(results.pop(), searchTerms);
|
||||
setTimeout(
|
||||
() => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
|
||||
() => _displayNextItem(results, resultCount, searchTerms),
|
||||
5
|
||||
);
|
||||
}
|
||||
|
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@ -164,7 +156,7 @@ const Search = {
|
|||
const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
|
||||
htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() });
|
||||
const docContent = htmlElement.querySelector('[role="main"]');
|
||||
if (docContent) return docContent.textContent;
|
||||
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."
|
||||
);
|
||||
|
|
@ -288,9 +280,9 @@ const Search = {
|
|||
let results = [];
|
||||
_removeChildren(document.getElementById("search-progress"));
|
||||
|
||||
const queryLower = query.toLowerCase().trim();
|
||||
const queryLower = query.toLowerCase();
|
||||
for (const [title, foundTitles] of Object.entries(allTitles)) {
|
||||
if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) {
|
||||
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([
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||||
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@ -368,7 +360,7 @@ const Search = {
|
|||
// console.info("search results:", Search.lastresults);
|
||||
|
||||
// print the results
|
||||
_displayNextItem(results, results.length, searchTerms, highlightTerms);
|
||||
_displayNextItem(results, results.length, searchTerms);
|
||||
},
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||||
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||||
/**
|
||||
|
|
|
|||
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@ -1,22 +1,23 @@
|
|||
<!DOCTYPE html>
|
||||
<html class="writer-html5" lang="en" data-content_root="./">
|
||||
<html class="writer-html5" lang="en">
|
||||
<head>
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>Index — QuaPy: A Python-based open-source framework for quantification 0.1.8 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="_static/pygments.css?v=92fd9be5" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
|
||||
<title>Index — QuaPy: A Python-based open-source framework for quantification 0.1.9 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="_static/pygments.css" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/css/theme.css" />
|
||||
|
||||
|
||||
<!--[if lt IE 9]>
|
||||
<script src="_static/js/html5shiv.min.js"></script>
|
||||
<![endif]-->
|
||||
|
||||
<script src="_static/jquery.js?v=5d32c60e"></script>
|
||||
<script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
|
||||
<script src="_static/documentation_options.js?v=22607128"></script>
|
||||
<script src="_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
|
||||
<script src="_static/jquery.js"></script>
|
||||
<script src="_static/underscore.js"></script>
|
||||
<script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
|
||||
<script src="_static/doctools.js"></script>
|
||||
<script src="_static/sphinx_highlight.js"></script>
|
||||
<script src="_static/js/theme.js"></script>
|
||||
<link rel="index" title="Index" href="#" />
|
||||
<link rel="search" title="Search" href="search.html" />
|
||||
|
|
@ -115,8 +116,6 @@
|
|||
<li><a href="quapy.html#quapy.error.acce">acce() (in module quapy.error)</a>
|
||||
</li>
|
||||
<li><a href="quapy.data.html#quapy.data.preprocessing.IndexTransformer.add_word">add_word() (quapy.data.preprocessing.IndexTransformer method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.functional.adjusted_quantification">adjusted_quantification() (in module quapy.functional)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.AdjustedClassifyAndCount">AdjustedClassifyAndCount (in module quapy.method.aggregative)</a>
|
||||
</li>
|
||||
|
|
@ -136,6 +135,8 @@
|
|||
<li><a href="quapy.method.html#quapy.method.aggregative.ACC.aggregate">(quapy.method.aggregative.ACC method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.aggregate">(quapy.method.aggregative.AggregativeQuantifier method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.BayesianCC.aggregate">(quapy.method.aggregative.BayesianCC method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.CC.aggregate">(quapy.method.aggregative.CC method)</a>
|
||||
</li>
|
||||
|
|
@ -174,6 +175,8 @@
|
|||
<li><a href="quapy.method.html#quapy.method.aggregative.ACC.aggregation_fit">(quapy.method.aggregative.ACC method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.aggregation_fit">(quapy.method.aggregative.AggregativeQuantifier method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.BayesianCC.aggregation_fit">(quapy.method.aggregative.BayesianCC method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.CC.aggregation_fit">(quapy.method.aggregative.CC method)</a>
|
||||
</li>
|
||||
|
|
@ -221,6 +224,8 @@
|
|||
<li><a href="quapy.method.html#quapy.method._kdey.KDEBase.BANDWIDTH_METHOD">BANDWIDTH_METHOD (quapy.method._kdey.KDEBase attribute)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.base.BaseQuantifier">BaseQuantifier (class in quapy.method.base)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.BayesianCC">BayesianCC (class in quapy.method.aggregative)</a>
|
||||
</li>
|
||||
<li><a href="quapy.classification.html#quapy.classification.calibration.BCTSCalibration">BCTSCalibration (class in quapy.classification.calibration)</a>
|
||||
</li>
|
||||
|
|
@ -284,11 +289,13 @@
|
|||
</ul></li>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.ClassifyAndCount">ClassifyAndCount (in module quapy.method.aggregative)</a>
|
||||
</li>
|
||||
</ul></td>
|
||||
<td style="width: 33%; vertical-align: top;"><ul>
|
||||
<li><a href="quapy.method.html#quapy.method._neural.QuaNetTrainer.clean_checkpoint">clean_checkpoint() (quapy.method._neural.QuaNetTrainer method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method._neural.QuaNetTrainer.clean_checkpoint_dir">clean_checkpoint_dir() (quapy.method._neural.QuaNetTrainer method)</a>
|
||||
</li>
|
||||
</ul></td>
|
||||
<td style="width: 33%; vertical-align: top;"><ul>
|
||||
<li><a href="quapy.html#quapy.functional.clip">clip() (in module quapy.functional)</a>
|
||||
</li>
|
||||
<li><a href="quapy.classification.html#quapy.classification.neural.CNNnet">CNNnet (class in quapy.classification.neural)</a>
|
||||
</li>
|
||||
|
|
@ -306,9 +313,13 @@
|
|||
<li><a href="quapy.method.html#quapy.method._threshold_optim.X.condition">(quapy.method._threshold_optim.X method)</a>
|
||||
</li>
|
||||
</ul></li>
|
||||
<li><a href="quapy.html#quapy.functional.condsoftmax">condsoftmax() (in module quapy.functional)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.model_selection.ConfigStatus">ConfigStatus (class in quapy.model_selection)</a>
|
||||
</li>
|
||||
<li><a href="quapy.data.html#quapy.data.base.LabelledCollection.counts">counts() (quapy.data.base.LabelledCollection method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.functional.counts_from_labels">counts_from_labels() (in module quapy.functional)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.util.create_if_not_exist">create_if_not_exist() (in module quapy.util)</a>
|
||||
</li>
|
||||
|
|
@ -472,6 +483,8 @@
|
|||
<li><a href="quapy.method.html#quapy.method.non_aggregative.DMx.fit">(quapy.method.non_aggregative.DMx method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.fit">(quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.non_aggregative.ReadMe.fit">(quapy.method.non_aggregative.ReadMe method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.model_selection.GridSearchQ.fit">(quapy.model_selection.GridSearchQ method)</a>
|
||||
</li>
|
||||
|
|
@ -505,6 +518,8 @@
|
|||
<table style="width: 100%" class="indextable genindextable"><tr>
|
||||
<td style="width: 33%; vertical-align: top;"><ul>
|
||||
<li><a href="quapy.html#quapy.protocol.OnLabelledCollectionProtocol.get_collator">get_collator() (quapy.protocol.OnLabelledCollectionProtocol class method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.BayesianCC.get_conditional_probability_samples">get_conditional_probability_samples() (quapy.method.aggregative.BayesianCC method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.functional.get_divergence">get_divergence() (in module quapy.functional)</a>
|
||||
</li>
|
||||
|
|
@ -542,6 +557,8 @@
|
|||
</ul></li>
|
||||
</ul></td>
|
||||
<td style="width: 33%; vertical-align: top;"><ul>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.BayesianCC.get_prevalence_samples">get_prevalence_samples() (quapy.method.aggregative.BayesianCC method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.meta.get_probability_distribution">get_probability_distribution() (in module quapy.method.meta)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.util.get_quapy_home">get_quapy_home() (in module quapy.util)</a>
|
||||
|
|
@ -628,18 +645,20 @@
|
|||
<h2 id="L">L</h2>
|
||||
<table style="width: 100%" class="indextable genindextable"><tr>
|
||||
<td style="width: 33%; vertical-align: top;"><ul>
|
||||
<li><a href="quapy.html#quapy.functional.l1_norm">l1_norm() (in module quapy.functional)</a>
|
||||
</li>
|
||||
<li><a href="quapy.data.html#quapy.data.base.LabelledCollection">LabelledCollection (class in quapy.data.base)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.functional.linear_search">linear_search() (in module quapy.functional)</a>
|
||||
</li>
|
||||
</ul></td>
|
||||
<td style="width: 33%; vertical-align: top;"><ul>
|
||||
<li><a href="quapy.data.html#quapy.data.base.Dataset.load">load() (quapy.data.base.Dataset class method)</a>
|
||||
|
||||
<ul>
|
||||
<li><a href="quapy.data.html#quapy.data.base.LabelledCollection.load">(quapy.data.base.LabelledCollection class method)</a>
|
||||
</li>
|
||||
</ul></li>
|
||||
</ul></td>
|
||||
<td style="width: 33%; vertical-align: top;"><ul>
|
||||
<li><a href="quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression">LowRankLogisticRegression (class in quapy.classification.methods)</a>
|
||||
</li>
|
||||
<li><a href="quapy.classification.html#quapy.classification.neural.LSTMnet">LSTMnet (class in quapy.classification.neural)</a>
|
||||
|
|
@ -673,6 +692,8 @@
|
|||
<li><a href="quapy.method.html#quapy.method.meta.MedianEstimator">MedianEstimator (class in quapy.method.meta)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.meta.MedianEstimator2">MedianEstimator2 (class in quapy.method.meta)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.ACC.METHODS">METHODS (quapy.method.aggregative.ACC attribute)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.error.mkld">mkld() (in module quapy.error)</a>
|
||||
</li>
|
||||
|
|
@ -686,7 +707,7 @@
|
|||
module
|
||||
|
||||
<ul>
|
||||
<li><a href="generated/quapy.html#module-quapy">quapy</a>, <a href="quapy.html#module-quapy">[1]</a>
|
||||
<li><a href="quapy.html#module-quapy">quapy</a>
|
||||
</li>
|
||||
<li><a href="quapy.classification.html#module-quapy.classification">quapy.classification</a>
|
||||
</li>
|
||||
|
|
@ -772,6 +793,8 @@
|
|||
<li><a href="quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer">NeuralClassifierTrainer (class in quapy.classification.neural)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.newELM">newELM() (in module quapy.method.aggregative)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.ACC.newInvariantRatioEstimation">newInvariantRatioEstimation() (quapy.method.aggregative.ACC class method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.base.newOneVsAll">newOneVsAll() (in module quapy.method.base)</a>
|
||||
</li>
|
||||
|
|
@ -786,6 +809,8 @@
|
|||
<li><a href="quapy.method.html#quapy.method.aggregative.newSVMRAE">newSVMRAE() (in module quapy.method.aggregative)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.error.nkld">nkld() (in module quapy.error)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.ACC.NORMALIZATIONS">NORMALIZATIONS (quapy.method.aggregative.ACC attribute)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.functional.normalize_prevalence">normalize_prevalence() (in module quapy.functional)</a>
|
||||
</li>
|
||||
|
|
@ -830,6 +855,8 @@
|
|||
<li><a href="quapy.method.html#quapy.method.aggregative.PACC">PACC (class in quapy.method.aggregative)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.util.parallel">parallel() (in module quapy.util)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.util.parallel_unpack">parallel_unpack() (in module quapy.util)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.PCC">PCC (class in quapy.method.aggregative)</a>
|
||||
</li>
|
||||
|
|
@ -880,6 +907,8 @@
|
|||
<li><a href="quapy.method.html#quapy.method.aggregative.ProbabilisticAdjustedClassifyAndCount">ProbabilisticAdjustedClassifyAndCount (in module quapy.method.aggregative)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.ProbabilisticClassifyAndCount">ProbabilisticClassifyAndCount (in module quapy.method.aggregative)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.functional.projection_simplex_sort">projection_simplex_sort() (in module quapy.functional)</a>
|
||||
</li>
|
||||
</ul></td>
|
||||
</tr></table>
|
||||
|
|
@ -911,6 +940,8 @@
|
|||
<li><a href="quapy.method.html#quapy.method.non_aggregative.DMx.quantify">(quapy.method.non_aggregative.DMx method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.quantify">(quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.non_aggregative.ReadMe.quantify">(quapy.method.non_aggregative.ReadMe method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.model_selection.GridSearchQ.quantify">(quapy.model_selection.GridSearchQ method)</a>
|
||||
</li>
|
||||
|
|
@ -919,7 +950,7 @@
|
|||
quapy
|
||||
|
||||
<ul>
|
||||
<li><a href="generated/quapy.html#module-quapy">module</a>, <a href="quapy.html#module-quapy">[1]</a>
|
||||
<li><a href="quapy.html#module-quapy">module</a>
|
||||
</li>
|
||||
</ul></li>
|
||||
<li>
|
||||
|
|
@ -1108,15 +1139,17 @@
|
|||
<li><a href="quapy.html#quapy.error.rae">rae() (in module quapy.error)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.protocol.AbstractStochasticSeededProtocol.random_state">random_state (quapy.protocol.AbstractStochasticSeededProtocol property)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.non_aggregative.ReadMe">ReadMe (class in quapy.method.non_aggregative)</a>
|
||||
</li>
|
||||
<li><a href="quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifier">RecalibratedProbabilisticClassifier (class in quapy.classification.calibration)</a>
|
||||
</li>
|
||||
<li><a href="quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase">RecalibratedProbabilisticClassifierBase (class in quapy.classification.calibration)</a>
|
||||
</li>
|
||||
<li><a href="quapy.data.html#quapy.data.base.Dataset.reduce">reduce() (quapy.data.base.Dataset method)</a>
|
||||
</li>
|
||||
</ul></td>
|
||||
<td style="width: 33%; vertical-align: top;"><ul>
|
||||
<li><a href="quapy.data.html#quapy.data.base.Dataset.reduce">reduce() (quapy.data.base.Dataset method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.data.html#quapy.data.preprocessing.reduce_columns">reduce_columns() (in module quapy.data.preprocessing)</a>
|
||||
</li>
|
||||
<li><a href="quapy.data.html#quapy.data.reader.reindex_labels">reindex_labels() (in module quapy.data.reader)</a>
|
||||
|
|
@ -1145,6 +1178,8 @@
|
|||
<li><a href="quapy.html#quapy.protocol.UPP.sample">(quapy.protocol.UPP method)</a>
|
||||
</li>
|
||||
</ul></li>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.BayesianCC.sample_from_posterior">sample_from_posterior() (quapy.method.aggregative.BayesianCC method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.protocol.AbstractStochasticSeededProtocol.samples_parameters">samples_parameters() (quapy.protocol.AbstractStochasticSeededProtocol method)</a>
|
||||
|
||||
<ul>
|
||||
|
|
@ -1193,7 +1228,13 @@
|
|||
</li>
|
||||
<li><a href="quapy.html#quapy.error.smooth">smooth() (in module quapy.error)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.ACC.solve_adjustment">solve_adjustment() (quapy.method.aggregative.ACC class method)</a>
|
||||
<li><a href="quapy.html#quapy.functional.softmax">softmax() (in module quapy.functional)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.functional.solve_adjustment">solve_adjustment() (in module quapy.functional)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.functional.solve_adjustment_binary">solve_adjustment_binary() (in module quapy.functional)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.ACC.SOLVERS">SOLVERS (quapy.method.aggregative.ACC attribute)</a>
|
||||
</li>
|
||||
<li><a href="quapy.data.html#quapy.data.base.LabelledCollection.split_random">split_random() (quapy.data.base.LabelledCollection method)</a>
|
||||
</li>
|
||||
|
|
@ -1210,6 +1251,8 @@
|
|||
</li>
|
||||
</ul></li>
|
||||
<li><a href="quapy.html#quapy.model_selection.Status">Status (class in quapy.model_selection)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.non_aggregative.ReadMe.std_constrained_linear_ls">std_constrained_linear_ls() (quapy.method.non_aggregative.ReadMe method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.functional.strprev">strprev() (in module quapy.functional)</a>
|
||||
</li>
|
||||
|
|
@ -1228,6 +1271,8 @@
|
|||
<li><a href="quapy.method.html#quapy.method._threshold_optim.T50">T50 (class in quapy.method._threshold_optim)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.util.temp_seed">temp_seed() (in module quapy.util)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.functional.ternary_search">ternary_search() (in module quapy.functional)</a>
|
||||
</li>
|
||||
<li><a href="quapy.data.html#quapy.data.preprocessing.text2tfidf">text2tfidf() (in module quapy.data.preprocessing)</a>
|
||||
</li>
|
||||
|
|
@ -1261,6 +1306,16 @@
|
|||
<td style="width: 33%; vertical-align: top;"><ul>
|
||||
<li><a href="quapy.data.html#quapy.data.base.Dataset.train_test">train_test (quapy.data.base.Dataset property)</a>
|
||||
</li>
|
||||
<li><a href="quapy.classification.html#quapy.classification.neural.CNNnet.training">training (quapy.classification.neural.CNNnet attribute)</a>
|
||||
|
||||
<ul>
|
||||
<li><a href="quapy.classification.html#quapy.classification.neural.LSTMnet.training">(quapy.classification.neural.LSTMnet attribute)</a>
|
||||
</li>
|
||||
<li><a href="quapy.classification.html#quapy.classification.neural.TextClassifierNet.training">(quapy.classification.neural.TextClassifierNet attribute)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method._neural.QuaNetModule.training">(quapy.method._neural.QuaNetModule attribute)</a>
|
||||
</li>
|
||||
</ul></li>
|
||||
<li><a href="quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.transform">transform() (quapy.classification.methods.LowRankLogisticRegression method)</a>
|
||||
|
||||
<ul>
|
||||
|
|
|
|||
|
|
@ -1,24 +1,24 @@
|
|||
<!DOCTYPE html>
|
||||
<html class="writer-html5" lang="en" data-content_root="./">
|
||||
<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 0.1.8 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="_static/pygments.css?v=92fd9be5" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
|
||||
<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" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/css/theme.css" />
|
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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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|
||||
<script async="async" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
|
||||
<script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
|
||||
<script src="_static/jquery.js"></script>
|
||||
<script src="_static/underscore.js"></script>
|
||||
<script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
|
||||
<script src="_static/doctools.js"></script>
|
||||
<script src="_static/sphinx_highlight.js"></script>
|
||||
<script src="_static/js/theme.js"></script>
|
||||
<link rel="index" title="Index" href="genindex.html" />
|
||||
<link rel="search" title="Search" href="search.html" />
|
||||
|
|
@ -73,21 +73,21 @@
|
|||
<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="Link to this heading"></a></h1>
|
||||
<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="Link to this heading"></a></h2>
|
||||
<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="Link to this heading"></a></h2>
|
||||
<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>
|
||||
<div class="toctree-wrapper compound">
|
||||
</div>
|
||||
</section>
|
||||
<section id="contents">
|
||||
<h2>Contents<a class="headerlink" href="#contents" title="Link to this heading"></a></h2>
|
||||
<h2>Contents<a class="headerlink" href="#contents" title="Permalink to this heading"></a></h2>
|
||||
<div class="toctree-wrapper compound">
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="modules.html">quapy</a><ul>
|
||||
|
|
@ -128,12 +128,14 @@
|
|||
<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>
|
||||
|
|
@ -154,6 +156,7 @@
|
|||
<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>
|
||||
|
|
@ -260,10 +263,13 @@
|
|||
<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.solve_adjustment"><code class="docutils literal notranslate"><span class="pre">ACC.solve_adjustment()</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>
|
||||
|
|
@ -289,6 +295,14 @@
|
|||
</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>
|
||||
|
|
@ -385,6 +399,7 @@
|
|||
<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>
|
||||
|
|
@ -494,6 +509,12 @@
|
|||
<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#module-quapy.method">Module contents</a></li>
|
||||
|
|
@ -543,12 +564,15 @@
|
|||
<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.adjusted_quantification"><code class="docutils literal notranslate"><span class="pre">adjusted_quantification()</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>
|
||||
|
|
@ -556,7 +580,12 @@
|
|||
<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_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>
|
||||
|
|
@ -657,6 +686,7 @@
|
|||
<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.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>
|
||||
|
|
@ -673,7 +703,7 @@
|
|||
</section>
|
||||
</section>
|
||||
<section id="indices-and-tables">
|
||||
<h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Link to this heading"></a></h1>
|
||||
<h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Permalink to this heading"></a></h1>
|
||||
<ul class="simple">
|
||||
<li><p><a class="reference internal" href="genindex.html"><span class="std std-ref">Index</span></a></p></li>
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|
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|
||||
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||||
<section id="quapy">
|
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<h1>quapy<a class="headerlink" href="#quapy" title="Link to this heading"></a></h1>
|
||||
<h1>quapy<a class="headerlink" href="#quapy" title="Permalink to this heading"></a></h1>
|
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|
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<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.adjusted_quantification"><code class="docutils literal notranslate"><span class="pre">adjusted_quantification()</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>
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|||
<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>
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||||
<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>
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||||
<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_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>
|
||||
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|
||||
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|
|||
<li class="toctree-l3"><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-l3"><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-l3"><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-l3"><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-l3"><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-l3"><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-l3"><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>
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<title>quapy.classification package — QuaPy: A Python-based open-source framework for quantification 0.1.8 documentation</title>
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@ -95,15 +96,15 @@
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|||
<div itemprop="articleBody">
|
||||
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||||
<section id="quapy-classification-package">
|
||||
<h1>quapy.classification package<a class="headerlink" href="#quapy-classification-package" title="Link to this heading"></a></h1>
|
||||
<h1>quapy.classification package<a class="headerlink" href="#quapy-classification-package" title="Permalink to this heading"></a></h1>
|
||||
<section id="submodules">
|
||||
<h2>Submodules<a class="headerlink" href="#submodules" title="Link to this heading"></a></h2>
|
||||
<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this heading"></a></h2>
|
||||
</section>
|
||||
<section id="module-quapy.classification.calibration">
|
||||
<span id="quapy-classification-calibration-module"></span><h2>quapy.classification.calibration module<a class="headerlink" href="#module-quapy.classification.calibration" title="Link to this heading"></a></h2>
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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>
|
||||
|
|
@ -124,7 +125,7 @@ training set afterwards. Default value is 5.</p></li>
|
|||
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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>
|
||||
|
|
@ -145,7 +146,7 @@ training set afterwards. Default value is 5.</p></li>
|
|||
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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
|
||||
|
|
@ -154,7 +155,7 @@ is hard-to-beat at label shift adaptation. In International Conference on Machin
|
|||
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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>
|
||||
|
|
@ -174,7 +175,7 @@ training set afterwards. Default value is 5.</p></li>
|
|||
</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="Link to this definition"></a></dt>
|
||||
<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>
|
||||
|
|
@ -185,7 +186,7 @@ training set afterwards. Default value is 5.</p></li>
|
|||
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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>
|
||||
|
|
@ -202,7 +203,7 @@ training set afterwards. Default value is 5.</p></li>
|
|||
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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>
|
||||
|
|
@ -221,7 +222,7 @@ The posterior probabilities thus generated are used for calibrating the outputs
|
|||
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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
|
||||
|
|
@ -241,7 +242,7 @@ the classifier. The classifier is not retrained on the whole dataset.</p>
|
|||
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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>
|
||||
|
|
@ -255,7 +256,7 @@ the classifier. The classifier is not retrained on the whole dataset.</p>
|
|||
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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>
|
||||
|
|
@ -271,7 +272,7 @@ the classifier. The classifier is not retrained on the whole dataset.</p>
|
|||
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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>
|
||||
|
|
@ -292,7 +293,7 @@ training set afterwards. Default value is 5.</p></li>
|
|||
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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>
|
||||
|
|
@ -313,10 +314,10 @@ training set afterwards. Default value is 5.</p></li>
|
|||
|
||||
</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="Link to this heading"></a></h2>
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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
|
||||
|
|
@ -335,7 +336,7 @@ while classification is performed using <code class="xref py py-class docutils l
|
|||
</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="Link to this definition"></a></dt>
|
||||
<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">
|
||||
|
|
@ -353,7 +354,7 @@ fitting <cite>TruncatedSVD</cite> and then <cite>LogisticRegression</cite> on th
|
|||
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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>
|
||||
|
|
@ -364,7 +365,7 @@ fitting <cite>TruncatedSVD</cite> and then <cite>LogisticRegression</cite> on th
|
|||
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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>
|
||||
|
|
@ -379,7 +380,7 @@ instances in <cite>X</cite></p>
|
|||
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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>
|
||||
|
|
@ -393,7 +394,7 @@ instances in <cite>X</cite></p>
|
|||
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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>
|
||||
|
|
@ -406,7 +407,7 @@ and eventually also <cite>n_components</cite> for <cite>TruncatedSVD</cite></p>
|
|||
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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">
|
||||
|
|
@ -423,10 +424,10 @@ and eventually also <cite>n_components</cite> for <cite>TruncatedSVD</cite></p>
|
|||
|
||||
</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="Link to this heading"></a></h2>
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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>
|
||||
|
|
@ -448,7 +449,7 @@ consecutive tokens that each kernel covers</p></li>
|
|||
</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="Link to this definition"></a></dt>
|
||||
<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">
|
||||
|
|
@ -466,7 +467,7 @@ dimensionality of the embedding</p>
|
|||
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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>
|
||||
|
|
@ -475,9 +476,14 @@ dimensionality of the embedding</p>
|
|||
</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="Link to this definition"></a></dt>
|
||||
<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>
|
||||
|
|
@ -490,7 +496,7 @@ dimensionality of the embedding</p>
|
|||
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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>
|
||||
|
|
@ -509,7 +515,7 @@ Long Short Term Memory networks.</p>
|
|||
</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="Link to this definition"></a></dt>
|
||||
<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">
|
||||
|
|
@ -527,7 +533,7 @@ dimensionality of the embedding</p>
|
|||
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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>
|
||||
|
|
@ -536,9 +542,14 @@ dimensionality of the embedding</p>
|
|||
</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="Link to this definition"></a></dt>
|
||||
<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>
|
||||
|
|
@ -551,7 +562,7 @@ dimensionality of the embedding</p>
|
|||
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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">
|
||||
|
|
@ -574,7 +585,7 @@ according to the evaluation in the held-out validation split (default ‘../chec
|
|||
</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="Link to this definition"></a></dt>
|
||||
<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>
|
||||
|
|
@ -585,7 +596,7 @@ according to the evaluation in the held-out validation split (default ‘../chec
|
|||
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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>
|
||||
|
|
@ -603,7 +614,7 @@ according to the evaluation in the held-out validation split (default ‘../chec
|
|||
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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>
|
||||
|
|
@ -614,7 +625,7 @@ according to the evaluation in the held-out validation split (default ‘../chec
|
|||
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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>
|
||||
|
|
@ -629,7 +640,7 @@ instances in <cite>X</cite></p>
|
|||
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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>
|
||||
|
|
@ -643,7 +654,7 @@ instances in <cite>X</cite></p>
|
|||
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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>
|
||||
|
|
@ -657,7 +668,7 @@ instances in <cite>X</cite></p>
|
|||
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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>
|
||||
|
|
@ -670,7 +681,7 @@ be disjoint.</p>
|
|||
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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>
|
||||
|
|
@ -687,12 +698,12 @@ where <cite>embed_size</cite> is defined by the classification network</p>
|
|||
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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>
|
||||
|
|
@ -703,7 +714,7 @@ where <cite>embed_size</cite> is defined by the classification network</p>
|
|||
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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">
|
||||
|
|
@ -721,7 +732,7 @@ dimensionality of the embedding</p>
|
|||
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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>
|
||||
|
|
@ -737,7 +748,7 @@ for each of the instances and classes</p>
|
|||
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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>
|
||||
|
|
@ -748,7 +759,7 @@ for each of the instances and classes</p>
|
|||
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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>
|
||||
|
|
@ -762,9 +773,14 @@ is length of the pad in the batch</p>
|
|||
</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="Link to this definition"></a></dt>
|
||||
<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>
|
||||
|
|
@ -775,7 +791,7 @@ is length of the pad in the batch</p>
|
|||
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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>
|
||||
|
||||
|
|
@ -783,7 +799,7 @@ is length of the pad in the batch</p>
|
|||
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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">
|
||||
|
|
@ -796,7 +812,7 @@ is length of the pad in the batch</p>
|
|||
</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="Link to this definition"></a></dt>
|
||||
<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">
|
||||
|
|
@ -820,10 +836,10 @@ applied, meaning that if the longest document in the batch is shorter than
|
|||
|
||||
</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="Link to this heading"></a></h2>
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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
|
||||
|
|
@ -848,7 +864,7 @@ for further details.</p>
|
|||
</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="Link to this definition"></a></dt>
|
||||
<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>
|
||||
|
|
@ -865,7 +881,7 @@ for further details.</p>
|
|||
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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>
|
||||
|
|
@ -882,7 +898,7 @@ for further details.</p>
|
|||
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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>
|
||||
|
|
@ -897,14 +913,14 @@ instances in <cite>X</cite></p>
|
|||
|
||||
<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="Link to this definition"></a></dt>
|
||||
<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="Link to this heading"></a></h2>
|
||||
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-quapy.classification" title="Permalink to this heading"></a></h2>
|
||||
</section>
|
||||
</section>
|
||||
|
||||
|
|
|
|||
|
|
@ -1,23 +1,24 @@
|
|||
<!DOCTYPE html>
|
||||
<html class="writer-html5" lang="en" data-content_root="./">
|
||||
<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>quapy.data package — QuaPy: A Python-based open-source framework for quantification 0.1.8 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="_static/pygments.css?v=92fd9be5" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
|
||||
<title>quapy.data package — QuaPy: A Python-based open-source framework for quantification 0.1.9 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="_static/pygments.css" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/css/theme.css" />
|
||||
|
||||
|
||||
<!--[if lt IE 9]>
|
||||
<script src="_static/js/html5shiv.min.js"></script>
|
||||
<![endif]-->
|
||||
|
||||
<script src="_static/jquery.js?v=5d32c60e"></script>
|
||||
<script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
|
||||
<script src="_static/documentation_options.js?v=22607128"></script>
|
||||
<script src="_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
|
||||
<script src="_static/jquery.js"></script>
|
||||
<script src="_static/underscore.js"></script>
|
||||
<script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
|
||||
<script src="_static/doctools.js"></script>
|
||||
<script src="_static/sphinx_highlight.js"></script>
|
||||
<script src="_static/js/theme.js"></script>
|
||||
<link rel="index" title="Index" href="genindex.html" />
|
||||
<link rel="search" title="Search" href="search.html" />
|
||||
|
|
@ -95,15 +96,15 @@
|
|||
<div itemprop="articleBody">
|
||||
|
||||
<section id="quapy-data-package">
|
||||
<h1>quapy.data package<a class="headerlink" href="#quapy-data-package" title="Link to this heading"></a></h1>
|
||||
<h1>quapy.data package<a class="headerlink" href="#quapy-data-package" title="Permalink to this heading"></a></h1>
|
||||
<section id="submodules">
|
||||
<h2>Submodules<a class="headerlink" href="#submodules" title="Link to this heading"></a></h2>
|
||||
<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this heading"></a></h2>
|
||||
</section>
|
||||
<section id="module-quapy.data.base">
|
||||
<span id="quapy-data-base-module"></span><h2>quapy.data.base module<a class="headerlink" href="#module-quapy.data.base" title="Link to this heading"></a></h2>
|
||||
<span id="quapy-data-base-module"></span><h2>quapy.data.base module<a class="headerlink" href="#module-quapy.data.base" title="Permalink to this heading"></a></h2>
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.Dataset">
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.data.base.</span></span><span class="sig-name descname"><span class="pre">Dataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">test</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">vocabulary</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dict</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#Dataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.Dataset" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.data.base.</span></span><span class="sig-name descname"><span class="pre">Dataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">test</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">vocabulary</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">dict</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#Dataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.Dataset" 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>Abstraction of training and test <a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">LabelledCollection</span></code></a> objects.</p>
|
||||
<dl class="field-list simple">
|
||||
|
|
@ -118,7 +119,7 @@
|
|||
</dl>
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.Dataset.SplitStratified">
|
||||
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">SplitStratified</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">collection</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.6</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#Dataset.SplitStratified"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.Dataset.SplitStratified" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">SplitStratified</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">collection</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.6</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#Dataset.SplitStratified"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.Dataset.SplitStratified" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Generates a <a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">Dataset</span></code></a> from a stratified split of a <a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">LabelledCollection</span></code></a> instance.
|
||||
See <a class="reference internal" href="#quapy.data.base.LabelledCollection.split_stratified" title="quapy.data.base.LabelledCollection.split_stratified"><code class="xref py py-meth docutils literal notranslate"><span class="pre">LabelledCollection.split_stratified()</span></code></a></p>
|
||||
<dl class="field-list simple">
|
||||
|
|
@ -136,7 +137,7 @@ See <a class="reference internal" href="#quapy.data.base.LabelledCollection.spli
|
|||
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.Dataset.binary">
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">binary</span></span><a class="headerlink" href="#quapy.data.base.Dataset.binary" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">binary</span></span><a class="headerlink" href="#quapy.data.base.Dataset.binary" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Returns True if the training collection is labelled according to two classes</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
|
|
@ -147,7 +148,7 @@ See <a class="reference internal" href="#quapy.data.base.LabelledCollection.spli
|
|||
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.Dataset.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.data.base.Dataset.classes_" title="Link to this definition"></a></dt>
|
||||
<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.data.base.Dataset.classes_" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>The classes according to which the training collection is labelled</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
|
|
@ -158,7 +159,7 @@ See <a class="reference internal" href="#quapy.data.base.LabelledCollection.spli
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.Dataset.kFCV">
|
||||
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">kFCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">nfolds</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">nrepeats</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">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#Dataset.kFCV"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.Dataset.kFCV" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">kFCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">nfolds</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">nrepeats</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">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#Dataset.kFCV"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.Dataset.kFCV" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Generator of stratified folds to be used in k-fold cross validation. This function is only a wrapper around
|
||||
<a class="reference internal" href="#quapy.data.base.LabelledCollection.kFCV" title="quapy.data.base.LabelledCollection.kFCV"><code class="xref py py-meth docutils literal notranslate"><span class="pre">LabelledCollection.kFCV()</span></code></a> that returns <a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">Dataset</span></code></a> instances made of training and test folds.</p>
|
||||
<dl class="field-list simple">
|
||||
|
|
@ -177,7 +178,7 @@ See <a class="reference internal" href="#quapy.data.base.LabelledCollection.spli
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.Dataset.load">
|
||||
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">train_path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loader_func</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">callable</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</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="o"><span class="pre">**</span></span><span class="n"><span class="pre">loader_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#Dataset.load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.Dataset.load" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">train_path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loader_func</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">callable</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</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="o"><span class="pre">**</span></span><span class="n"><span class="pre">loader_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#Dataset.load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.Dataset.load" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Loads a training and a test labelled set of data and convert it into a <a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">Dataset</span></code></a> instance.
|
||||
The function in charge of reading the instances must be specified. This function can be a custom one, or any of
|
||||
the reading functions defined in <a class="reference internal" href="#module-quapy.data.reader" title="quapy.data.reader"><code class="xref py py-mod docutils literal notranslate"><span class="pre">quapy.data.reader</span></code></a> module.</p>
|
||||
|
|
@ -201,7 +202,7 @@ See <a class="reference internal" href="#quapy.data.base.LabelledCollection.load
|
|||
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.Dataset.n_classes">
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">n_classes</span></span><a class="headerlink" href="#quapy.data.base.Dataset.n_classes" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">n_classes</span></span><a class="headerlink" href="#quapy.data.base.Dataset.n_classes" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>The number of classes according to which the training collection is labelled</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
|
|
@ -212,7 +213,7 @@ See <a class="reference internal" href="#quapy.data.base.LabelledCollection.load
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.Dataset.reduce">
|
||||
<span class="sig-name descname"><span class="pre">reduce</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_train</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">n_test</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#Dataset.reduce"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.Dataset.reduce" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">reduce</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_train</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">n_test</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#Dataset.reduce"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.Dataset.reduce" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Reduce the number of instances in place for quick experiments. Preserves the prevalence of each set.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
|
|
@ -229,7 +230,7 @@ See <a class="reference internal" href="#quapy.data.base.LabelledCollection.load
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.Dataset.stats">
|
||||
<span class="sig-name descname"><span class="pre">stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">show</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#Dataset.stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.Dataset.stats" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">show</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#Dataset.stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.Dataset.stats" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Returns (and eventually prints) a dictionary with some stats of this dataset. E.g.,:</p>
|
||||
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_reviews</span><span class="p">(</span><span class="s1">'kindle'</span><span class="p">,</span> <span class="n">tfidf</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
|
||||
<span class="gp">>>> </span><span class="n">data</span><span class="o">.</span><span class="n">stats</span><span class="p">()</span>
|
||||
|
|
@ -252,7 +253,7 @@ the collection), <cite>prevs</cite> (the prevalence values for each class)</p>
|
|||
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.Dataset.train_test">
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">train_test</span></span><a class="headerlink" href="#quapy.data.base.Dataset.train_test" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">train_test</span></span><a class="headerlink" href="#quapy.data.base.Dataset.train_test" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Alias to <cite>self.training</cite> and <cite>self.test</cite></p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
|
|
@ -266,7 +267,7 @@ the collection), <cite>prevs</cite> (the prevalence values for each class)</p>
|
|||
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.Dataset.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.data.base.Dataset.vocabulary_size" title="Link to this definition"></a></dt>
|
||||
<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.data.base.Dataset.vocabulary_size" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>If the dataset is textual, and the vocabulary was indicated, returns the size of the vocabulary</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
|
|
@ -279,7 +280,7 @@ the collection), <cite>prevs</cite> (the prevalence values for each class)</p>
|
|||
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection">
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.data.base.</span></span><span class="sig-name descname"><span class="pre">LabelledCollection</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">classes</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/data/base.html#LabelledCollection"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.data.base.</span></span><span class="sig-name descname"><span class="pre">LabelledCollection</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">classes</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/data/base.html#LabelledCollection"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection" 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>A LabelledCollection is a set of objects each with a label attached to each of them.
|
||||
This class implements several sampling routines and other utilities.</p>
|
||||
|
|
@ -296,7 +297,7 @@ from the labels. The classes must be indicated in cases in which some of the lab
|
|||
</dl>
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.X">
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">X</span></span><a class="headerlink" href="#quapy.data.base.LabelledCollection.X" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">X</span></span><a class="headerlink" href="#quapy.data.base.LabelledCollection.X" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>An alias to self.instances</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
|
|
@ -307,7 +308,7 @@ from the labels. The classes must be indicated in cases in which some of the lab
|
|||
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.Xp">
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">Xp</span></span><a class="headerlink" href="#quapy.data.base.LabelledCollection.Xp" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">Xp</span></span><a class="headerlink" href="#quapy.data.base.LabelledCollection.Xp" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Gets the instances and the true prevalence. This is useful when implementing evaluation protocols from
|
||||
a <a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">LabelledCollection</span></code></a> object.</p>
|
||||
<dl class="field-list simple">
|
||||
|
|
@ -319,7 +320,7 @@ a <a class="reference internal" href="#quapy.data.base.LabelledCollection" title
|
|||
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.Xy">
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">Xy</span></span><a class="headerlink" href="#quapy.data.base.LabelledCollection.Xy" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">Xy</span></span><a class="headerlink" href="#quapy.data.base.LabelledCollection.Xy" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Gets the instances and labels. This is useful when working with <cite>sklearn</cite> estimators, e.g.:</p>
|
||||
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">svm</span> <span class="o">=</span> <span class="n">LinearSVC</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="o">*</span><span class="n">my_collection</span><span class="o">.</span><span class="n">Xy</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
|
|
@ -333,7 +334,7 @@ a <a class="reference internal" href="#quapy.data.base.LabelledCollection" title
|
|||
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.binary">
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">binary</span></span><a class="headerlink" href="#quapy.data.base.LabelledCollection.binary" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">binary</span></span><a class="headerlink" href="#quapy.data.base.LabelledCollection.binary" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Returns True if the number of classes is 2</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
|
|
@ -344,7 +345,7 @@ a <a class="reference internal" href="#quapy.data.base.LabelledCollection" title
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.counts">
|
||||
<span class="sig-name descname"><span class="pre">counts</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.counts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.counts" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">counts</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.counts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.counts" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Returns the number of instances for each of the classes in the codeframe.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
|
|
@ -356,7 +357,7 @@ as listed by <cite>self.classes_</cite></p>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.join">
|
||||
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">join</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><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Iterable</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.join"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.join" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">join</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><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Iterable</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.join"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.join" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Returns a new <a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">LabelledCollection</span></code></a> as the union of the collections given in input.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
|
|
@ -370,7 +371,7 @@ as listed by <cite>self.classes_</cite></p>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.kFCV">
|
||||
<span class="sig-name descname"><span class="pre">kFCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nfolds</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">nrepeats</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">random_state</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/data/base.html#LabelledCollection.kFCV"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.kFCV" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">kFCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nfolds</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">nrepeats</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">random_state</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/data/base.html#LabelledCollection.kFCV"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.kFCV" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Generator of stratified folds to be used in k-fold cross validation.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
|
|
@ -388,7 +389,7 @@ as listed by <cite>self.classes_</cite></p>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.load">
|
||||
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loader_func</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">callable</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</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="o"><span class="pre">**</span></span><span class="n"><span class="pre">loader_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.load" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loader_func</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">callable</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</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="o"><span class="pre">**</span></span><span class="n"><span class="pre">loader_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.load" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Loads a labelled set of data and convert it into a <a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">LabelledCollection</span></code></a> instance. The function in charge
|
||||
of reading the instances must be specified. This function can be a custom one, or any of the reading functions
|
||||
defined in <a class="reference internal" href="#module-quapy.data.reader" title="quapy.data.reader"><code class="xref py py-mod docutils literal notranslate"><span class="pre">quapy.data.reader</span></code></a> module.</p>
|
||||
|
|
@ -411,7 +412,7 @@ these arguments are used to call <cite>loader_func(path, **loader_kwargs)</cite>
|
|||
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.n_classes">
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">n_classes</span></span><a class="headerlink" href="#quapy.data.base.LabelledCollection.n_classes" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">n_classes</span></span><a class="headerlink" href="#quapy.data.base.LabelledCollection.n_classes" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>The number of classes</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
|
|
@ -422,7 +423,7 @@ these arguments are used to call <cite>loader_func(path, **loader_kwargs)</cite>
|
|||
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.p">
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">p</span></span><a class="headerlink" href="#quapy.data.base.LabelledCollection.p" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">p</span></span><a class="headerlink" href="#quapy.data.base.LabelledCollection.p" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>An alias to self.prevalence()</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
|
|
@ -433,7 +434,7 @@ these arguments are used to call <cite>loader_func(path, **loader_kwargs)</cite>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.prevalence">
|
||||
<span class="sig-name descname"><span class="pre">prevalence</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.prevalence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.prevalence" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">prevalence</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.prevalence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.prevalence" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Returns the prevalence, or relative frequency, of the classes in the codeframe.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
|
|
@ -445,7 +446,7 @@ as listed by <cite>self.classes_</cite></p>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.sampling">
|
||||
<span class="sig-name descname"><span class="pre">sampling</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</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/data/base.html#LabelledCollection.sampling"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.sampling" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">sampling</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</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/data/base.html#LabelledCollection.sampling"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.sampling" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Return a random sample (an instance of <a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">LabelledCollection</span></code></a>) of desired size and desired prevalence
|
||||
values. For each class, the sampling is drawn without replacement if the requested prevalence is larger than
|
||||
the actual prevalence of the class, or with replacement otherwise.</p>
|
||||
|
|
@ -469,7 +470,7 @@ prevalence == <cite>prevs</cite> if the exact prevalence values can be met as pr
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.sampling_from_index">
|
||||
<span class="sig-name descname"><span class="pre">sampling_from_index</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">index</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.sampling_from_index"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.sampling_from_index" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">sampling_from_index</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">index</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.sampling_from_index"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.sampling_from_index" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Returns an instance of <a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">LabelledCollection</span></code></a> whose elements are sampled from this collection using the
|
||||
index.</p>
|
||||
<dl class="field-list simple">
|
||||
|
|
@ -484,7 +485,7 @@ index.</p>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.sampling_index">
|
||||
<span class="sig-name descname"><span class="pre">sampling_index</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</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/data/base.html#LabelledCollection.sampling_index"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.sampling_index" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">sampling_index</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</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/data/base.html#LabelledCollection.sampling_index"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.sampling_index" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Returns an index to be used to extract a random sample of desired size and desired prevalence values. If the
|
||||
prevalence values are not specified, then returns the index of a uniform sampling.
|
||||
For each class, the sampling is drawn with replacement if the requested prevalence is larger than
|
||||
|
|
@ -508,7 +509,7 @@ it is constrained. E.g., for binary collections, only the prevalence <cite>p</ci
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.split_random">
|
||||
<span class="sig-name descname"><span class="pre">split_random</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">train_prop</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</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/data/base.html#LabelledCollection.split_random"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.split_random" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">split_random</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">train_prop</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</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/data/base.html#LabelledCollection.split_random"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.split_random" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Returns two instances of <a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">LabelledCollection</span></code></a> split randomly from this collection, at desired
|
||||
proportion.</p>
|
||||
<dl class="field-list simple">
|
||||
|
|
@ -529,7 +530,7 @@ second one with <cite>1-train_prop</cite> elements</p>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.split_stratified">
|
||||
<span class="sig-name descname"><span class="pre">split_stratified</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">train_prop</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</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/data/base.html#LabelledCollection.split_stratified"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.split_stratified" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">split_stratified</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">train_prop</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</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/data/base.html#LabelledCollection.split_stratified"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.split_stratified" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Returns two instances of <a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">LabelledCollection</span></code></a> split with stratification from this collection, at desired
|
||||
proportion.</p>
|
||||
<dl class="field-list simple">
|
||||
|
|
@ -550,7 +551,7 @@ second one with <cite>1-train_prop</cite> elements</p>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.stats">
|
||||
<span class="sig-name descname"><span class="pre">stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">show</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.stats" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">show</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.stats" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Returns (and eventually prints) a dictionary with some stats of this collection. E.g.,:</p>
|
||||
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_reviews</span><span class="p">(</span><span class="s1">'kindle'</span><span class="p">,</span> <span class="n">tfidf</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
|
||||
<span class="gp">>>> </span><span class="n">data</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">stats</span><span class="p">()</span>
|
||||
|
|
@ -572,7 +573,7 @@ values for each class)</p>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.uniform_sampling">
|
||||
<span class="sig-name descname"><span class="pre">uniform_sampling</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</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/data/base.html#LabelledCollection.uniform_sampling"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.uniform_sampling" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">uniform_sampling</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</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/data/base.html#LabelledCollection.uniform_sampling"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.uniform_sampling" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Returns a uniform sample (an instance of <a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">LabelledCollection</span></code></a>) of desired size. The sampling is drawn
|
||||
with replacement if the requested size is greater than the number of instances, or without replacement
|
||||
otherwise.</p>
|
||||
|
|
@ -591,7 +592,7 @@ otherwise.</p>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.uniform_sampling_index">
|
||||
<span class="sig-name descname"><span class="pre">uniform_sampling_index</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</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/data/base.html#LabelledCollection.uniform_sampling_index"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.uniform_sampling_index" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">uniform_sampling_index</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</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/data/base.html#LabelledCollection.uniform_sampling_index"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.uniform_sampling_index" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Returns an index to be used to extract a uniform sample of desired size. The sampling is drawn
|
||||
with replacement if the requested size is greater than the number of instances, or without replacement
|
||||
otherwise.</p>
|
||||
|
|
@ -610,7 +611,7 @@ otherwise.</p>
|
|||
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.y">
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">y</span></span><a class="headerlink" href="#quapy.data.base.LabelledCollection.y" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">y</span></span><a class="headerlink" href="#quapy.data.base.LabelledCollection.y" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>An alias to self.labels</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
|
|
@ -623,10 +624,10 @@ otherwise.</p>
|
|||
|
||||
</section>
|
||||
<section id="module-quapy.data.datasets">
|
||||
<span id="quapy-data-datasets-module"></span><h2>quapy.data.datasets module<a class="headerlink" href="#module-quapy.data.datasets" title="Link to this heading"></a></h2>
|
||||
<span id="quapy-data-datasets-module"></span><h2>quapy.data.datasets module<a class="headerlink" href="#module-quapy.data.datasets" title="Permalink to this heading"></a></h2>
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.datasets.fetch_IFCB">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_IFCB</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">single_sample_train</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">for_model_selection</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">data_home</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/data/datasets.html#fetch_IFCB"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.fetch_IFCB" title="Link to this definition"></a></dt>
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_IFCB</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">single_sample_train</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">for_model_selection</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">data_home</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/data/datasets.html#fetch_IFCB"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.fetch_IFCB" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Loads the IFCB dataset for quantification from <a class="reference external" href="https://zenodo.org/records/10036244">Zenodo</a> (for more
|
||||
information on this dataset, please follow the zenodo link).
|
||||
This dataset is based on the data available publicly at
|
||||
|
|
@ -658,7 +659,7 @@ i.e., a sampling protocol that returns a series of samples labelled by prevalenc
|
|||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.datasets.fetch_UCIBinaryDataset">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_UCIBinaryDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</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">test_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</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> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">Dataset</span></a></span></span><a class="reference internal" href="_modules/quapy/data/datasets.html#fetch_UCIBinaryDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.fetch_UCIBinaryDataset" title="Link to this definition"></a></dt>
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_UCIBinaryDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</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">test_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</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> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">Dataset</span></a></span></span><a class="reference internal" href="_modules/quapy/data/datasets.html#fetch_UCIBinaryDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.fetch_UCIBinaryDataset" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Loads a UCI dataset as an instance of <a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.Dataset</span></code></a>, as used in
|
||||
<a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S1566253516300628">Pérez-Gállego, P., Quevedo, J. R., & del Coz, J. J. (2017).
|
||||
Using ensembles for problems with characterizable changes in data distribution: A case study on quantification.
|
||||
|
|
@ -688,7 +689,7 @@ The list of valid dataset names can be accessed in <cite>quapy.data.datasets.UCI
|
|||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.datasets.fetch_UCIBinaryLabelledCollection">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_UCIBinaryLabelledCollection</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</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> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></span><a class="reference internal" href="_modules/quapy/data/datasets.html#fetch_UCIBinaryLabelledCollection"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.fetch_UCIBinaryLabelledCollection" title="Link to this definition"></a></dt>
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_UCIBinaryLabelledCollection</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</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> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></span><a class="reference internal" href="_modules/quapy/data/datasets.html#fetch_UCIBinaryLabelledCollection"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.fetch_UCIBinaryLabelledCollection" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Loads a UCI collection as an instance of <a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a>, as used in
|
||||
<a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S1566253516300628">Pérez-Gállego, P., Quevedo, J. R., & del Coz, J. J. (2017).
|
||||
Using ensembles for problems with characterizable changes in data distribution: A case study on quantification.
|
||||
|
|
@ -725,7 +726,7 @@ This can be reproduced by using <a class="reference internal" href="#quapy.data.
|
|||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.datasets.fetch_UCIMulticlassDataset">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_UCIMulticlassDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</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">test_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</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> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">Dataset</span></a></span></span><a class="reference internal" href="_modules/quapy/data/datasets.html#fetch_UCIMulticlassDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.fetch_UCIMulticlassDataset" title="Link to this definition"></a></dt>
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_UCIMulticlassDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</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">test_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</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> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">Dataset</span></a></span></span><a class="reference internal" href="_modules/quapy/data/datasets.html#fetch_UCIMulticlassDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.fetch_UCIMulticlassDataset" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Loads a UCI multiclass dataset as an instance of <a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.Dataset</span></code></a>.</p>
|
||||
<p>The list of available datasets is taken from <a class="reference external" href="https://archive.ics.uci.edu/">https://archive.ics.uci.edu/</a>, following these criteria:
|
||||
- It has more than 1000 instances
|
||||
|
|
@ -758,7 +759,7 @@ This can be reproduced by using <a class="reference internal" href="#quapy.data.
|
|||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.datasets.fetch_UCIMulticlassLabelledCollection">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_UCIMulticlassLabelledCollection</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</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> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></span><a class="reference internal" href="_modules/quapy/data/datasets.html#fetch_UCIMulticlassLabelledCollection"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.fetch_UCIMulticlassLabelledCollection" title="Link to this definition"></a></dt>
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_UCIMulticlassLabelledCollection</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</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> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></span><a class="reference internal" href="_modules/quapy/data/datasets.html#fetch_UCIMulticlassLabelledCollection"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.fetch_UCIMulticlassLabelledCollection" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Loads a UCI multiclass collection as an instance of <a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a>.</p>
|
||||
<p>The list of available datasets is taken from <a class="reference external" href="https://archive.ics.uci.edu/">https://archive.ics.uci.edu/</a>, following these criteria:
|
||||
- It has more than 1000 instances
|
||||
|
|
@ -791,7 +792,7 @@ This can be reproduced by using <a class="reference internal" href="#quapy.data.
|
|||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.datasets.fetch_lequa2022">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_lequa2022</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">task</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</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/data/datasets.html#fetch_lequa2022"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.fetch_lequa2022" title="Link to this definition"></a></dt>
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_lequa2022</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">task</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</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/data/datasets.html#fetch_lequa2022"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.fetch_lequa2022" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Loads the official datasets provided for the <a class="reference external" href="https://lequa2022.github.io/index">LeQua</a> competition.
|
||||
In brief, there are 4 tasks (T1A, T1B, T2A, T2B) having to do with text quantification
|
||||
problems. Tasks T1A and T1B provide documents in vector form, while T2A and T2B provide raw documents instead.
|
||||
|
|
@ -822,7 +823,7 @@ that return a series of samples stored in a directory which are labelled by prev
|
|||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.datasets.fetch_reviews">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_reviews</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tfidf</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">min_df</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">data_home</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">pickle</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> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">Dataset</span></a></span></span><a class="reference internal" href="_modules/quapy/data/datasets.html#fetch_reviews"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.fetch_reviews" title="Link to this definition"></a></dt>
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_reviews</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tfidf</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">min_df</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">data_home</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">pickle</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> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">Dataset</span></a></span></span><a class="reference internal" href="_modules/quapy/data/datasets.html#fetch_reviews"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.fetch_reviews" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Loads a Reviews dataset as a Dataset instance, as used in
|
||||
<a class="reference external" href="https://dl.acm.org/doi/abs/10.1145/3269206.3269287">Esuli, A., Moreo, A., and Sebastiani, F. “A recurrent neural network for sentiment quantification.”
|
||||
Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 2018.</a>.
|
||||
|
|
@ -848,7 +849,7 @@ faster subsequent invokations</p></li>
|
|||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.datasets.fetch_twitter">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_twitter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">for_model_selection</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">min_df</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">data_home</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">pickle</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> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">Dataset</span></a></span></span><a class="reference internal" href="_modules/quapy/data/datasets.html#fetch_twitter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.fetch_twitter" title="Link to this definition"></a></dt>
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_twitter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">for_model_selection</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">min_df</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">data_home</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">pickle</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> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">Dataset</span></a></span></span><a class="reference internal" href="_modules/quapy/data/datasets.html#fetch_twitter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.fetch_twitter" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Loads a Twitter dataset as a <a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.Dataset</span></code></a> instance, as used in:
|
||||
<a class="reference external" href="https://link.springer.com/content/pdf/10.1007/s13278-016-0327-z.pdf">Gao, W., Sebastiani, F.: From classification to quantification in tweet sentiment analysis.
|
||||
Social Network Analysis and Mining6(19), 1–22 (2016)</a>
|
||||
|
|
@ -879,15 +880,15 @@ faster subsequent invokations</p></li>
|
|||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.datasets.warn">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">warn</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/data/datasets.html#warn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.warn" title="Link to this definition"></a></dt>
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">warn</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/data/datasets.html#warn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.warn" title="Permalink to this definition"></a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</section>
|
||||
<section id="module-quapy.data.preprocessing">
|
||||
<span id="quapy-data-preprocessing-module"></span><h2>quapy.data.preprocessing module<a class="headerlink" href="#module-quapy.data.preprocessing" title="Link to this heading"></a></h2>
|
||||
<span id="quapy-data-preprocessing-module"></span><h2>quapy.data.preprocessing module<a class="headerlink" href="#module-quapy.data.preprocessing" title="Permalink to this heading"></a></h2>
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.data.preprocessing.IndexTransformer">
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.data.preprocessing.</span></span><span class="sig-name descname"><span class="pre">IndexTransformer</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">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/preprocessing.html#IndexTransformer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.IndexTransformer" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.data.preprocessing.</span></span><span class="sig-name descname"><span class="pre">IndexTransformer</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">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/preprocessing.html#IndexTransformer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.IndexTransformer" 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>This class implements a sklearn’s-style transformer that indexes text as numerical ids for the tokens it
|
||||
contains, and that would be generated by sklearn’s
|
||||
|
|
@ -901,7 +902,7 @@ contains, and that would be generated by sklearn’s
|
|||
</dl>
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.preprocessing.IndexTransformer.add_word">
|
||||
<span class="sig-name descname"><span class="pre">add_word</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">word</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">id</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">nogaps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/preprocessing.html#IndexTransformer.add_word"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.IndexTransformer.add_word" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">add_word</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">word</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">id</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">nogaps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/preprocessing.html#IndexTransformer.add_word"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.IndexTransformer.add_word" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Adds a new token (regardless of whether it has been found in the text or not), with dedicated id.
|
||||
Useful to define special tokens for codifying unknown words, or padding tokens.</p>
|
||||
<dl class="field-list simple">
|
||||
|
|
@ -922,7 +923,7 @@ precedent ids stored so far</p></li>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.preprocessing.IndexTransformer.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><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/preprocessing.html#IndexTransformer.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.IndexTransformer.fit" title="Link to this definition"></a></dt>
|
||||
<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><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/preprocessing.html#IndexTransformer.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.IndexTransformer.fit" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Fits the transformer, i.e., decides on the vocabulary, given a list of strings.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
|
|
@ -936,7 +937,7 @@ precedent ids stored so far</p></li>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.preprocessing.IndexTransformer.fit_transform">
|
||||
<span class="sig-name descname"><span class="pre">fit_transform</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">n_jobs</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/data/preprocessing.html#IndexTransformer.fit_transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.IndexTransformer.fit_transform" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">fit_transform</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">n_jobs</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/data/preprocessing.html#IndexTransformer.fit_transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.IndexTransformer.fit_transform" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Fits the transform on <cite>X</cite> and transforms it.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
|
|
@ -953,7 +954,7 @@ precedent ids stored so far</p></li>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.preprocessing.IndexTransformer.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>, <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><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/preprocessing.html#IndexTransformer.transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.IndexTransformer.transform" title="Link to this definition"></a></dt>
|
||||
<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>, <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><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/preprocessing.html#IndexTransformer.transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.IndexTransformer.transform" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Transforms the strings in <cite>X</cite> as lists of numerical ids</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
|
|
@ -970,7 +971,7 @@ precedent ids stored so far</p></li>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.preprocessing.IndexTransformer.vocabulary_size">
|
||||
<span class="sig-name descname"><span class="pre">vocabulary_size</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/preprocessing.html#IndexTransformer.vocabulary_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.IndexTransformer.vocabulary_size" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">vocabulary_size</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/preprocessing.html#IndexTransformer.vocabulary_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.IndexTransformer.vocabulary_size" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Gets the length of the vocabulary according to which the document tokens have been indexed</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
|
|
@ -983,7 +984,7 @@ precedent ids stored so far</p></li>
|
|||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.preprocessing.index">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.preprocessing.</span></span><span class="sig-name descname"><span class="pre">index</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">Dataset</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_df</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">inplace</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="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/data/preprocessing.html#index"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.index" title="Link to this definition"></a></dt>
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.preprocessing.</span></span><span class="sig-name descname"><span class="pre">index</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">Dataset</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_df</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">inplace</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="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/data/preprocessing.html#index"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.index" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Indexes the tokens of a textual <a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.Dataset</span></code></a> of string documents.
|
||||
To index a document means to replace each different token by a unique numerical index.
|
||||
Rare words (i.e., words occurring less than <cite>min_df</cite> times) are replaced by a special token <cite>UNK</cite></p>
|
||||
|
|
@ -1007,7 +1008,7 @@ are lists of str</p></li>
|
|||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.preprocessing.reduce_columns">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.preprocessing.</span></span><span class="sig-name descname"><span class="pre">reduce_columns</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">Dataset</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_df</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">inplace</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/data/preprocessing.html#reduce_columns"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.reduce_columns" title="Link to this definition"></a></dt>
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.preprocessing.</span></span><span class="sig-name descname"><span class="pre">reduce_columns</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">Dataset</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_df</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">inplace</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/data/preprocessing.html#reduce_columns"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.reduce_columns" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Reduces the dimensionality of the instances, represented as a <cite>csr_matrix</cite> (or any subtype of
|
||||
<cite>scipy.sparse.spmatrix</cite>), of training and test documents by removing the columns of words which are not present
|
||||
in at least <cite>min_df</cite> instances in the training set</p>
|
||||
|
|
@ -1030,7 +1031,7 @@ in the training set have been removed</p>
|
|||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.preprocessing.standardize">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.preprocessing.</span></span><span class="sig-name descname"><span class="pre">standardize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">Dataset</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</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/data/preprocessing.html#standardize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.standardize" title="Link to this definition"></a></dt>
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.preprocessing.</span></span><span class="sig-name descname"><span class="pre">standardize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">Dataset</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</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/data/preprocessing.html#standardize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.standardize" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Standardizes the real-valued columns of a <a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.Dataset</span></code></a>.
|
||||
Standardization, aka z-scoring, of a variable <cite>X</cite> comes down to subtracting the average and normalizing by the
|
||||
standard deviation.</p>
|
||||
|
|
@ -1050,7 +1051,7 @@ standard deviation.</p>
|
|||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.preprocessing.text2tfidf">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.preprocessing.</span></span><span class="sig-name descname"><span class="pre">text2tfidf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">Dataset</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_df</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sublinear_tf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</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="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/data/preprocessing.html#text2tfidf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.text2tfidf" title="Link to this definition"></a></dt>
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.preprocessing.</span></span><span class="sig-name descname"><span class="pre">text2tfidf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">Dataset</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_df</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sublinear_tf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</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="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/data/preprocessing.html#text2tfidf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.text2tfidf" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Transforms a <a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.Dataset</span></code></a> of textual instances into a <a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.Dataset</span></code></a> of
|
||||
tfidf weighted sparse vectors</p>
|
||||
<dl class="field-list simple">
|
||||
|
|
@ -1074,10 +1075,10 @@ current Dataset (if inplace=True) where the instances are stored in a <cite>csr_
|
|||
|
||||
</section>
|
||||
<section id="module-quapy.data.reader">
|
||||
<span id="quapy-data-reader-module"></span><h2>quapy.data.reader module<a class="headerlink" href="#module-quapy.data.reader" title="Link to this heading"></a></h2>
|
||||
<span id="quapy-data-reader-module"></span><h2>quapy.data.reader module<a class="headerlink" href="#module-quapy.data.reader" title="Permalink to this heading"></a></h2>
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.reader.binarize">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.reader.</span></span><span class="sig-name descname"><span class="pre">binarize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_class</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/reader.html#binarize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.reader.binarize" title="Link to this definition"></a></dt>
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.reader.</span></span><span class="sig-name descname"><span class="pre">binarize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_class</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/reader.html#binarize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.reader.binarize" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Binarizes a categorical array-like collection of labels towards the positive class <cite>pos_class</cite>. E.g.,:</p>
|
||||
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">binarize</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
|
||||
<span class="gp">>>> </span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
|
||||
|
|
@ -1099,7 +1100,7 @@ current Dataset (if inplace=True) where the instances are stored in a <cite>csr_
|
|||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.reader.from_csv">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.reader.</span></span><span class="sig-name descname"><span class="pre">from_csv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">encoding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'utf-8'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/reader.html#from_csv"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.reader.from_csv" title="Link to this definition"></a></dt>
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.reader.</span></span><span class="sig-name descname"><span class="pre">from_csv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">encoding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'utf-8'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/reader.html#from_csv"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.reader.from_csv" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Reads a csv file in which columns are separated by ‘,’.
|
||||
File format <label>,<feat1>,<feat2>,…,<featn></p>
|
||||
<dl class="field-list simple">
|
||||
|
|
@ -1117,7 +1118,7 @@ File format <label>,<feat1>,<feat2>,…,<featn></p>
|
|||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.reader.from_sparse">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.reader.</span></span><span class="sig-name descname"><span class="pre">from_sparse</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/reader.html#from_sparse"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.reader.from_sparse" title="Link to this definition"></a></dt>
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.reader.</span></span><span class="sig-name descname"><span class="pre">from_sparse</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/reader.html#from_sparse"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.reader.from_sparse" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Reads a labelled collection of real-valued instances expressed in sparse format
|
||||
File format <-1 or 0 or 1>[s col(int):val(float)]</p>
|
||||
<dl class="field-list simple">
|
||||
|
|
@ -1132,7 +1133,7 @@ File format <-1 or 0 or 1>[s col(int):val(float)]</p>
|
|||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.reader.from_text">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.reader.</span></span><span class="sig-name descname"><span class="pre">from_text</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">encoding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'utf-8'</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">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class2int</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/reader.html#from_text"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.reader.from_text" title="Link to this definition"></a></dt>
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.reader.</span></span><span class="sig-name descname"><span class="pre">from_text</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">encoding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'utf-8'</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">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class2int</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/reader.html#from_text"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.reader.from_text" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Reads a labelled colletion of documents.
|
||||
File fomart <0 or 1> <document></p>
|
||||
<dl class="field-list simple">
|
||||
|
|
@ -1151,7 +1152,7 @@ File fomart <0 or 1> <document></p>
|
|||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.reader.reindex_labels">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.reader.</span></span><span class="sig-name descname"><span class="pre">reindex_labels</span></span><span class="sig-paren">(</span><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/data/reader.html#reindex_labels"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.reader.reindex_labels" title="Link to this definition"></a></dt>
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.reader.</span></span><span class="sig-name descname"><span class="pre">reindex_labels</span></span><span class="sig-paren">(</span><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/data/reader.html#reindex_labels"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.reader.reindex_labels" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Re-indexes a list of labels as a list of indexes, and returns the classnames corresponding to the indexes.
|
||||
E.g.:</p>
|
||||
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">reindex_labels</span><span class="p">([</span><span class="s1">'B'</span><span class="p">,</span> <span class="s1">'B'</span><span class="p">,</span> <span class="s1">'A'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">])</span>
|
||||
|
|
@ -1170,7 +1171,7 @@ E.g.:</p>
|
|||
|
||||
</section>
|
||||
<section id="module-quapy.data">
|
||||
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-quapy.data" title="Link to this heading"></a></h2>
|
||||
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-quapy.data" title="Permalink to this heading"></a></h2>
|
||||
</section>
|
||||
</section>
|
||||
|
||||
|
|
|
|||
File diff suppressed because one or more lines are too long
File diff suppressed because it is too large
Load Diff
|
|
@ -1,11 +1,11 @@
|
|||
<!DOCTYPE html>
|
||||
<html class="writer-html5" lang="en" data-content_root="./">
|
||||
<html class="writer-html5" lang="en">
|
||||
<head>
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>Search — QuaPy: A Python-based open-source framework for quantification 0.1.8 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="_static/pygments.css?v=92fd9be5" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
|
||||
<title>Search — QuaPy: A Python-based open-source framework for quantification 0.1.9 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="_static/pygments.css" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/css/theme.css" />
|
||||
|
||||
|
||||
|
||||
|
|
@ -13,11 +13,12 @@
|
|||
<script src="_static/js/html5shiv.min.js"></script>
|
||||
<![endif]-->
|
||||
|
||||
<script src="_static/jquery.js?v=5d32c60e"></script>
|
||||
<script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
|
||||
<script src="_static/documentation_options.js?v=22607128"></script>
|
||||
<script src="_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
|
||||
<script src="_static/jquery.js"></script>
|
||||
<script src="_static/underscore.js"></script>
|
||||
<script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
|
||||
<script src="_static/doctools.js"></script>
|
||||
<script src="_static/sphinx_highlight.js"></script>
|
||||
<script src="_static/js/theme.js"></script>
|
||||
<script src="_static/searchtools.js"></script>
|
||||
<script src="_static/language_data.js"></script>
|
||||
|
|
|
|||
File diff suppressed because one or more lines are too long
|
|
@ -0,0 +1,35 @@
|
|||
@ECHO OFF
|
||||
|
||||
pushd %~dp0
|
||||
|
||||
REM Command file for Sphinx documentation
|
||||
|
||||
if "%SPHINXBUILD%" == "" (
|
||||
set SPHINXBUILD=sphinx-build
|
||||
)
|
||||
set SOURCEDIR=source
|
||||
set BUILDDIR=build
|
||||
|
||||
%SPHINXBUILD% >NUL 2>NUL
|
||||
if errorlevel 9009 (
|
||||
echo.
|
||||
echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
|
||||
echo.installed, then set the SPHINXBUILD environment variable to point
|
||||
echo.to the full path of the 'sphinx-build' executable. Alternatively you
|
||||
echo.may add the Sphinx directory to PATH.
|
||||
echo.
|
||||
echo.If you don't have Sphinx installed, grab it from
|
||||
echo.https://www.sphinx-doc.org/
|
||||
exit /b 1
|
||||
)
|
||||
|
||||
if "%1" == "" goto help
|
||||
|
||||
%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
|
||||
goto end
|
||||
|
||||
:help
|
||||
%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
|
||||
|
||||
:end
|
||||
popd
|
||||
|
|
@ -0,0 +1,55 @@
|
|||
# Configuration file for the Sphinx documentation builder.
|
||||
#
|
||||
# For the full list of built-in configuration values, see the documentation:
|
||||
# https://www.sphinx-doc.org/en/master/usage/configuration.html
|
||||
|
||||
# -- Project information -----------------------------------------------------
|
||||
# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
|
||||
|
||||
import pathlib
|
||||
import sys
|
||||
from os.path import join
|
||||
quapy_path = join(pathlib.Path(__file__).parents[2].resolve().as_posix(), 'quapy')
|
||||
print(f'quapy path={quapy_path}')
|
||||
sys.path.insert(0, quapy_path)
|
||||
|
||||
|
||||
project = 'QuaPy: A Python-based open-source framework for quantification'
|
||||
copyright = '2024, Alejandro Moreo'
|
||||
author = 'Alejandro Moreo'
|
||||
|
||||
|
||||
|
||||
import quapy
|
||||
|
||||
release = quapy.__version__
|
||||
|
||||
# -- General configuration ---------------------------------------------------
|
||||
# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
|
||||
|
||||
extensions = [
|
||||
'sphinx.ext.duration',
|
||||
'sphinx.ext.doctest',
|
||||
'sphinx.ext.autodoc',
|
||||
'sphinx.ext.autosummary',
|
||||
'sphinx.ext.viewcode',
|
||||
'sphinx.ext.napoleon'
|
||||
]
|
||||
|
||||
templates_path = ['_templates']
|
||||
|
||||
# List of patterns, relative to source directory, that match files and
|
||||
# directories to ignore when looking for source files.
|
||||
# This pattern also affects html_static_path and html_extra_path.
|
||||
exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
|
||||
|
||||
|
||||
# -- Options for HTML output -------------------------------------------------
|
||||
# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
|
||||
|
||||
html_theme = 'sphinx_rtd_theme'
|
||||
# html_theme = 'furo'
|
||||
# need to be installed: pip install furo (not working...)
|
||||
html_static_path = ['_static']
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,41 @@
|
|||
.. QuaPy: A Python-based open-source framework for quantification documentation master file, created by
|
||||
sphinx-quickstart on Wed Feb 7 16:26:46 2024.
|
||||
You can adapt this file completely to your liking, but it should at least
|
||||
contain the root `toctree` directive.
|
||||
|
||||
Welcome to QuaPy's documentation!
|
||||
==========================================================================================
|
||||
|
||||
QuaPy is a Python-based open-source framework for quantification.
|
||||
|
||||
This document contains the API of the modules included in QuaPy.
|
||||
|
||||
Installation
|
||||
------------
|
||||
|
||||
`pip install quapy`
|
||||
|
||||
GitHub
|
||||
------------
|
||||
|
||||
QuaPy is hosted in GitHub at `https://github.com/HLT-ISTI/QuaPy <https://github.com/HLT-ISTI/QuaPy>`_
|
||||
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: Contents:
|
||||
|
||||
Contents
|
||||
--------
|
||||
|
||||
.. toctree::
|
||||
|
||||
modules
|
||||
|
||||
|
||||
Indices and tables
|
||||
==================
|
||||
|
||||
* :ref:`genindex`
|
||||
* :ref:`modindex`
|
||||
* :ref:`search`
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
quapy
|
||||
=====
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 4
|
||||
|
||||
quapy
|
||||
|
|
@ -0,0 +1,45 @@
|
|||
quapy.classification package
|
||||
============================
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
quapy.classification.calibration module
|
||||
---------------------------------------
|
||||
|
||||
.. automodule:: quapy.classification.calibration
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
quapy.classification.methods module
|
||||
-----------------------------------
|
||||
|
||||
.. automodule:: quapy.classification.methods
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
quapy.classification.neural module
|
||||
----------------------------------
|
||||
|
||||
.. automodule:: quapy.classification.neural
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
quapy.classification.svmperf module
|
||||
-----------------------------------
|
||||
|
||||
.. automodule:: quapy.classification.svmperf
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Module contents
|
||||
---------------
|
||||
|
||||
.. automodule:: quapy.classification
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
|
@ -0,0 +1,46 @@
|
|||
quapy.data package
|
||||
==================
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
quapy.data.base module
|
||||
----------------------
|
||||
|
||||
.. automodule:: quapy.data.base
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
quapy.data.datasets module
|
||||
--------------------------
|
||||
|
||||
.. 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:
|
||||
|
|
@ -0,0 +1,61 @@
|
|||
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:
|
||||
|
||||
Module contents
|
||||
---------------
|
||||
|
||||
.. automodule:: quapy.method
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
|
@ -0,0 +1,80 @@
|
|||
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:
|
||||
|
|
@ -0,0 +1,194 @@
|
|||
"""
|
||||
.. author:: Paweł Czyż
|
||||
|
||||
This example shows how to use Bayesian quantification (https://arxiv.org/abs/2302.09159),
|
||||
which is suitable for low-data situations and when the uncertainty of the prevalence estimate is of interest.
|
||||
|
||||
For this, we will need to install extra dependencies:
|
||||
|
||||
```
|
||||
$ pip install quapy[bayesian]
|
||||
```
|
||||
|
||||
Running the script via:
|
||||
|
||||
```
|
||||
$ python examples/bayesian_quantification.py
|
||||
```
|
||||
|
||||
will produce a plot `bayesian_quantification.pdf`.
|
||||
|
||||
Due to a low sample size and the fact that classes 2 and 3 are hard to distinguish,
|
||||
it is hard to estimate the proportions accurately, what is visible by looking at the posterior samples,
|
||||
showing large uncertainty.
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import quapy as qp
|
||||
|
||||
from sklearn.ensemble import RandomForestClassifier
|
||||
|
||||
from quapy.method.aggregative import BayesianCC, ACC, PACC
|
||||
from quapy.data import LabelledCollection, Dataset
|
||||
|
||||
|
||||
FIGURE_PATH = "bayesian_quantification.pdf"
|
||||
|
||||
|
||||
def simulate_data(rng) -> Dataset:
|
||||
"""Generates a simulated data set with three classes."""
|
||||
|
||||
# Number of examples of each class in both data sets
|
||||
n_train = [400, 400, 400]
|
||||
n_test = [40, 25, 15]
|
||||
|
||||
# Mean vectors and shared covariance of P(X|Y) distributions
|
||||
mus = [np.zeros(2), np.array([1, 1.5]), np.array([1.5, 1])]
|
||||
cov = np.eye(2)
|
||||
|
||||
def gen_Xy(centers, sizes):
|
||||
X = np.concatenate([rng.multivariate_normal(mu_i, cov, size_i) for mu_i, size_i in zip(centers, sizes)])
|
||||
y = np.concatenate([[i] * n for i, n in enumerate(sizes)])
|
||||
return X, y
|
||||
|
||||
# Generate the features accordingly
|
||||
train = LabelledCollection(*gen_Xy(centers=mus, sizes=n_train))
|
||||
test = LabelledCollection(*gen_Xy(centers=mus, sizes=n_test))
|
||||
|
||||
return Dataset(training=train, test=test)
|
||||
|
||||
|
||||
def plot_simulated_data(axs, data: Dataset) -> None:
|
||||
"""Plots a simulated data set.
|
||||
|
||||
:param axs: a list of three `plt.Axes` objects, on which the samples will be plotted.
|
||||
:param data: the simulated data set.
|
||||
"""
|
||||
train, test = data.train_test
|
||||
xlim = (
|
||||
-0.3 + min(train.X[:, 0].min(), test.X[:, 0].min()),
|
||||
0.3 + max(train.X[:, 0].max(), test.X[:, 0].max())
|
||||
)
|
||||
ylim = (
|
||||
-0.3 + min(train.X[:, 1].min(), test.X[:, 1].min()),
|
||||
0.3 + max(train.X[:, 1].max(), test.X[:, 1].max())
|
||||
)
|
||||
|
||||
for ax in axs:
|
||||
ax.set_xlabel("$X_1$")
|
||||
ax.set_ylabel("$X_2$")
|
||||
ax.set_aspect("equal")
|
||||
ax.set_xlim(*xlim)
|
||||
ax.set_ylim(*ylim)
|
||||
ax.set_xticks([])
|
||||
ax.set_yticks([])
|
||||
|
||||
ax = axs[0]
|
||||
ax.set_title("Training set")
|
||||
for i in range(data.n_classes):
|
||||
ax.scatter(train.X[train.y == i, 0], train.X[train.y == i, 1], c=f"C{i}", s=3, rasterized=True)
|
||||
|
||||
ax = axs[1]
|
||||
ax.set_title("Test set\n(with labels)")
|
||||
for i in range(data.n_classes):
|
||||
ax.scatter(test.X[test.y == i, 0], test.X[test.y == i, 1], c=f"C{i}", s=3, rasterized=True)
|
||||
|
||||
ax = axs[2]
|
||||
ax.set_title("Test set\n(as observed)")
|
||||
ax.scatter(test.X[:, 0], test.X[:, 1], c="C5", s=3, rasterized=True)
|
||||
|
||||
|
||||
def plot_true_proportions(ax: plt.Axes, test_prevalence: np.ndarray) -> None:
|
||||
"""Plots the true proportions."""
|
||||
n_classes = len(test_prevalence)
|
||||
x_ax = np.arange(n_classes)
|
||||
ax.plot(x_ax, test_prevalence, c="black", linewidth=2, label="True")
|
||||
|
||||
ax.set_xlabel("Class")
|
||||
ax.set_ylabel("Prevalence")
|
||||
ax.set_xticks(x_ax, x_ax + 1)
|
||||
ax.set_yticks([0, 0.25, 0.5, 0.75, 1.0])
|
||||
ax.set_xlim(-0.1, n_classes - 0.9)
|
||||
ax.set_ylim(-0.01, 1.01)
|
||||
|
||||
|
||||
def get_random_forest() -> RandomForestClassifier:
|
||||
"""An auxiliary factory method to generate a random forest."""
|
||||
return RandomForestClassifier(n_estimators=10, random_state=5)
|
||||
|
||||
|
||||
def _get_estimate(estimator_class, training: LabelledCollection, test: np.ndarray) -> None:
|
||||
"""Auxiliary method for running ACC and PACC."""
|
||||
estimator = estimator_class(get_random_forest())
|
||||
estimator.fit(training)
|
||||
return estimator.quantify(test)
|
||||
|
||||
|
||||
def train_and_plot_bayesian_quantification(ax: plt.Axes, training: LabelledCollection, test: LabelledCollection) -> None:
|
||||
"""Fits Bayesian quantification and plots posterior mean as well as individual samples"""
|
||||
print('training model Bayesian CC...', end='')
|
||||
quantifier = BayesianCC(classifier=get_random_forest())
|
||||
quantifier.fit(training)
|
||||
|
||||
# Obtain mean prediction
|
||||
mean_prediction = quantifier.quantify(test.X)
|
||||
mae = qp.error.mae(test.prevalence(), mean_prediction)
|
||||
x_ax = np.arange(training.n_classes)
|
||||
ax.plot(x_ax, mean_prediction, c="salmon", linewidth=2, linestyle=":", label="Bayesian")
|
||||
|
||||
# Obtain individual samples
|
||||
samples = quantifier.get_prevalence_samples()
|
||||
for sample in samples[::5, :]:
|
||||
ax.plot(x_ax, sample, c="salmon", alpha=0.1, linewidth=0.3, rasterized=True)
|
||||
print(f'MAE={mae:.4f} [done]')
|
||||
|
||||
|
||||
def train_and_plot_acc(ax: plt.Axes, training: LabelledCollection, test: LabelledCollection) -> None:
|
||||
print('training model ACC...', end='')
|
||||
estimate = _get_estimate(ACC, training, test.X)
|
||||
mae = qp.error.mae(test.prevalence(), estimate)
|
||||
ax.plot(np.arange(training.n_classes), estimate, c="darkblue", linewidth=2, linestyle=":", label="ACC")
|
||||
print(f'MAE={mae:.4f} [done]')
|
||||
|
||||
|
||||
def train_and_plot_pacc(ax: plt.Axes, training: LabelledCollection, test: LabelledCollection) -> None:
|
||||
print('training model PACC...', end='')
|
||||
estimate = _get_estimate(PACC, training, test.X)
|
||||
mae = qp.error.mae(test.prevalence(), estimate)
|
||||
ax.plot(np.arange(training.n_classes), estimate, c="limegreen", linewidth=2, linestyle=":", label="PACC")
|
||||
print(f'MAE={mae:.4f} [done]')
|
||||
|
||||
|
||||
def main() -> None:
|
||||
# --- Simulate data ---
|
||||
print('generating simulated data')
|
||||
rng = np.random.default_rng(42)
|
||||
data = simulate_data(rng)
|
||||
training, test = data.train_test
|
||||
|
||||
# --- Plot simulated data ---
|
||||
fig, axs = plt.subplots(1, 4, figsize=(13, 3), dpi=300)
|
||||
for ax in axs:
|
||||
ax.spines[['top', 'right']].set_visible(False)
|
||||
plot_simulated_data(axs[:3], data)
|
||||
|
||||
# --- Plot quantification results ---
|
||||
ax = axs[3]
|
||||
plot_true_proportions(ax, test_prevalence=test.prevalence())
|
||||
|
||||
train_and_plot_acc(ax, training=training, test=test)
|
||||
train_and_plot_pacc(ax, training=training, test=test)
|
||||
train_and_plot_bayesian_quantification(ax=ax, training=training, test=test)
|
||||
print('[done]')
|
||||
|
||||
ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', frameon=False)
|
||||
|
||||
print(f'saving plot in path {FIGURE_PATH}...', end='')
|
||||
fig.tight_layout()
|
||||
fig.savefig(FIGURE_PATH)
|
||||
print('[done]')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
|
|
@ -1,43 +1,28 @@
|
|||
import itertools
|
||||
import warnings
|
||||
from collections import defaultdict
|
||||
from typing import Union, Callable
|
||||
from typing import Literal, Union, Callable
|
||||
from numpy.typing import ArrayLike
|
||||
|
||||
import scipy
|
||||
import numpy as np
|
||||
|
||||
|
||||
def prevalence_linspace(n_prevalences=21, repeats=1, smooth_limits_epsilon=0.01):
|
||||
"""
|
||||
Produces an array of uniformly separated values of prevalence.
|
||||
By default, produces an array of 21 prevalence values, with
|
||||
step 0.05 and with the limits smoothed, i.e.:
|
||||
[0.01, 0.05, 0.10, 0.15, ..., 0.90, 0.95, 0.99]
|
||||
# ------------------------------------------------------------------------------------------
|
||||
# Counter utils
|
||||
# ------------------------------------------------------------------------------------------
|
||||
|
||||
:param n_prevalences: the number of prevalence values to sample from the [0,1] interval (default 21)
|
||||
:param repeats: number of times each prevalence is to be repeated (defaults to 1)
|
||||
:param smooth_limits_epsilon: the quantity to add and subtract to the limits 0 and 1
|
||||
:return: an array of uniformly separated prevalence values
|
||||
def counts_from_labels(labels: ArrayLike, classes: ArrayLike) -> np.ndarray:
|
||||
"""
|
||||
p = np.linspace(0., 1., num=n_prevalences, endpoint=True)
|
||||
p[0] += smooth_limits_epsilon
|
||||
p[-1] -= smooth_limits_epsilon
|
||||
if p[0] > p[1]:
|
||||
raise ValueError(f'the smoothing in the limits is greater than the prevalence step')
|
||||
if repeats > 1:
|
||||
p = np.repeat(p, repeats)
|
||||
return p
|
||||
|
||||
|
||||
def counts_from_labels(labels, classes):
|
||||
"""
|
||||
Computes the count values from a vector of labels.
|
||||
Computes the raw count values from a vector of labels.
|
||||
|
||||
:param labels: array-like of shape `(n_instances,)` with the label for each instance
|
||||
:param classes: the class labels. This is needed in order to correctly compute the prevalence vector even when
|
||||
some classes have no examples.
|
||||
:return: an ndarray of shape `(len(classes),)` with the occurrence counts of each class
|
||||
:return: ndarray of shape `(len(classes),)` with the raw counts for each class, in the same order
|
||||
as they appear in `classes`
|
||||
"""
|
||||
if labels.ndim != 1:
|
||||
if np.asarray(labels).ndim != 1:
|
||||
raise ValueError(f'param labels does not seem to be a ndarray of label predictions')
|
||||
unique, counts = np.unique(labels, return_counts=True)
|
||||
by_class = defaultdict(lambda:0, dict(zip(unique, counts)))
|
||||
|
|
@ -45,20 +30,22 @@ def counts_from_labels(labels, classes):
|
|||
return counts
|
||||
|
||||
|
||||
def prevalence_from_labels(labels, classes):
|
||||
def prevalence_from_labels(labels: ArrayLike, classes: ArrayLike):
|
||||
"""
|
||||
Computes the prevalence values from a vector of labels.
|
||||
|
||||
:param labels: array-like of shape `(n_instances,)` with the label for each instance
|
||||
:param classes: the class labels. This is needed in order to correctly compute the prevalence vector even when
|
||||
some classes have no examples.
|
||||
:return: an ndarray of shape `(len(classes))` with the class prevalence values
|
||||
:return: ndarray of shape `(len(classes),)` with the class proportions for each class, in the same order
|
||||
as they appear in `classes`
|
||||
"""
|
||||
counts = np.array(counts_from_labels(labels, classes), dtype=float)
|
||||
return counts / np.sum(counts)
|
||||
counts = counts_from_labels(labels, classes)
|
||||
prevalences = counts.astype(float) / np.sum(counts)
|
||||
return prevalences
|
||||
|
||||
|
||||
def prevalence_from_probabilities(posteriors, binarize: bool = False):
|
||||
def prevalence_from_probabilities(posteriors: ArrayLike, binarize: bool = False):
|
||||
"""
|
||||
Returns a vector of prevalence values from a matrix of posterior probabilities.
|
||||
|
||||
|
|
@ -67,8 +54,9 @@ def prevalence_from_probabilities(posteriors, binarize: bool = False):
|
|||
converting the vectors of posterior probabilities into class indices, by taking the argmax).
|
||||
:return: array of shape `(n_classes,)` containing the prevalence values
|
||||
"""
|
||||
posteriors = np.asarray(posteriors)
|
||||
if posteriors.ndim != 2:
|
||||
raise ValueError(f'param posteriors does not seem to be a ndarray of posteior probabilities')
|
||||
raise ValueError(f'param posteriors does not seem to be a ndarray of posterior probabilities')
|
||||
if binarize:
|
||||
predictions = np.argmax(posteriors, axis=-1)
|
||||
return prevalence_from_labels(predictions, np.arange(posteriors.shape[1]))
|
||||
|
|
@ -78,25 +66,264 @@ def prevalence_from_probabilities(posteriors, binarize: bool = False):
|
|||
return prevalences
|
||||
|
||||
|
||||
def as_binary_prevalence(positive_prevalence: Union[float, np.ndarray], clip_if_necessary=False):
|
||||
def num_prevalence_combinations(n_prevpoints:int, n_classes:int, n_repeats:int=1) -> int:
|
||||
"""
|
||||
Computes the number of valid prevalence combinations in the n_classes-dimensional simplex if `n_prevpoints` equally
|
||||
distant prevalence values are generated and `n_repeats` repetitions are requested.
|
||||
The computation comes down to calculating:
|
||||
|
||||
.. math::
|
||||
\\binom{N+C-1}{C-1} \\times r
|
||||
|
||||
where `N` is `n_prevpoints-1`, i.e., the number of probability mass blocks to allocate, `C` is the number of
|
||||
classes, and `r` is `n_repeats`. This solution comes from the
|
||||
`Stars and Bars <https://brilliant.org/wiki/integer-equations-star-and-bars/>`_ problem.
|
||||
|
||||
:param int n_classes: number of classes
|
||||
:param int n_prevpoints: number of prevalence points.
|
||||
:param int n_repeats: number of repetitions for each prevalence combination
|
||||
:return: The number of possible combinations. For example, if `n_classes`=2, `n_prevpoints`=5, `n_repeats`=1,
|
||||
then the number of possible combinations are 5, i.e.: [0,1], [0.25,0.75], [0.50,0.50], [0.75,0.25],
|
||||
and [1.0,0.0]
|
||||
"""
|
||||
N = n_prevpoints-1
|
||||
C = n_classes
|
||||
r = n_repeats
|
||||
return int(scipy.special.binom(N + C - 1, C - 1) * r)
|
||||
|
||||
|
||||
def get_nprevpoints_approximation(combinations_budget:int, n_classes:int, n_repeats:int=1) -> int:
|
||||
"""
|
||||
Searches for the largest number of (equidistant) prevalence points to define for each of the `n_classes` classes so
|
||||
that the number of valid prevalence values generated as combinations of prevalence points (points in a
|
||||
`n_classes`-dimensional simplex) do not exceed combinations_budget.
|
||||
|
||||
:param int combinations_budget: maximum number of combinations allowed
|
||||
:param int n_classes: number of classes
|
||||
:param int n_repeats: number of repetitions for each prevalence combination
|
||||
:return: the largest number of prevalence points that generate less than combinations_budget valid prevalences
|
||||
"""
|
||||
assert n_classes > 0 and n_repeats > 0 and combinations_budget > 0, 'parameters must be positive integers'
|
||||
n_prevpoints = 1
|
||||
while True:
|
||||
combinations = num_prevalence_combinations(n_prevpoints, n_classes, n_repeats)
|
||||
if combinations > combinations_budget:
|
||||
return n_prevpoints-1
|
||||
else:
|
||||
n_prevpoints += 1
|
||||
|
||||
|
||||
# ------------------------------------------------------------------------------------------
|
||||
# Prevalence vectors
|
||||
# ------------------------------------------------------------------------------------------
|
||||
|
||||
def as_binary_prevalence(positive_prevalence: Union[float, ArrayLike], clip_if_necessary: bool=False) -> np.ndarray:
|
||||
"""
|
||||
Helper that, given a float representing the prevalence for the positive class, returns a np.ndarray of two
|
||||
values representing a binary distribution.
|
||||
|
||||
:param positive_prevalence: prevalence for the positive class
|
||||
:param clip_if_necessary: if True, clips the value in [0,1] in order to guarantee the resulting distribution
|
||||
:param positive_prevalence: float or array-like of floats with the prevalence for the positive class
|
||||
:param bool clip_if_necessary: if True, clips the value in [0,1] in order to guarantee the resulting distribution
|
||||
is valid. If False, it then checks that the value is in the valid range, and raises an error if not.
|
||||
:return: np.ndarray of shape `(2,)`
|
||||
"""
|
||||
positive_prevalence = np.asarray(positive_prevalence, float)
|
||||
if clip_if_necessary:
|
||||
positive_prevalence = np.clip(positive_prevalence, 0, 1)
|
||||
else:
|
||||
assert 0 <= positive_prevalence <= 1, 'the value provided is not a valid prevalence for the positive class'
|
||||
assert np.logical_and(0 <= positive_prevalence, positive_prevalence <= 1).all(), \
|
||||
'the value provided is not a valid prevalence for the positive class'
|
||||
return np.asarray([1-positive_prevalence, positive_prevalence]).T
|
||||
|
||||
|
||||
def strprev(prevalences: ArrayLike, prec: int=3) -> str:
|
||||
"""
|
||||
Returns a string representation for a prevalence vector. E.g.,
|
||||
|
||||
def HellingerDistance(P, Q) -> float:
|
||||
>>> strprev([1/3, 2/3], prec=2)
|
||||
>>> '[0.33, 0.67]'
|
||||
|
||||
:param prevalences: array-like of prevalence values
|
||||
:param prec: int, indicates the float precision (number of decimal values to print)
|
||||
:return: string
|
||||
"""
|
||||
return '['+ ', '.join([f'{p:.{prec}f}' for p in prevalences]) + ']'
|
||||
|
||||
|
||||
def check_prevalence_vector(prevalences: ArrayLike, raise_exception: bool=False, tolerance: float=1e-08, aggr=True):
|
||||
"""
|
||||
Checks that `prevalences` is a valid prevalence vector, i.e., it contains values in [0,1] and
|
||||
the values sum up to 1. In other words, verifies that the `prevalences` vectors lies in the
|
||||
probability simplex.
|
||||
|
||||
:param ArrayLike prevalences: the prevalence vector, or vectors, to check
|
||||
:param bool raise_exception: whether to raise an exception if the vector (or any of the vectors) does
|
||||
not lie in the simplex (default False)
|
||||
:param float tolerance: error tolerance for the check `sum(prevalences) - 1 = 0`
|
||||
:param bool aggr: if True (default) returns one single bool (True if all prevalence vectors are valid,
|
||||
False otherwise), if False returns an array of bool, one for each prevalence vector
|
||||
:return: a single bool True if `prevalences` is a vector of prevalence values that lies on the simplex,
|
||||
or False otherwise; alternatively, if `prevalences` is a matrix of shape `(num_vectors, n_classes,)`
|
||||
then it returns one such bool for each prevalence vector
|
||||
"""
|
||||
prevalences = np.asarray(prevalences)
|
||||
|
||||
all_positive = prevalences>=0
|
||||
if not all_positive.all():
|
||||
if raise_exception:
|
||||
raise ValueError('some prevalence vectors contain negative numbers; '
|
||||
'consider using the qp.functional.normalize_prevalence with '
|
||||
'any method from ["clip", "mapsimplex", "softmax"]')
|
||||
|
||||
all_close_1 = np.isclose(prevalences.sum(axis=-1), 1, atol=tolerance)
|
||||
if not all_close_1.all():
|
||||
if raise_exception:
|
||||
raise ValueError('some prevalence vectors do not sum up to 1; '
|
||||
'consider using the qp.functional.normalize_prevalence with '
|
||||
'any method from ["l1", "clip", "mapsimplex", "softmax"]')
|
||||
|
||||
valid = np.logical_and(all_positive.all(axis=-1), all_close_1)
|
||||
if aggr:
|
||||
return valid.all()
|
||||
else:
|
||||
return valid
|
||||
|
||||
|
||||
def normalize_prevalence(prevalences: ArrayLike, method='l1'):
|
||||
"""
|
||||
Normalizes a vector or matrix of prevalence values. The normalization consists of applying a L1 normalization in
|
||||
cases in which the prevalence values are not all-zeros, and to convert the prevalence values into `1/n_classes` in
|
||||
cases in which all values are zero.
|
||||
|
||||
:param prevalences: array-like of shape `(n_classes,)` or of shape `(n_samples, n_classes,)` with prevalence values
|
||||
:param str method: indicates the normalization method to employ, options are:
|
||||
|
||||
* `l1`: applies L1 normalization (default); a 0 vector is mapped onto the uniform prevalence
|
||||
* `clip`: clip values in [0,1] and then rescales so that the L1 norm is 1
|
||||
* `mapsimplex`: projects vectors onto the probability simplex. This implementation relies on
|
||||
`Mathieu Blondel's projection_simplex_sort <https://gist.github.com/mblondel/6f3b7aaad90606b98f71>`_
|
||||
* `softmax`: applies softmax to all vectors
|
||||
* `condsoftmax`: applies softmax only to invalid prevalence vectors
|
||||
|
||||
:return: a normalized vector or matrix of prevalence values
|
||||
"""
|
||||
if method in ['none', None]:
|
||||
return prevalences
|
||||
|
||||
prevalences = np.asarray(prevalences, dtype=float)
|
||||
|
||||
if method=='l1':
|
||||
normalized = l1_norm(prevalences)
|
||||
check_prevalence_vector(normalized, raise_exception=True)
|
||||
elif method=='clip':
|
||||
normalized = clip(prevalences) # no need to check afterwards
|
||||
elif method=='mapsimplex':
|
||||
normalized = projection_simplex_sort(prevalences)
|
||||
elif method=='softmax':
|
||||
normalized = softmax(prevalences)
|
||||
elif method=='condsoftmax':
|
||||
normalized = condsoftmax(prevalences)
|
||||
else:
|
||||
raise ValueError(f'unknown {method=}, valid ones are ["l1", "clip", "mapsimplex", "softmax"]')
|
||||
|
||||
return normalized
|
||||
|
||||
|
||||
def l1_norm(prevalences: ArrayLike) -> np.ndarray:
|
||||
"""
|
||||
Applies L1 normalization to the `unnormalized_arr` so that it becomes a valid prevalence
|
||||
vector. Zero vectors are mapped onto the uniform distribution. Raises an exception if
|
||||
the resulting vectors are not valid distributions. This may happen when the original
|
||||
prevalence vectors contain negative values. Use the `clip` normalization function
|
||||
instead to avoid this possibility.
|
||||
|
||||
:param prevalences: array-like of shape `(n_classes,)` or of shape `(n_samples, n_classes,)` with prevalence values
|
||||
:return: np.ndarray representing a valid distribution
|
||||
"""
|
||||
n_classes = prevalences.shape[-1]
|
||||
accum = prevalences.sum(axis=-1, keepdims=True)
|
||||
prevalences = np.true_divide(prevalences, accum, where=accum > 0)
|
||||
allzeros = accum.flatten() == 0
|
||||
if any(allzeros):
|
||||
if prevalences.ndim == 1:
|
||||
prevalences = np.full(shape=n_classes, fill_value=1. / n_classes)
|
||||
else:
|
||||
prevalences[allzeros] = np.full(shape=n_classes, fill_value=1. / n_classes)
|
||||
return prevalences
|
||||
|
||||
|
||||
def clip(prevalences: ArrayLike) -> np.ndarray:
|
||||
"""
|
||||
Clips the values in [0,1] and then applies the L1 normalization.
|
||||
|
||||
:param prevalences: array-like of shape `(n_classes,)` or of shape `(n_samples, n_classes,)` with prevalence values
|
||||
:return: np.ndarray representing a valid distribution
|
||||
"""
|
||||
clipped = np.clip(prevalences, 0, 1)
|
||||
normalized = l1_norm(clipped)
|
||||
return normalized
|
||||
|
||||
|
||||
def projection_simplex_sort(unnormalized_arr: ArrayLike) -> np.ndarray:
|
||||
"""Projects a point onto the probability simplex.
|
||||
|
||||
The code is adapted from Mathieu Blondel's BSD-licensed
|
||||
`implementation <https://gist.github.com/mblondel/6f3b7aaad90606b98f71>`_
|
||||
(see function `projection_simplex_sort` in their repo) which is accompanying the paper
|
||||
|
||||
Mathieu Blondel, Akinori Fujino, and Naonori Ueda.
|
||||
Large-scale Multiclass Support Vector Machine Training via Euclidean Projection onto the Simplex,
|
||||
ICPR 2014, `URL <http://www.mblondel.org/publications/mblondel-icpr2014.pdf>`_
|
||||
|
||||
:param `unnormalized_arr`: point in n-dimensional space, shape `(n,)`
|
||||
:return: projection of `unnormalized_arr` onto the (n-1)-dimensional probability simplex, shape `(n,)`
|
||||
"""
|
||||
unnormalized_arr = np.asarray(unnormalized_arr)
|
||||
n = len(unnormalized_arr)
|
||||
u = np.sort(unnormalized_arr)[::-1]
|
||||
cssv = np.cumsum(u) - 1.0
|
||||
ind = np.arange(1, n + 1)
|
||||
cond = u - cssv / ind > 0
|
||||
rho = ind[cond][-1]
|
||||
theta = cssv[cond][-1] / float(rho)
|
||||
return np.maximum(unnormalized_arr - theta, 0)
|
||||
|
||||
|
||||
def softmax(prevalences: ArrayLike) -> np.ndarray:
|
||||
"""
|
||||
Applies the softmax function to all vectors even if the original vectors were valid distributions.
|
||||
If you want to leave valid vectors untouched, use condsoftmax instead.
|
||||
|
||||
:param prevalences: array-like of shape `(n_classes,)` or of shape `(n_samples, n_classes,)` with prevalence values
|
||||
:return: np.ndarray representing a valid distribution
|
||||
"""
|
||||
normalized = scipy.special.softmax(prevalences, axis=-1)
|
||||
return normalized
|
||||
|
||||
|
||||
def condsoftmax(prevalences: ArrayLike) -> np.ndarray:
|
||||
"""
|
||||
Applies the softmax function only to vectors that do not represent valid distributions.
|
||||
|
||||
:param prevalences: array-like of shape `(n_classes,)` or of shape `(n_samples, n_classes,)` with prevalence values
|
||||
:return: np.ndarray representing a valid distribution
|
||||
"""
|
||||
invalid_idx = ~ check_prevalence_vector(prevalences, aggr=False, raise_exception=False)
|
||||
if isinstance(invalid_idx, np.bool_) and invalid_idx:
|
||||
# only one vector
|
||||
normalized = scipy.special.softmax(prevalences)
|
||||
else:
|
||||
prevalences = np.copy(prevalences)
|
||||
prevalences[invalid_idx] = scipy.special.softmax(prevalences[invalid_idx], axis=-1)
|
||||
normalized = prevalences
|
||||
return normalized
|
||||
|
||||
|
||||
# ------------------------------------------------------------------------------------------
|
||||
# Divergences
|
||||
# ------------------------------------------------------------------------------------------
|
||||
|
||||
def HellingerDistance(P: np.ndarray, Q: np.ndarray) -> float:
|
||||
"""
|
||||
Computes the Hellingher Distance (HD) between (discretized) distributions `P` and `Q`.
|
||||
The HD for two discrete distributions of `k` bins is defined as:
|
||||
|
|
@ -111,7 +338,7 @@ def HellingerDistance(P, Q) -> float:
|
|||
return np.sqrt(np.sum((np.sqrt(P) - np.sqrt(Q))**2))
|
||||
|
||||
|
||||
def TopsoeDistance(P, Q, epsilon=1e-20):
|
||||
def TopsoeDistance(P: np.ndarray, Q: np.ndarray, epsilon: float=1e-20):
|
||||
"""
|
||||
Topsoe distance between two (discretized) distributions `P` and `Q`.
|
||||
The Topsoe distance for two discrete distributions of `k` bins is defined as:
|
||||
|
|
@ -127,7 +354,130 @@ def TopsoeDistance(P, Q, epsilon=1e-20):
|
|||
return np.sum(P*np.log((2*P+epsilon)/(P+Q+epsilon)) + Q*np.log((2*Q+epsilon)/(P+Q+epsilon)))
|
||||
|
||||
|
||||
def uniform_prevalence_sampling(n_classes, size=1):
|
||||
def get_divergence(divergence: Union[str, Callable]):
|
||||
"""
|
||||
Guarantees that the divergence received as argument is a function. That is, if this argument is already
|
||||
a callable, then it is returned, if it is instead a string, then tries to instantiate the corresponding
|
||||
divergence from the string name.
|
||||
|
||||
:param divergence: callable or string indicating the name of the divergence function
|
||||
:return: callable
|
||||
"""
|
||||
if isinstance(divergence, str):
|
||||
if divergence=='HD':
|
||||
return HellingerDistance
|
||||
elif divergence=='topsoe':
|
||||
return TopsoeDistance
|
||||
else:
|
||||
raise ValueError(f'unknown divergence {divergence}')
|
||||
elif callable(divergence):
|
||||
return divergence
|
||||
else:
|
||||
raise ValueError(f'argument "divergence" not understood; use a str or a callable function')
|
||||
|
||||
|
||||
# ------------------------------------------------------------------------------------------
|
||||
# Solvers
|
||||
# ------------------------------------------------------------------------------------------
|
||||
|
||||
def argmin_prevalence(loss: Callable,
|
||||
n_classes: int,
|
||||
method: Literal["optim_minimize", "linear_search", "ternary_search"]='optim_minimize'):
|
||||
"""
|
||||
Searches for the prevalence vector that minimizes a loss function.
|
||||
|
||||
:param loss: callable, the function to minimize
|
||||
:param n_classes: int, number of classes
|
||||
:param method: string indicating the search strategy. Possible values are::
|
||||
'optim_minimize': uses scipy.optim
|
||||
'linear_search': carries out a linear search for binary problems in the space [0, 0.01, 0.02, ..., 1]
|
||||
'ternary_search': implements the ternary search (not yet implemented)
|
||||
:return: np.ndarray, a prevalence vector
|
||||
"""
|
||||
if method == 'optim_minimize':
|
||||
return optim_minimize(loss, n_classes)
|
||||
elif method == 'linear_search':
|
||||
return linear_search(loss, n_classes)
|
||||
elif method == 'ternary_search':
|
||||
ternary_search(loss, n_classes)
|
||||
else:
|
||||
raise NotImplementedError()
|
||||
|
||||
|
||||
def optim_minimize(loss: Callable, n_classes: int):
|
||||
"""
|
||||
Searches for the optimal prevalence values, i.e., an `n_classes`-dimensional vector of the (`n_classes`-1)-simplex
|
||||
that yields the smallest lost. This optimization is carried out by means of a constrained search using scipy's
|
||||
SLSQP routine.
|
||||
|
||||
:param loss: (callable) the function to minimize
|
||||
:param n_classes: (int) the number of classes, i.e., the dimensionality of the prevalence vector
|
||||
:return: (ndarray) the best prevalence vector found
|
||||
"""
|
||||
from scipy import optimize
|
||||
|
||||
# the initial point is set as the uniform distribution
|
||||
uniform_distribution = np.full(fill_value=1 / n_classes, shape=(n_classes,))
|
||||
|
||||
# solutions are bounded to those contained in the unit-simplex
|
||||
bounds = tuple((0, 1) for _ in range(n_classes)) # values in [0,1]
|
||||
constraints = ({'type': 'eq', 'fun': lambda x: 1 - sum(x)}) # values summing up to 1
|
||||
r = optimize.minimize(loss, x0=uniform_distribution, method='SLSQP', bounds=bounds, constraints=constraints)
|
||||
return r.x
|
||||
|
||||
|
||||
def linear_search(loss: Callable, n_classes: int):
|
||||
"""
|
||||
Performs a linear search for the best prevalence value in binary problems. The search is carried out by exploring
|
||||
the range [0,1] stepping by 0.01. This search is inefficient, and is added only for completeness (some of the
|
||||
early methods in quantification literature used it, e.g., HDy). A most powerful alternative is `optim_minimize`.
|
||||
|
||||
:param loss: (callable) the function to minimize
|
||||
:param n_classes: (int) the number of classes, i.e., the dimensionality of the prevalence vector
|
||||
:return: (ndarray) the best prevalence vector found
|
||||
"""
|
||||
assert n_classes==2, 'linear search is only available for binary problems'
|
||||
|
||||
prev_selected, min_score = None, None
|
||||
for prev in prevalence_linspace(grid_points=100, repeats=1, smooth_limits_epsilon=0.0):
|
||||
score = loss(np.asarray([1 - prev, prev]))
|
||||
if min_score is None or score < min_score:
|
||||
prev_selected, min_score = prev, score
|
||||
|
||||
return np.asarray([1 - prev_selected, prev_selected])
|
||||
|
||||
|
||||
def ternary_search(loss: Callable, n_classes: int):
|
||||
raise NotImplementedError()
|
||||
|
||||
|
||||
# ------------------------------------------------------------------------------------------
|
||||
# Sampling utils
|
||||
# ------------------------------------------------------------------------------------------
|
||||
|
||||
def prevalence_linspace(grid_points:int=21, repeats:int=1, smooth_limits_epsilon:float=0.01) -> np.ndarray:
|
||||
"""
|
||||
Produces an array of uniformly separated values of prevalence.
|
||||
By default, produces an array of 21 prevalence values, with
|
||||
step 0.05 and with the limits smoothed, i.e.:
|
||||
[0.01, 0.05, 0.10, 0.15, ..., 0.90, 0.95, 0.99]
|
||||
|
||||
:param grid_points: the number of prevalence values to sample from the [0,1] interval (default 21)
|
||||
:param repeats: number of times each prevalence is to be repeated (defaults to 1)
|
||||
:param smooth_limits_epsilon: the quantity to add and subtract to the limits 0 and 1
|
||||
:return: an array of uniformly separated prevalence values
|
||||
"""
|
||||
p = np.linspace(0., 1., num=grid_points, endpoint=True)
|
||||
p[0] += smooth_limits_epsilon
|
||||
p[-1] -= smooth_limits_epsilon
|
||||
if p[0] > p[1]:
|
||||
raise ValueError(f'the smoothing in the limits is greater than the prevalence step')
|
||||
if repeats > 1:
|
||||
p = np.repeat(p, repeats)
|
||||
return p
|
||||
|
||||
|
||||
def uniform_prevalence_sampling(n_classes: int, size: int=1) -> np.ndarray:
|
||||
"""
|
||||
Implements the `Kraemer algorithm <http://www.cs.cmu.edu/~nasmith/papers/smith+tromble.tr04.pdf>`_
|
||||
for sampling uniformly at random from the unit simplex. This implementation is adapted from this
|
||||
|
|
@ -156,21 +506,11 @@ def uniform_prevalence_sampling(n_classes, size=1):
|
|||
uniform_simplex_sampling = uniform_prevalence_sampling
|
||||
|
||||
|
||||
def strprev(prevalences, prec=3):
|
||||
"""
|
||||
Returns a string representation for a prevalence vector. E.g.,
|
||||
# ------------------------------------------------------------------------------------------
|
||||
# Adjustment
|
||||
# ------------------------------------------------------------------------------------------
|
||||
|
||||
>>> strprev([1/3, 2/3], prec=2)
|
||||
>>> '[0.33, 0.67]'
|
||||
|
||||
:param prevalences: a vector of prevalence values
|
||||
:param prec: float precision
|
||||
:return: string
|
||||
"""
|
||||
return '['+ ', '.join([f'{p:.{prec}f}' for p in prevalences]) + ']'
|
||||
|
||||
|
||||
def adjusted_quantification(prevalence_estim, tpr, fpr, clip=True):
|
||||
def solve_adjustment_binary(prevalence_estim: ArrayLike, tpr: float, fpr: float, clip: bool=True):
|
||||
"""
|
||||
Implements the adjustment of ACC and PACC for the binary case. The adjustment for a prevalence estimate of the
|
||||
positive class `p` comes down to computing:
|
||||
|
|
@ -178,10 +518,10 @@ def adjusted_quantification(prevalence_estim, tpr, fpr, clip=True):
|
|||
.. math::
|
||||
ACC(p) = \\frac{ p - fpr }{ tpr - fpr }
|
||||
|
||||
:param prevalence_estim: float, the estimated value for the positive class
|
||||
:param tpr: float, the true positive rate of the classifier
|
||||
:param fpr: float, the false positive rate of the classifier
|
||||
:param clip: set to True (default) to clip values that might exceed the range [0,1]
|
||||
:param float prevalence_estim: the estimated value for the positive class (`p` in the formula)
|
||||
:param float tpr: the true positive rate of the classifier
|
||||
:param float fpr: the false positive rate of the classifier
|
||||
:param bool clip: set to True (default) to clip values that might exceed the range [0,1]
|
||||
:return: float, the adjusted count
|
||||
"""
|
||||
|
||||
|
|
@ -194,184 +534,75 @@ def adjusted_quantification(prevalence_estim, tpr, fpr, clip=True):
|
|||
return adjusted
|
||||
|
||||
|
||||
def normalize_prevalence(prevalences):
|
||||
def solve_adjustment(
|
||||
class_conditional_rates: np.ndarray,
|
||||
unadjusted_counts: np.ndarray,
|
||||
method: Literal["inversion", "invariant-ratio"],
|
||||
solver: Literal["exact", "minimize", "exact-raise", "exact-cc"]) -> np.ndarray:
|
||||
"""
|
||||
Normalize a vector or matrix of prevalence values. The normalization consists of applying a L1 normalization in
|
||||
cases in which the prevalence values are not all-zeros, and to convert the prevalence values into `1/n_classes` in
|
||||
cases in which all values are zero.
|
||||
Function that tries to solve for :math:`p` the equation :math:`q = M p`, where :math:`q` is the vector of
|
||||
`unadjusted counts` (as estimated, e.g., via classify and count) with :math:`q_i` an estimate of
|
||||
:math:`P(\hat{Y}=y_i)`, and where :math:`M` is the matrix of `class-conditional rates` with :math:`M_{ij}` an
|
||||
estimate of :math:`P(\hat{Y}=y_i|Y=y_j)`.
|
||||
|
||||
:param prevalences: array-like of shape `(n_classes,)` or of shape `(n_samples, n_classes,)` with prevalence values
|
||||
:return: a normalized vector or matrix of prevalence values
|
||||
:param class_conditional_rates: array of shape `(n_classes, n_classes,)` with entry `(i,j)` being the estimate
|
||||
of :math:`P(\hat{Y}=y_i|Y=y_j)`, that is, the probability that an instance that belongs to class :math:`y_j`
|
||||
ends up being classified as belonging to class :math:`y_i`
|
||||
|
||||
:param unadjusted_counts: array of shape `(n_classes,)` containing the unadjusted prevalence values (e.g., as
|
||||
estimated by CC or PCC)
|
||||
|
||||
:param str method: indicates the adjustment method to be used. Valid options are:
|
||||
|
||||
* `inversion`: tries to solve the equation :math:`q = M p` as :math:`p = M^{-1} q` where
|
||||
:math:`M^{-1}` is the matrix inversion of :math:`M`. This inversion may not exist in
|
||||
degenerated cases.
|
||||
* `invariant-ratio`: invariant ratio estimator of `Vaz et al. 2018 <https://jmlr.org/papers/v20/18-456.html>`_,
|
||||
which replaces the last equation in :math:`M` with the normalization condition (i.e., that the sum of
|
||||
all prevalence values must equal 1).
|
||||
|
||||
:param str solver: the method to use for solving the system of linear equations. Valid options are:
|
||||
|
||||
* `exact-raise`: tries to solve the system using matrix inversion. Raises an error if the matrix has rank
|
||||
strictly lower than `n_classes`.
|
||||
* `exact-cc`: if the matrix is not full rank, returns :math:`q` (i.e., the unadjusted counts) as the estimates
|
||||
* `exact`: deprecated, defaults to 'exact-cc' (will be removed in future versions)
|
||||
* `minimize`: minimizes a loss, so the solution always exists
|
||||
"""
|
||||
prevalences = np.asarray(prevalences)
|
||||
n_classes = prevalences.shape[-1]
|
||||
accum = prevalences.sum(axis=-1, keepdims=True)
|
||||
prevalences = np.true_divide(prevalences, accum, where=accum>0)
|
||||
allzeros = accum.flatten()==0
|
||||
if any(allzeros):
|
||||
if prevalences.ndim == 1:
|
||||
prevalences = np.full(shape=n_classes, fill_value=1./n_classes)
|
||||
else:
|
||||
prevalences[accum.flatten()==0] = np.full(shape=n_classes, fill_value=1./n_classes)
|
||||
return prevalences
|
||||
if solver == "exact":
|
||||
warnings.warn(
|
||||
"The 'exact' solver is deprecated. Use 'exact-raise' or 'exact-cc'", DeprecationWarning, stacklevel=2)
|
||||
solver = "exact-cc"
|
||||
|
||||
A = np.asarray(class_conditional_rates, dtype=float)
|
||||
B = np.asarray(unadjusted_counts, dtype=float)
|
||||
|
||||
def __num_prevalence_combinations_depr(n_prevpoints:int, n_classes:int, n_repeats:int=1):
|
||||
"""
|
||||
Computes the number of prevalence combinations in the n_classes-dimensional simplex if `nprevpoints` equally distant
|
||||
prevalence values are generated and `n_repeats` repetitions are requested.
|
||||
|
||||
:param n_classes: integer, number of classes
|
||||
:param n_prevpoints: integer, number of prevalence points.
|
||||
:param n_repeats: integer, number of repetitions for each prevalence combination
|
||||
:return: The number of possible combinations. For example, if n_classes=2, n_prevpoints=5, n_repeats=1, then the
|
||||
number of possible combinations are 5, i.e.: [0,1], [0.25,0.75], [0.50,0.50], [0.75,0.25], and [1.0,0.0]
|
||||
"""
|
||||
__cache={}
|
||||
def __f(nc,np):
|
||||
if (nc,np) in __cache: # cached result
|
||||
return __cache[(nc,np)]
|
||||
if nc==1: # stop condition
|
||||
return 1
|
||||
else: # recursive call
|
||||
x = sum([__f(nc-1, np-i) for i in range(np)])
|
||||
__cache[(nc,np)] = x
|
||||
return x
|
||||
return __f(n_classes, n_prevpoints) * n_repeats
|
||||
|
||||
|
||||
def num_prevalence_combinations(n_prevpoints:int, n_classes:int, n_repeats:int=1):
|
||||
"""
|
||||
Computes the number of valid prevalence combinations in the n_classes-dimensional simplex if `n_prevpoints` equally
|
||||
distant prevalence values are generated and `n_repeats` repetitions are requested.
|
||||
The computation comes down to calculating:
|
||||
|
||||
.. math::
|
||||
\\binom{N+C-1}{C-1} \\times r
|
||||
|
||||
where `N` is `n_prevpoints-1`, i.e., the number of probability mass blocks to allocate, `C` is the number of
|
||||
classes, and `r` is `n_repeats`. This solution comes from the
|
||||
`Stars and Bars <https://brilliant.org/wiki/integer-equations-star-and-bars/>`_ problem.
|
||||
|
||||
:param n_classes: integer, number of classes
|
||||
:param n_prevpoints: integer, number of prevalence points.
|
||||
:param n_repeats: integer, number of repetitions for each prevalence combination
|
||||
:return: The number of possible combinations. For example, if n_classes=2, n_prevpoints=5, n_repeats=1, then the
|
||||
number of possible combinations are 5, i.e.: [0,1], [0.25,0.75], [0.50,0.50], [0.75,0.25], and [1.0,0.0]
|
||||
"""
|
||||
N = n_prevpoints-1
|
||||
C = n_classes
|
||||
r = n_repeats
|
||||
return int(scipy.special.binom(N + C - 1, C - 1) * r)
|
||||
|
||||
|
||||
def get_nprevpoints_approximation(combinations_budget:int, n_classes:int, n_repeats:int=1):
|
||||
"""
|
||||
Searches for the largest number of (equidistant) prevalence points to define for each of the `n_classes` classes so
|
||||
that the number of valid prevalence values generated as combinations of prevalence points (points in a
|
||||
`n_classes`-dimensional simplex) do not exceed combinations_budget.
|
||||
|
||||
:param combinations_budget: integer, maximum number of combinations allowed
|
||||
:param n_classes: integer, number of classes
|
||||
:param n_repeats: integer, number of repetitions for each prevalence combination
|
||||
:return: the largest number of prevalence points that generate less than combinations_budget valid prevalences
|
||||
"""
|
||||
assert n_classes > 0 and n_repeats > 0 and combinations_budget > 0, 'parameters must be positive integers'
|
||||
n_prevpoints = 1
|
||||
while True:
|
||||
combinations = num_prevalence_combinations(n_prevpoints, n_classes, n_repeats)
|
||||
if combinations > combinations_budget:
|
||||
return n_prevpoints-1
|
||||
else:
|
||||
n_prevpoints += 1
|
||||
|
||||
|
||||
def check_prevalence_vector(p, raise_exception=False, toleranze=1e-08):
|
||||
"""
|
||||
Checks that p is a valid prevalence vector, i.e., that it contains values in [0,1] and that the values sum up to 1.
|
||||
|
||||
:param p: the prevalence vector to check
|
||||
:return: True if `p` is valid, False otherwise
|
||||
"""
|
||||
p = np.asarray(p)
|
||||
if not all(p>=0):
|
||||
if raise_exception:
|
||||
raise ValueError('the prevalence vector contains negative numbers')
|
||||
return False
|
||||
if not all(p<=1):
|
||||
if raise_exception:
|
||||
raise ValueError('the prevalence vector contains values >1')
|
||||
return False
|
||||
if not np.isclose(p.sum(), 1, atol=toleranze):
|
||||
if raise_exception:
|
||||
raise ValueError('the prevalence vector does not sum up to 1')
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def get_divergence(divergence: Union[str, Callable]):
|
||||
if isinstance(divergence, str):
|
||||
if divergence=='HD':
|
||||
return HellingerDistance
|
||||
elif divergence=='topsoe':
|
||||
return TopsoeDistance
|
||||
else:
|
||||
raise ValueError(f'unknown divergence {divergence}')
|
||||
elif callable(divergence):
|
||||
return divergence
|
||||
if method == "inversion":
|
||||
pass # We leave A and B unchanged
|
||||
elif method == "invariant-ratio":
|
||||
# Change the last equation to replace it with the normalization condition
|
||||
A[-1, :] = 1.0
|
||||
B[-1] = 1.0
|
||||
else:
|
||||
raise ValueError(f'argument "divergence" not understood; use a str or a callable function')
|
||||
raise ValueError(f"unknown {method=}")
|
||||
|
||||
|
||||
def argmin_prevalence(loss, n_classes, method='optim_minimize'):
|
||||
if method == 'optim_minimize':
|
||||
return optim_minimize(loss, n_classes)
|
||||
elif method == 'linear_search':
|
||||
return linear_search(loss, n_classes)
|
||||
elif method == 'ternary_search':
|
||||
raise NotImplementedError()
|
||||
if solver == "minimize":
|
||||
def loss(prev):
|
||||
return np.linalg.norm(A @ prev - B)
|
||||
return optim_minimize(loss, n_classes=A.shape[0])
|
||||
elif solver in ["exact-raise", "exact-cc"]:
|
||||
# Solvers based on matrix inversion, so we use try/except block
|
||||
try:
|
||||
return np.linalg.solve(A, B)
|
||||
except np.linalg.LinAlgError:
|
||||
# The matrix is not invertible.
|
||||
# Depending on the solver, we either raise an error
|
||||
# or return the classifier predictions without adjustment
|
||||
if solver == "exact-raise":
|
||||
raise
|
||||
elif solver == "exact-cc":
|
||||
return unadjusted_counts
|
||||
else:
|
||||
raise ValueError(f"Solver {solver} not known.")
|
||||
else:
|
||||
raise NotImplementedError()
|
||||
|
||||
|
||||
def optim_minimize(loss, n_classes):
|
||||
"""
|
||||
Searches for the optimal prevalence values, i.e., an `n_classes`-dimensional vector of the (`n_classes`-1)-simplex
|
||||
that yields the smallest lost. This optimization is carried out by means of a constrained search using scipy's
|
||||
SLSQP routine.
|
||||
|
||||
:param loss: (callable) the function to minimize
|
||||
:param n_classes: (int) the number of classes, i.e., the dimensionality of the prevalence vector
|
||||
:return: (ndarray) the best prevalence vector found
|
||||
"""
|
||||
from scipy import optimize
|
||||
|
||||
# the initial point is set as the uniform distribution
|
||||
uniform_distribution = np.full(fill_value=1 / n_classes, shape=(n_classes,))
|
||||
|
||||
# solutions are bounded to those contained in the unit-simplex
|
||||
bounds = tuple((0, 1) for _ in range(n_classes)) # values in [0,1]
|
||||
constraints = ({'type': 'eq', 'fun': lambda x: 1 - sum(x)}) # values summing up to 1
|
||||
r = optimize.minimize(loss, x0=uniform_distribution, method='SLSQP', bounds=bounds, constraints=constraints)
|
||||
return r.x
|
||||
|
||||
|
||||
def linear_search(loss, n_classes):
|
||||
"""
|
||||
Performs a linear search for the best prevalence value in binary problems. The search is carried out by exploring
|
||||
the range [0,1] stepping by 0.01. This search is inefficient, and is added only for completeness (some of the
|
||||
early methods in quantification literature used it, e.g., HDy). A most powerful alternative is `optim_minimize`.
|
||||
|
||||
:param loss: (callable) the function to minimize
|
||||
:param n_classes: (int) the number of classes, i.e., the dimensionality of the prevalence vector
|
||||
:return: (ndarray) the best prevalence vector found
|
||||
"""
|
||||
assert n_classes==2, 'linear search is only available for binary problems'
|
||||
|
||||
prev_selected, min_score = None, None
|
||||
for prev in prevalence_linspace(n_prevalences=100, repeats=1, smooth_limits_epsilon=0.0):
|
||||
score = loss(np.asarray([1 - prev, prev]))
|
||||
if min_score is None or score < min_score:
|
||||
prev_selected, min_score = prev, score
|
||||
|
||||
return np.asarray([1 - prev_selected, prev_selected])
|
||||
raise ValueError(f'unknown {solver=}')
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
from abc import ABC, abstractmethod
|
||||
from copy import deepcopy
|
||||
from typing import Callable, Union
|
||||
from typing import Callable, Literal, Union
|
||||
import numpy as np
|
||||
from abstention.calibration import NoBiasVectorScaling, TempScaling, VectorScaling
|
||||
from scipy import optimize
|
||||
|
|
@ -352,113 +352,6 @@ class CC(AggregativeCrispQuantifier):
|
|||
return F.prevalence_from_labels(classif_predictions, self.classes_)
|
||||
|
||||
|
||||
class ACC(AggregativeCrispQuantifier):
|
||||
"""
|
||||
`Adjusted Classify & Count <https://link.springer.com/article/10.1007/s10618-008-0097-y>`_,
|
||||
the "adjusted" variant of :class:`CC`, that corrects the predictions of CC
|
||||
according to the `misclassification rates`.
|
||||
|
||||
:param classifier: a sklearn's Estimator that generates a classifier
|
||||
:param val_split: specifies the data used for generating classifier predictions. This specification
|
||||
can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to
|
||||
be extracted from the training set; or as an integer (default 5), indicating that the predictions
|
||||
are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value
|
||||
for `k`); or as a collection defining the specific set of data to use for validation.
|
||||
Alternatively, this set can be specified at fit time by indicating the exact set of data
|
||||
on which the predictions are to be generated.
|
||||
:param n_jobs: number of parallel workers
|
||||
:param solver: indicates the method to be used for obtaining the final estimates. The choice
|
||||
'exact' comes down to solving the system of linear equations :math:`Ax=B` where `A` is a
|
||||
matrix containing the class-conditional probabilities of the predictions (e.g., the tpr and fpr in
|
||||
binary) and `B` is the vector of prevalence values estimated via CC, as :math:`x=A^{-1}B`. This solution
|
||||
might not exist for degenerated classifiers, in which case the method defaults to classify and count
|
||||
(i.e., does not attempt any adjustment).
|
||||
Another option is to search for the prevalence vector that minimizes the L2 norm of :math:`|Ax-B|`. The latter
|
||||
is achieved by indicating solver='minimize'. This one generally works better, and is the default parameter.
|
||||
More details about this can be consulted in `Bunse, M. "On Multi-Class Extensions of Adjusted Classify and
|
||||
Count", on proceedings of the 2nd International Workshop on Learning to Quantify: Methods and Applications
|
||||
(LQ 2022), ECML/PKDD 2022, Grenoble (France) <https://lq-2022.github.io/proceedings/CompleteVolume.pdf>`_.
|
||||
"""
|
||||
|
||||
def __init__(self, classifier: BaseEstimator, val_split=5, n_jobs=None, solver='minimize'):
|
||||
self.classifier = classifier
|
||||
self.val_split = val_split
|
||||
self.n_jobs = qp._get_njobs(n_jobs)
|
||||
self.solver = solver
|
||||
|
||||
def _check_init_parameters(self):
|
||||
if self.solver not in ['exact', 'minimize']:
|
||||
raise ValueError("unknown solver; valid ones are 'exact', 'minimize'")
|
||||
|
||||
def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
|
||||
"""
|
||||
Estimates the misclassification rates.
|
||||
|
||||
:param classif_predictions: a :class:`quapy.data.base.LabelledCollection` containing,
|
||||
as instances, the label predictions issued by the classifier and, as labels, the true labels
|
||||
:param data: a :class:`quapy.data.base.LabelledCollection` consisting of the training data
|
||||
"""
|
||||
pred_labels, true_labels = classif_predictions.Xy
|
||||
self.cc = CC(self.classifier)
|
||||
self.Pte_cond_estim_ = self.getPteCondEstim(self.classifier.classes_, true_labels, pred_labels)
|
||||
|
||||
@classmethod
|
||||
def getPteCondEstim(cls, classes, y, y_):
|
||||
# estimate the matrix with entry (i,j) being the estimate of P(hat_yi|yj), that is, the probability that a
|
||||
# document that belongs to yj ends up being classified as belonging to yi
|
||||
conf = confusion_matrix(y, y_, labels=classes).T
|
||||
conf = conf.astype(float)
|
||||
class_counts = conf.sum(axis=0)
|
||||
for i, _ in enumerate(classes):
|
||||
if class_counts[i] == 0:
|
||||
conf[i, i] = 1
|
||||
else:
|
||||
conf[:, i] /= class_counts[i]
|
||||
return conf
|
||||
|
||||
def aggregate(self, classif_predictions):
|
||||
prevs_estim = self.cc.aggregate(classif_predictions)
|
||||
return ACC.solve_adjustment(self.Pte_cond_estim_, prevs_estim, solver=self.solver)
|
||||
|
||||
@classmethod
|
||||
def solve_adjustment(cls, PteCondEstim, prevs_estim, solver='exact'):
|
||||
"""
|
||||
Solves the system linear system :math:`Ax = B` with :math:`A` = `PteCondEstim` and :math:`B` = `prevs_estim`
|
||||
|
||||
:param PteCondEstim: a `np.ndarray` of shape `(n_classes,n_classes,)` with entry `(i,j)` being the estimate
|
||||
of :math:`P(y_i|y_j)`, that is, the probability that an instance that belongs to :math:`y_j` ends up being
|
||||
classified as belonging to :math:`y_i`
|
||||
:param prevs_estim: a `np.ndarray` of shape `(n_classes,)` with the class prevalence estimates
|
||||
:param solver: indicates the method to use for solving the system of linear equations. Valid options are
|
||||
'exact' (tries to solve the system --may fail if the misclassificatin matrix has rank < n_classes) or
|
||||
'optim_minimize' (minimizes a norm --always exists).
|
||||
:return: an adjusted `np.ndarray` of shape `(n_classes,)` with the corrected class prevalence estimates
|
||||
"""
|
||||
|
||||
A = PteCondEstim
|
||||
B = prevs_estim
|
||||
|
||||
if solver == 'exact':
|
||||
# attempts an exact solution of the linear system (may fail)
|
||||
|
||||
try:
|
||||
adjusted_prevs = np.linalg.solve(A, B)
|
||||
adjusted_prevs = np.clip(adjusted_prevs, 0, 1)
|
||||
adjusted_prevs /= adjusted_prevs.sum()
|
||||
except np.linalg.LinAlgError:
|
||||
adjusted_prevs = prevs_estim # no way to adjust them!
|
||||
|
||||
return adjusted_prevs
|
||||
|
||||
elif solver == 'minimize':
|
||||
# poses the problem as an optimization one, and tries to minimize the norm of the differences
|
||||
|
||||
def loss(prev):
|
||||
return np.linalg.norm(A @ prev - B)
|
||||
|
||||
return F.optim_minimize(loss, n_classes=A.shape[0])
|
||||
|
||||
|
||||
class PCC(AggregativeSoftQuantifier):
|
||||
"""
|
||||
`Probabilistic Classify & Count <https://ieeexplore.ieee.org/abstract/document/5694031>`_,
|
||||
|
|
@ -483,41 +376,209 @@ class PCC(AggregativeSoftQuantifier):
|
|||
return F.prevalence_from_probabilities(classif_posteriors, binarize=False)
|
||||
|
||||
|
||||
class ACC(AggregativeCrispQuantifier):
|
||||
"""
|
||||
`Adjusted Classify & Count <https://link.springer.com/article/10.1007/s10618-008-0097-y>`_,
|
||||
the "adjusted" variant of :class:`CC`, that corrects the predictions of CC
|
||||
according to the `misclassification rates`.
|
||||
|
||||
:param classifier: a sklearn's Estimator that generates a classifier
|
||||
|
||||
:param val_split: specifies the data used for generating classifier predictions. This specification
|
||||
can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to
|
||||
be extracted from the training set; or as an integer (default 5), indicating that the predictions
|
||||
are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value
|
||||
for `k`); or as a collection defining the specific set of data to use for validation.
|
||||
Alternatively, this set can be specified at fit time by indicating the exact set of data
|
||||
on which the predictions are to be generated.
|
||||
|
||||
:param str method: adjustment method to be used:
|
||||
|
||||
* 'inversion': matrix inversion method based on the matrix equality :math:`P(C)=P(C|Y)P(Y)`,
|
||||
which tries to invert :math:`P(C|Y)` matrix.
|
||||
* 'invariant-ratio': invariant ratio estimator of `Vaz et al. 2018 <https://jmlr.org/papers/v20/18-456.html>`_,
|
||||
which replaces the last equation with the normalization condition.
|
||||
|
||||
:param str solver: indicates the method to use for solving the system of linear equations. Valid options are:
|
||||
|
||||
* 'exact-raise': tries to solve the system using matrix inversion. Raises an error if the matrix has rank
|
||||
strictly less than `n_classes`.
|
||||
* 'exact-cc': if the matrix is not of full rank, returns `p_c` as the estimates, which corresponds to
|
||||
no adjustment (i.e., the classify and count method. See :class:`quapy.method.aggregative.CC`)
|
||||
* 'exact': deprecated, defaults to 'exact-cc'
|
||||
* 'minimize': minimizes the L2 norm of :math:`|Ax-B|`. This one generally works better, and is the
|
||||
default parameter. More details about this can be consulted in `Bunse, M. "On Multi-Class Extensions of
|
||||
Adjusted Classify and Count", on proceedings of the 2nd International Workshop on Learning to Quantify:
|
||||
Methods and Applications (LQ 2022), ECML/PKDD 2022, Grenoble (France)
|
||||
<https://lq-2022.github.io/proceedings/CompleteVolume.pdf>`_.
|
||||
|
||||
:param str norm: the method to use for normalization.
|
||||
|
||||
* `clip`, the values are clipped to the range [0,1] and then L1-normalized.
|
||||
* `mapsimplex` projects vectors onto the probability simplex. This implementation relies on
|
||||
`Mathieu Blondel's projection_simplex_sort <https://gist.github.com/mblondel/6f3b7aaad90606b98f71>`_
|
||||
* `condsoftmax`, applies a softmax normalization only to prevalence vectors that lie outside the simplex
|
||||
|
||||
:param n_jobs: number of parallel workers
|
||||
"""
|
||||
def __init__(
|
||||
self,
|
||||
classifier: BaseEstimator,
|
||||
val_split=5,
|
||||
solver: Literal['minimize', 'exact', 'exact-raise', 'exact-cc'] = 'minimize',
|
||||
method: Literal['inversion', 'invariant-ratio'] = 'inversion',
|
||||
norm: Literal['clip', 'mapsimplex', 'condsoftmax'] = 'clip',
|
||||
n_jobs=None,
|
||||
):
|
||||
self.classifier = classifier
|
||||
self.val_split = val_split
|
||||
self.n_jobs = qp._get_njobs(n_jobs)
|
||||
self.solver = solver
|
||||
self.method = method
|
||||
self.norm = norm
|
||||
|
||||
SOLVERS = ['exact', 'minimize', 'exact-raise', 'exact-cc']
|
||||
METHODS = ['inversion', 'invariant-ratio']
|
||||
NORMALIZATIONS = ['clip', 'mapsimplex', 'condsoftmax', None]
|
||||
|
||||
@classmethod
|
||||
def newInvariantRatioEstimation(cls, classifier: BaseEstimator, val_split=5, n_jobs=None):
|
||||
"""
|
||||
Constructs a quantifier that implements the Invariant Ratio Estimator of
|
||||
`Vaz et al. 2018 <https://jmlr.org/papers/v20/18-456.html>`_. This amounts
|
||||
to setting method to 'invariant-ratio' and clipping to 'project'.
|
||||
|
||||
:param classifier: a sklearn's Estimator that generates a classifier
|
||||
:param val_split: specifies the data used for generating classifier predictions. This specification
|
||||
can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to
|
||||
be extracted from the training set; or as an integer (default 5), indicating that the predictions
|
||||
are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value
|
||||
for `k`); or as a collection defining the specific set of data to use for validation.
|
||||
Alternatively, this set can be specified at fit time by indicating the exact set of data
|
||||
on which the predictions are to be generated.
|
||||
:param n_jobs: number of parallel workers
|
||||
:return: an instance of ACC configured so that it implements the Invariant Ratio Estimator
|
||||
"""
|
||||
return ACC(classifier, val_split=val_split, method='invariant-ratio', norm='mapsimplex', n_jobs=n_jobs)
|
||||
|
||||
def _check_init_parameters(self):
|
||||
if self.solver not in ACC.SOLVERS:
|
||||
raise ValueError(f"unknown solver; valid ones are {ACC.SOLVERS}")
|
||||
if self.method not in ACC.METHODS:
|
||||
raise ValueError(f"unknown method; valid ones are {ACC.METHODS}")
|
||||
if self.norm not in ACC.NORMALIZATIONS:
|
||||
raise ValueError(f"unknown clipping; valid ones are {ACC.NORMALIZATIONS}")
|
||||
|
||||
def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
|
||||
"""
|
||||
Estimates the misclassification rates.
|
||||
|
||||
:param classif_predictions: a :class:`quapy.data.base.LabelledCollection` containing,
|
||||
as instances, the label predictions issued by the classifier and, as labels, the true labels
|
||||
:param data: a :class:`quapy.data.base.LabelledCollection` consisting of the training data
|
||||
"""
|
||||
pred_labels, true_labels = classif_predictions.Xy
|
||||
self.cc = CC(self.classifier)
|
||||
self.Pte_cond_estim_ = ACC.getPteCondEstim(self.classifier.classes_, true_labels, pred_labels)
|
||||
|
||||
@classmethod
|
||||
def getPteCondEstim(cls, classes, y, y_):
|
||||
"""
|
||||
Estimate the matrix with entry (i,j) being the estimate of P(hat_yi|yj), that is, the probability that a
|
||||
document that belongs to yj ends up being classified as belonging to yi
|
||||
|
||||
:param classes: array-like with the class names
|
||||
:param y: array-like with the true labels
|
||||
:param y_: array-like with the estimated labels
|
||||
:return: np.ndarray
|
||||
"""
|
||||
conf = confusion_matrix(y, y_, labels=classes).T
|
||||
conf = conf.astype(float)
|
||||
class_counts = conf.sum(axis=0)
|
||||
for i, _ in enumerate(classes):
|
||||
if class_counts[i] == 0:
|
||||
conf[i, i] = 1
|
||||
else:
|
||||
conf[:, i] /= class_counts[i]
|
||||
return conf
|
||||
|
||||
def aggregate(self, classif_predictions):
|
||||
prevs_estim = self.cc.aggregate(classif_predictions)
|
||||
estimate = F.solve_adjustment(
|
||||
class_conditional_rates=self.Pte_cond_estim_,
|
||||
unadjusted_counts=prevs_estim,
|
||||
solver=self.solver,
|
||||
method=self.method,
|
||||
)
|
||||
return F.normalize_prevalence(estimate, method=self.norm)
|
||||
|
||||
|
||||
class PACC(AggregativeSoftQuantifier):
|
||||
"""
|
||||
`Probabilistic Adjusted Classify & Count <https://ieeexplore.ieee.org/abstract/document/5694031>`_,
|
||||
the probabilistic variant of ACC that relies on the posterior probabilities returned by a probabilistic classifier.
|
||||
|
||||
:param classifier: a sklearn's Estimator that generates a classifier
|
||||
|
||||
:param val_split: specifies the data used for generating classifier predictions. This specification
|
||||
can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to
|
||||
be extracted from the training set; or as an integer (default 5), indicating that the predictions
|
||||
are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value
|
||||
for `k`). Alternatively, this set can be specified at fit time by indicating the exact set of data
|
||||
on which the predictions are to be generated.
|
||||
|
||||
:param str method: adjustment method to be used:
|
||||
|
||||
* 'inversion': matrix inversion method based on the matrix equality :math:`P(C)=P(C|Y)P(Y)`,
|
||||
which tries to invert `P(C|Y)` matrix.
|
||||
* 'invariant-ratio': invariant ratio estimator of `Vaz et al. <https://jmlr.org/papers/v20/18-456.html>`_,
|
||||
which replaces the last equation with the normalization condition.
|
||||
|
||||
:param str solver: the method to use for solving the system of linear equations. Valid options are:
|
||||
|
||||
* 'exact-raise': tries to solve the system using matrix inversion.
|
||||
Raises an error if the matrix has rank strictly less than `n_classes`.
|
||||
* 'exact-cc': if the matrix is not of full rank, returns `p_c` as the estimates, which
|
||||
corresponds to no adjustment (i.e., the classify and count method. See :class:`quapy.method.aggregative.CC`)
|
||||
* 'exact': deprecated, defaults to 'exact-cc'
|
||||
* 'minimize': minimizes the L2 norm of :math:`|Ax-B|`. This one generally works better, and is the
|
||||
default parameter. More details about this can be consulted in `Bunse, M. "On Multi-Class Extensions
|
||||
of Adjusted Classify and Count", on proceedings of the 2nd International Workshop on Learning to
|
||||
Quantify: Methods and Applications (LQ 2022), ECML/PKDD 2022, Grenoble (France)
|
||||
<https://lq-2022.github.io/proceedings/CompleteVolume.pdf>`_.
|
||||
|
||||
:param str norm: the method to use for normalization.
|
||||
|
||||
* `clip`, the values are clipped to the range [0,1] and then L1-normalized.
|
||||
* `mapsimplex` projects vectors onto the probability simplex. This implementation relies on
|
||||
`Mathieu Blondel's projection_simplex_sort <https://gist.github.com/mblondel/6f3b7aaad90606b98f71>`_
|
||||
* `condsoftmax`, applies a softmax normalization only to prevalence vectors that lie outside the simplex
|
||||
|
||||
:param n_jobs: number of parallel workers
|
||||
:param solver: indicates the method to be used for obtaining the final estimates. The choice
|
||||
'exact' comes down to solving the system of linear equations :math:`Ax=B` where `A` is a
|
||||
matrix containing the class-conditional probabilities of the predictions (e.g., the tpr and fpr in
|
||||
binary) and `B` is the vector of prevalence values estimated via CC, as :math:`x=A^{-1}B`. This solution
|
||||
might not exist for degenerated classifiers, in which case the method defaults to classify and count
|
||||
(i.e., does not attempt any adjustment).
|
||||
Another option is to search for the prevalence vector that minimizes the L2 norm of :math:`|Ax-B|`. The latter
|
||||
is achieved by indicating solver='minimize'. This one generally works better, and is the default parameter.
|
||||
More details about this can be consulted in `Bunse, M. "On Multi-Class Extensions of Adjusted Classify and
|
||||
Count", on proceedings of the 2nd International Workshop on Learning to Quantify: Methods and Applications
|
||||
(LQ 2022), ECML/PKDD 2022, Grenoble (France) <https://lq-2022.github.io/proceedings/CompleteVolume.pdf>`_.
|
||||
|
||||
"""
|
||||
|
||||
def __init__(self, classifier: BaseEstimator, val_split=5, n_jobs=None, solver='minimize'):
|
||||
def __init__(
|
||||
self,
|
||||
classifier: BaseEstimator,
|
||||
val_split=5,
|
||||
solver: Literal['minimize', 'exact', 'exact-raise', 'exact-cc'] = 'minimize',
|
||||
method: Literal['inversion', 'invariant-ratio'] = 'inversion',
|
||||
norm: Literal['clip', 'mapsimplex', 'condsoftmax'] = 'clip',
|
||||
n_jobs=None
|
||||
):
|
||||
self.classifier = classifier
|
||||
self.val_split = val_split
|
||||
self.n_jobs = qp._get_njobs(n_jobs)
|
||||
self.solver = solver
|
||||
self.method = method
|
||||
self.norm = norm
|
||||
|
||||
def _check_init_parameters(self):
|
||||
assert self.solver in ['exact', 'minimize'], "unknown solver; valid ones are 'exact', 'minimize'"
|
||||
if self.solver not in ACC.SOLVERS:
|
||||
raise ValueError(f"unknown solver; valid ones are {ACC.SOLVERS}")
|
||||
if self.method not in ACC.METHODS:
|
||||
raise ValueError(f"unknown method; valid ones are {ACC.METHODS}")
|
||||
if self.clipping not in ACC.NORMALIZATIONS:
|
||||
raise ValueError(f"unknown clipping; valid ones are {ACC.NORMALIZATIONS}")
|
||||
|
||||
def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
|
||||
"""
|
||||
|
|
@ -529,11 +590,18 @@ class PACC(AggregativeSoftQuantifier):
|
|||
"""
|
||||
posteriors, true_labels = classif_predictions.Xy
|
||||
self.pcc = PCC(self.classifier)
|
||||
self.Pte_cond_estim_ = self.getPteCondEstim(self.classifier.classes_, true_labels, posteriors)
|
||||
self.Pte_cond_estim_ = PACC.getPteCondEstim(self.classifier.classes_, true_labels, posteriors)
|
||||
|
||||
def aggregate(self, classif_posteriors):
|
||||
prevs_estim = self.pcc.aggregate(classif_posteriors)
|
||||
return ACC.solve_adjustment(self.Pte_cond_estim_, prevs_estim, solver=self.solver)
|
||||
|
||||
estimate = F.solve_adjustment(
|
||||
class_conditional_rates=self.Pte_cond_estim_,
|
||||
unadjusted_counts=prevs_estim,
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solver=self.solver,
|
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method=self.method,
|
||||
)
|
||||
return F.normalize_prevalence(estimate, method=self.norm)
|
||||
|
||||
@classmethod
|
||||
def getPteCondEstim(cls, classes, y, y_):
|
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|
|
@ -885,7 +953,7 @@ class HDy(AggregativeSoftQuantifier, BinaryAggregativeQuantifier):
|
|||
# at small steps (modern implementations resort to an optimization procedure,
|
||||
# see class DistributionMatching)
|
||||
prev_selected, min_dist = None, None
|
||||
for prev in F.prevalence_linspace(n_prevalences=101, repeats=1, smooth_limits_epsilon=0.0):
|
||||
for prev in F.prevalence_linspace(grid_points=101, repeats=1, smooth_limits_epsilon=0.0):
|
||||
Px_train = prev * Pxy1_density + (1 - prev) * Pxy0_density
|
||||
hdy = F.HellingerDistance(Px_train, Px_test)
|
||||
if prev_selected is None or hdy < min_dist:
|
||||
|
|
|
|||
|
|
@ -25,6 +25,7 @@ class Status(Enum):
|
|||
|
||||
|
||||
class ConfigStatus:
|
||||
|
||||
def __init__(self, params, status, msg=''):
|
||||
self.params = params
|
||||
self.status = status
|
||||
|
|
|
|||
Loading…
Reference in New Issue