From 17896c5556599d1c3ff043ebfa73e1db993a4388 Mon Sep 17 00:00:00 2001
From: Dominique Benielli <dominique.benielli@lis-lab.fr>
Date: Thu, 23 Jan 2020 16:41:28 +0100
Subject: [PATCH] doc passed
---
copyrightstamp.txt | 1 +
doc/doccumbo/conf.py | 185 -----------
doc/doccumbo/credits.rst | 59 ----
doc/doccumbo/index.rst | 29 --
doc/doccumbo/reference/index.rst | 16 -
doc/doccumbo/reference/modules.rst | 12 -
doc/docmetricexamples/conf.py | 277 ----------------
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doc/docmetricexamples/reference/lpMKL.rst | 11 -
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doc/docmetricexamples/reference/modules.rst | 11 -
doc/docmetricexamples/reference/mvml.rst | 11 -
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doc/docmumbo/api.rst | 9 -
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.../cumbo/plot_cumbo_2_views_2_classes.py | 127 ++++++++
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examples/mumbo/README.txt | 11 +
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examples/mvml/README.txt | 10 +
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multimodal/boosting/cumbo.py | 31 +-
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create mode 100644 doc/tutorial/backreferences/multimodal.datasets.data_sample.MultiModalSparseArray.arcsin.examples
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create mode 100644 doc/tutorial/backreferences/multimodal.datasets.data_sample.MultiModalSparseArray.deg2rad.examples
create mode 100644 doc/tutorial/backreferences/multimodal.datasets.data_sample.MultiModalSparseArray.diagonal.examples
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create mode 100644 doc/tutorial/backreferences/multimodal.datasets.data_sample.MultiModalSparseArray.getnnz.examples
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create mode 100644 doc/tutorial/backreferences/multimodal.datasets.data_sample.MultiModalSparseArray.has_sorted_indices.examples
create mode 100644 doc/tutorial/backreferences/multimodal.datasets.data_sample.MultiModalSparseArray.log1p.examples
create mode 100644 doc/tutorial/backreferences/multimodal.datasets.data_sample.MultiModalSparseArray.max.examples
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create mode 100644 doc/tutorial/backreferences/multimodal.datasets.data_sample.MultiModalSparseArray.min.examples
create mode 100644 doc/tutorial/backreferences/multimodal.datasets.data_sample.MultiModalSparseArray.minimum.examples
create mode 100644 doc/tutorial/backreferences/multimodal.datasets.data_sample.MultiModalSparseArray.multiply.examples
create mode 100644 doc/tutorial/backreferences/multimodal.datasets.data_sample.MultiModalSparseArray.nnz.examples
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create mode 100644 doc/tutorial/backreferences/multimodal.datasets.data_sample.MultiModalSparseArray.resize.examples
create mode 100644 doc/tutorial/backreferences/multimodal.datasets.data_sample.MultiModalSparseArray.rint.examples
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create mode 100644 doc/tutorial/backreferences/multimodal.datasets.data_sample.MultiModalSparseArray.setdiag.examples
create mode 100644 doc/tutorial/backreferences/multimodal.datasets.data_sample.MultiModalSparseArray.shape.examples
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create mode 100644 doc/tutorial/backreferences/multimodal.datasets.data_sample.MultiModalSparseArray.sin.examples
create mode 100644 doc/tutorial/backreferences/multimodal.datasets.data_sample.MultiModalSparseArray.sinh.examples
create mode 100644 doc/tutorial/backreferences/multimodal.datasets.data_sample.MultiModalSparseArray.sort_indices.examples
create mode 100644 doc/tutorial/backreferences/multimodal.datasets.data_sample.MultiModalSparseArray.sorted_indices.examples
create mode 100644 doc/tutorial/backreferences/multimodal.datasets.data_sample.MultiModalSparseArray.sqrt.examples
create mode 100644 doc/tutorial/backreferences/multimodal.datasets.data_sample.MultiModalSparseArray.sum.examples
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create mode 100644 doc/tutorial/backreferences/multimodal.datasets.data_sample.MultiModalSparseArray.tan.examples
create mode 100644 doc/tutorial/backreferences/multimodal.datasets.data_sample.MultiModalSparseArray.tanh.examples
create mode 100644 doc/tutorial/backreferences/multimodal.datasets.data_sample.MultiModalSparseArray.toarray.examples
create mode 100644 doc/tutorial/backreferences/multimodal.datasets.data_sample.MultiModalSparseArray.tobsr.examples
create mode 100644 doc/tutorial/backreferences/multimodal.datasets.data_sample.MultiModalSparseArray.tocoo.examples
create mode 100644 doc/tutorial/backreferences/multimodal.datasets.data_sample.MultiModalSparseArray.tocsc.examples
create mode 100644 doc/tutorial/backreferences/multimodal.datasets.data_sample.MultiModalSparseArray.tocsr.examples
create mode 100644 doc/tutorial/backreferences/multimodal.datasets.data_sample.MultiModalSparseArray.todense.examples
create mode 100644 doc/tutorial/backreferences/multimodal.datasets.data_sample.MultiModalSparseArray.todia.examples
create mode 100644 doc/tutorial/backreferences/multimodal.datasets.data_sample.MultiModalSparseArray.todok.examples
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create mode 100644 doc/tutorial/backreferences/multimodal.datasets.data_sample.MultiModalSparseArray.trunc.examples
create mode 100644 doc/tutorial/backreferences/multimodal.datasets.data_sample.examples
create mode 100644 doc/tutorial/backreferences/multimodal.kernels.lpMKL.MKL.examples
create mode 100644 doc/tutorial/backreferences/multimodal.kernels.lpMKL.MKL.fit.examples
create mode 100644 doc/tutorial/backreferences/multimodal.kernels.lpMKL.MKL.get_params.examples
create mode 100644 doc/tutorial/backreferences/multimodal.kernels.lpMKL.MKL.learn_lpMKL.examples
create mode 100644 doc/tutorial/backreferences/multimodal.kernels.lpMKL.MKL.lpMKL_predict.examples
create mode 100644 doc/tutorial/backreferences/multimodal.kernels.lpMKL.MKL.predict.examples
create mode 100644 doc/tutorial/backreferences/multimodal.kernels.lpMKL.MKL.score.examples
create mode 100644 doc/tutorial/backreferences/multimodal.kernels.lpMKL.MKL.set_params.examples
create mode 100644 doc/tutorial/backreferences/multimodal.kernels.lpMKL.examples
create mode 100644 doc/tutorial/backreferences/multimodal.kernels.mkernel.MKernel.examples
create mode 100644 doc/tutorial/backreferences/multimodal.kernels.mkernel.examples
create mode 100644 doc/tutorial/backreferences/multimodal.kernels.mvml.MVML.examples
create mode 100644 doc/tutorial/backreferences/multimodal.kernels.mvml.MVML.fit.examples
create mode 100644 doc/tutorial/backreferences/multimodal.kernels.mvml.MVML.get_params.examples
create mode 100644 doc/tutorial/backreferences/multimodal.kernels.mvml.MVML.learn_mvml.examples
create mode 100644 doc/tutorial/backreferences/multimodal.kernels.mvml.MVML.predict.examples
create mode 100644 doc/tutorial/backreferences/multimodal.kernels.mvml.MVML.predict_mvml.examples
create mode 100644 doc/tutorial/backreferences/multimodal.kernels.mvml.MVML.score.examples
create mode 100644 doc/tutorial/backreferences/multimodal.kernels.mvml.MVML.set_params.examples
create mode 100644 doc/tutorial/backreferences/multimodal.kernels.mvml.examples
rename doc/{docmumbo => tutorial}/backreferences/multimodalboost.MumboClassifier.examples (100%)
rename doc/{docmumbo => tutorial}/backreferences/multimodalboost.MumboClassifier.fit.examples (100%)
rename doc/{docmumbo => tutorial}/backreferences/multimodalboost.MumboClassifier.staged_score.examples (100%)
rename doc/{docmumbo => tutorial}/backreferences/multimodalboost.mumbo.MumboClassifier.best_views_.examples (100%)
create mode 100644 doc/tutorial/backreferences/multimodalboost.mumbo.MumboClassifier.decision_function.examples
rename doc/{docmumbo => tutorial}/backreferences/multimodalboost.mumbo.MumboClassifier.estimator_weights_.examples (100%)
rename doc/{docmumbo => tutorial}/backreferences/multimodalboost.mumbo.MumboClassifier.estimators_.examples (100%)
create mode 100644 doc/tutorial/backreferences/multimodalboost.mumbo.MumboClassifier.examples
create mode 100644 doc/tutorial/backreferences/multimodalboost.mumbo.MumboClassifier.fit.examples
create mode 100644 doc/tutorial/backreferences/multimodalboost.mumbo.MumboClassifier.get_params.examples
create mode 100644 doc/tutorial/backreferences/multimodalboost.mumbo.MumboClassifier.predict.examples
create mode 100644 doc/tutorial/backreferences/multimodalboost.mumbo.MumboClassifier.score.examples
create mode 100644 doc/tutorial/backreferences/multimodalboost.mumbo.MumboClassifier.set_params.examples
create mode 100644 doc/tutorial/backreferences/multimodalboost.mumbo.MumboClassifier.staged_decision_function.examples
create mode 100644 doc/tutorial/backreferences/multimodalboost.mumbo.MumboClassifier.staged_predict.examples
create mode 100644 doc/tutorial/backreferences/multimodalboost.mumbo.MumboClassifier.staged_score.examples
create mode 100644 doc/tutorial/backreferences/multimodalboost.mumbo.examples
create mode 100644 doc/tutorial/backreferences/numpy.core._internal._ctypes.data.examples
create mode 100644 doc/tutorial/backreferences/numpy.core._internal._ctypes.data_as.examples
create mode 100644 doc/tutorial/backreferences/numpy.core._internal._ctypes.shape.examples
create mode 100644 doc/tutorial/backreferences/numpy.core._internal._ctypes.shape_as.examples
create mode 100644 doc/tutorial/backreferences/numpy.core._internal._ctypes.strides.examples
create mode 100644 doc/tutorial/backreferences/numpy.core._internal._ctypes.strides_as.examples
rename doc/{docmumbo => tutorial}/credits.rst (53%)
rename doc/{doccumbo => tutorial}/install_devel.rst (69%)
create mode 100644 examples/cumbo/README.txt
create mode 100644 examples/cumbo/plot_cumbo_2_views_2_classes.py
create mode 100644 examples/cumbo/plot_cumbo_3_views_3_classes.py
create mode 100644 examples/mumbo/README.txt
create mode 100644 examples/mumbo/plot_mumbo_2_views_2_classes.py
create mode 100644 examples/mumbo/plot_mumbo_3_views_3_classes.py
create mode 100644 examples/mvml/README.txt
create mode 100644 examples/mvml/plot_mvml_.py
diff --git a/copyrightstamp.txt b/copyrightstamp.txt
index bb382b5..64f2083 100644
--- a/copyrightstamp.txt
+++ b/copyrightstamp.txt
@@ -17,6 +17,7 @@ Contributors:
* Dominique Benielli <dominique.benielli_AT_univ-amu.fr>
* Riikka Huusari <rikka.huusari_AT_univ-amu.fr>
* Baptiste Bauvin <baptiste.bauvin_AT_univ-amu.fr>
+* Hachem Kadri <hachem.kadri_AT_lis-lab.fr>
Description:
-----------
diff --git a/doc/doccumbo/conf.py b/doc/doccumbo/conf.py
deleted file mode 100644
index e7414e8..0000000
--- a/doc/doccumbo/conf.py
+++ /dev/null
@@ -1,185 +0,0 @@
-# -*- coding: utf-8 -*-
-
-# import os, sys
-#
-# MultiviewPlatform documentation build configuration file, created by
-# sphinx-quickstart on Mon Jan 29 17:13:09 2018.
-#
-# This file is execfile()d with the current directory set to its
-# containing dir.
-#
-# Note that not all possible configuration values are present in this
-# autogenerated file.
-#
-# All configuration values have a default; values that are commented out
-# serve to show the default.
-
-# If extensions (or modules to document with autodoc) are in another directory,
-# add these directories to sys.path here. If the directory is relative to the
-# documentation root, use os.path.abspath to make it absolute, like shown here.
-#
-import os
-import sys
-
-sys.path.insert(0, os.path.abspath('.'))
-sys.path.insert(0, os.path.abspath('../../multiconfusion'))
-sys.path.insert(0, os.path.abspath('../..'))
-file_loc = os.path.split(__file__)[0]
-sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(file_loc), '.')))
-import multiconfusion
-# -- General configuration ------------------------------------------------
-
-# If your documentation needs a minimal Sphinx version, state it here.
-#
-# needs_sphinx = '1.0'
-
-add_module_names = False
-
-# sys.path.append(os.path.abspath('sphinxext'))
-
-# Add any Sphinx extension module names here, as strings. They can be
-# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
-# ones.
-extensions = ['sphinx.ext.autodoc',
-# 'sphinx.ext.doctest',
-# 'sphinx.ext.intersphinx',
-# 'sphinx.ext.todo',
-# 'nbsphinx',
- 'sphinx.ext.coverage',
- 'sphinx.ext.imgmath',
-# 'sphinx.ext.mathjax',
-# 'sphinx.ext.ifconfig',
-# 'sphinx.ext.viewcode',
-# 'sphinx.ext.githubpages',
- 'sphinx.ext.napoleon',
- 'm2r',]
-
-# Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
-
-# The suffix(es) of source filenames.
-# You can specify multiple suffix as a list of string:
-#
-# source_suffix = {'.rst': 'restructuredtext', '.md': 'markdown'}
-# source_suffix = '.rst'
-source_suffix = ['.rst', '.md']
-
-# source_parsers = {
-# '.md': CommonMarkParser,
-# }
-
-# The master toctree document.
-master_doc = 'index'
-
-# General information about the project.
-project = u'MultiConfusion'
-copyright = u'2019, Dominique Benielli'
-author = u'Dominique Benielli'
-
-# The version info for the project you're documenting, acts as replacement for
-# |version| and |release|, also used in various other places throughout the
-# built documents.
-#
-# The short X.Y version.
-version = u'0.0.0'
-# The full version, including alpha/beta/rc tags.
-release = u'0'
-
-# The language for content autogenerated by Sphinx. Refer to documentation
-# for a list of supported languages.
-#
-# This is also used if you do content translation via gettext catalogs.
-# Usually you set "language" from the command line for these cases.
-language = None
-
-# List of patterns, relative to source directory, that match files and
-# directories to ignore when looking for source files.
-# This patterns also effect to html_static_path and html_extra_path
-exclude_patterns = []
-
-# The name of the Pygments (syntax highlighting) style to use.
-pygments_style = 'sphinx'
-
-# If true, `todo` and `todoList` produce output, else they produce nothing.
-todo_include_todos = True
-
-# -- Options for HTML output ----------------------------------------------
-
-# The theme to use for HTML and HTML Help pages. See the documentation for
-# a list of builtin themes.
-#
-# html_theme = 'sphinx_rtd_theme'
-html_theme = 'classic'
-
-# Theme options are theme-specific and customize the look and feel of a theme
-# further. For a list of options available for each theme, see the
-# documentation.
-#
-# html_theme_options = {}
-
-# Add any paths that contain custom static files (such as style sheets) here,
-# relative to this directory. They are copied after the builtin static files,
-# so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = []
-
-# -- Options for HTMLHelp output ------------------------------------------
-
-# Output file base name for HTML help builder.
-htmlhelp_basename = 'MultiConfusiondoc'
-
-# -- Options for LaTeX output ---------------------------------------------
-
-latex_elements = {
- # The paper size ('letterpaper' or 'a4paper').
- #
- # 'papersize': 'letterpaper',
-
- # The font size ('10pt', '11pt' or '12pt').
- #
- # 'pointsize': '10pt',
-
- # Additional stuff for the LaTeX preamble.
- #
- # 'preamble': '',
-
- # Latex figure (float) alignment
- #
- # 'figure_align': 'htbp',
-}
-
-# Grouping the document tree into LaTeX files. List of tuples
-# (source start file, target name, title,
-# author, documentclass [howto, manual, or own class]).
-latex_documents = [
- (master_doc, 'MultiConfusion.tex', u'MultiConfusion Documentation',
- u'Dominique Benielli', 'manual'),
-]
-
-# -- Options for manual page output ---------------------------------------
-
-# One entry per manual page. List of tuples
-# (source start file, name, description, authors, manual section).
-man_pages = [
- (master_doc, 'confusion', u'MultiConfusion Documentation',
- [author], 1)
-]
-
-# -- Options for Texinfo output -------------------------------------------
-
-# Grouping the document tree into Texinfo files. List of tuples
-# (source start file, target name, title, author,
-# dir menu entry, description, category)
-texinfo_documents = [
- (master_doc, 'MultiConfusion', u'MultiConfusion Documentation',
- author, 'MultiConfusion', 'One line description of project.',
- 'Miscellaneous'),
-]
-
-# Example configuration for intersphinx: refer to the Python standard library.
-intersphinx_mapping = {'https://docs.python.org/': None}
-
-# def setup(app):
-# app.add_config_value('recommonmark_config', {
-# 'auto_toc_tree_section': 'Contents',
-# }, True)
-# app.add_transform(AutoStructify)
diff --git a/doc/doccumbo/credits.rst b/doc/doccumbo/credits.rst
deleted file mode 100644
index 9da33fd..0000000
--- a/doc/doccumbo/credits.rst
+++ /dev/null
@@ -1,59 +0,0 @@
-Credits
-=======
-
-**multiconfusion* is developped by the
-`development team <https://developpement.lis-lab.fr/>`_ of the
-`LIS <http://www.lis-lab.fr/>`_.
-
-If you use **multiconfusion** in a scientific publication, please cite the
-following paper::
-
- @InProceedings{Koco:2011:BAM,
- author={Ko\c{c}o, Sokol and Capponi, C{\'e}cile},
- editor={Gunopulos, Dimitrios and Hofmann, Thomas and Malerba, Donato
- and Vazirgiannis, Michalis},
- title={A Boosting Approach to Multiview Classification with Cooperation},
- booktitle={Proceedings of the 2011 European Conference on Machine Learning
- and Knowledge Discovery in Databases - Volume Part II},
- year={2011},
- location={Athens, Greece},
- publisher={Springer-Verlag},
- address={Berlin, Heidelberg},
- pages={209--228},
- numpages = {20},
- isbn={978-3-642-23783-6}
- url={https://link.springer.com/chapter/10.1007/978-3-642-23783-6_14},
- keywords={boosting, classification, multiview learning,
- supervised learning},
- }
-
-References
-----------
-
-* Sokol Koço, Cécile Capponi,
- `"A boosting approach to multiview classification with cooperation"
- <https://link.springer.com/chapter/10.1007/978-3-642-23783-6_14>`_,
- Proceedings of the 2011 European Conference on Machine Learning (ECML),
- Athens, Greece, pp.209-228, 2011, Springer-Verlag.
-
-* Sokol Koço,
- `"Tackling the uneven views problem with cooperation based ensemble
- learning methods" <http://www.theses.fr/en/2013AIXM4101>`_,
- PhD Thesis, Aix-Marseille Université, 2013.
-
-Copyright
----------
-
-Université d'Aix Marseille (AMU) -
-Centre National de la Recherche Scientifique (CNRS) -
-Université de Toulon (UTLN).
-
-Copyright © 2019-2020 AMU, CNRS, UTLN
-
-License
--------
-
-**multiconfusion** is free software: you can redistribute it and/or modify
-it under the terms of the **GNU Lesser General Public License** as published by
-the Free Software Foundation, either **version 3** of the License, or
-(at your option) any later version.
diff --git a/doc/doccumbo/index.rst b/doc/doccumbo/index.rst
deleted file mode 100644
index 740f792..0000000
--- a/doc/doccumbo/index.rst
+++ /dev/null
@@ -1,29 +0,0 @@
-.. Multiconfusion documentation master file, created by
- sphinx-quickstart on Mon Sep 2 12:12:08 2019.
- You can adapt this file completely to your liking, but it should at least
- contain the root `toctree` directive.
-
-Welcome to Mucumbo's documentation!
-===================================
-
-**multiconfusion** is a Python package implementing boost algorithms for
-machine learning with multimodal with confusion matrix data.
-
-It is compatible with `scikit-learn <http://scikit-learn.org/>`_, a popular
-package for machine learning in Python.
-
-.. toctree::
- :maxdepth: 2
- :caption: Contents:
-
- install_devel
- reference/index
- credits
-
-
-Indices and tables
-==================
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
diff --git a/doc/doccumbo/reference/index.rst b/doc/doccumbo/reference/index.rst
deleted file mode 100644
index 6d289a7..0000000
--- a/doc/doccumbo/reference/index.rst
+++ /dev/null
@@ -1,16 +0,0 @@
-Welcome to Multi-View Mu Cumbo's reference!
-===========================================
-
-.. toctree::
- :maxdepth: 2
- :caption: Contents:
-
- modules
-
-
-Indices and tables
-==================
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
diff --git a/doc/doccumbo/reference/modules.rst b/doc/doccumbo/reference/modules.rst
deleted file mode 100644
index b6d9bd9..0000000
--- a/doc/doccumbo/reference/modules.rst
+++ /dev/null
@@ -1,12 +0,0 @@
-multiconfusion
-==============
-
-.. toctree::
- :maxdepth: 3
-
-.. automodule:: multiconfusion.cumbo
- :members:
- :inherited-members:
-
- datasets
-
diff --git a/doc/docmetricexamples/conf.py b/doc/docmetricexamples/conf.py
deleted file mode 100644
index a45f0cf..0000000
--- a/doc/docmetricexamples/conf.py
+++ /dev/null
@@ -1,277 +0,0 @@
-# -*- coding: utf-8 -*-
-
-from datetime import date
-import os
-import sys
-sys.path.insert(0, os.path.abspath('../metriclearning'))
-sys.path.insert(0, os.path.abspath('../..'))
-sys.path.insert(0, os.path.abspath("."))
-sys.path.append(os.path.join(os.path.dirname(__name__), '..'))
-sys.path.append(os.path.join(os.path.abspath(os.path.dirname(__file__)), 'sphinxext'))
-import metriclearning
-
-# If extensions (or modules to document with autodoc) are in another directory,
-# add these directories to sys.path here. If the directory is relative to the
-# documentation root, use os.path.abspath to make it absolute, like shown here.
-# sys.path.insert(0, os.path.abspath('.'))
-
-# -- General configuration ------------------------------------------------
-
-# If your documentation needs a minimal Sphinx version, state it here.
-# needs_sphinx = '1.0'
-
-# Add any Sphinx extension module names here, as strings. They can be
-# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
-# ones.
-extensions = [
- 'sphinx.ext.autodoc',
- 'sphinx.ext.doctest',
- 'sphinx.ext.imgmath',
- 'numpydoc',
- # 'sphinx_gallery.gen_gallery'
-]
-
-# Add any paths that contain templates here, relative to this directory.
-# templates_path = ['_templates']
-
-# The suffix of source filenames.
-source_suffix = '.rst'
-
-# The encoding of source files.
-source_encoding = 'utf-8'
-
-# The master toctree document.
-master_doc = 'index'
-
-# General information about the project.
-project = 'metriclearning'
-author = 'Dominique Benielli'
-copyright = '2017-{}, LIS UMR 7020'.format(date.today().year)
-
-# The version info for the project you're documenting, acts as replacement for
-# |version| and |release|, also used in various other places throughout the
-# built documents.
-#
-# The short X.Y version.
-version = metriclearning.__version__
-# The full version, including alpha/beta/rc tags.
-release = metriclearning.__version__
-
-# The language for content autogenerated by Sphinx. Refer to documentation
-# for a list of supported languages.
-# language = None
-
-# There are two options for replacing |today|: either, you set today to some
-# non-false value, then it is used:
-# today = ''
-# Else, today_fmt is used as the format for a strftime call.
-# today_fmt = '%B %d, %Y'
-
-# List of patterns, relative to source directory, that match files and
-# directories to ignore when looking for source files.
-exclude_patterns = []
-
-# The reST default role (used for this markup: `text`) to use for all
-# documents.
-# default_role = None
-
-# If true, '()' will be appended to :func: etc. cross-reference text.
-# add_function_parentheses = True
-
-# If true, the current module name will be prepended to all description
-# unit titles (such as .. function::).
-# add_module_names = True
-
-# If true, sectionauthor and moduleauthor directives will be shown in the
-# output. They are ignored by default.
-show_authors = False
-
-# The name of the Pygments (syntax highlighting) style to use.
-pygments_style = 'sphinx'
-
-# A list of ignored prefixes for module index sorting.
-# modindex_common_prefix = []
-
-# If true, keep warnings as "system message" paragraphs in the built documents.
-# keep_warnings = False
-
-
-# -- Options for HTML output ----------------------------------------------
-
-# The theme to use for HTML and HTML Help pages. See the documentation for
-# a list of builtin themes.
-html_theme = 'nature'
-
-# Theme options are theme-specific and customize the look and feel of a theme
-# further. For a list of options available for each theme, see the
-# documentation.
-# html_theme_options = {}
-
-# Add any paths that contain custom themes here, relative to this directory.
-# html_theme_path = []
-
-# The name for this set of Sphinx documents. If None, it defaults to
-# "<project> v<release> documentation".
-# html_title = None
-
-# A shorter title for the navigation bar. Default is the same as html_title.
-# html_short_title = None
-
-# The name of an image file (relative to this directory) to place at the top
-# of the sidebar.
-# html_logo = None
-
-# The name of an image file (within the static path) to use as favicon of the
-# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32
-# pixels large.
-# html_favicon = None
-
-# Add any paths that contain custom static files (such as style sheets) here,
-# relative to this directory. They are copied after the builtin static files,
-# so a file named "default.css" will overwrite the builtin "default.css".
-# html_static_path = ['_static']
-
-# Add any extra paths that contain custom files (such as robots.txt or
-# .htaccess) here, relative to this directory. These files are copied
-# directly to the root of the documentation.
-# html_extra_path = []
-
-# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
-# using the given strftime format.
-# html_last_updated_fmt = '%b %d, %Y'
-
-# If true, SmartyPants will be used to convert quotes and dashes to
-# typographically correct entities.
-# html_use_smartypants = True
-
-# Custom sidebar templates, maps document names to template names.
-# html_sidebars = {}
-
-# Additional templates that should be rendered to pages, maps page names to
-# template names.
-# html_additional_pages = {}
-
-# If false, no module index is generated.
-# html_domain_indices = True
-
-# If false, no index is generated.
-# html_use_index = True
-
-# If true, the index is split into individual pages for each letter.
-# html_split_index = False
-
-# If true, links to the reST sources are added to the pages.
-# html_show_sourcelink = True
-
-# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
-# html_show_sphinx = True
-
-# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
-# html_show_copyright = True
-
-# If true, an OpenSearch description file will be output, and all pages will
-# contain a <link> tag referring to it. The value of this option must be the
-# base URL from which the finished HTML is served.
-# html_use_opensearch = ''
-
-# This is the file name suffix for HTML files (e.g. ".xhtml").
-# html_file_suffix = None
-
-# Output file base name for HTML help builder.
-htmlhelp_basename = '{}doc'.format(project)
-
-
-# -- Options for LaTeX output ---------------------------------------------
-
-latex_elements = {
- # The paper size ('letterpaper' or 'a4paper').
- 'papersize': 'a4paper',
-
- # The font size ('10pt', '11pt' or '12pt').
- 'pointsize': '10pt',
-
- # Additional stuff for the LaTeX preamble.
- # 'preamble': '',
-
- # Latex figure (float) alignment
- 'figure_align': 'htbp'}
-
-# Grouping the document tree into LaTeX files. List of tuples
-# (source start file, target name, title,
-# author, documentclass [howto, manual, or own class]).
-latex_documents = [
- (master_doc, '{}.tex'.format(project), '{} Documentation'.format(project),
- author, 'manual')]
-
-# The name of an image file (relative to this directory) to place at the top of
-# the title page.
-# latex_logo = None
-
-# For "manual" documents, if this is true, then toplevel headings are parts,
-# not chapters.
-# latex_use_parts = False
-
-# If true, show page references after internal links.
-# latex_show_pagerefs = False
-
-# If true, show URL addresses after external links.
-# latex_show_urls = False
-
-# Documents to append as an appendix to all manuals.
-# latex_appendices = []
-
-# If false, no module index is generated.
-# latex_domain_indices = True
-
-
-# -- Options for manual page output ---------------------------------------
-
-# One entry per manual page. List of tuples
-# (source start file, name, description, authors, manual section).
-man_pages = [
- (master_doc, project, '{} Documentation'.format(project),
- [author], 1)
-]
-
-# If true, show URL addresses after external links.
-# man_show_urls = False
-
-# -- Options for Texinfo output -------------------------------------------
-
-# Grouping the document tree into Texinfo files. List of tuples
-# (source start file, target name, title, author,
-# dir menu entry, description, category)
-texinfo_documents = [
- (master_doc, project, '{} Documentation'.format(project), author, project,
- 'Multi-View Metric Learning in Vector-Valued Kernel Spaces for machine learning.',
- 'Miscellaneous')]
-
-# Documents to append as an appendix to all manuals.
-# texinfo_appendices = []
-
-# If false, no module index is generated.
-# texinfo_domain_indices = True
-
-# How to display URL addresses: 'footnote', 'no', or 'inline'.
-# texinfo_show_urls = 'footnote'
-
-# If true, do not generate a @detailmenu in the "Top" node's menu.
-# texinfo_no_detailmenu = False
-
-# Example configuration for intersphinx: refer to the Python standard library.
-intersphinx_mapping = {
- 'sklearn': ('http://scikit-learn.org/stable', None)
-}
-
-numpydoc_show_class_members = False
-
-sphinx_gallery_conf = {
- 'doc_module': (project,),
- 'backreferences_dir': 'backreferences',
- # path to your examples scripts
- 'examples_dirs': '../examples',
- # path where to save gallery generated examples
- 'gallery_dirs': 'auto_examples'}
-
-# Generate the plots for the gallery
-plot_gallery = 'True'
diff --git a/doc/docmetricexamples/index.rst b/doc/docmetricexamples/index.rst
deleted file mode 100644
index 71e56a0..0000000
--- a/doc/docmetricexamples/index.rst
+++ /dev/null
@@ -1,21 +0,0 @@
-.. metriclearning documentation master file, created by
- sphinx-quickstart on Mon Sep 2 12:12:08 2019.
- You can adapt this file completely to your liking, but it should at least
- contain the root `toctree` directive.
-
-Welcome to metriclearning's documentation!
-==========================================
-
-.. toctree::
- :maxdepth: 2
- :caption: Contents:
-
- reference/index
-
-
-Indices and tables
-==================
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
diff --git a/doc/docmetricexamples/reference/datasets.rst b/doc/docmetricexamples/reference/datasets.rst
deleted file mode 100644
index 21d17f4..0000000
--- a/doc/docmetricexamples/reference/datasets.rst
+++ /dev/null
@@ -1,14 +0,0 @@
-datasets
-========
-
-.. automodule:: metriclearning.datasets.base
- :members:
- :undoc-members:
- :show-inheritance:
-
-
-
-.. automodule:: metriclearning.datasets.data_sample
- :members:
- :undoc-members:
- :show-inheritance:
diff --git a/doc/docmetricexamples/reference/index.rst b/doc/docmetricexamples/reference/index.rst
deleted file mode 100644
index f0c60bf..0000000
--- a/doc/docmetricexamples/reference/index.rst
+++ /dev/null
@@ -1,16 +0,0 @@
-Welcome to Multi-View metriclearning's reference!
-=================================================
-
-.. toctree::
- :maxdepth: 2
- :caption: Contents:
-
- modules
-
-
-Indices and tables
-==================
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
diff --git a/doc/docmetricexamples/reference/lpMKL.rst b/doc/docmetricexamples/reference/lpMKL.rst
deleted file mode 100644
index 27a1e37..0000000
--- a/doc/docmetricexamples/reference/lpMKL.rst
+++ /dev/null
@@ -1,11 +0,0 @@
-lpMKL
-=====
-
- .. automodule:: metriclearning.lpMKL
- :members: MKL
- :undoc-members:
- :show-inheritance:
-
-
-
-
diff --git a/doc/docmetricexamples/reference/mkernel.rst b/doc/docmetricexamples/reference/mkernel.rst
deleted file mode 100644
index b3eaf1c..0000000
--- a/doc/docmetricexamples/reference/mkernel.rst
+++ /dev/null
@@ -1,9 +0,0 @@
-abstract mkernel
-================
-
-.. automodule:: metriclearning.mkernel
- :members: MKernel
- :undoc-members:
- :show-inheritance:
-
-
diff --git a/doc/docmetricexamples/reference/modules.rst b/doc/docmetricexamples/reference/modules.rst
deleted file mode 100644
index 6d61b3e..0000000
--- a/doc/docmetricexamples/reference/modules.rst
+++ /dev/null
@@ -1,11 +0,0 @@
-metriclearning
-==============
-
-.. toctree::
- :maxdepth: 3
-
- mkernel
- mvml
- lpMKL
- datasets
-
diff --git a/doc/docmetricexamples/reference/mvml.rst b/doc/docmetricexamples/reference/mvml.rst
deleted file mode 100644
index 3dae7b8..0000000
--- a/doc/docmetricexamples/reference/mvml.rst
+++ /dev/null
@@ -1,11 +0,0 @@
-MVML of metriclearning
-======================
-
-.. automodule:: metriclearning.mvml
- :members: MVML
- :undoc-members:
- :show-inheritance:
-
-
-
-
diff --git a/doc/docmetricexamples/tes-demo.py b/doc/docmetricexamples/tes-demo.py
deleted file mode 100644
index 2632e5e..0000000
--- a/doc/docmetricexamples/tes-demo.py
+++ /dev/null
@@ -1,97 +0,0 @@
-import numpy as np
-from sklearn import datasets
-from sklearn.metrics.pairwise import rbf_kernel
-from metriclearning.mvml import MVML
-from metriclearning.lpMKL import MKL
-from metriclearning.datasets.data_sample import DataSample
-from metriclearning.tests.datasets.get_dataset_path import get_dataset_path
-import pickle
-np.random.seed(4)
-
-# =========== create a simple dataset ============
-
-n_tot = 200
-half = int(n_tot/2)
-n_tr = 120
-
-# create a bit more data than needed so that we can take "half" amount of samples for each class
-X0, y0 = datasets.make_moons(n_samples=n_tot+2, noise=0.3, shuffle=False)
-X1, y1 = datasets.make_circles(n_samples=n_tot+2, noise=0.1, shuffle=False)
-
-# make multi-view correspondence (select equal number of samples for both classes and order the data same way
-# in both views)
-
-yinds0 = np.append(np.where(y0 == 0)[0][0:half], np.where(y0 == 1)[0][0:half])
-yinds1 = np.append(np.where(y1 == 0)[0][0:half], np.where(y1 == 1)[0][0:half])
-
-X0 = X0[yinds0, :]
-X1 = X1[yinds1, :]
-Y = np.append(np.zeros(half)-1, np.ones(half)) # labels -1 and 1
-
-n_tot = 200
-half = int(n_tot/2)
-n_tr = 120
-
-# create a bit more data than needed so that we can take "half" amount of samples for each class
-X0, y0 = datasets.make_moons(n_samples=n_tot+2, noise=0.3, shuffle=False)
-X1, y1 = datasets.make_circles(n_samples=n_tot+2, noise=0.1, shuffle=False)
-
-# make multi-view correspondence (select equal number of samples for both classes and order the data same way
-# in both views)
-
-yinds0 = np.append(np.where(y0 == 0)[0][0:half], np.where(y0 == 1)[0][0:half])
-yinds1 = np.append(np.where(y1 == 0)[0][0:half], np.where(y1 == 1)[0][0:half])
-
-X0 = X0[yinds0, :]
-X1 = X1[yinds1, :]
-Y = np.append(np.zeros(half)-1, np.ones(half)) # labels -1 and 1
-
-
-# shuffle
-order = np.random.permutation(n_tot)
-X0 = X0[order, :]
-X1 = X1[order, :]
-Y = Y[order]
-
-# make kernel dictionaries
-kernel_dict = {}
-test_kernel_dict = {}
-kernel_dict[0] = rbf_kernel(X0[0:n_tr, :])
-kernel_dict[1] = rbf_kernel(X1[0:n_tr, :])
-test_kernel_dict[0] = rbf_kernel(X0[n_tr:n_tot, :], X0[0:n_tr, :])
-test_kernel_dict[1] = rbf_kernel(X1[n_tr:n_tot, :], X1[0:n_tr, :])
-
-d= DataSample(kernel_dict)
-a = d.data
-# np.save(input_x, kernel_dict)
-# np.save(input_y, Y)
-# f = open(input_x, "wb")
-# pickle.dump(input_x, f)
-#input_x = get_dataset_path("input_x_dic.pkl")
-#f = open(input_x, "r")
-#dicoc = pickle.load(f)
-# pickle.dump(kernel_dict, f)
-#f.close()
-# =========== use MVML in classifying the data ============
-
-# demo on how the code is intended to be used; parameters are not cross-validated, just picked some
-# mvml = MVML(kernel_dict, Y[0:n_tr], [0.1, 1], nystrom_param=0.2)
-
-
-mvml = MVML( [0.1, 1], nystrom_param=0.2)
-mvml.fit(a, Y[0:n_tr])
-print("x shape", mvml.X_.shape)
-print("x shape int",mvml.X_.shapes_int)
-dd = DataSample(test_kernel_dict)
-X_test = dd.data
-red1 = mvml.predict(X_test)
-
-mkl = MKL(lmbda=0.1)
-mkl.fit(kernel_dict,Y[0:n_tr] )
-
-mkl.predict(X_test)
-#red1 = np.sign(mvml.predict_mvml(test_kernel_dict, g1, w1))
-
-
-
-
diff --git a/doc/docmumbo/api.rst b/doc/docmumbo/api.rst
deleted file mode 100644
index 5a9ad85..0000000
--- a/doc/docmumbo/api.rst
+++ /dev/null
@@ -1,9 +0,0 @@
-API Documentation
-=================
-
-multimodalboost.mumbo
----------------------
-
-.. automodule:: multimodalboost.mumbo
- :members:
- :inherited-members:
diff --git a/doc/docmumbo/auto_examples/auto_examples_jupyter.zip b/doc/docmumbo/auto_examples/auto_examples_jupyter.zip
deleted file mode 100644
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diff --git a/doc/docmumbo/auto_examples/index.rst b/doc/docmumbo/auto_examples/index.rst
deleted file mode 100644
index ba7c2af..0000000
--- a/doc/docmumbo/auto_examples/index.rst
+++ /dev/null
@@ -1,83 +0,0 @@
-:orphan:
-
-
-
-.. _sphx_glr_auto_examples:
-
-.. _examples:
-
-Examples
-========
-
-The following toy examples illustrate how the MuMBo algorithm exploits
-cooperation between views for classification.
-
-
-
-.. raw:: html
-
- <div class="sphx-glr-thumbcontainer" tooltip="In this toy example, we generate data from two classes, split between two two-dimensional views...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_2_views_2_classes_thumb.png
-
- :ref:`sphx_glr_auto_examples_plot_2_views_2_classes.py`
-
-.. raw:: html
-
- </div>
-
-
-.. toctree::
- :hidden:
-
- /auto_examples/plot_2_views_2_classes
-
-.. raw:: html
-
- <div class="sphx-glr-thumbcontainer" tooltip="In this toy example, we generate data from three classes, split between three two-dimensional v...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_3_views_3_classes_thumb.png
-
- :ref:`sphx_glr_auto_examples_plot_3_views_3_classes.py`
-
-.. raw:: html
-
- </div>
-
-
-.. toctree::
- :hidden:
-
- /auto_examples/plot_3_views_3_classes
-.. raw:: html
-
- <div class="sphx-glr-clear"></div>
-
-
-
-.. only :: html
-
- .. container:: sphx-glr-footer
- :class: sphx-glr-footer-gallery
-
-
- .. container:: sphx-glr-download
-
- :download:`Download all examples in Python source code: auto_examples_python.zip <//home/dominique/projets/ANR-Lives/multimodal/multimodalboost/doc/auto_examples/auto_examples_python.zip>`
-
-
-
- .. container:: sphx-glr-download
-
- :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip <//home/dominique/projets/ANR-Lives/multimodal/multimodalboost/doc/auto_examples/auto_examples_jupyter.zip>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
- `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/doc/docmumbo/auto_examples/plot_2_views_2_classes.ipynb b/doc/docmumbo/auto_examples/plot_2_views_2_classes.ipynb
deleted file mode 100644
index 958f7f5..0000000
--- a/doc/docmumbo/auto_examples/plot_2_views_2_classes.ipynb
+++ /dev/null
@@ -1,54 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n==========================\n2 views, 2 classes example\n==========================\n\nIn this toy example, we generate data from two classes, split between two\ntwo-dimensional views.\n\nFor each view, the data are generated so that half of the points of each class\nare well separated in the plane, while the other half of the points are not\nseparated and placed in the same area. We also insure that the points that are\nnot separated in one view are well separated in the other view.\n\nThus, in the figure representing the data, the points represented by crosses\n(x) are well separated in view 0 while they are not separated in view 1, while\nthe points represented by dots (.) are well separated in view 1 while they are\nnot separated in view 0. In this figure, the blue symbols represent points\nof class 0, while red symbols represent points of class 1.\n\nThe MuMBo algorithm take adavantage of the complementarity of the two views to\nrightly classify the points.\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Universit\u00e9 d'Aix Marseille (AMU) -\n# Centre National de la Recherche Scientifique (CNRS) -\n# Universit\u00e9 de Toulon (UTLN).\n# Copyright \u00a9 2017-2018 AMU, CNRS, UTLN\n#\n# This file is part of multimodalboost.\n#\n# multimodalboost is free software: you can redistribute it and/or modify\n# it under the terms of the GNU Lesser General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# multimodalboost is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU Lesser General Public License for more details.\n#\n# You should have received a copy of the GNU Lesser General Public License\n# along with multimodalboost. If not, see <http://www.gnu.org/licenses/>.\n#\n# Author: Florent JAILLET - Laboratoire d'Informatique et Syst\u00e8mes - UMR 7020\n\nimport numpy as np\nfrom multimodalboost.mumbo import MumboClassifier\nfrom matplotlib import pyplot as plt\n\n\ndef generate_data(n_samples, lim):\n \"\"\"Generate random data in a rectangle\"\"\"\n lim = np.array(lim)\n n_features = lim.shape[0]\n data = np.random.random((n_samples, n_features))\n data = (lim[:, 1]-lim[:, 0]) * data + lim[:, 0]\n return data\n\n\nseed = 12\nnp.random.seed(seed)\n\nn_samples = 100\n\nview_0 = np.concatenate((generate_data(n_samples, [[0., 1.], [0., 1.]]),\n generate_data(n_samples, [[1., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [1., 2.]])))\n\nview_1 = np.concatenate((generate_data(n_samples, [[1., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [1., 2.]]),\n generate_data(n_samples, [[0., 1.], [0., 1.]])))\n\nX = np.concatenate((view_0, view_1), axis=1)\n\ny = np.zeros(4*n_samples, dtype=np.int64)\ny[2*n_samples:] = 1\n\nviews_ind = np.array([0, 2, 4])\n\nn_estimators = 3\nclf = MumboClassifier(n_estimators=n_estimators)\nclf.fit(X, y, views_ind)\n\nprint('\\nAfter 3 iterations, the MuMBo classifier reaches exact '\n 'classification for the\\nlearning samples:')\nfor ind, score in enumerate(clf.staged_score(X, y)):\n print(' - iteration {}, score: {}'.format(ind + 1, score))\n\n\nprint('\\nThe resulting MuMBo classifier uses three sub-classifiers that are '\n 'wheighted\\nusing the following weights:\\n'\n ' estimator weights: {}'.format(clf.estimator_weights_))\n\nprint('\\nThe two first sub-classifiers use the data of view 0 to compute '\n 'their\\nclassification results, while the third one uses the data of '\n 'view 1:\\n'\n ' best views: {}'. format(clf.best_views_))\n\nprint('\\nThe first figure displays the data, splitting the representation '\n 'between the\\ntwo views.')\n\nfig = plt.figure(figsize=(10., 8.))\nfig.suptitle('Representation of the data', size=16)\nfor ind_view in range(2):\n ax = plt.subplot(2, 1, ind_view + 1)\n ax.set_title('View {}'.format(ind_view))\n ind_feature = ind_view * 2\n styles = ('.b', 'xb', '.r', 'xr')\n labels = ('non-separated', 'separated')\n for ind in range(4):\n ind_class = ind // 2\n label = labels[(ind + ind_view) % 2]\n ax.plot(X[n_samples*ind:n_samples*(ind+1), ind_feature],\n X[n_samples*ind:n_samples*(ind+1), ind_feature + 1],\n styles[ind],\n label='Class {} ({})'.format(ind_class, label))\n ax.legend()\n\nprint('\\nThe second figure displays the classification results for the '\n 'sub-classifiers\\non the learning sample data.\\n')\n\nstyles = ('.b', '.r')\nfig = plt.figure(figsize=(12., 7.))\nfig.suptitle('Classification results on the learning data for the '\n 'sub-classifiers', size=16)\nfor ind_estimator in range(n_estimators):\n best_view = clf.best_views_[ind_estimator]\n y_pred = clf.estimators_[ind_estimator].predict(\n X[:, 2*best_view:2*best_view+2])\n background_color = (1.0, 1.0, 0.9)\n for ind_view in range(2):\n ax = plt.subplot(2, 3, ind_estimator + 3*ind_view + 1)\n if ind_view == best_view:\n ax.set_facecolor(background_color)\n ax.set_title(\n 'Sub-classifier {} - View {}'.format(ind_estimator, ind_view))\n ind_feature = ind_view * 2\n for ind_class in range(2):\n ind_samples = (y_pred == ind_class)\n ax.plot(X[ind_samples, ind_feature],\n X[ind_samples, ind_feature + 1],\n styles[ind_class],\n label='Class {}'.format(ind_class))\n ax.legend(title='Predicted class:')\n\nplt.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
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- "language": "python",
- "name": "python3"
- },
- "language_info": {
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- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/doc/docmumbo/auto_examples/plot_2_views_2_classes.py.md5 b/doc/docmumbo/auto_examples/plot_2_views_2_classes.py.md5
deleted file mode 100644
index 1172a53..0000000
--- a/doc/docmumbo/auto_examples/plot_2_views_2_classes.py.md5
+++ /dev/null
@@ -1 +0,0 @@
-6bfb170b1af59e9ec2958eea239976e2
\ No newline at end of file
diff --git a/doc/docmumbo/auto_examples/plot_2_views_2_classes_codeobj.pickle b/doc/docmumbo/auto_examples/plot_2_views_2_classes_codeobj.pickle
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diff --git a/doc/docmumbo/auto_examples/plot_3_views_3_classes.ipynb b/doc/docmumbo/auto_examples/plot_3_views_3_classes.ipynb
deleted file mode 100644
index bf392e7..0000000
--- a/doc/docmumbo/auto_examples/plot_3_views_3_classes.ipynb
+++ /dev/null
@@ -1,54 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n==========================\n3 views, 3 classes example\n==========================\n\nIn this toy example, we generate data from three classes, split between three\ntwo-dimensional views.\n\nFor each view, the data are generated so that the points for two classes are\nwell seperated, while the points for the third class are not seperated with\nthe two other classes. That means that, taken separately, none of the single\nviews allows for a good classification of the data.\n\nNevertheless, the MuMBo algorithm take adavantage of the complementarity of\nthe views to rightly classify the points.\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Universit\u00e9 d'Aix Marseille (AMU) -\n# Centre National de la Recherche Scientifique (CNRS) -\n# Universit\u00e9 de Toulon (UTLN).\n# Copyright \u00a9 2017-2018 AMU, CNRS, UTLN\n#\n# This file is part of multimodalboost.\n#\n# multimodalboost is free software: you can redistribute it and/or modify\n# it under the terms of the GNU Lesser General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# multimodalboost is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU Lesser General Public License for more details.\n#\n# You should have received a copy of the GNU Lesser General Public License\n# along with multimodalboost. If not, see <http://www.gnu.org/licenses/>.\n#\n# Author: Florent JAILLET - Laboratoire d'Informatique et Syst\u00e8mes - UMR 7020\n\nimport numpy as np\nfrom multimodalboost.mumbo import MumboClassifier\nfrom matplotlib import pyplot as plt\n\n\ndef generate_data(n_samples, lim):\n \"\"\"Generate random data in a rectangle\"\"\"\n lim = np.array(lim)\n n_features = lim.shape[0]\n data = np.random.random((n_samples, n_features))\n data = (lim[:, 1]-lim[:, 0]) * data + lim[:, 0]\n return data\n\n\nseed = 12\nnp.random.seed(seed)\n\nn_samples = 300\n\nview_0 = np.concatenate((generate_data(n_samples, [[0., 1.], [0., 1.]]),\n generate_data(n_samples, [[1., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 2.], [0., 1.]])))\n\nview_1 = np.concatenate((generate_data(n_samples, [[1., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [0., 1.]])))\n\nview_2 = np.concatenate((generate_data(n_samples, [[0., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [0., 1.]]),\n generate_data(n_samples, [[1., 2.], [0., 1.]])))\n\nX = np.concatenate((view_0, view_1, view_2), axis=1)\n\ny = np.zeros(3*n_samples, dtype=np.int64)\ny[n_samples:2*n_samples] = 1\ny[2*n_samples:] = 2\n\nviews_ind = np.array([0, 2, 4, 6])\n\nn_estimators = 4\nclf = MumboClassifier(n_estimators=n_estimators)\nclf.fit(X, y, views_ind)\n\nprint('\\nAfter 4 iterations, the MuMBo classifier reaches exact '\n 'classification for the\\nlearning samples:')\nfor ind, score in enumerate(clf.staged_score(X, y)):\n print(' - iteration {}, score: {}'.format(ind + 1, score))\n\nprint('\\nThe resulting MuMBo classifier uses four sub-classifiers that are '\n 'wheighted\\nusing the following weights:\\n'\n ' estimator weights: {}'.format(clf.estimator_weights_))\n\nprint('\\nThe first sub-classifier uses the data of view 0 to compute '\n 'its classification\\nresults, the second and third sub-classifiers use '\n 'the data of view 1, while the\\nfourth one uses the data of '\n 'view 2:\\n'\n ' best views: {}'. format(clf.best_views_))\n\nprint('\\nThe first figure displays the data, splitting the representation '\n 'between the\\nthree views.')\n\nstyles = ('.b', '.r', '.g')\nfig = plt.figure(figsize=(12., 11.))\nfig.suptitle('Representation of the data', size=16)\nfor ind_view in range(3):\n ax = plt.subplot(3, 1, ind_view + 1)\n ax.set_title('View {}'.format(ind_view))\n ind_feature = ind_view * 2\n for ind_class in range(3):\n ind_samples = (y == ind_class)\n ax.plot(X[ind_samples, ind_feature],\n X[ind_samples, ind_feature + 1],\n styles[ind_class],\n label='Class {}'.format(ind_class))\n ax.legend(loc='upper left', framealpha=0.9)\n\nprint('\\nThe second figure displays the classification results for the '\n 'sub-classifiers\\non the learning sample data.\\n')\n\nfig = plt.figure(figsize=(14., 11.))\nfig.suptitle('Classification results on the learning data for the '\n 'sub-classifiers', size=16)\nfor ind_estimator in range(n_estimators):\n best_view = clf.best_views_[ind_estimator]\n y_pred = clf.estimators_[ind_estimator].predict(\n X[:, 2*best_view:2*best_view+2])\n background_color = (1.0, 1.0, 0.9)\n for ind_view in range(3):\n ax = plt.subplot(3, 4, ind_estimator + 4*ind_view + 1)\n if ind_view == best_view:\n ax.set_facecolor(background_color)\n ax.set_title(\n 'Sub-classifier {} - View {}'.format(ind_estimator, ind_view))\n ind_feature = ind_view * 2\n for ind_class in range(3):\n ind_samples = (y_pred == ind_class)\n ax.plot(X[ind_samples, ind_feature],\n X[ind_samples, ind_feature + 1],\n styles[ind_class],\n label='Class {}'.format(ind_class))\n ax.legend(title='Predicted class:', loc='upper left', framealpha=0.9)\n\nplt.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
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- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/doc/docmumbo/auto_examples/plot_3_views_3_classes.py.md5 b/doc/docmumbo/auto_examples/plot_3_views_3_classes.py.md5
deleted file mode 100644
index e7abc15..0000000
--- a/doc/docmumbo/auto_examples/plot_3_views_3_classes.py.md5
+++ /dev/null
@@ -1 +0,0 @@
-2dba08fc2c1e223dbeac65eeda17b109
\ No newline at end of file
diff --git a/doc/docmumbo/auto_examples/plot_3_views_3_classes_codeobj.pickle b/doc/docmumbo/auto_examples/plot_3_views_3_classes_codeobj.pickle
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diff --git a/doc/docmumbo/auto_examples/sg_execution_times.rst b/doc/docmumbo/auto_examples/sg_execution_times.rst
deleted file mode 100644
index 2ac4607..0000000
--- a/doc/docmumbo/auto_examples/sg_execution_times.rst
+++ /dev/null
@@ -1,14 +0,0 @@
-
-:orphan:
-
-.. _sphx_glr_auto_examples_sg_execution_times:
-
-Computation times
-=================
-**00:02.026** total execution time for **auto_examples** files:
-
-+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_plot_3_views_3_classes.py` (``plot_3_views_3_classes.py``) | 00:01.293 | 0.0 MB |
-+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_plot_2_views_2_classes.py` (``plot_2_views_2_classes.py``) | 00:00.733 | 0.0 MB |
-+-----------------------------------------------------------------------------------------+-----------+--------+
diff --git a/doc/docmumbo/conf.py b/doc/docmumbo/conf.py
deleted file mode 100644
index e989eef..0000000
--- a/doc/docmumbo/conf.py
+++ /dev/null
@@ -1,270 +0,0 @@
-# -*- coding: utf-8 -*-
-
-from datetime import date
-import os
-
-import multimodalboost
-
-# If extensions (or modules to document with autodoc) are in another directory,
-# add these directories to sys.path here. If the directory is relative to the
-# documentation root, use os.path.abspath to make it absolute, like shown here.
-# sys.path.insert(0, os.path.abspath('.'))
-
-# -- General configuration ------------------------------------------------
-
-# If your documentation needs a minimal Sphinx version, state it here.
-# needs_sphinx = '1.0'
-
-# Add any Sphinx extension module names here, as strings. They can be
-# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
-# ones.
-extensions = [
- 'sphinx.ext.autodoc',
- 'sphinx.ext.doctest',
- 'numpydoc',
- 'sphinx_gallery.gen_gallery']
-
-# Add any paths that contain templates here, relative to this directory.
-# templates_path = ['_templates']
-
-# The suffix of source filenames.
-source_suffix = '.rst'
-
-# The encoding of source files.
-source_encoding = 'utf-8'
-
-# The master toctree document.
-master_doc = 'index'
-
-# General information about the project.
-project = 'multimodalboost'
-author = 'Florent Jaillet'
-copyright = '2017-{}, LIS UMR 7020'.format(date.today().year)
-
-# The version info for the project you're documenting, acts as replacement for
-# |version| and |release|, also used in various other places throughout the
-# built documents.
-#
-# The short X.Y version.
-version = multimodalboost.__version__
-# The full version, including alpha/beta/rc tags.
-release = multimodalboost.__version__
-
-# The language for content autogenerated by Sphinx. Refer to documentation
-# for a list of supported languages.
-# language = None
-
-# There are two options for replacing |today|: either, you set today to some
-# non-false value, then it is used:
-# today = ''
-# Else, today_fmt is used as the format for a strftime call.
-# today_fmt = '%B %d, %Y'
-
-# List of patterns, relative to source directory, that match files and
-# directories to ignore when looking for source files.
-exclude_patterns = ['_build']
-
-# The reST default role (used for this markup: `text`) to use for all
-# documents.
-# default_role = None
-
-# If true, '()' will be appended to :func: etc. cross-reference text.
-# add_function_parentheses = True
-
-# If true, the current module name will be prepended to all description
-# unit titles (such as .. function::).
-# add_module_names = True
-
-# If true, sectionauthor and moduleauthor directives will be shown in the
-# output. They are ignored by default.
-show_authors = False
-
-# The name of the Pygments (syntax highlighting) style to use.
-pygments_style = 'sphinx'
-
-# A list of ignored prefixes for module index sorting.
-# modindex_common_prefix = []
-
-# If true, keep warnings as "system message" paragraphs in the built documents.
-# keep_warnings = False
-
-
-# -- Options for HTML output ----------------------------------------------
-
-# The theme to use for HTML and HTML Help pages. See the documentation for
-# a list of builtin themes.
-html_theme = 'nature'
-
-# Theme options are theme-specific and customize the look and feel of a theme
-# further. For a list of options available for each theme, see the
-# documentation.
-# html_theme_options = {}
-
-# Add any paths that contain custom themes here, relative to this directory.
-# html_theme_path = []
-
-# The name for this set of Sphinx documents. If None, it defaults to
-# "<project> v<release> documentation".
-# html_title = None
-
-# A shorter title for the navigation bar. Default is the same as html_title.
-# html_short_title = None
-
-# The name of an image file (relative to this directory) to place at the top
-# of the sidebar.
-# html_logo = None
-
-# The name of an image file (within the static path) to use as favicon of the
-# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32
-# pixels large.
-# html_favicon = None
-
-# Add any paths that contain custom static files (such as style sheets) here,
-# relative to this directory. They are copied after the builtin static files,
-# so a file named "default.css" will overwrite the builtin "default.css".
-# html_static_path = ['_static']
-
-# Add any extra paths that contain custom files (such as robots.txt or
-# .htaccess) here, relative to this directory. These files are copied
-# directly to the root of the documentation.
-# html_extra_path = []
-
-# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
-# using the given strftime format.
-# html_last_updated_fmt = '%b %d, %Y'
-
-# If true, SmartyPants will be used to convert quotes and dashes to
-# typographically correct entities.
-# html_use_smartypants = True
-
-# Custom sidebar templates, maps document names to template names.
-# html_sidebars = {}
-
-# Additional templates that should be rendered to pages, maps page names to
-# template names.
-# html_additional_pages = {}
-
-# If false, no module index is generated.
-# html_domain_indices = True
-
-# If false, no index is generated.
-# html_use_index = True
-
-# If true, the index is split into individual pages for each letter.
-# html_split_index = False
-
-# If true, links to the reST sources are added to the pages.
-# html_show_sourcelink = True
-
-# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
-# html_show_sphinx = True
-
-# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
-# html_show_copyright = True
-
-# If true, an OpenSearch description file will be output, and all pages will
-# contain a <link> tag referring to it. The value of this option must be the
-# base URL from which the finished HTML is served.
-# html_use_opensearch = ''
-
-# This is the file name suffix for HTML files (e.g. ".xhtml").
-# html_file_suffix = None
-
-# Output file base name for HTML help builder.
-htmlhelp_basename = '{}doc'.format(project)
-
-
-# -- Options for LaTeX output ---------------------------------------------
-
-latex_elements = {
- # The paper size ('letterpaper' or 'a4paper').
- 'papersize': 'a4paper',
-
- # The font size ('10pt', '11pt' or '12pt').
- 'pointsize': '10pt',
-
- # Additional stuff for the LaTeX preamble.
- # 'preamble': '',
-
- # Latex figure (float) alignment
- 'figure_align': 'htbp'}
-
-# Grouping the document tree into LaTeX files. List of tuples
-# (source start file, target name, title,
-# author, documentclass [howto, manual, or own class]).
-latex_documents = [
- (master_doc, '{}.tex'.format(project), '{} Documentation'.format(project),
- author, 'manual')]
-
-# The name of an image file (relative to this directory) to place at the top of
-# the title page.
-# latex_logo = None
-
-# For "manual" documents, if this is true, then toplevel headings are parts,
-# not chapters.
-# latex_use_parts = False
-
-# If true, show page references after internal links.
-# latex_show_pagerefs = False
-
-# If true, show URL addresses after external links.
-# latex_show_urls = False
-
-# Documents to append as an appendix to all manuals.
-# latex_appendices = []
-
-# If false, no module index is generated.
-# latex_domain_indices = True
-
-
-# -- Options for manual page output ---------------------------------------
-
-# One entry per manual page. List of tuples
-# (source start file, name, description, authors, manual section).
-man_pages = [
- (master_doc, project, '{} Documentation'.format(project),
- [author], 1)
-]
-
-# If true, show URL addresses after external links.
-# man_show_urls = False
-
-# -- Options for Texinfo output -------------------------------------------
-
-# Grouping the document tree into Texinfo files. List of tuples
-# (source start file, target name, title, author,
-# dir menu entry, description, category)
-texinfo_documents = [
- (master_doc, project, '{} Documentation'.format(project), author, project,
- 'Boost algorithms for machine learning with multimodal data.',
- 'Miscellaneous')]
-
-# Documents to append as an appendix to all manuals.
-# texinfo_appendices = []
-
-# If false, no module index is generated.
-# texinfo_domain_indices = True
-
-# How to display URL addresses: 'footnote', 'no', or 'inline'.
-# texinfo_show_urls = 'footnote'
-
-# If true, do not generate a @detailmenu in the "Top" node's menu.
-# texinfo_no_detailmenu = False
-
-# Example configuration for intersphinx: refer to the Python standard library.
-intersphinx_mapping = {
- 'sklearn': ('http://scikit-learn.org/stable', None)
-}
-
-numpydoc_show_class_members = False
-
-sphinx_gallery_conf = {
- 'doc_module': (project,),
- 'backreferences_dir': 'backreferences',
- # path to your examples scripts
- 'examples_dirs': '../examples',
- # path where to save gallery generated examples
- 'gallery_dirs': 'auto_examples'}
-
-# Generate the plots for the gallery
-plot_gallery = 'True'
diff --git a/doc/docmumbo/index.rst b/doc/docmumbo/index.rst
deleted file mode 100644
index 3687ae3..0000000
--- a/doc/docmumbo/index.rst
+++ /dev/null
@@ -1,34 +0,0 @@
-.. project-template documentation master file, created by
- sphinx-quickstart on Mon Jan 18 14:44:12 2016.
- You can adapt this file completely to your liking, but it should at least
- contain the root `toctree` directive.
-
-multimodalboost
-===============
-
-**multimodalboost** is a Python package implementing boost algorithms for
-machine learning with multimodal data.
-
-It is compatible with `scikit-learn <http://scikit-learn.org/>`_, a popular
-package for machine learning in Python.
-
-Documentation
--------------
-
-:Release: |version|
-:Date: |today|
-
-.. toctree::
- :maxdepth: 1
-
- install_devel
- api
- auto_examples/index
- credits
-
-Indices and tables
-------------------
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
diff --git a/doc/index.rst b/doc/index.rst
index 6e58256..d21b6f5 100644
--- a/doc/index.rst
+++ b/doc/index.rst
@@ -24,7 +24,7 @@ Documentation
reference/api
tutorial/install_devel
tutorial/auto_examples/index
-
+ tutorial/auto_examples/sg_execution_times
Indices and tables
diff --git a/doc/tutorial/auto_examples/auto_examples_jupyter.zip b/doc/tutorial/auto_examples/auto_examples_jupyter.zip
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diff --git a/doc/tutorial/auto_examples/cumbo/cumbo_plot_2_views_2_classes.ipynb b/doc/tutorial/auto_examples/cumbo/cumbo_plot_2_views_2_classes.ipynb
new file mode 100644
index 0000000..81b05f4
--- /dev/null
+++ b/doc/tutorial/auto_examples/cumbo/cumbo_plot_2_views_2_classes.ipynb
@@ -0,0 +1,54 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n==================================\nMuCombo 2 views, 2 classes example\n==================================\n\nIn this toy example, we generate data from two classes, split between two\ntwo-dimensional views.\n\nFor each view, the data are generated so that half of the points of each class\nare well separated in the plane, while the other half of the points are not\nseparated and placed in the same area. We also insure that the points that are\nnot separated in one view are well separated in the other view.\n\nThus, in the figure representing the data, the points represented by crosses\n(x) are well separated in view 0 while they are not separated in view 1, while\nthe points represented by dots (.) are well separated in view 1 while they are\nnot separated in view 0. In this figure, the blue symbols represent points\nof class 0, while red symbols represent points of class 1.\n\nThe MuCuMBo algorithm take adavantage of the complementarity of the two views to\nrightly classify the points.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\nfrom multimodal.boosting.cumbo import MuCumboClassifier\nfrom matplotlib import pyplot as plt\n\n\ndef generate_data(n_samples, lim):\n \"\"\"Generate random data in a rectangle\"\"\"\n lim = np.array(lim)\n n_features = lim.shape[0]\n data = np.random.random((n_samples, n_features))\n data = (lim[:, 1]-lim[:, 0]) * data + lim[:, 0]\n return data\n\n\nseed = 12\nnp.random.seed(seed)\n\nn_samples = 100\n\nview_0 = np.concatenate((generate_data(n_samples, [[0., 1.], [0., 1.]]),\n generate_data(n_samples, [[1., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [1., 2.]])))\n\nview_1 = np.concatenate((generate_data(n_samples, [[1., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [1., 2.]]),\n generate_data(n_samples, [[0., 1.], [0., 1.]])))\n\nX = np.concatenate((view_0, view_1), axis=1)\n\ny = np.zeros(4*n_samples, dtype=np.int64)\ny[2*n_samples:] = 1\n\nviews_ind = np.array([0, 2, 4])\n\nn_estimators = 3\nclf = MuCumboClassifier(n_estimators=n_estimators)\nclf.fit(X, y, views_ind)\n\nprint('\\nAfter 3 iterations, the MuCuMBo classifier reaches exact '\n 'classification for the\\nlearning samples:')\nfor ind, score in enumerate(clf.staged_score(X, y)):\n print(' - iteration {}, score: {}'.format(ind + 1, score))\n\n\nprint('\\nThe resulting MuCuMBo classifier uses three sub-classifiers that are '\n 'wheighted\\nusing the following weights:\\n'\n ' estimator weights: {}'.format(clf.estimator_weights_alpha_))\n\n# print('\\nThe two first sub-classifiers use the data of view 0 to compute '\n# 'their\\nclassification results, while the third one uses the data of '\n# 'view 1:\\n'\n# ' best views: {}'. format(clf.best_views_))\n\nprint('\\nThe first figure displays the data, splitting the representation '\n 'between the\\ntwo views.')\n\nfig = plt.figure(figsize=(10., 8.))\nfig.suptitle('Representation of the data', size=16)\nfor ind_view in range(2):\n ax = plt.subplot(2, 1, ind_view + 1)\n ax.set_title('View {}'.format(ind_view))\n ind_feature = ind_view * 2\n styles = ('.b', 'xb', '.r', 'xr')\n labels = ('non-separated', 'separated')\n for ind in range(4):\n ind_class = ind // 2\n label = labels[(ind + ind_view) % 2]\n ax.plot(X[n_samples*ind:n_samples*(ind+1), ind_feature],\n X[n_samples*ind:n_samples*(ind+1), ind_feature + 1],\n styles[ind],\n label='Class {} ({})'.format(ind_class, label))\n ax.legend()\n\nprint('\\nThe second figure displays the classification results for the '\n 'sub-classifiers\\non the learning sample data.\\n')\n\nstyles = ('.b', '.r')\n# fig = plt.figure(figsize=(12., 7.))\n# fig.suptitle('Classification results on the learning data for the '\n# 'sub-classifiers', size=16)\n# for ind_estimator in range(n_estimators):\n# best_view = clf.best_views_[ind_estimator]\n# y_pred = clf.estimators_[ind_estimator].predict(\n# X[:, 2*best_view:2*best_view+2])\n# background_color = (1.0, 1.0, 0.9)\n# for ind_view in range(2):\n# ax = plt.subplot(2, 3, ind_estimator + 3*ind_view + 1)\n# if ind_view == best_view:\n# ax.set_facecolor(background_color)\n# ax.set_title(\n# 'Sub-classifier {} - View {}'.format(ind_estimator, ind_view))\n# ind_feature = ind_view * 2\n# for ind_class in range(2):\n# ind_samples = (y_pred == ind_class)\n# ax.plot(X[ind_samples, ind_feature],\n# X[ind_samples, ind_feature + 1],\n# styles[ind_class],\n# label='Class {}'.format(ind_class))\n# ax.legend(title='Predicted class:')\n\nplt.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/examples/cumbo_plot_2_views_2_classes.py b/doc/tutorial/auto_examples/cumbo/cumbo_plot_2_views_2_classes.py
similarity index 94%
rename from examples/cumbo_plot_2_views_2_classes.py
rename to doc/tutorial/auto_examples/cumbo/cumbo_plot_2_views_2_classes.py
index 28760f5..c669f69 100644
--- a/examples/cumbo_plot_2_views_2_classes.py
+++ b/doc/tutorial/auto_examples/cumbo/cumbo_plot_2_views_2_classes.py
@@ -1,8 +1,8 @@
# -*- coding: utf-8 -*-
"""
-==========================
-2 views, 2 classes example
-==========================
+==================================
+MuCombo 2 views, 2 classes example
+==================================
In this toy example, we generate data from two classes, split between two
two-dimensional views.
@@ -18,7 +18,7 @@ the points represented by dots (.) are well separated in view 1 while they are
not separated in view 0. In this figure, the blue symbols represent points
of class 0, while red symbols represent points of class 1.
-The MuMBo algorithm take adavantage of the complementarity of the two views to
+The MuCuMBo algorithm take adavantage of the complementarity of the two views to
rightly classify the points.
"""
@@ -62,7 +62,7 @@ n_estimators = 3
clf = MuCumboClassifier(n_estimators=n_estimators)
clf.fit(X, y, views_ind)
-print('\nAfter 3 iterations, the MuMBo classifier reaches exact '
+print('\nAfter 3 iterations, the MuCuMBo classifier reaches exact '
'classification for the\nlearning samples:')
for ind, score in enumerate(clf.staged_score(X, y)):
print(' - iteration {}, score: {}'.format(ind + 1, score))
diff --git a/doc/tutorial/auto_examples/cumbo/cumbo_plot_2_views_2_classes.rst b/doc/tutorial/auto_examples/cumbo/cumbo_plot_2_views_2_classes.rst
new file mode 100644
index 0000000..e539862
--- /dev/null
+++ b/doc/tutorial/auto_examples/cumbo/cumbo_plot_2_views_2_classes.rst
@@ -0,0 +1,170 @@
+.. note::
+ :class: sphx-glr-download-link-note
+
+ Click :ref:`here <sphx_glr_download_tutorial_auto_examples_cumbo_cumbo_plot_2_views_2_classes.py>` to download the full example code
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorial_auto_examples_cumbo_cumbo_plot_2_views_2_classes.py:
+
+
+==================================
+MuCombo 2 views, 2 classes example
+==================================
+
+In this toy example, we generate data from two classes, split between two
+two-dimensional views.
+
+For each view, the data are generated so that half of the points of each class
+are well separated in the plane, while the other half of the points are not
+separated and placed in the same area. We also insure that the points that are
+not separated in one view are well separated in the other view.
+
+Thus, in the figure representing the data, the points represented by crosses
+(x) are well separated in view 0 while they are not separated in view 1, while
+the points represented by dots (.) are well separated in view 1 while they are
+not separated in view 0. In this figure, the blue symbols represent points
+of class 0, while red symbols represent points of class 1.
+
+The MuCuMBo algorithm take adavantage of the complementarity of the two views to
+rightly classify the points.
+
+
+.. code-block:: default
+
+
+ import numpy as np
+ from multimodal.boosting.cumbo import MuCumboClassifier
+ from matplotlib import pyplot as plt
+
+
+ def generate_data(n_samples, lim):
+ """Generate random data in a rectangle"""
+ lim = np.array(lim)
+ n_features = lim.shape[0]
+ data = np.random.random((n_samples, n_features))
+ data = (lim[:, 1]-lim[:, 0]) * data + lim[:, 0]
+ return data
+
+
+ seed = 12
+ np.random.seed(seed)
+
+ n_samples = 100
+
+ view_0 = np.concatenate((generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [1., 2.]])))
+
+ view_1 = np.concatenate((generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [1., 2.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]])))
+
+ X = np.concatenate((view_0, view_1), axis=1)
+
+ y = np.zeros(4*n_samples, dtype=np.int64)
+ y[2*n_samples:] = 1
+
+ views_ind = np.array([0, 2, 4])
+
+ n_estimators = 3
+ clf = MuCumboClassifier(n_estimators=n_estimators)
+ clf.fit(X, y, views_ind)
+
+ print('\nAfter 3 iterations, the MuCuMBo classifier reaches exact '
+ 'classification for the\nlearning samples:')
+ for ind, score in enumerate(clf.staged_score(X, y)):
+ print(' - iteration {}, score: {}'.format(ind + 1, score))
+
+
+ print('\nThe resulting MuCuMBo classifier uses three sub-classifiers that are '
+ 'wheighted\nusing the following weights:\n'
+ ' estimator weights: {}'.format(clf.estimator_weights_alpha_))
+
+ # print('\nThe two first sub-classifiers use the data of view 0 to compute '
+ # 'their\nclassification results, while the third one uses the data of '
+ # 'view 1:\n'
+ # ' best views: {}'. format(clf.best_views_))
+
+ print('\nThe first figure displays the data, splitting the representation '
+ 'between the\ntwo views.')
+
+ fig = plt.figure(figsize=(10., 8.))
+ fig.suptitle('Representation of the data', size=16)
+ for ind_view in range(2):
+ ax = plt.subplot(2, 1, ind_view + 1)
+ ax.set_title('View {}'.format(ind_view))
+ ind_feature = ind_view * 2
+ styles = ('.b', 'xb', '.r', 'xr')
+ labels = ('non-separated', 'separated')
+ for ind in range(4):
+ ind_class = ind // 2
+ label = labels[(ind + ind_view) % 2]
+ ax.plot(X[n_samples*ind:n_samples*(ind+1), ind_feature],
+ X[n_samples*ind:n_samples*(ind+1), ind_feature + 1],
+ styles[ind],
+ label='Class {} ({})'.format(ind_class, label))
+ ax.legend()
+
+ print('\nThe second figure displays the classification results for the '
+ 'sub-classifiers\non the learning sample data.\n')
+
+ styles = ('.b', '.r')
+ # fig = plt.figure(figsize=(12., 7.))
+ # fig.suptitle('Classification results on the learning data for the '
+ # 'sub-classifiers', size=16)
+ # for ind_estimator in range(n_estimators):
+ # best_view = clf.best_views_[ind_estimator]
+ # y_pred = clf.estimators_[ind_estimator].predict(
+ # X[:, 2*best_view:2*best_view+2])
+ # background_color = (1.0, 1.0, 0.9)
+ # for ind_view in range(2):
+ # ax = plt.subplot(2, 3, ind_estimator + 3*ind_view + 1)
+ # if ind_view == best_view:
+ # ax.set_facecolor(background_color)
+ # ax.set_title(
+ # 'Sub-classifier {} - View {}'.format(ind_estimator, ind_view))
+ # ind_feature = ind_view * 2
+ # for ind_class in range(2):
+ # ind_samples = (y_pred == ind_class)
+ # ax.plot(X[ind_samples, ind_feature],
+ # X[ind_samples, ind_feature + 1],
+ # styles[ind_class],
+ # label='Class {}'.format(ind_class))
+ # ax.legend(title='Predicted class:')
+
+ plt.show()
+
+
+.. rst-class:: sphx-glr-timing
+
+ **Total running time of the script:** ( 0 minutes 0.000 seconds)
+
+
+.. _sphx_glr_download_tutorial_auto_examples_cumbo_cumbo_plot_2_views_2_classes.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+ :class: sphx-glr-footer-example
+
+
+
+ .. container:: sphx-glr-download
+
+ :download:`Download Python source code: cumbo_plot_2_views_2_classes.py <cumbo_plot_2_views_2_classes.py>`
+
+
+
+ .. container:: sphx-glr-download
+
+ :download:`Download Jupyter notebook: cumbo_plot_2_views_2_classes.ipynb <cumbo_plot_2_views_2_classes.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+ `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/doc/tutorial/auto_examples/cumbo/cumbo_plot_2_views_2_classes_codeobj.pickle b/doc/tutorial/auto_examples/cumbo/cumbo_plot_2_views_2_classes_codeobj.pickle
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diff --git a/doc/tutorial/auto_examples/cumbo/cumbo_plot_3_views_3_classes.ipynb b/doc/tutorial/auto_examples/cumbo/cumbo_plot_3_views_3_classes.ipynb
new file mode 100644
index 0000000..a6981bb
--- /dev/null
+++ b/doc/tutorial/auto_examples/cumbo/cumbo_plot_3_views_3_classes.ipynb
@@ -0,0 +1,54 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n==================================\nMuCumbo 3 views, 3 classes example\n==================================\n\nIn this toy example, we generate data from three classes, split between three\ntwo-dimensional views.\n\nFor each view, the data are generated so that the points for two classes are\nwell seperated, while the points for the third class are not seperated with\nthe two other classes. That means that, taken separately, none of the single\nviews allows for a good classification of the data.\n\nNevertheless, the MuCuMbo algorithm take adavantage of the complementarity of\nthe views to rightly classify the points.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\nfrom multimodal.boosting.cumbo import MuCumboClassifier\nfrom matplotlib import pyplot as plt\n\n\ndef generate_data(n_samples, lim):\n \"\"\"Generate random data in a rectangle\"\"\"\n lim = np.array(lim)\n n_features = lim.shape[0]\n data = np.random.random((n_samples, n_features))\n data = (lim[:, 1]-lim[:, 0]) * data + lim[:, 0]\n return data\n\n\nseed = 12\nnp.random.seed(seed)\n\nn_samples = 300\n\nview_0 = np.concatenate((generate_data(n_samples, [[0., 1.], [0., 1.]]),\n generate_data(n_samples, [[1., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 2.], [0., 1.]])))\n\nview_1 = np.concatenate((generate_data(n_samples, [[1., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [0., 1.]])))\n\nview_2 = np.concatenate((generate_data(n_samples, [[0., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [0., 1.]]),\n generate_data(n_samples, [[1., 2.], [0., 1.]])))\n\nX = np.concatenate((view_0, view_1, view_2), axis=1)\n\ny = np.zeros(3*n_samples, dtype=np.int64)\ny[n_samples:2*n_samples] = 1\ny[2*n_samples:] = 2\n\nviews_ind = np.array([0, 2, 4, 6])\n\nn_estimators = 4\nclf = MuCumboClassifier(n_estimators=n_estimators)\nclf.fit(X, y, views_ind)\n\nprint('\\nAfter 4 iterations, the MuCuMBo classifier reaches exact '\n 'classification for the\\nlearning samples:')\nfor ind, score in enumerate(clf.staged_score(X, y)):\n print(' - iteration {}, score: {}'.format(ind + 1, score))\n\nprint('\\nThe resulting MuCuMBo classifier uses four sub-classifiers that are '\n 'wheighted\\nusing the following weights:\\n'\n ' estimator weights alpha: {}'.format(clf.estimator_weights_alpha_))\n\n# print('\\nThe first sub-classifier uses the data of view 0 to compute '\n# 'its classification\\nresults, the second and third sub-classifiers use '\n# 'the data of view 1, while the\\nfourth one uses the data of '\n# 'view 2:\\n'\n# ' best views: {}'. format(clf.best_views_))\n\nprint('\\nThe first figure displays the data, splitting the representation '\n 'between the\\nthree views.')\n\nstyles = ('.b', '.r', '.g')\nfig = plt.figure(figsize=(12., 11.))\nfig.suptitle('Representation of the data', size=16)\nfor ind_view in range(3):\n ax = plt.subplot(3, 1, ind_view + 1)\n ax.set_title('View {}'.format(ind_view))\n ind_feature = ind_view * 2\n for ind_class in range(3):\n ind_samples = (y == ind_class)\n ax.plot(X[ind_samples, ind_feature],\n X[ind_samples, ind_feature + 1],\n styles[ind_class],\n label='Class {}'.format(ind_class))\n ax.legend(loc='upper left', framealpha=0.9)\n\nprint('\\nThe second figure displays the classification results for the '\n 'sub-classifiers\\non the learning sample data.\\n')\n\n# fig = plt.figure(figsize=(14., 11.))\n# fig.suptitle('Classification results on the learning data for the '\n# 'sub-classifiers', size=16)\n# for ind_estimator in range(n_estimators):\n# best_view = clf.best_views_[ind_estimator]\n# y_pred = clf.estimators_[ind_estimator].predict(\n# X[:, 2*best_view:2*best_view+2])\n# background_color = (1.0, 1.0, 0.9)\n# for ind_view in range(3):\n# ax = plt.subplot(3, 4, ind_estimator + 4*ind_view + 1)\n# if ind_view == best_view:\n# ax.set_facecolor(background_color)\n# ax.set_title(\n# 'Sub-classifier {} - View {}'.format(ind_estimator, ind_view))\n# ind_feature = ind_view * 2\n# for ind_class in range(3):\n# ind_samples = (y_pred == ind_class)\n# ax.plot(X[ind_samples, ind_feature],\n# X[ind_samples, ind_feature + 1],\n# styles[ind_class],\n# label='Class {}'.format(ind_class))\n# ax.legend(title='Predicted class:', loc='upper left', framealpha=0.9)\n\nplt.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/examples/cumbo_plot_3_views_3_classes.py b/doc/tutorial/auto_examples/cumbo/cumbo_plot_3_views_3_classes.py
similarity index 96%
rename from examples/cumbo_plot_3_views_3_classes.py
rename to doc/tutorial/auto_examples/cumbo/cumbo_plot_3_views_3_classes.py
index 6a03a10..058b2dc 100644
--- a/examples/cumbo_plot_3_views_3_classes.py
+++ b/doc/tutorial/auto_examples/cumbo/cumbo_plot_3_views_3_classes.py
@@ -1,8 +1,8 @@
# -*- coding: utf-8 -*-
"""
-==========================
-3 views, 3 classes example
-==========================
+==================================
+MuCumbo 3 views, 3 classes example
+==================================
In this toy example, we generate data from three classes, split between three
two-dimensional views.
@@ -12,7 +12,7 @@ well seperated, while the points for the third class are not seperated with
the two other classes. That means that, taken separately, none of the single
views allows for a good classification of the data.
-Nevertheless, the MuMBo algorithm take adavantage of the complementarity of
+Nevertheless, the MuCuMbo algorithm take adavantage of the complementarity of
the views to rightly classify the points.
"""
diff --git a/doc/tutorial/auto_examples/cumbo/cumbo_plot_3_views_3_classes.rst b/doc/tutorial/auto_examples/cumbo/cumbo_plot_3_views_3_classes.rst
new file mode 100644
index 0000000..aa7cfab
--- /dev/null
+++ b/doc/tutorial/auto_examples/cumbo/cumbo_plot_3_views_3_classes.rst
@@ -0,0 +1,164 @@
+.. note::
+ :class: sphx-glr-download-link-note
+
+ Click :ref:`here <sphx_glr_download_tutorial_auto_examples_cumbo_cumbo_plot_3_views_3_classes.py>` to download the full example code
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorial_auto_examples_cumbo_cumbo_plot_3_views_3_classes.py:
+
+
+==================================
+MuCumbo 3 views, 3 classes example
+==================================
+
+In this toy example, we generate data from three classes, split between three
+two-dimensional views.
+
+For each view, the data are generated so that the points for two classes are
+well seperated, while the points for the third class are not seperated with
+the two other classes. That means that, taken separately, none of the single
+views allows for a good classification of the data.
+
+Nevertheless, the MuCuMbo algorithm take adavantage of the complementarity of
+the views to rightly classify the points.
+
+
+.. code-block:: default
+
+
+ import numpy as np
+ from multimodal.boosting.cumbo import MuCumboClassifier
+ from matplotlib import pyplot as plt
+
+
+ def generate_data(n_samples, lim):
+ """Generate random data in a rectangle"""
+ lim = np.array(lim)
+ n_features = lim.shape[0]
+ data = np.random.random((n_samples, n_features))
+ data = (lim[:, 1]-lim[:, 0]) * data + lim[:, 0]
+ return data
+
+
+ seed = 12
+ np.random.seed(seed)
+
+ n_samples = 300
+
+ view_0 = np.concatenate((generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 2.], [0., 1.]])))
+
+ view_1 = np.concatenate((generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]])))
+
+ view_2 = np.concatenate((generate_data(n_samples, [[0., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[1., 2.], [0., 1.]])))
+
+ X = np.concatenate((view_0, view_1, view_2), axis=1)
+
+ y = np.zeros(3*n_samples, dtype=np.int64)
+ y[n_samples:2*n_samples] = 1
+ y[2*n_samples:] = 2
+
+ views_ind = np.array([0, 2, 4, 6])
+
+ n_estimators = 4
+ clf = MuCumboClassifier(n_estimators=n_estimators)
+ clf.fit(X, y, views_ind)
+
+ print('\nAfter 4 iterations, the MuCuMBo classifier reaches exact '
+ 'classification for the\nlearning samples:')
+ for ind, score in enumerate(clf.staged_score(X, y)):
+ print(' - iteration {}, score: {}'.format(ind + 1, score))
+
+ print('\nThe resulting MuCuMBo classifier uses four sub-classifiers that are '
+ 'wheighted\nusing the following weights:\n'
+ ' estimator weights alpha: {}'.format(clf.estimator_weights_alpha_))
+
+ # print('\nThe first sub-classifier uses the data of view 0 to compute '
+ # 'its classification\nresults, the second and third sub-classifiers use '
+ # 'the data of view 1, while the\nfourth one uses the data of '
+ # 'view 2:\n'
+ # ' best views: {}'. format(clf.best_views_))
+
+ print('\nThe first figure displays the data, splitting the representation '
+ 'between the\nthree views.')
+
+ styles = ('.b', '.r', '.g')
+ fig = plt.figure(figsize=(12., 11.))
+ fig.suptitle('Representation of the data', size=16)
+ for ind_view in range(3):
+ ax = plt.subplot(3, 1, ind_view + 1)
+ ax.set_title('View {}'.format(ind_view))
+ ind_feature = ind_view * 2
+ for ind_class in range(3):
+ ind_samples = (y == ind_class)
+ ax.plot(X[ind_samples, ind_feature],
+ X[ind_samples, ind_feature + 1],
+ styles[ind_class],
+ label='Class {}'.format(ind_class))
+ ax.legend(loc='upper left', framealpha=0.9)
+
+ print('\nThe second figure displays the classification results for the '
+ 'sub-classifiers\non the learning sample data.\n')
+
+ # fig = plt.figure(figsize=(14., 11.))
+ # fig.suptitle('Classification results on the learning data for the '
+ # 'sub-classifiers', size=16)
+ # for ind_estimator in range(n_estimators):
+ # best_view = clf.best_views_[ind_estimator]
+ # y_pred = clf.estimators_[ind_estimator].predict(
+ # X[:, 2*best_view:2*best_view+2])
+ # background_color = (1.0, 1.0, 0.9)
+ # for ind_view in range(3):
+ # ax = plt.subplot(3, 4, ind_estimator + 4*ind_view + 1)
+ # if ind_view == best_view:
+ # ax.set_facecolor(background_color)
+ # ax.set_title(
+ # 'Sub-classifier {} - View {}'.format(ind_estimator, ind_view))
+ # ind_feature = ind_view * 2
+ # for ind_class in range(3):
+ # ind_samples = (y_pred == ind_class)
+ # ax.plot(X[ind_samples, ind_feature],
+ # X[ind_samples, ind_feature + 1],
+ # styles[ind_class],
+ # label='Class {}'.format(ind_class))
+ # ax.legend(title='Predicted class:', loc='upper left', framealpha=0.9)
+
+ plt.show()
+
+
+.. rst-class:: sphx-glr-timing
+
+ **Total running time of the script:** ( 0 minutes 0.000 seconds)
+
+
+.. _sphx_glr_download_tutorial_auto_examples_cumbo_cumbo_plot_3_views_3_classes.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+ :class: sphx-glr-footer-example
+
+
+
+ .. container:: sphx-glr-download
+
+ :download:`Download Python source code: cumbo_plot_3_views_3_classes.py <cumbo_plot_3_views_3_classes.py>`
+
+
+
+ .. container:: sphx-glr-download
+
+ :download:`Download Jupyter notebook: cumbo_plot_3_views_3_classes.ipynb <cumbo_plot_3_views_3_classes.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+ `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/doc/tutorial/auto_examples/cumbo/cumbo_plot_3_views_3_classes_codeobj.pickle b/doc/tutorial/auto_examples/cumbo/cumbo_plot_3_views_3_classes_codeobj.pickle
new file mode 100644
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diff --git a/doc/docmumbo/auto_examples/images/sphx_glr_plot_2_views_2_classes_001.png b/doc/tutorial/auto_examples/cumbo/images/sphx_glr_plot_cumbo_2_views_2_classes_001.png
similarity index 100%
rename from doc/docmumbo/auto_examples/images/sphx_glr_plot_2_views_2_classes_001.png
rename to doc/tutorial/auto_examples/cumbo/images/sphx_glr_plot_cumbo_2_views_2_classes_001.png
diff --git a/doc/docmumbo/auto_examples/images/sphx_glr_plot_3_views_3_classes_001.png b/doc/tutorial/auto_examples/cumbo/images/sphx_glr_plot_cumbo_3_views_3_classes_001.png
similarity index 100%
rename from doc/docmumbo/auto_examples/images/sphx_glr_plot_3_views_3_classes_001.png
rename to doc/tutorial/auto_examples/cumbo/images/sphx_glr_plot_cumbo_3_views_3_classes_001.png
diff --git a/doc/tutorial/auto_examples/cumbo/images/thumb/sphx_glr_cumbo_plot_2_views_2_classes_thumb.png b/doc/tutorial/auto_examples/cumbo/images/thumb/sphx_glr_cumbo_plot_2_views_2_classes_thumb.png
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diff --git a/doc/tutorial/auto_examples/cumbo/images/thumb/sphx_glr_cumbo_plot_3_views_3_classes_thumb.png b/doc/tutorial/auto_examples/cumbo/images/thumb/sphx_glr_cumbo_plot_3_views_3_classes_thumb.png
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diff --git a/doc/docmumbo/auto_examples/images/thumb/sphx_glr_plot_2_views_2_classes_thumb.png b/doc/tutorial/auto_examples/cumbo/images/thumb/sphx_glr_plot_cumbo_2_views_2_classes_thumb.png
similarity index 100%
rename from doc/docmumbo/auto_examples/images/thumb/sphx_glr_plot_2_views_2_classes_thumb.png
rename to doc/tutorial/auto_examples/cumbo/images/thumb/sphx_glr_plot_cumbo_2_views_2_classes_thumb.png
diff --git a/doc/docmumbo/auto_examples/images/thumb/sphx_glr_plot_3_views_3_classes_thumb.png b/doc/tutorial/auto_examples/cumbo/images/thumb/sphx_glr_plot_cumbo_3_views_3_classes_thumb.png
similarity index 100%
rename from doc/docmumbo/auto_examples/images/thumb/sphx_glr_plot_3_views_3_classes_thumb.png
rename to doc/tutorial/auto_examples/cumbo/images/thumb/sphx_glr_plot_cumbo_3_views_3_classes_thumb.png
diff --git a/doc/tutorial/auto_examples/cumbo/plot_cumbo_2_views_2_classes.ipynb b/doc/tutorial/auto_examples/cumbo/plot_cumbo_2_views_2_classes.ipynb
new file mode 100644
index 0000000..81b05f4
--- /dev/null
+++ b/doc/tutorial/auto_examples/cumbo/plot_cumbo_2_views_2_classes.ipynb
@@ -0,0 +1,54 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n==================================\nMuCombo 2 views, 2 classes example\n==================================\n\nIn this toy example, we generate data from two classes, split between two\ntwo-dimensional views.\n\nFor each view, the data are generated so that half of the points of each class\nare well separated in the plane, while the other half of the points are not\nseparated and placed in the same area. We also insure that the points that are\nnot separated in one view are well separated in the other view.\n\nThus, in the figure representing the data, the points represented by crosses\n(x) are well separated in view 0 while they are not separated in view 1, while\nthe points represented by dots (.) are well separated in view 1 while they are\nnot separated in view 0. In this figure, the blue symbols represent points\nof class 0, while red symbols represent points of class 1.\n\nThe MuCuMBo algorithm take adavantage of the complementarity of the two views to\nrightly classify the points.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\nfrom multimodal.boosting.cumbo import MuCumboClassifier\nfrom matplotlib import pyplot as plt\n\n\ndef generate_data(n_samples, lim):\n \"\"\"Generate random data in a rectangle\"\"\"\n lim = np.array(lim)\n n_features = lim.shape[0]\n data = np.random.random((n_samples, n_features))\n data = (lim[:, 1]-lim[:, 0]) * data + lim[:, 0]\n return data\n\n\nseed = 12\nnp.random.seed(seed)\n\nn_samples = 100\n\nview_0 = np.concatenate((generate_data(n_samples, [[0., 1.], [0., 1.]]),\n generate_data(n_samples, [[1., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [1., 2.]])))\n\nview_1 = np.concatenate((generate_data(n_samples, [[1., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [1., 2.]]),\n generate_data(n_samples, [[0., 1.], [0., 1.]])))\n\nX = np.concatenate((view_0, view_1), axis=1)\n\ny = np.zeros(4*n_samples, dtype=np.int64)\ny[2*n_samples:] = 1\n\nviews_ind = np.array([0, 2, 4])\n\nn_estimators = 3\nclf = MuCumboClassifier(n_estimators=n_estimators)\nclf.fit(X, y, views_ind)\n\nprint('\\nAfter 3 iterations, the MuCuMBo classifier reaches exact '\n 'classification for the\\nlearning samples:')\nfor ind, score in enumerate(clf.staged_score(X, y)):\n print(' - iteration {}, score: {}'.format(ind + 1, score))\n\n\nprint('\\nThe resulting MuCuMBo classifier uses three sub-classifiers that are '\n 'wheighted\\nusing the following weights:\\n'\n ' estimator weights: {}'.format(clf.estimator_weights_alpha_))\n\n# print('\\nThe two first sub-classifiers use the data of view 0 to compute '\n# 'their\\nclassification results, while the third one uses the data of '\n# 'view 1:\\n'\n# ' best views: {}'. format(clf.best_views_))\n\nprint('\\nThe first figure displays the data, splitting the representation '\n 'between the\\ntwo views.')\n\nfig = plt.figure(figsize=(10., 8.))\nfig.suptitle('Representation of the data', size=16)\nfor ind_view in range(2):\n ax = plt.subplot(2, 1, ind_view + 1)\n ax.set_title('View {}'.format(ind_view))\n ind_feature = ind_view * 2\n styles = ('.b', 'xb', '.r', 'xr')\n labels = ('non-separated', 'separated')\n for ind in range(4):\n ind_class = ind // 2\n label = labels[(ind + ind_view) % 2]\n ax.plot(X[n_samples*ind:n_samples*(ind+1), ind_feature],\n X[n_samples*ind:n_samples*(ind+1), ind_feature + 1],\n styles[ind],\n label='Class {} ({})'.format(ind_class, label))\n ax.legend()\n\nprint('\\nThe second figure displays the classification results for the '\n 'sub-classifiers\\non the learning sample data.\\n')\n\nstyles = ('.b', '.r')\n# fig = plt.figure(figsize=(12., 7.))\n# fig.suptitle('Classification results on the learning data for the '\n# 'sub-classifiers', size=16)\n# for ind_estimator in range(n_estimators):\n# best_view = clf.best_views_[ind_estimator]\n# y_pred = clf.estimators_[ind_estimator].predict(\n# X[:, 2*best_view:2*best_view+2])\n# background_color = (1.0, 1.0, 0.9)\n# for ind_view in range(2):\n# ax = plt.subplot(2, 3, ind_estimator + 3*ind_view + 1)\n# if ind_view == best_view:\n# ax.set_facecolor(background_color)\n# ax.set_title(\n# 'Sub-classifier {} - View {}'.format(ind_estimator, ind_view))\n# ind_feature = ind_view * 2\n# for ind_class in range(2):\n# ind_samples = (y_pred == ind_class)\n# ax.plot(X[ind_samples, ind_feature],\n# X[ind_samples, ind_feature + 1],\n# styles[ind_class],\n# label='Class {}'.format(ind_class))\n# ax.legend(title='Predicted class:')\n\nplt.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/doc/tutorial/auto_examples/cumbo/plot_cumbo_2_views_2_classes.py b/doc/tutorial/auto_examples/cumbo/plot_cumbo_2_views_2_classes.py
new file mode 100644
index 0000000..c669f69
--- /dev/null
+++ b/doc/tutorial/auto_examples/cumbo/plot_cumbo_2_views_2_classes.py
@@ -0,0 +1,127 @@
+# -*- coding: utf-8 -*-
+"""
+==================================
+MuCombo 2 views, 2 classes example
+==================================
+
+In this toy example, we generate data from two classes, split between two
+two-dimensional views.
+
+For each view, the data are generated so that half of the points of each class
+are well separated in the plane, while the other half of the points are not
+separated and placed in the same area. We also insure that the points that are
+not separated in one view are well separated in the other view.
+
+Thus, in the figure representing the data, the points represented by crosses
+(x) are well separated in view 0 while they are not separated in view 1, while
+the points represented by dots (.) are well separated in view 1 while they are
+not separated in view 0. In this figure, the blue symbols represent points
+of class 0, while red symbols represent points of class 1.
+
+The MuCuMBo algorithm take adavantage of the complementarity of the two views to
+rightly classify the points.
+"""
+
+import numpy as np
+from multimodal.boosting.cumbo import MuCumboClassifier
+from matplotlib import pyplot as plt
+
+
+def generate_data(n_samples, lim):
+ """Generate random data in a rectangle"""
+ lim = np.array(lim)
+ n_features = lim.shape[0]
+ data = np.random.random((n_samples, n_features))
+ data = (lim[:, 1]-lim[:, 0]) * data + lim[:, 0]
+ return data
+
+
+seed = 12
+np.random.seed(seed)
+
+n_samples = 100
+
+view_0 = np.concatenate((generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [1., 2.]])))
+
+view_1 = np.concatenate((generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [1., 2.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]])))
+
+X = np.concatenate((view_0, view_1), axis=1)
+
+y = np.zeros(4*n_samples, dtype=np.int64)
+y[2*n_samples:] = 1
+
+views_ind = np.array([0, 2, 4])
+
+n_estimators = 3
+clf = MuCumboClassifier(n_estimators=n_estimators)
+clf.fit(X, y, views_ind)
+
+print('\nAfter 3 iterations, the MuCuMBo classifier reaches exact '
+ 'classification for the\nlearning samples:')
+for ind, score in enumerate(clf.staged_score(X, y)):
+ print(' - iteration {}, score: {}'.format(ind + 1, score))
+
+
+print('\nThe resulting MuCuMBo classifier uses three sub-classifiers that are '
+ 'wheighted\nusing the following weights:\n'
+ ' estimator weights: {}'.format(clf.estimator_weights_alpha_))
+
+# print('\nThe two first sub-classifiers use the data of view 0 to compute '
+# 'their\nclassification results, while the third one uses the data of '
+# 'view 1:\n'
+# ' best views: {}'. format(clf.best_views_))
+
+print('\nThe first figure displays the data, splitting the representation '
+ 'between the\ntwo views.')
+
+fig = plt.figure(figsize=(10., 8.))
+fig.suptitle('Representation of the data', size=16)
+for ind_view in range(2):
+ ax = plt.subplot(2, 1, ind_view + 1)
+ ax.set_title('View {}'.format(ind_view))
+ ind_feature = ind_view * 2
+ styles = ('.b', 'xb', '.r', 'xr')
+ labels = ('non-separated', 'separated')
+ for ind in range(4):
+ ind_class = ind // 2
+ label = labels[(ind + ind_view) % 2]
+ ax.plot(X[n_samples*ind:n_samples*(ind+1), ind_feature],
+ X[n_samples*ind:n_samples*(ind+1), ind_feature + 1],
+ styles[ind],
+ label='Class {} ({})'.format(ind_class, label))
+ ax.legend()
+
+print('\nThe second figure displays the classification results for the '
+ 'sub-classifiers\non the learning sample data.\n')
+
+styles = ('.b', '.r')
+# fig = plt.figure(figsize=(12., 7.))
+# fig.suptitle('Classification results on the learning data for the '
+# 'sub-classifiers', size=16)
+# for ind_estimator in range(n_estimators):
+# best_view = clf.best_views_[ind_estimator]
+# y_pred = clf.estimators_[ind_estimator].predict(
+# X[:, 2*best_view:2*best_view+2])
+# background_color = (1.0, 1.0, 0.9)
+# for ind_view in range(2):
+# ax = plt.subplot(2, 3, ind_estimator + 3*ind_view + 1)
+# if ind_view == best_view:
+# ax.set_facecolor(background_color)
+# ax.set_title(
+# 'Sub-classifier {} - View {}'.format(ind_estimator, ind_view))
+# ind_feature = ind_view * 2
+# for ind_class in range(2):
+# ind_samples = (y_pred == ind_class)
+# ax.plot(X[ind_samples, ind_feature],
+# X[ind_samples, ind_feature + 1],
+# styles[ind_class],
+# label='Class {}'.format(ind_class))
+# ax.legend(title='Predicted class:')
+
+plt.show()
diff --git a/doc/tutorial/auto_examples/cumbo/plot_cumbo_2_views_2_classes.py.md5 b/doc/tutorial/auto_examples/cumbo/plot_cumbo_2_views_2_classes.py.md5
new file mode 100644
index 0000000..06630ff
--- /dev/null
+++ b/doc/tutorial/auto_examples/cumbo/plot_cumbo_2_views_2_classes.py.md5
@@ -0,0 +1 @@
+5b1bad9179e6c052ce9a8198c8443c9a
\ No newline at end of file
diff --git a/doc/tutorial/auto_examples/cumbo/plot_cumbo_2_views_2_classes.rst b/doc/tutorial/auto_examples/cumbo/plot_cumbo_2_views_2_classes.rst
new file mode 100644
index 0000000..f47b92a
--- /dev/null
+++ b/doc/tutorial/auto_examples/cumbo/plot_cumbo_2_views_2_classes.rst
@@ -0,0 +1,208 @@
+.. note::
+ :class: sphx-glr-download-link-note
+
+ Click :ref:`here <sphx_glr_download_tutorial_auto_examples_cumbo_plot_cumbo_2_views_2_classes.py>` to download the full example code
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorial_auto_examples_cumbo_plot_cumbo_2_views_2_classes.py:
+
+
+==================================
+MuCombo 2 views, 2 classes example
+==================================
+
+In this toy example, we generate data from two classes, split between two
+two-dimensional views.
+
+For each view, the data are generated so that half of the points of each class
+are well separated in the plane, while the other half of the points are not
+separated and placed in the same area. We also insure that the points that are
+not separated in one view are well separated in the other view.
+
+Thus, in the figure representing the data, the points represented by crosses
+(x) are well separated in view 0 while they are not separated in view 1, while
+the points represented by dots (.) are well separated in view 1 while they are
+not separated in view 0. In this figure, the blue symbols represent points
+of class 0, while red symbols represent points of class 1.
+
+The MuCuMBo algorithm take adavantage of the complementarity of the two views to
+rightly classify the points.
+
+
+
+.. image:: /tutorial/auto_examples/cumbo/images/sphx_glr_plot_cumbo_2_views_2_classes_001.png
+ :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+ /home/dominique/.virtualenvs/env_multiv/local/lib/python3.6/site-packages/sklearn/ensemble/weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.
+ from numpy.core.umath_tests import inner1d
+
+ After 3 iterations, the MuCuMBo classifier reaches exact classification for the
+ learning samples:
+ - iteration 1, score: 0.75
+ - iteration 2, score: 0.75
+
+ The resulting MuCuMBo classifier uses three sub-classifiers that are wheighted
+ using the following weights:
+ estimator weights: [[0.54930614 0.54930614]]
+
+ The first figure displays the data, splitting the representation between the
+ two views.
+
+ The second figure displays the classification results for the sub-classifiers
+ on the learning sample data.
+
+
+
+
+
+
+
+|
+
+
+.. code-block:: default
+
+
+ import numpy as np
+ from multimodal.boosting.cumbo import MuCumboClassifier
+ from matplotlib import pyplot as plt
+
+
+ def generate_data(n_samples, lim):
+ """Generate random data in a rectangle"""
+ lim = np.array(lim)
+ n_features = lim.shape[0]
+ data = np.random.random((n_samples, n_features))
+ data = (lim[:, 1]-lim[:, 0]) * data + lim[:, 0]
+ return data
+
+
+ seed = 12
+ np.random.seed(seed)
+
+ n_samples = 100
+
+ view_0 = np.concatenate((generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [1., 2.]])))
+
+ view_1 = np.concatenate((generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [1., 2.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]])))
+
+ X = np.concatenate((view_0, view_1), axis=1)
+
+ y = np.zeros(4*n_samples, dtype=np.int64)
+ y[2*n_samples:] = 1
+
+ views_ind = np.array([0, 2, 4])
+
+ n_estimators = 3
+ clf = MuCumboClassifier(n_estimators=n_estimators)
+ clf.fit(X, y, views_ind)
+
+ print('\nAfter 3 iterations, the MuCuMBo classifier reaches exact '
+ 'classification for the\nlearning samples:')
+ for ind, score in enumerate(clf.staged_score(X, y)):
+ print(' - iteration {}, score: {}'.format(ind + 1, score))
+
+
+ print('\nThe resulting MuCuMBo classifier uses three sub-classifiers that are '
+ 'wheighted\nusing the following weights:\n'
+ ' estimator weights: {}'.format(clf.estimator_weights_alpha_))
+
+ # print('\nThe two first sub-classifiers use the data of view 0 to compute '
+ # 'their\nclassification results, while the third one uses the data of '
+ # 'view 1:\n'
+ # ' best views: {}'. format(clf.best_views_))
+
+ print('\nThe first figure displays the data, splitting the representation '
+ 'between the\ntwo views.')
+
+ fig = plt.figure(figsize=(10., 8.))
+ fig.suptitle('Representation of the data', size=16)
+ for ind_view in range(2):
+ ax = plt.subplot(2, 1, ind_view + 1)
+ ax.set_title('View {}'.format(ind_view))
+ ind_feature = ind_view * 2
+ styles = ('.b', 'xb', '.r', 'xr')
+ labels = ('non-separated', 'separated')
+ for ind in range(4):
+ ind_class = ind // 2
+ label = labels[(ind + ind_view) % 2]
+ ax.plot(X[n_samples*ind:n_samples*(ind+1), ind_feature],
+ X[n_samples*ind:n_samples*(ind+1), ind_feature + 1],
+ styles[ind],
+ label='Class {} ({})'.format(ind_class, label))
+ ax.legend()
+
+ print('\nThe second figure displays the classification results for the '
+ 'sub-classifiers\non the learning sample data.\n')
+
+ styles = ('.b', '.r')
+ # fig = plt.figure(figsize=(12., 7.))
+ # fig.suptitle('Classification results on the learning data for the '
+ # 'sub-classifiers', size=16)
+ # for ind_estimator in range(n_estimators):
+ # best_view = clf.best_views_[ind_estimator]
+ # y_pred = clf.estimators_[ind_estimator].predict(
+ # X[:, 2*best_view:2*best_view+2])
+ # background_color = (1.0, 1.0, 0.9)
+ # for ind_view in range(2):
+ # ax = plt.subplot(2, 3, ind_estimator + 3*ind_view + 1)
+ # if ind_view == best_view:
+ # ax.set_facecolor(background_color)
+ # ax.set_title(
+ # 'Sub-classifier {} - View {}'.format(ind_estimator, ind_view))
+ # ind_feature = ind_view * 2
+ # for ind_class in range(2):
+ # ind_samples = (y_pred == ind_class)
+ # ax.plot(X[ind_samples, ind_feature],
+ # X[ind_samples, ind_feature + 1],
+ # styles[ind_class],
+ # label='Class {}'.format(ind_class))
+ # ax.legend(title='Predicted class:')
+
+ plt.show()
+
+
+.. rst-class:: sphx-glr-timing
+
+ **Total running time of the script:** ( 0 minutes 0.616 seconds)
+
+
+.. _sphx_glr_download_tutorial_auto_examples_cumbo_plot_cumbo_2_views_2_classes.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+ :class: sphx-glr-footer-example
+
+
+
+ .. container:: sphx-glr-download
+
+ :download:`Download Python source code: plot_cumbo_2_views_2_classes.py <plot_cumbo_2_views_2_classes.py>`
+
+
+
+ .. container:: sphx-glr-download
+
+ :download:`Download Jupyter notebook: plot_cumbo_2_views_2_classes.ipynb <plot_cumbo_2_views_2_classes.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+ `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/doc/tutorial/auto_examples/cumbo/plot_cumbo_2_views_2_classes_codeobj.pickle b/doc/tutorial/auto_examples/cumbo/plot_cumbo_2_views_2_classes_codeobj.pickle
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diff --git a/doc/tutorial/auto_examples/cumbo/plot_cumbo_3_views_3_classes.ipynb b/doc/tutorial/auto_examples/cumbo/plot_cumbo_3_views_3_classes.ipynb
new file mode 100644
index 0000000..a6981bb
--- /dev/null
+++ b/doc/tutorial/auto_examples/cumbo/plot_cumbo_3_views_3_classes.ipynb
@@ -0,0 +1,54 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n==================================\nMuCumbo 3 views, 3 classes example\n==================================\n\nIn this toy example, we generate data from three classes, split between three\ntwo-dimensional views.\n\nFor each view, the data are generated so that the points for two classes are\nwell seperated, while the points for the third class are not seperated with\nthe two other classes. That means that, taken separately, none of the single\nviews allows for a good classification of the data.\n\nNevertheless, the MuCuMbo algorithm take adavantage of the complementarity of\nthe views to rightly classify the points.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\nfrom multimodal.boosting.cumbo import MuCumboClassifier\nfrom matplotlib import pyplot as plt\n\n\ndef generate_data(n_samples, lim):\n \"\"\"Generate random data in a rectangle\"\"\"\n lim = np.array(lim)\n n_features = lim.shape[0]\n data = np.random.random((n_samples, n_features))\n data = (lim[:, 1]-lim[:, 0]) * data + lim[:, 0]\n return data\n\n\nseed = 12\nnp.random.seed(seed)\n\nn_samples = 300\n\nview_0 = np.concatenate((generate_data(n_samples, [[0., 1.], [0., 1.]]),\n generate_data(n_samples, [[1., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 2.], [0., 1.]])))\n\nview_1 = np.concatenate((generate_data(n_samples, [[1., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [0., 1.]])))\n\nview_2 = np.concatenate((generate_data(n_samples, [[0., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [0., 1.]]),\n generate_data(n_samples, [[1., 2.], [0., 1.]])))\n\nX = np.concatenate((view_0, view_1, view_2), axis=1)\n\ny = np.zeros(3*n_samples, dtype=np.int64)\ny[n_samples:2*n_samples] = 1\ny[2*n_samples:] = 2\n\nviews_ind = np.array([0, 2, 4, 6])\n\nn_estimators = 4\nclf = MuCumboClassifier(n_estimators=n_estimators)\nclf.fit(X, y, views_ind)\n\nprint('\\nAfter 4 iterations, the MuCuMBo classifier reaches exact '\n 'classification for the\\nlearning samples:')\nfor ind, score in enumerate(clf.staged_score(X, y)):\n print(' - iteration {}, score: {}'.format(ind + 1, score))\n\nprint('\\nThe resulting MuCuMBo classifier uses four sub-classifiers that are '\n 'wheighted\\nusing the following weights:\\n'\n ' estimator weights alpha: {}'.format(clf.estimator_weights_alpha_))\n\n# print('\\nThe first sub-classifier uses the data of view 0 to compute '\n# 'its classification\\nresults, the second and third sub-classifiers use '\n# 'the data of view 1, while the\\nfourth one uses the data of '\n# 'view 2:\\n'\n# ' best views: {}'. format(clf.best_views_))\n\nprint('\\nThe first figure displays the data, splitting the representation '\n 'between the\\nthree views.')\n\nstyles = ('.b', '.r', '.g')\nfig = plt.figure(figsize=(12., 11.))\nfig.suptitle('Representation of the data', size=16)\nfor ind_view in range(3):\n ax = plt.subplot(3, 1, ind_view + 1)\n ax.set_title('View {}'.format(ind_view))\n ind_feature = ind_view * 2\n for ind_class in range(3):\n ind_samples = (y == ind_class)\n ax.plot(X[ind_samples, ind_feature],\n X[ind_samples, ind_feature + 1],\n styles[ind_class],\n label='Class {}'.format(ind_class))\n ax.legend(loc='upper left', framealpha=0.9)\n\nprint('\\nThe second figure displays the classification results for the '\n 'sub-classifiers\\non the learning sample data.\\n')\n\n# fig = plt.figure(figsize=(14., 11.))\n# fig.suptitle('Classification results on the learning data for the '\n# 'sub-classifiers', size=16)\n# for ind_estimator in range(n_estimators):\n# best_view = clf.best_views_[ind_estimator]\n# y_pred = clf.estimators_[ind_estimator].predict(\n# X[:, 2*best_view:2*best_view+2])\n# background_color = (1.0, 1.0, 0.9)\n# for ind_view in range(3):\n# ax = plt.subplot(3, 4, ind_estimator + 4*ind_view + 1)\n# if ind_view == best_view:\n# ax.set_facecolor(background_color)\n# ax.set_title(\n# 'Sub-classifier {} - View {}'.format(ind_estimator, ind_view))\n# ind_feature = ind_view * 2\n# for ind_class in range(3):\n# ind_samples = (y_pred == ind_class)\n# ax.plot(X[ind_samples, ind_feature],\n# X[ind_samples, ind_feature + 1],\n# styles[ind_class],\n# label='Class {}'.format(ind_class))\n# ax.legend(title='Predicted class:', loc='upper left', framealpha=0.9)\n\nplt.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/doc/tutorial/auto_examples/cumbo/plot_cumbo_3_views_3_classes.py b/doc/tutorial/auto_examples/cumbo/plot_cumbo_3_views_3_classes.py
new file mode 100644
index 0000000..058b2dc
--- /dev/null
+++ b/doc/tutorial/auto_examples/cumbo/plot_cumbo_3_views_3_classes.py
@@ -0,0 +1,121 @@
+# -*- coding: utf-8 -*-
+"""
+==================================
+MuCumbo 3 views, 3 classes example
+==================================
+
+In this toy example, we generate data from three classes, split between three
+two-dimensional views.
+
+For each view, the data are generated so that the points for two classes are
+well seperated, while the points for the third class are not seperated with
+the two other classes. That means that, taken separately, none of the single
+views allows for a good classification of the data.
+
+Nevertheless, the MuCuMbo algorithm take adavantage of the complementarity of
+the views to rightly classify the points.
+"""
+
+import numpy as np
+from multimodal.boosting.cumbo import MuCumboClassifier
+from matplotlib import pyplot as plt
+
+
+def generate_data(n_samples, lim):
+ """Generate random data in a rectangle"""
+ lim = np.array(lim)
+ n_features = lim.shape[0]
+ data = np.random.random((n_samples, n_features))
+ data = (lim[:, 1]-lim[:, 0]) * data + lim[:, 0]
+ return data
+
+
+seed = 12
+np.random.seed(seed)
+
+n_samples = 300
+
+view_0 = np.concatenate((generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 2.], [0., 1.]])))
+
+view_1 = np.concatenate((generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]])))
+
+view_2 = np.concatenate((generate_data(n_samples, [[0., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[1., 2.], [0., 1.]])))
+
+X = np.concatenate((view_0, view_1, view_2), axis=1)
+
+y = np.zeros(3*n_samples, dtype=np.int64)
+y[n_samples:2*n_samples] = 1
+y[2*n_samples:] = 2
+
+views_ind = np.array([0, 2, 4, 6])
+
+n_estimators = 4
+clf = MuCumboClassifier(n_estimators=n_estimators)
+clf.fit(X, y, views_ind)
+
+print('\nAfter 4 iterations, the MuCuMBo classifier reaches exact '
+ 'classification for the\nlearning samples:')
+for ind, score in enumerate(clf.staged_score(X, y)):
+ print(' - iteration {}, score: {}'.format(ind + 1, score))
+
+print('\nThe resulting MuCuMBo classifier uses four sub-classifiers that are '
+ 'wheighted\nusing the following weights:\n'
+ ' estimator weights alpha: {}'.format(clf.estimator_weights_alpha_))
+
+# print('\nThe first sub-classifier uses the data of view 0 to compute '
+# 'its classification\nresults, the second and third sub-classifiers use '
+# 'the data of view 1, while the\nfourth one uses the data of '
+# 'view 2:\n'
+# ' best views: {}'. format(clf.best_views_))
+
+print('\nThe first figure displays the data, splitting the representation '
+ 'between the\nthree views.')
+
+styles = ('.b', '.r', '.g')
+fig = plt.figure(figsize=(12., 11.))
+fig.suptitle('Representation of the data', size=16)
+for ind_view in range(3):
+ ax = plt.subplot(3, 1, ind_view + 1)
+ ax.set_title('View {}'.format(ind_view))
+ ind_feature = ind_view * 2
+ for ind_class in range(3):
+ ind_samples = (y == ind_class)
+ ax.plot(X[ind_samples, ind_feature],
+ X[ind_samples, ind_feature + 1],
+ styles[ind_class],
+ label='Class {}'.format(ind_class))
+ ax.legend(loc='upper left', framealpha=0.9)
+
+print('\nThe second figure displays the classification results for the '
+ 'sub-classifiers\non the learning sample data.\n')
+
+# fig = plt.figure(figsize=(14., 11.))
+# fig.suptitle('Classification results on the learning data for the '
+# 'sub-classifiers', size=16)
+# for ind_estimator in range(n_estimators):
+# best_view = clf.best_views_[ind_estimator]
+# y_pred = clf.estimators_[ind_estimator].predict(
+# X[:, 2*best_view:2*best_view+2])
+# background_color = (1.0, 1.0, 0.9)
+# for ind_view in range(3):
+# ax = plt.subplot(3, 4, ind_estimator + 4*ind_view + 1)
+# if ind_view == best_view:
+# ax.set_facecolor(background_color)
+# ax.set_title(
+# 'Sub-classifier {} - View {}'.format(ind_estimator, ind_view))
+# ind_feature = ind_view * 2
+# for ind_class in range(3):
+# ind_samples = (y_pred == ind_class)
+# ax.plot(X[ind_samples, ind_feature],
+# X[ind_samples, ind_feature + 1],
+# styles[ind_class],
+# label='Class {}'.format(ind_class))
+# ax.legend(title='Predicted class:', loc='upper left', framealpha=0.9)
+
+plt.show()
diff --git a/doc/tutorial/auto_examples/cumbo/plot_cumbo_3_views_3_classes.py.md5 b/doc/tutorial/auto_examples/cumbo/plot_cumbo_3_views_3_classes.py.md5
new file mode 100644
index 0000000..c3653c3
--- /dev/null
+++ b/doc/tutorial/auto_examples/cumbo/plot_cumbo_3_views_3_classes.py.md5
@@ -0,0 +1 @@
+62ad2fbd5b04b88cb8fe2f86822f60b9
\ No newline at end of file
diff --git a/doc/tutorial/auto_examples/cumbo/plot_cumbo_3_views_3_classes.rst b/doc/tutorial/auto_examples/cumbo/plot_cumbo_3_views_3_classes.rst
new file mode 100644
index 0000000..07d4669
--- /dev/null
+++ b/doc/tutorial/auto_examples/cumbo/plot_cumbo_3_views_3_classes.rst
@@ -0,0 +1,201 @@
+.. note::
+ :class: sphx-glr-download-link-note
+
+ Click :ref:`here <sphx_glr_download_tutorial_auto_examples_cumbo_plot_cumbo_3_views_3_classes.py>` to download the full example code
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorial_auto_examples_cumbo_plot_cumbo_3_views_3_classes.py:
+
+
+==================================
+MuCumbo 3 views, 3 classes example
+==================================
+
+In this toy example, we generate data from three classes, split between three
+two-dimensional views.
+
+For each view, the data are generated so that the points for two classes are
+well seperated, while the points for the third class are not seperated with
+the two other classes. That means that, taken separately, none of the single
+views allows for a good classification of the data.
+
+Nevertheless, the MuCuMbo algorithm take adavantage of the complementarity of
+the views to rightly classify the points.
+
+
+
+.. image:: /tutorial/auto_examples/cumbo/images/sphx_glr_plot_cumbo_3_views_3_classes_001.png
+ :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+
+ After 4 iterations, the MuCuMBo classifier reaches exact classification for the
+ learning samples:
+ - iteration 1, score: 0.6666666666666666
+ - iteration 2, score: 0.6666666666666666
+ - iteration 3, score: 1.0
+
+ The resulting MuCuMBo classifier uses four sub-classifiers that are wheighted
+ using the following weights:
+ estimator weights alpha: [[0.54930614 0.54930614 0.54930614]]
+
+ The first figure displays the data, splitting the representation between the
+ three views.
+
+ The second figure displays the classification results for the sub-classifiers
+ on the learning sample data.
+
+
+
+
+
+
+
+|
+
+
+.. code-block:: default
+
+
+ import numpy as np
+ from multimodal.boosting.cumbo import MuCumboClassifier
+ from matplotlib import pyplot as plt
+
+
+ def generate_data(n_samples, lim):
+ """Generate random data in a rectangle"""
+ lim = np.array(lim)
+ n_features = lim.shape[0]
+ data = np.random.random((n_samples, n_features))
+ data = (lim[:, 1]-lim[:, 0]) * data + lim[:, 0]
+ return data
+
+
+ seed = 12
+ np.random.seed(seed)
+
+ n_samples = 300
+
+ view_0 = np.concatenate((generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 2.], [0., 1.]])))
+
+ view_1 = np.concatenate((generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]])))
+
+ view_2 = np.concatenate((generate_data(n_samples, [[0., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[1., 2.], [0., 1.]])))
+
+ X = np.concatenate((view_0, view_1, view_2), axis=1)
+
+ y = np.zeros(3*n_samples, dtype=np.int64)
+ y[n_samples:2*n_samples] = 1
+ y[2*n_samples:] = 2
+
+ views_ind = np.array([0, 2, 4, 6])
+
+ n_estimators = 4
+ clf = MuCumboClassifier(n_estimators=n_estimators)
+ clf.fit(X, y, views_ind)
+
+ print('\nAfter 4 iterations, the MuCuMBo classifier reaches exact '
+ 'classification for the\nlearning samples:')
+ for ind, score in enumerate(clf.staged_score(X, y)):
+ print(' - iteration {}, score: {}'.format(ind + 1, score))
+
+ print('\nThe resulting MuCuMBo classifier uses four sub-classifiers that are '
+ 'wheighted\nusing the following weights:\n'
+ ' estimator weights alpha: {}'.format(clf.estimator_weights_alpha_))
+
+ # print('\nThe first sub-classifier uses the data of view 0 to compute '
+ # 'its classification\nresults, the second and third sub-classifiers use '
+ # 'the data of view 1, while the\nfourth one uses the data of '
+ # 'view 2:\n'
+ # ' best views: {}'. format(clf.best_views_))
+
+ print('\nThe first figure displays the data, splitting the representation '
+ 'between the\nthree views.')
+
+ styles = ('.b', '.r', '.g')
+ fig = plt.figure(figsize=(12., 11.))
+ fig.suptitle('Representation of the data', size=16)
+ for ind_view in range(3):
+ ax = plt.subplot(3, 1, ind_view + 1)
+ ax.set_title('View {}'.format(ind_view))
+ ind_feature = ind_view * 2
+ for ind_class in range(3):
+ ind_samples = (y == ind_class)
+ ax.plot(X[ind_samples, ind_feature],
+ X[ind_samples, ind_feature + 1],
+ styles[ind_class],
+ label='Class {}'.format(ind_class))
+ ax.legend(loc='upper left', framealpha=0.9)
+
+ print('\nThe second figure displays the classification results for the '
+ 'sub-classifiers\non the learning sample data.\n')
+
+ # fig = plt.figure(figsize=(14., 11.))
+ # fig.suptitle('Classification results on the learning data for the '
+ # 'sub-classifiers', size=16)
+ # for ind_estimator in range(n_estimators):
+ # best_view = clf.best_views_[ind_estimator]
+ # y_pred = clf.estimators_[ind_estimator].predict(
+ # X[:, 2*best_view:2*best_view+2])
+ # background_color = (1.0, 1.0, 0.9)
+ # for ind_view in range(3):
+ # ax = plt.subplot(3, 4, ind_estimator + 4*ind_view + 1)
+ # if ind_view == best_view:
+ # ax.set_facecolor(background_color)
+ # ax.set_title(
+ # 'Sub-classifier {} - View {}'.format(ind_estimator, ind_view))
+ # ind_feature = ind_view * 2
+ # for ind_class in range(3):
+ # ind_samples = (y_pred == ind_class)
+ # ax.plot(X[ind_samples, ind_feature],
+ # X[ind_samples, ind_feature + 1],
+ # styles[ind_class],
+ # label='Class {}'.format(ind_class))
+ # ax.legend(title='Predicted class:', loc='upper left', framealpha=0.9)
+
+ plt.show()
+
+
+.. rst-class:: sphx-glr-timing
+
+ **Total running time of the script:** ( 0 minutes 0.497 seconds)
+
+
+.. _sphx_glr_download_tutorial_auto_examples_cumbo_plot_cumbo_3_views_3_classes.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+ :class: sphx-glr-footer-example
+
+
+
+ .. container:: sphx-glr-download
+
+ :download:`Download Python source code: plot_cumbo_3_views_3_classes.py <plot_cumbo_3_views_3_classes.py>`
+
+
+
+ .. container:: sphx-glr-download
+
+ :download:`Download Jupyter notebook: plot_cumbo_3_views_3_classes.ipynb <plot_cumbo_3_views_3_classes.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+ `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/doc/tutorial/auto_examples/cumbo/plot_cumbo_3_views_3_classes_codeobj.pickle b/doc/tutorial/auto_examples/cumbo/plot_cumbo_3_views_3_classes_codeobj.pickle
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diff --git a/doc/tutorial/auto_examples/cumbo/sg_execution_times.rst b/doc/tutorial/auto_examples/cumbo/sg_execution_times.rst
new file mode 100644
index 0000000..8d71d51
--- /dev/null
+++ b/doc/tutorial/auto_examples/cumbo/sg_execution_times.rst
@@ -0,0 +1,14 @@
+
+:orphan:
+
+.. _sphx_glr_tutorial_auto_examples_cumbo_sg_execution_times:
+
+Computation times
+=================
+**00:01.113** total execution time for **tutorial_auto_examples_cumbo** files:
+
++--------------------------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorial_auto_examples_cumbo_plot_cumbo_2_views_2_classes.py` (``plot_cumbo_2_views_2_classes.py``) | 00:00.616 | 0.0 MB |
++--------------------------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorial_auto_examples_cumbo_plot_cumbo_3_views_3_classes.py` (``plot_cumbo_3_views_3_classes.py``) | 00:00.497 | 0.0 MB |
++--------------------------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/doc/tutorial/auto_examples/cumbo_plot_2_views_2_classes.ipynb b/doc/tutorial/auto_examples/cumbo_plot_2_views_2_classes.ipynb
new file mode 100644
index 0000000..81b05f4
--- /dev/null
+++ b/doc/tutorial/auto_examples/cumbo_plot_2_views_2_classes.ipynb
@@ -0,0 +1,54 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n==================================\nMuCombo 2 views, 2 classes example\n==================================\n\nIn this toy example, we generate data from two classes, split between two\ntwo-dimensional views.\n\nFor each view, the data are generated so that half of the points of each class\nare well separated in the plane, while the other half of the points are not\nseparated and placed in the same area. We also insure that the points that are\nnot separated in one view are well separated in the other view.\n\nThus, in the figure representing the data, the points represented by crosses\n(x) are well separated in view 0 while they are not separated in view 1, while\nthe points represented by dots (.) are well separated in view 1 while they are\nnot separated in view 0. In this figure, the blue symbols represent points\nof class 0, while red symbols represent points of class 1.\n\nThe MuCuMBo algorithm take adavantage of the complementarity of the two views to\nrightly classify the points.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\nfrom multimodal.boosting.cumbo import MuCumboClassifier\nfrom matplotlib import pyplot as plt\n\n\ndef generate_data(n_samples, lim):\n \"\"\"Generate random data in a rectangle\"\"\"\n lim = np.array(lim)\n n_features = lim.shape[0]\n data = np.random.random((n_samples, n_features))\n data = (lim[:, 1]-lim[:, 0]) * data + lim[:, 0]\n return data\n\n\nseed = 12\nnp.random.seed(seed)\n\nn_samples = 100\n\nview_0 = np.concatenate((generate_data(n_samples, [[0., 1.], [0., 1.]]),\n generate_data(n_samples, [[1., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [1., 2.]])))\n\nview_1 = np.concatenate((generate_data(n_samples, [[1., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [1., 2.]]),\n generate_data(n_samples, [[0., 1.], [0., 1.]])))\n\nX = np.concatenate((view_0, view_1), axis=1)\n\ny = np.zeros(4*n_samples, dtype=np.int64)\ny[2*n_samples:] = 1\n\nviews_ind = np.array([0, 2, 4])\n\nn_estimators = 3\nclf = MuCumboClassifier(n_estimators=n_estimators)\nclf.fit(X, y, views_ind)\n\nprint('\\nAfter 3 iterations, the MuCuMBo classifier reaches exact '\n 'classification for the\\nlearning samples:')\nfor ind, score in enumerate(clf.staged_score(X, y)):\n print(' - iteration {}, score: {}'.format(ind + 1, score))\n\n\nprint('\\nThe resulting MuCuMBo classifier uses three sub-classifiers that are '\n 'wheighted\\nusing the following weights:\\n'\n ' estimator weights: {}'.format(clf.estimator_weights_alpha_))\n\n# print('\\nThe two first sub-classifiers use the data of view 0 to compute '\n# 'their\\nclassification results, while the third one uses the data of '\n# 'view 1:\\n'\n# ' best views: {}'. format(clf.best_views_))\n\nprint('\\nThe first figure displays the data, splitting the representation '\n 'between the\\ntwo views.')\n\nfig = plt.figure(figsize=(10., 8.))\nfig.suptitle('Representation of the data', size=16)\nfor ind_view in range(2):\n ax = plt.subplot(2, 1, ind_view + 1)\n ax.set_title('View {}'.format(ind_view))\n ind_feature = ind_view * 2\n styles = ('.b', 'xb', '.r', 'xr')\n labels = ('non-separated', 'separated')\n for ind in range(4):\n ind_class = ind // 2\n label = labels[(ind + ind_view) % 2]\n ax.plot(X[n_samples*ind:n_samples*(ind+1), ind_feature],\n X[n_samples*ind:n_samples*(ind+1), ind_feature + 1],\n styles[ind],\n label='Class {} ({})'.format(ind_class, label))\n ax.legend()\n\nprint('\\nThe second figure displays the classification results for the '\n 'sub-classifiers\\non the learning sample data.\\n')\n\nstyles = ('.b', '.r')\n# fig = plt.figure(figsize=(12., 7.))\n# fig.suptitle('Classification results on the learning data for the '\n# 'sub-classifiers', size=16)\n# for ind_estimator in range(n_estimators):\n# best_view = clf.best_views_[ind_estimator]\n# y_pred = clf.estimators_[ind_estimator].predict(\n# X[:, 2*best_view:2*best_view+2])\n# background_color = (1.0, 1.0, 0.9)\n# for ind_view in range(2):\n# ax = plt.subplot(2, 3, ind_estimator + 3*ind_view + 1)\n# if ind_view == best_view:\n# ax.set_facecolor(background_color)\n# ax.set_title(\n# 'Sub-classifier {} - View {}'.format(ind_estimator, ind_view))\n# ind_feature = ind_view * 2\n# for ind_class in range(2):\n# ind_samples = (y_pred == ind_class)\n# ax.plot(X[ind_samples, ind_feature],\n# X[ind_samples, ind_feature + 1],\n# styles[ind_class],\n# label='Class {}'.format(ind_class))\n# ax.legend(title='Predicted class:')\n\nplt.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/doc/tutorial/auto_examples/cumbo_plot_2_views_2_classes.py b/doc/tutorial/auto_examples/cumbo_plot_2_views_2_classes.py
new file mode 100644
index 0000000..c669f69
--- /dev/null
+++ b/doc/tutorial/auto_examples/cumbo_plot_2_views_2_classes.py
@@ -0,0 +1,127 @@
+# -*- coding: utf-8 -*-
+"""
+==================================
+MuCombo 2 views, 2 classes example
+==================================
+
+In this toy example, we generate data from two classes, split between two
+two-dimensional views.
+
+For each view, the data are generated so that half of the points of each class
+are well separated in the plane, while the other half of the points are not
+separated and placed in the same area. We also insure that the points that are
+not separated in one view are well separated in the other view.
+
+Thus, in the figure representing the data, the points represented by crosses
+(x) are well separated in view 0 while they are not separated in view 1, while
+the points represented by dots (.) are well separated in view 1 while they are
+not separated in view 0. In this figure, the blue symbols represent points
+of class 0, while red symbols represent points of class 1.
+
+The MuCuMBo algorithm take adavantage of the complementarity of the two views to
+rightly classify the points.
+"""
+
+import numpy as np
+from multimodal.boosting.cumbo import MuCumboClassifier
+from matplotlib import pyplot as plt
+
+
+def generate_data(n_samples, lim):
+ """Generate random data in a rectangle"""
+ lim = np.array(lim)
+ n_features = lim.shape[0]
+ data = np.random.random((n_samples, n_features))
+ data = (lim[:, 1]-lim[:, 0]) * data + lim[:, 0]
+ return data
+
+
+seed = 12
+np.random.seed(seed)
+
+n_samples = 100
+
+view_0 = np.concatenate((generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [1., 2.]])))
+
+view_1 = np.concatenate((generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [1., 2.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]])))
+
+X = np.concatenate((view_0, view_1), axis=1)
+
+y = np.zeros(4*n_samples, dtype=np.int64)
+y[2*n_samples:] = 1
+
+views_ind = np.array([0, 2, 4])
+
+n_estimators = 3
+clf = MuCumboClassifier(n_estimators=n_estimators)
+clf.fit(X, y, views_ind)
+
+print('\nAfter 3 iterations, the MuCuMBo classifier reaches exact '
+ 'classification for the\nlearning samples:')
+for ind, score in enumerate(clf.staged_score(X, y)):
+ print(' - iteration {}, score: {}'.format(ind + 1, score))
+
+
+print('\nThe resulting MuCuMBo classifier uses three sub-classifiers that are '
+ 'wheighted\nusing the following weights:\n'
+ ' estimator weights: {}'.format(clf.estimator_weights_alpha_))
+
+# print('\nThe two first sub-classifiers use the data of view 0 to compute '
+# 'their\nclassification results, while the third one uses the data of '
+# 'view 1:\n'
+# ' best views: {}'. format(clf.best_views_))
+
+print('\nThe first figure displays the data, splitting the representation '
+ 'between the\ntwo views.')
+
+fig = plt.figure(figsize=(10., 8.))
+fig.suptitle('Representation of the data', size=16)
+for ind_view in range(2):
+ ax = plt.subplot(2, 1, ind_view + 1)
+ ax.set_title('View {}'.format(ind_view))
+ ind_feature = ind_view * 2
+ styles = ('.b', 'xb', '.r', 'xr')
+ labels = ('non-separated', 'separated')
+ for ind in range(4):
+ ind_class = ind // 2
+ label = labels[(ind + ind_view) % 2]
+ ax.plot(X[n_samples*ind:n_samples*(ind+1), ind_feature],
+ X[n_samples*ind:n_samples*(ind+1), ind_feature + 1],
+ styles[ind],
+ label='Class {} ({})'.format(ind_class, label))
+ ax.legend()
+
+print('\nThe second figure displays the classification results for the '
+ 'sub-classifiers\non the learning sample data.\n')
+
+styles = ('.b', '.r')
+# fig = plt.figure(figsize=(12., 7.))
+# fig.suptitle('Classification results on the learning data for the '
+# 'sub-classifiers', size=16)
+# for ind_estimator in range(n_estimators):
+# best_view = clf.best_views_[ind_estimator]
+# y_pred = clf.estimators_[ind_estimator].predict(
+# X[:, 2*best_view:2*best_view+2])
+# background_color = (1.0, 1.0, 0.9)
+# for ind_view in range(2):
+# ax = plt.subplot(2, 3, ind_estimator + 3*ind_view + 1)
+# if ind_view == best_view:
+# ax.set_facecolor(background_color)
+# ax.set_title(
+# 'Sub-classifier {} - View {}'.format(ind_estimator, ind_view))
+# ind_feature = ind_view * 2
+# for ind_class in range(2):
+# ind_samples = (y_pred == ind_class)
+# ax.plot(X[ind_samples, ind_feature],
+# X[ind_samples, ind_feature + 1],
+# styles[ind_class],
+# label='Class {}'.format(ind_class))
+# ax.legend(title='Predicted class:')
+
+plt.show()
diff --git a/doc/tutorial/auto_examples/cumbo_plot_2_views_2_classes.rst b/doc/tutorial/auto_examples/cumbo_plot_2_views_2_classes.rst
new file mode 100644
index 0000000..74f22bd
--- /dev/null
+++ b/doc/tutorial/auto_examples/cumbo_plot_2_views_2_classes.rst
@@ -0,0 +1,170 @@
+.. note::
+ :class: sphx-glr-download-link-note
+
+ Click :ref:`here <sphx_glr_download_tutorial_auto_examples_cumbo_plot_2_views_2_classes.py>` to download the full example code
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorial_auto_examples_cumbo_plot_2_views_2_classes.py:
+
+
+==================================
+MuCombo 2 views, 2 classes example
+==================================
+
+In this toy example, we generate data from two classes, split between two
+two-dimensional views.
+
+For each view, the data are generated so that half of the points of each class
+are well separated in the plane, while the other half of the points are not
+separated and placed in the same area. We also insure that the points that are
+not separated in one view are well separated in the other view.
+
+Thus, in the figure representing the data, the points represented by crosses
+(x) are well separated in view 0 while they are not separated in view 1, while
+the points represented by dots (.) are well separated in view 1 while they are
+not separated in view 0. In this figure, the blue symbols represent points
+of class 0, while red symbols represent points of class 1.
+
+The MuCuMBo algorithm take adavantage of the complementarity of the two views to
+rightly classify the points.
+
+
+.. code-block:: default
+
+
+ import numpy as np
+ from multimodal.boosting.cumbo import MuCumboClassifier
+ from matplotlib import pyplot as plt
+
+
+ def generate_data(n_samples, lim):
+ """Generate random data in a rectangle"""
+ lim = np.array(lim)
+ n_features = lim.shape[0]
+ data = np.random.random((n_samples, n_features))
+ data = (lim[:, 1]-lim[:, 0]) * data + lim[:, 0]
+ return data
+
+
+ seed = 12
+ np.random.seed(seed)
+
+ n_samples = 100
+
+ view_0 = np.concatenate((generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [1., 2.]])))
+
+ view_1 = np.concatenate((generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [1., 2.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]])))
+
+ X = np.concatenate((view_0, view_1), axis=1)
+
+ y = np.zeros(4*n_samples, dtype=np.int64)
+ y[2*n_samples:] = 1
+
+ views_ind = np.array([0, 2, 4])
+
+ n_estimators = 3
+ clf = MuCumboClassifier(n_estimators=n_estimators)
+ clf.fit(X, y, views_ind)
+
+ print('\nAfter 3 iterations, the MuCuMBo classifier reaches exact '
+ 'classification for the\nlearning samples:')
+ for ind, score in enumerate(clf.staged_score(X, y)):
+ print(' - iteration {}, score: {}'.format(ind + 1, score))
+
+
+ print('\nThe resulting MuCuMBo classifier uses three sub-classifiers that are '
+ 'wheighted\nusing the following weights:\n'
+ ' estimator weights: {}'.format(clf.estimator_weights_alpha_))
+
+ # print('\nThe two first sub-classifiers use the data of view 0 to compute '
+ # 'their\nclassification results, while the third one uses the data of '
+ # 'view 1:\n'
+ # ' best views: {}'. format(clf.best_views_))
+
+ print('\nThe first figure displays the data, splitting the representation '
+ 'between the\ntwo views.')
+
+ fig = plt.figure(figsize=(10., 8.))
+ fig.suptitle('Representation of the data', size=16)
+ for ind_view in range(2):
+ ax = plt.subplot(2, 1, ind_view + 1)
+ ax.set_title('View {}'.format(ind_view))
+ ind_feature = ind_view * 2
+ styles = ('.b', 'xb', '.r', 'xr')
+ labels = ('non-separated', 'separated')
+ for ind in range(4):
+ ind_class = ind // 2
+ label = labels[(ind + ind_view) % 2]
+ ax.plot(X[n_samples*ind:n_samples*(ind+1), ind_feature],
+ X[n_samples*ind:n_samples*(ind+1), ind_feature + 1],
+ styles[ind],
+ label='Class {} ({})'.format(ind_class, label))
+ ax.legend()
+
+ print('\nThe second figure displays the classification results for the '
+ 'sub-classifiers\non the learning sample data.\n')
+
+ styles = ('.b', '.r')
+ # fig = plt.figure(figsize=(12., 7.))
+ # fig.suptitle('Classification results on the learning data for the '
+ # 'sub-classifiers', size=16)
+ # for ind_estimator in range(n_estimators):
+ # best_view = clf.best_views_[ind_estimator]
+ # y_pred = clf.estimators_[ind_estimator].predict(
+ # X[:, 2*best_view:2*best_view+2])
+ # background_color = (1.0, 1.0, 0.9)
+ # for ind_view in range(2):
+ # ax = plt.subplot(2, 3, ind_estimator + 3*ind_view + 1)
+ # if ind_view == best_view:
+ # ax.set_facecolor(background_color)
+ # ax.set_title(
+ # 'Sub-classifier {} - View {}'.format(ind_estimator, ind_view))
+ # ind_feature = ind_view * 2
+ # for ind_class in range(2):
+ # ind_samples = (y_pred == ind_class)
+ # ax.plot(X[ind_samples, ind_feature],
+ # X[ind_samples, ind_feature + 1],
+ # styles[ind_class],
+ # label='Class {}'.format(ind_class))
+ # ax.legend(title='Predicted class:')
+
+ plt.show()
+
+
+.. rst-class:: sphx-glr-timing
+
+ **Total running time of the script:** ( 0 minutes 0.000 seconds)
+
+
+.. _sphx_glr_download_tutorial_auto_examples_cumbo_plot_2_views_2_classes.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+ :class: sphx-glr-footer-example
+
+
+
+ .. container:: sphx-glr-download
+
+ :download:`Download Python source code: cumbo_plot_2_views_2_classes.py <cumbo_plot_2_views_2_classes.py>`
+
+
+
+ .. container:: sphx-glr-download
+
+ :download:`Download Jupyter notebook: cumbo_plot_2_views_2_classes.ipynb <cumbo_plot_2_views_2_classes.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+ `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/doc/tutorial/auto_examples/cumbo_plot_2_views_2_classes_codeobj.pickle b/doc/tutorial/auto_examples/cumbo_plot_2_views_2_classes_codeobj.pickle
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diff --git a/doc/tutorial/auto_examples/cumbo_plot_3_views_3_classes.ipynb b/doc/tutorial/auto_examples/cumbo_plot_3_views_3_classes.ipynb
new file mode 100644
index 0000000..a6981bb
--- /dev/null
+++ b/doc/tutorial/auto_examples/cumbo_plot_3_views_3_classes.ipynb
@@ -0,0 +1,54 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n==================================\nMuCumbo 3 views, 3 classes example\n==================================\n\nIn this toy example, we generate data from three classes, split between three\ntwo-dimensional views.\n\nFor each view, the data are generated so that the points for two classes are\nwell seperated, while the points for the third class are not seperated with\nthe two other classes. That means that, taken separately, none of the single\nviews allows for a good classification of the data.\n\nNevertheless, the MuCuMbo algorithm take adavantage of the complementarity of\nthe views to rightly classify the points.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\nfrom multimodal.boosting.cumbo import MuCumboClassifier\nfrom matplotlib import pyplot as plt\n\n\ndef generate_data(n_samples, lim):\n \"\"\"Generate random data in a rectangle\"\"\"\n lim = np.array(lim)\n n_features = lim.shape[0]\n data = np.random.random((n_samples, n_features))\n data = (lim[:, 1]-lim[:, 0]) * data + lim[:, 0]\n return data\n\n\nseed = 12\nnp.random.seed(seed)\n\nn_samples = 300\n\nview_0 = np.concatenate((generate_data(n_samples, [[0., 1.], [0., 1.]]),\n generate_data(n_samples, [[1., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 2.], [0., 1.]])))\n\nview_1 = np.concatenate((generate_data(n_samples, [[1., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [0., 1.]])))\n\nview_2 = np.concatenate((generate_data(n_samples, [[0., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [0., 1.]]),\n generate_data(n_samples, [[1., 2.], [0., 1.]])))\n\nX = np.concatenate((view_0, view_1, view_2), axis=1)\n\ny = np.zeros(3*n_samples, dtype=np.int64)\ny[n_samples:2*n_samples] = 1\ny[2*n_samples:] = 2\n\nviews_ind = np.array([0, 2, 4, 6])\n\nn_estimators = 4\nclf = MuCumboClassifier(n_estimators=n_estimators)\nclf.fit(X, y, views_ind)\n\nprint('\\nAfter 4 iterations, the MuCuMBo classifier reaches exact '\n 'classification for the\\nlearning samples:')\nfor ind, score in enumerate(clf.staged_score(X, y)):\n print(' - iteration {}, score: {}'.format(ind + 1, score))\n\nprint('\\nThe resulting MuCuMBo classifier uses four sub-classifiers that are '\n 'wheighted\\nusing the following weights:\\n'\n ' estimator weights alpha: {}'.format(clf.estimator_weights_alpha_))\n\n# print('\\nThe first sub-classifier uses the data of view 0 to compute '\n# 'its classification\\nresults, the second and third sub-classifiers use '\n# 'the data of view 1, while the\\nfourth one uses the data of '\n# 'view 2:\\n'\n# ' best views: {}'. format(clf.best_views_))\n\nprint('\\nThe first figure displays the data, splitting the representation '\n 'between the\\nthree views.')\n\nstyles = ('.b', '.r', '.g')\nfig = plt.figure(figsize=(12., 11.))\nfig.suptitle('Representation of the data', size=16)\nfor ind_view in range(3):\n ax = plt.subplot(3, 1, ind_view + 1)\n ax.set_title('View {}'.format(ind_view))\n ind_feature = ind_view * 2\n for ind_class in range(3):\n ind_samples = (y == ind_class)\n ax.plot(X[ind_samples, ind_feature],\n X[ind_samples, ind_feature + 1],\n styles[ind_class],\n label='Class {}'.format(ind_class))\n ax.legend(loc='upper left', framealpha=0.9)\n\nprint('\\nThe second figure displays the classification results for the '\n 'sub-classifiers\\non the learning sample data.\\n')\n\n# fig = plt.figure(figsize=(14., 11.))\n# fig.suptitle('Classification results on the learning data for the '\n# 'sub-classifiers', size=16)\n# for ind_estimator in range(n_estimators):\n# best_view = clf.best_views_[ind_estimator]\n# y_pred = clf.estimators_[ind_estimator].predict(\n# X[:, 2*best_view:2*best_view+2])\n# background_color = (1.0, 1.0, 0.9)\n# for ind_view in range(3):\n# ax = plt.subplot(3, 4, ind_estimator + 4*ind_view + 1)\n# if ind_view == best_view:\n# ax.set_facecolor(background_color)\n# ax.set_title(\n# 'Sub-classifier {} - View {}'.format(ind_estimator, ind_view))\n# ind_feature = ind_view * 2\n# for ind_class in range(3):\n# ind_samples = (y_pred == ind_class)\n# ax.plot(X[ind_samples, ind_feature],\n# X[ind_samples, ind_feature + 1],\n# styles[ind_class],\n# label='Class {}'.format(ind_class))\n# ax.legend(title='Predicted class:', loc='upper left', framealpha=0.9)\n\nplt.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/doc/tutorial/auto_examples/cumbo_plot_3_views_3_classes.py b/doc/tutorial/auto_examples/cumbo_plot_3_views_3_classes.py
new file mode 100644
index 0000000..058b2dc
--- /dev/null
+++ b/doc/tutorial/auto_examples/cumbo_plot_3_views_3_classes.py
@@ -0,0 +1,121 @@
+# -*- coding: utf-8 -*-
+"""
+==================================
+MuCumbo 3 views, 3 classes example
+==================================
+
+In this toy example, we generate data from three classes, split between three
+two-dimensional views.
+
+For each view, the data are generated so that the points for two classes are
+well seperated, while the points for the third class are not seperated with
+the two other classes. That means that, taken separately, none of the single
+views allows for a good classification of the data.
+
+Nevertheless, the MuCuMbo algorithm take adavantage of the complementarity of
+the views to rightly classify the points.
+"""
+
+import numpy as np
+from multimodal.boosting.cumbo import MuCumboClassifier
+from matplotlib import pyplot as plt
+
+
+def generate_data(n_samples, lim):
+ """Generate random data in a rectangle"""
+ lim = np.array(lim)
+ n_features = lim.shape[0]
+ data = np.random.random((n_samples, n_features))
+ data = (lim[:, 1]-lim[:, 0]) * data + lim[:, 0]
+ return data
+
+
+seed = 12
+np.random.seed(seed)
+
+n_samples = 300
+
+view_0 = np.concatenate((generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 2.], [0., 1.]])))
+
+view_1 = np.concatenate((generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]])))
+
+view_2 = np.concatenate((generate_data(n_samples, [[0., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[1., 2.], [0., 1.]])))
+
+X = np.concatenate((view_0, view_1, view_2), axis=1)
+
+y = np.zeros(3*n_samples, dtype=np.int64)
+y[n_samples:2*n_samples] = 1
+y[2*n_samples:] = 2
+
+views_ind = np.array([0, 2, 4, 6])
+
+n_estimators = 4
+clf = MuCumboClassifier(n_estimators=n_estimators)
+clf.fit(X, y, views_ind)
+
+print('\nAfter 4 iterations, the MuCuMBo classifier reaches exact '
+ 'classification for the\nlearning samples:')
+for ind, score in enumerate(clf.staged_score(X, y)):
+ print(' - iteration {}, score: {}'.format(ind + 1, score))
+
+print('\nThe resulting MuCuMBo classifier uses four sub-classifiers that are '
+ 'wheighted\nusing the following weights:\n'
+ ' estimator weights alpha: {}'.format(clf.estimator_weights_alpha_))
+
+# print('\nThe first sub-classifier uses the data of view 0 to compute '
+# 'its classification\nresults, the second and third sub-classifiers use '
+# 'the data of view 1, while the\nfourth one uses the data of '
+# 'view 2:\n'
+# ' best views: {}'. format(clf.best_views_))
+
+print('\nThe first figure displays the data, splitting the representation '
+ 'between the\nthree views.')
+
+styles = ('.b', '.r', '.g')
+fig = plt.figure(figsize=(12., 11.))
+fig.suptitle('Representation of the data', size=16)
+for ind_view in range(3):
+ ax = plt.subplot(3, 1, ind_view + 1)
+ ax.set_title('View {}'.format(ind_view))
+ ind_feature = ind_view * 2
+ for ind_class in range(3):
+ ind_samples = (y == ind_class)
+ ax.plot(X[ind_samples, ind_feature],
+ X[ind_samples, ind_feature + 1],
+ styles[ind_class],
+ label='Class {}'.format(ind_class))
+ ax.legend(loc='upper left', framealpha=0.9)
+
+print('\nThe second figure displays the classification results for the '
+ 'sub-classifiers\non the learning sample data.\n')
+
+# fig = plt.figure(figsize=(14., 11.))
+# fig.suptitle('Classification results on the learning data for the '
+# 'sub-classifiers', size=16)
+# for ind_estimator in range(n_estimators):
+# best_view = clf.best_views_[ind_estimator]
+# y_pred = clf.estimators_[ind_estimator].predict(
+# X[:, 2*best_view:2*best_view+2])
+# background_color = (1.0, 1.0, 0.9)
+# for ind_view in range(3):
+# ax = plt.subplot(3, 4, ind_estimator + 4*ind_view + 1)
+# if ind_view == best_view:
+# ax.set_facecolor(background_color)
+# ax.set_title(
+# 'Sub-classifier {} - View {}'.format(ind_estimator, ind_view))
+# ind_feature = ind_view * 2
+# for ind_class in range(3):
+# ind_samples = (y_pred == ind_class)
+# ax.plot(X[ind_samples, ind_feature],
+# X[ind_samples, ind_feature + 1],
+# styles[ind_class],
+# label='Class {}'.format(ind_class))
+# ax.legend(title='Predicted class:', loc='upper left', framealpha=0.9)
+
+plt.show()
diff --git a/doc/tutorial/auto_examples/cumbo_plot_3_views_3_classes.rst b/doc/tutorial/auto_examples/cumbo_plot_3_views_3_classes.rst
new file mode 100644
index 0000000..a002f63
--- /dev/null
+++ b/doc/tutorial/auto_examples/cumbo_plot_3_views_3_classes.rst
@@ -0,0 +1,164 @@
+.. note::
+ :class: sphx-glr-download-link-note
+
+ Click :ref:`here <sphx_glr_download_tutorial_auto_examples_cumbo_plot_3_views_3_classes.py>` to download the full example code
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorial_auto_examples_cumbo_plot_3_views_3_classes.py:
+
+
+==================================
+MuCumbo 3 views, 3 classes example
+==================================
+
+In this toy example, we generate data from three classes, split between three
+two-dimensional views.
+
+For each view, the data are generated so that the points for two classes are
+well seperated, while the points for the third class are not seperated with
+the two other classes. That means that, taken separately, none of the single
+views allows for a good classification of the data.
+
+Nevertheless, the MuCuMbo algorithm take adavantage of the complementarity of
+the views to rightly classify the points.
+
+
+.. code-block:: default
+
+
+ import numpy as np
+ from multimodal.boosting.cumbo import MuCumboClassifier
+ from matplotlib import pyplot as plt
+
+
+ def generate_data(n_samples, lim):
+ """Generate random data in a rectangle"""
+ lim = np.array(lim)
+ n_features = lim.shape[0]
+ data = np.random.random((n_samples, n_features))
+ data = (lim[:, 1]-lim[:, 0]) * data + lim[:, 0]
+ return data
+
+
+ seed = 12
+ np.random.seed(seed)
+
+ n_samples = 300
+
+ view_0 = np.concatenate((generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 2.], [0., 1.]])))
+
+ view_1 = np.concatenate((generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]])))
+
+ view_2 = np.concatenate((generate_data(n_samples, [[0., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[1., 2.], [0., 1.]])))
+
+ X = np.concatenate((view_0, view_1, view_2), axis=1)
+
+ y = np.zeros(3*n_samples, dtype=np.int64)
+ y[n_samples:2*n_samples] = 1
+ y[2*n_samples:] = 2
+
+ views_ind = np.array([0, 2, 4, 6])
+
+ n_estimators = 4
+ clf = MuCumboClassifier(n_estimators=n_estimators)
+ clf.fit(X, y, views_ind)
+
+ print('\nAfter 4 iterations, the MuCuMBo classifier reaches exact '
+ 'classification for the\nlearning samples:')
+ for ind, score in enumerate(clf.staged_score(X, y)):
+ print(' - iteration {}, score: {}'.format(ind + 1, score))
+
+ print('\nThe resulting MuCuMBo classifier uses four sub-classifiers that are '
+ 'wheighted\nusing the following weights:\n'
+ ' estimator weights alpha: {}'.format(clf.estimator_weights_alpha_))
+
+ # print('\nThe first sub-classifier uses the data of view 0 to compute '
+ # 'its classification\nresults, the second and third sub-classifiers use '
+ # 'the data of view 1, while the\nfourth one uses the data of '
+ # 'view 2:\n'
+ # ' best views: {}'. format(clf.best_views_))
+
+ print('\nThe first figure displays the data, splitting the representation '
+ 'between the\nthree views.')
+
+ styles = ('.b', '.r', '.g')
+ fig = plt.figure(figsize=(12., 11.))
+ fig.suptitle('Representation of the data', size=16)
+ for ind_view in range(3):
+ ax = plt.subplot(3, 1, ind_view + 1)
+ ax.set_title('View {}'.format(ind_view))
+ ind_feature = ind_view * 2
+ for ind_class in range(3):
+ ind_samples = (y == ind_class)
+ ax.plot(X[ind_samples, ind_feature],
+ X[ind_samples, ind_feature + 1],
+ styles[ind_class],
+ label='Class {}'.format(ind_class))
+ ax.legend(loc='upper left', framealpha=0.9)
+
+ print('\nThe second figure displays the classification results for the '
+ 'sub-classifiers\non the learning sample data.\n')
+
+ # fig = plt.figure(figsize=(14., 11.))
+ # fig.suptitle('Classification results on the learning data for the '
+ # 'sub-classifiers', size=16)
+ # for ind_estimator in range(n_estimators):
+ # best_view = clf.best_views_[ind_estimator]
+ # y_pred = clf.estimators_[ind_estimator].predict(
+ # X[:, 2*best_view:2*best_view+2])
+ # background_color = (1.0, 1.0, 0.9)
+ # for ind_view in range(3):
+ # ax = plt.subplot(3, 4, ind_estimator + 4*ind_view + 1)
+ # if ind_view == best_view:
+ # ax.set_facecolor(background_color)
+ # ax.set_title(
+ # 'Sub-classifier {} - View {}'.format(ind_estimator, ind_view))
+ # ind_feature = ind_view * 2
+ # for ind_class in range(3):
+ # ind_samples = (y_pred == ind_class)
+ # ax.plot(X[ind_samples, ind_feature],
+ # X[ind_samples, ind_feature + 1],
+ # styles[ind_class],
+ # label='Class {}'.format(ind_class))
+ # ax.legend(title='Predicted class:', loc='upper left', framealpha=0.9)
+
+ plt.show()
+
+
+.. rst-class:: sphx-glr-timing
+
+ **Total running time of the script:** ( 0 minutes 0.000 seconds)
+
+
+.. _sphx_glr_download_tutorial_auto_examples_cumbo_plot_3_views_3_classes.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+ :class: sphx-glr-footer-example
+
+
+
+ .. container:: sphx-glr-download
+
+ :download:`Download Python source code: cumbo_plot_3_views_3_classes.py <cumbo_plot_3_views_3_classes.py>`
+
+
+
+ .. container:: sphx-glr-download
+
+ :download:`Download Jupyter notebook: cumbo_plot_3_views_3_classes.ipynb <cumbo_plot_3_views_3_classes.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+ `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/doc/tutorial/auto_examples/cumbo_plot_3_views_3_classes_codeobj.pickle b/doc/tutorial/auto_examples/cumbo_plot_3_views_3_classes_codeobj.pickle
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diff --git a/doc/docmumbo/auto_examples/images/sphx_glr_plot_2_views_2_classes_002.png b/doc/tutorial/auto_examples/images/sphx_glr_plot_2_views_2_classes_002.png
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diff --git a/doc/tutorial/auto_examples/images/sphx_glr_plot_3_views_3_classes_001.png b/doc/tutorial/auto_examples/images/sphx_glr_plot_3_views_3_classes_001.png
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diff --git a/doc/docmumbo/auto_examples/images/sphx_glr_plot_3_views_3_classes_002.png b/doc/tutorial/auto_examples/images/sphx_glr_plot_3_views_3_classes_002.png
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diff --git a/doc/tutorial/auto_examples/images/thumb/sphx_glr_cumbo_plot_2_views_2_classes_thumb.png b/doc/tutorial/auto_examples/images/thumb/sphx_glr_cumbo_plot_2_views_2_classes_thumb.png
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diff --git a/doc/tutorial/auto_examples/images/thumb/sphx_glr_plot_2_views_2_classes_thumb.png b/doc/tutorial/auto_examples/images/thumb/sphx_glr_plot_2_views_2_classes_thumb.png
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diff --git a/doc/tutorial/auto_examples/images/thumb/sphx_glr_plot_3_views_3_classes_thumb.png b/doc/tutorial/auto_examples/images/thumb/sphx_glr_plot_3_views_3_classes_thumb.png
new file mode 100644
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diff --git a/doc/tutorial/auto_examples/index.rst b/doc/tutorial/auto_examples/index.rst
new file mode 100644
index 0000000..17002d9
--- /dev/null
+++ b/doc/tutorial/auto_examples/index.rst
@@ -0,0 +1,205 @@
+:orphan:
+
+
+
+.. _sphx_glr_tutorial_auto_examples:
+
+.. _examples:
+
+Multimodal Examples
+===================
+
+
+
+
+
+.. raw:: html
+
+ <div class="sphx-glr-clear"></div>
+
+
+
+.. _sphx_glr_tutorial_auto_examples_cumbo:
+
+.. _examples:
+
+Examples
+========
+
+MuCuMBo Examples
+----------------
+
+The following toy examples illustrate how the MuCuMBo algorithm exploits
+cooperation between views for classification.
+
+
+
+
+.. raw:: html
+
+ <div class="sphx-glr-thumbcontainer" tooltip="In this toy example, we generate data from two classes, split between two two-dimensional views...">
+
+.. only:: html
+
+ .. figure:: /tutorial/auto_examples/cumbo/images/thumb/sphx_glr_plot_cumbo_2_views_2_classes_thumb.png
+
+ :ref:`sphx_glr_tutorial_auto_examples_cumbo_plot_cumbo_2_views_2_classes.py`
+
+.. raw:: html
+
+ </div>
+
+
+.. toctree::
+ :hidden:
+
+ /tutorial/auto_examples/cumbo/plot_cumbo_2_views_2_classes
+
+.. raw:: html
+
+ <div class="sphx-glr-thumbcontainer" tooltip="In this toy example, we generate data from three classes, split between three two-dimensional v...">
+
+.. only:: html
+
+ .. figure:: /tutorial/auto_examples/cumbo/images/thumb/sphx_glr_plot_cumbo_3_views_3_classes_thumb.png
+
+ :ref:`sphx_glr_tutorial_auto_examples_cumbo_plot_cumbo_3_views_3_classes.py`
+
+.. raw:: html
+
+ </div>
+
+
+.. toctree::
+ :hidden:
+
+ /tutorial/auto_examples/cumbo/plot_cumbo_3_views_3_classes
+.. raw:: html
+
+ <div class="sphx-glr-clear"></div>
+
+
+
+.. _sphx_glr_tutorial_auto_examples_mumbo:
+
+.. _examples:
+
+Examples
+========
+
+MuMBo Examples
+--------------
+
+The following toy examples illustrate how the MuMBo algorithm exploits
+cooperation between views for classification.
+
+
+
+
+.. raw:: html
+
+ <div class="sphx-glr-thumbcontainer" tooltip="In this toy example, we generate data from two classes, split between two two-dimensional views...">
+
+.. only:: html
+
+ .. figure:: /tutorial/auto_examples/mumbo/images/thumb/sphx_glr_plot_mumbo_2_views_2_classes_thumb.png
+
+ :ref:`sphx_glr_tutorial_auto_examples_mumbo_plot_mumbo_2_views_2_classes.py`
+
+.. raw:: html
+
+ </div>
+
+
+.. toctree::
+ :hidden:
+
+ /tutorial/auto_examples/mumbo/plot_mumbo_2_views_2_classes
+
+.. raw:: html
+
+ <div class="sphx-glr-thumbcontainer" tooltip="In this toy example, we generate data from three classes, split between three two-dimensional v...">
+
+.. only:: html
+
+ .. figure:: /tutorial/auto_examples/mumbo/images/thumb/sphx_glr_plot_mumbo_3_views_3_classes_thumb.png
+
+ :ref:`sphx_glr_tutorial_auto_examples_mumbo_plot_mumbo_3_views_3_classes.py`
+
+.. raw:: html
+
+ </div>
+
+
+.. toctree::
+ :hidden:
+
+ /tutorial/auto_examples/mumbo/plot_mumbo_3_views_3_classes
+.. raw:: html
+
+ <div class="sphx-glr-clear"></div>
+
+
+
+.. _sphx_glr_tutorial_auto_examples_mvml:
+
+.. _examples:
+
+Examples
+========
+
+MVML
+----
+
+The following toy examples illustrate how the MVML algorithm
+
+
+
+
+.. raw:: html
+
+ <div class="sphx-glr-thumbcontainer" tooltip="Demonstration uses scikit-learn for retrieving datasets and for calculating rbf kernel function...">
+
+.. only:: html
+
+ .. figure:: /tutorial/auto_examples/mvml/images/thumb/sphx_glr_plot_mvml__thumb.png
+
+ :ref:`sphx_glr_tutorial_auto_examples_mvml_plot_mvml_.py`
+
+.. raw:: html
+
+ </div>
+
+
+.. toctree::
+ :hidden:
+
+ /tutorial/auto_examples/mvml/plot_mvml_
+.. raw:: html
+
+ <div class="sphx-glr-clear"></div>
+
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+ :class: sphx-glr-footer-gallery
+
+
+ .. container:: sphx-glr-download
+
+ :download:`Download all examples in Python source code: auto_examples_python.zip <//home/dominique/projets/ANR-Lives/scikit-multimodallearn/doc/tutorial/auto_examples/auto_examples_python.zip>`
+
+
+
+ .. container:: sphx-glr-download
+
+ :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip <//home/dominique/projets/ANR-Lives/scikit-multimodallearn/doc/tutorial/auto_examples/auto_examples_jupyter.zip>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+ `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/doc/tutorial/auto_examples/mumbo/images/sphx_glr_plot_mumbo_2_views_2_classes_001.png b/doc/tutorial/auto_examples/mumbo/images/sphx_glr_plot_mumbo_2_views_2_classes_001.png
new file mode 100644
index 0000000000000000000000000000000000000000..6c10bfca50d38ffb88d84b3bd7603ff549a6f5e2
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diff --git a/doc/tutorial/auto_examples/mumbo/images/thumb/sphx_glr_mumbo_plot_3_views_3_classes_thumb.png b/doc/tutorial/auto_examples/mumbo/images/thumb/sphx_glr_mumbo_plot_3_views_3_classes_thumb.png
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diff --git a/doc/tutorial/auto_examples/mumbo/mumbo_plot_2_views_2_classes.ipynb b/doc/tutorial/auto_examples/mumbo/mumbo_plot_2_views_2_classes.ipynb
new file mode 100644
index 0000000..bfbb2d3
--- /dev/null
+++ b/doc/tutorial/auto_examples/mumbo/mumbo_plot_2_views_2_classes.ipynb
@@ -0,0 +1,54 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n================================\nMumbo 2 views, 2 classes example\n================================\n\nIn this toy example, we generate data from two classes, split between two\ntwo-dimensional views.\n\nFor each view, the data are generated so that half of the points of each class\nare well separated in the plane, while the other half of the points are not\nseparated and placed in the same area. We also insure that the points that are\nnot separated in one view are well separated in the other view.\n\nThus, in the figure representing the data, the points represented by crosses\n(x) are well separated in view 0 while they are not separated in view 1, while\nthe points represented by dots (.) are well separated in view 1 while they are\nnot separated in view 0. In this figure, the blue symbols represent points\nof class 0, while red symbols represent points of class 1.\n\nThe MuMBo algorithm take adavantage of the complementarity of the two views to\nrightly classify the points.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\nfrom multimodal.boosting.mumbo import MumboClassifier\nfrom matplotlib import pyplot as plt\n\n\ndef generate_data(n_samples, lim):\n \"\"\"Generate random data in a rectangle\"\"\"\n lim = np.array(lim)\n n_features = lim.shape[0]\n data = np.random.random((n_samples, n_features))\n data = (lim[:, 1]-lim[:, 0]) * data + lim[:, 0]\n return data\n\n\nseed = 12\nnp.random.seed(seed)\n\nn_samples = 100\n\nview_0 = np.concatenate((generate_data(n_samples, [[0., 1.], [0., 1.]]),\n generate_data(n_samples, [[1., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [1., 2.]])))\n\nview_1 = np.concatenate((generate_data(n_samples, [[1., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [1., 2.]]),\n generate_data(n_samples, [[0., 1.], [0., 1.]])))\n\nX = np.concatenate((view_0, view_1), axis=1)\n\ny = np.zeros(4*n_samples, dtype=np.int64)\ny[2*n_samples:] = 1\n\nviews_ind = np.array([0, 2, 4])\n\nn_estimators = 3\nclf = MumboClassifier(n_estimators=n_estimators)\nclf.fit(X, y, views_ind)\n\nprint('\\nAfter 3 iterations, the MuMBo classifier reaches exact '\n 'classification for the\\nlearning samples:')\nfor ind, score in enumerate(clf.staged_score(X, y)):\n print(' - iteration {}, score: {}'.format(ind + 1, score))\n\n\nprint('\\nThe resulting MuMBo classifier uses three sub-classifiers that are '\n 'wheighted\\nusing the following weights:\\n'\n ' estimator weights: {}'.format(clf.estimator_weights_))\n\nprint('\\nThe two first sub-classifiers use the data of view 0 to compute '\n 'their\\nclassification results, while the third one uses the data of '\n 'view 1:\\n'\n ' best views: {}'. format(clf.best_views_))\n\nprint('\\nThe first figure displays the data, splitting the representation '\n 'between the\\ntwo views.')\n\nfig = plt.figure(figsize=(10., 8.))\nfig.suptitle('Representation of the data', size=16)\nfor ind_view in range(2):\n ax = plt.subplot(2, 1, ind_view + 1)\n ax.set_title('View {}'.format(ind_view))\n ind_feature = ind_view * 2\n styles = ('.b', 'xb', '.r', 'xr')\n labels = ('non-separated', 'separated')\n for ind in range(4):\n ind_class = ind // 2\n label = labels[(ind + ind_view) % 2]\n ax.plot(X[n_samples*ind:n_samples*(ind+1), ind_feature],\n X[n_samples*ind:n_samples*(ind+1), ind_feature + 1],\n styles[ind],\n label='Class {} ({})'.format(ind_class, label))\n ax.legend()\n\nprint('\\nThe second figure displays the classification results for the '\n 'sub-classifiers\\non the learning sample data.\\n')\n\nstyles = ('.b', '.r')\nfig = plt.figure(figsize=(12., 7.))\nfig.suptitle('Classification results on the learning data for the '\n 'sub-classifiers', size=16)\nfor ind_estimator in range(n_estimators):\n best_view = clf.best_views_[ind_estimator]\n y_pred = clf.estimators_[ind_estimator].predict(\n X[:, 2*best_view:2*best_view+2])\n background_color = (1.0, 1.0, 0.9)\n for ind_view in range(2):\n ax = plt.subplot(2, 3, ind_estimator + 3*ind_view + 1)\n if ind_view == best_view:\n ax.set_facecolor(background_color)\n ax.set_title(\n 'Sub-classifier {} - View {}'.format(ind_estimator, ind_view))\n ind_feature = ind_view * 2\n for ind_class in range(2):\n ind_samples = (y_pred == ind_class)\n ax.plot(X[ind_samples, ind_feature],\n X[ind_samples, ind_feature + 1],\n styles[ind_class],\n label='Class {}'.format(ind_class))\n ax.legend(title='Predicted class:')\n\nplt.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/examples/mumbo_plot_2_views_2_classes.py b/doc/tutorial/auto_examples/mumbo/mumbo_plot_2_views_2_classes.py
similarity index 97%
rename from examples/mumbo_plot_2_views_2_classes.py
rename to doc/tutorial/auto_examples/mumbo/mumbo_plot_2_views_2_classes.py
index a2f864e..91f0dc7 100644
--- a/examples/mumbo_plot_2_views_2_classes.py
+++ b/doc/tutorial/auto_examples/mumbo/mumbo_plot_2_views_2_classes.py
@@ -1,8 +1,8 @@
# -*- coding: utf-8 -*-
"""
-==========================
-2 views, 2 classes example
-==========================
+================================
+Mumbo 2 views, 2 classes example
+================================
In this toy example, we generate data from two classes, split between two
two-dimensional views.
diff --git a/doc/tutorial/auto_examples/mumbo/mumbo_plot_2_views_2_classes.rst b/doc/tutorial/auto_examples/mumbo/mumbo_plot_2_views_2_classes.rst
new file mode 100644
index 0000000..712a45e
--- /dev/null
+++ b/doc/tutorial/auto_examples/mumbo/mumbo_plot_2_views_2_classes.rst
@@ -0,0 +1,170 @@
+.. note::
+ :class: sphx-glr-download-link-note
+
+ Click :ref:`here <sphx_glr_download_tutorial_auto_examples_mumbo_mumbo_plot_2_views_2_classes.py>` to download the full example code
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorial_auto_examples_mumbo_mumbo_plot_2_views_2_classes.py:
+
+
+================================
+Mumbo 2 views, 2 classes example
+================================
+
+In this toy example, we generate data from two classes, split between two
+two-dimensional views.
+
+For each view, the data are generated so that half of the points of each class
+are well separated in the plane, while the other half of the points are not
+separated and placed in the same area. We also insure that the points that are
+not separated in one view are well separated in the other view.
+
+Thus, in the figure representing the data, the points represented by crosses
+(x) are well separated in view 0 while they are not separated in view 1, while
+the points represented by dots (.) are well separated in view 1 while they are
+not separated in view 0. In this figure, the blue symbols represent points
+of class 0, while red symbols represent points of class 1.
+
+The MuMBo algorithm take adavantage of the complementarity of the two views to
+rightly classify the points.
+
+
+.. code-block:: default
+
+
+ import numpy as np
+ from multimodal.boosting.mumbo import MumboClassifier
+ from matplotlib import pyplot as plt
+
+
+ def generate_data(n_samples, lim):
+ """Generate random data in a rectangle"""
+ lim = np.array(lim)
+ n_features = lim.shape[0]
+ data = np.random.random((n_samples, n_features))
+ data = (lim[:, 1]-lim[:, 0]) * data + lim[:, 0]
+ return data
+
+
+ seed = 12
+ np.random.seed(seed)
+
+ n_samples = 100
+
+ view_0 = np.concatenate((generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [1., 2.]])))
+
+ view_1 = np.concatenate((generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [1., 2.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]])))
+
+ X = np.concatenate((view_0, view_1), axis=1)
+
+ y = np.zeros(4*n_samples, dtype=np.int64)
+ y[2*n_samples:] = 1
+
+ views_ind = np.array([0, 2, 4])
+
+ n_estimators = 3
+ clf = MumboClassifier(n_estimators=n_estimators)
+ clf.fit(X, y, views_ind)
+
+ print('\nAfter 3 iterations, the MuMBo classifier reaches exact '
+ 'classification for the\nlearning samples:')
+ for ind, score in enumerate(clf.staged_score(X, y)):
+ print(' - iteration {}, score: {}'.format(ind + 1, score))
+
+
+ print('\nThe resulting MuMBo classifier uses three sub-classifiers that are '
+ 'wheighted\nusing the following weights:\n'
+ ' estimator weights: {}'.format(clf.estimator_weights_))
+
+ print('\nThe two first sub-classifiers use the data of view 0 to compute '
+ 'their\nclassification results, while the third one uses the data of '
+ 'view 1:\n'
+ ' best views: {}'. format(clf.best_views_))
+
+ print('\nThe first figure displays the data, splitting the representation '
+ 'between the\ntwo views.')
+
+ fig = plt.figure(figsize=(10., 8.))
+ fig.suptitle('Representation of the data', size=16)
+ for ind_view in range(2):
+ ax = plt.subplot(2, 1, ind_view + 1)
+ ax.set_title('View {}'.format(ind_view))
+ ind_feature = ind_view * 2
+ styles = ('.b', 'xb', '.r', 'xr')
+ labels = ('non-separated', 'separated')
+ for ind in range(4):
+ ind_class = ind // 2
+ label = labels[(ind + ind_view) % 2]
+ ax.plot(X[n_samples*ind:n_samples*(ind+1), ind_feature],
+ X[n_samples*ind:n_samples*(ind+1), ind_feature + 1],
+ styles[ind],
+ label='Class {} ({})'.format(ind_class, label))
+ ax.legend()
+
+ print('\nThe second figure displays the classification results for the '
+ 'sub-classifiers\non the learning sample data.\n')
+
+ styles = ('.b', '.r')
+ fig = plt.figure(figsize=(12., 7.))
+ fig.suptitle('Classification results on the learning data for the '
+ 'sub-classifiers', size=16)
+ for ind_estimator in range(n_estimators):
+ best_view = clf.best_views_[ind_estimator]
+ y_pred = clf.estimators_[ind_estimator].predict(
+ X[:, 2*best_view:2*best_view+2])
+ background_color = (1.0, 1.0, 0.9)
+ for ind_view in range(2):
+ ax = plt.subplot(2, 3, ind_estimator + 3*ind_view + 1)
+ if ind_view == best_view:
+ ax.set_facecolor(background_color)
+ ax.set_title(
+ 'Sub-classifier {} - View {}'.format(ind_estimator, ind_view))
+ ind_feature = ind_view * 2
+ for ind_class in range(2):
+ ind_samples = (y_pred == ind_class)
+ ax.plot(X[ind_samples, ind_feature],
+ X[ind_samples, ind_feature + 1],
+ styles[ind_class],
+ label='Class {}'.format(ind_class))
+ ax.legend(title='Predicted class:')
+
+ plt.show()
+
+
+.. rst-class:: sphx-glr-timing
+
+ **Total running time of the script:** ( 0 minutes 0.000 seconds)
+
+
+.. _sphx_glr_download_tutorial_auto_examples_mumbo_mumbo_plot_2_views_2_classes.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+ :class: sphx-glr-footer-example
+
+
+
+ .. container:: sphx-glr-download
+
+ :download:`Download Python source code: mumbo_plot_2_views_2_classes.py <mumbo_plot_2_views_2_classes.py>`
+
+
+
+ .. container:: sphx-glr-download
+
+ :download:`Download Jupyter notebook: mumbo_plot_2_views_2_classes.ipynb <mumbo_plot_2_views_2_classes.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+ `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/doc/tutorial/auto_examples/mumbo/mumbo_plot_2_views_2_classes_codeobj.pickle b/doc/tutorial/auto_examples/mumbo/mumbo_plot_2_views_2_classes_codeobj.pickle
new file mode 100644
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diff --git a/doc/tutorial/auto_examples/mumbo/mumbo_plot_3_views_3_classes.ipynb b/doc/tutorial/auto_examples/mumbo/mumbo_plot_3_views_3_classes.ipynb
new file mode 100644
index 0000000..371d83e
--- /dev/null
+++ b/doc/tutorial/auto_examples/mumbo/mumbo_plot_3_views_3_classes.ipynb
@@ -0,0 +1,54 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n================================\nMumbo 3 views, 3 classes example\n================================\n\nIn this toy example, we generate data from three classes, split between three\ntwo-dimensional views.\n\nFor each view, the data are generated so that the points for two classes are\nwell seperated, while the points for the third class are not seperated with\nthe two other classes. That means that, taken separately, none of the single\nviews allows for a good classification of the data.\n\nNevertheless, the MuMBo algorithm take adavantage of the complementarity of\nthe views to rightly classify the points.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\nfrom multimodal.boosting.mumbo import MumboClassifier\nfrom matplotlib import pyplot as plt\n\n\ndef generate_data(n_samples, lim):\n \"\"\"Generate random data in a rectangle\"\"\"\n lim = np.array(lim)\n n_features = lim.shape[0]\n data = np.random.random((n_samples, n_features))\n data = (lim[:, 1]-lim[:, 0]) * data + lim[:, 0]\n return data\n\n\nseed = 12\nnp.random.seed(seed)\n\nn_samples = 300\n\nview_0 = np.concatenate((generate_data(n_samples, [[0., 1.], [0., 1.]]),\n generate_data(n_samples, [[1., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 2.], [0., 1.]])))\n\nview_1 = np.concatenate((generate_data(n_samples, [[1., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [0., 1.]])))\n\nview_2 = np.concatenate((generate_data(n_samples, [[0., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [0., 1.]]),\n generate_data(n_samples, [[1., 2.], [0., 1.]])))\n\nX = np.concatenate((view_0, view_1, view_2), axis=1)\n\ny = np.zeros(3*n_samples, dtype=np.int64)\ny[n_samples:2*n_samples] = 1\ny[2*n_samples:] = 2\n\nviews_ind = np.array([0, 2, 4, 6])\n\nn_estimators = 4\nclf = MumboClassifier(n_estimators=n_estimators)\nclf.fit(X, y, views_ind)\n\nprint('\\nAfter 4 iterations, the MuMBo classifier reaches exact '\n 'classification for the\\nlearning samples:')\nfor ind, score in enumerate(clf.staged_score(X, y)):\n print(' - iteration {}, score: {}'.format(ind + 1, score))\n\nprint('\\nThe resulting MuMBo classifier uses four sub-classifiers that are '\n 'wheighted\\nusing the following weights:\\n'\n ' estimator weights: {}'.format(clf.estimator_weights_))\n\nprint('\\nThe first sub-classifier uses the data of view 0 to compute '\n 'its classification\\nresults, the second and third sub-classifiers use '\n 'the data of view 1, while the\\nfourth one uses the data of '\n 'view 2:\\n'\n ' best views: {}'. format(clf.best_views_))\n\nprint('\\nThe first figure displays the data, splitting the representation '\n 'between the\\nthree views.')\n\nstyles = ('.b', '.r', '.g')\nfig = plt.figure(figsize=(12., 11.))\nfig.suptitle('Representation of the data', size=16)\nfor ind_view in range(3):\n ax = plt.subplot(3, 1, ind_view + 1)\n ax.set_title('View {}'.format(ind_view))\n ind_feature = ind_view * 2\n for ind_class in range(3):\n ind_samples = (y == ind_class)\n ax.plot(X[ind_samples, ind_feature],\n X[ind_samples, ind_feature + 1],\n styles[ind_class],\n label='Class {}'.format(ind_class))\n ax.legend(loc='upper left', framealpha=0.9)\n\nprint('\\nThe second figure displays the classification results for the '\n 'sub-classifiers\\non the learning sample data.\\n')\n\nfig = plt.figure(figsize=(14., 11.))\nfig.suptitle('Classification results on the learning data for the '\n 'sub-classifiers', size=16)\nfor ind_estimator in range(n_estimators):\n best_view = clf.best_views_[ind_estimator]\n y_pred = clf.estimators_[ind_estimator].predict(\n X[:, 2*best_view:2*best_view+2])\n background_color = (1.0, 1.0, 0.9)\n for ind_view in range(3):\n ax = plt.subplot(3, 4, ind_estimator + 4*ind_view + 1)\n if ind_view == best_view:\n ax.set_facecolor(background_color)\n ax.set_title(\n 'Sub-classifier {} - View {}'.format(ind_estimator, ind_view))\n ind_feature = ind_view * 2\n for ind_class in range(3):\n ind_samples = (y_pred == ind_class)\n ax.plot(X[ind_samples, ind_feature],\n X[ind_samples, ind_feature + 1],\n styles[ind_class],\n label='Class {}'.format(ind_class))\n ax.legend(title='Predicted class:', loc='upper left', framealpha=0.9)\n\nplt.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/examples/mumbo_plot_3_views_3_classes.py b/doc/tutorial/auto_examples/mumbo/mumbo_plot_3_views_3_classes.py
similarity index 97%
rename from examples/mumbo_plot_3_views_3_classes.py
rename to doc/tutorial/auto_examples/mumbo/mumbo_plot_3_views_3_classes.py
index 624a791..62cd311 100644
--- a/examples/mumbo_plot_3_views_3_classes.py
+++ b/doc/tutorial/auto_examples/mumbo/mumbo_plot_3_views_3_classes.py
@@ -1,8 +1,8 @@
# -*- coding: utf-8 -*-
"""
-==========================
-3 views, 3 classes example
-==========================
+================================
+Mumbo 3 views, 3 classes example
+================================
In this toy example, we generate data from three classes, split between three
two-dimensional views.
diff --git a/doc/tutorial/auto_examples/mumbo/mumbo_plot_3_views_3_classes.rst b/doc/tutorial/auto_examples/mumbo/mumbo_plot_3_views_3_classes.rst
new file mode 100644
index 0000000..4aedb22
--- /dev/null
+++ b/doc/tutorial/auto_examples/mumbo/mumbo_plot_3_views_3_classes.rst
@@ -0,0 +1,164 @@
+.. note::
+ :class: sphx-glr-download-link-note
+
+ Click :ref:`here <sphx_glr_download_tutorial_auto_examples_mumbo_mumbo_plot_3_views_3_classes.py>` to download the full example code
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorial_auto_examples_mumbo_mumbo_plot_3_views_3_classes.py:
+
+
+================================
+Mumbo 3 views, 3 classes example
+================================
+
+In this toy example, we generate data from three classes, split between three
+two-dimensional views.
+
+For each view, the data are generated so that the points for two classes are
+well seperated, while the points for the third class are not seperated with
+the two other classes. That means that, taken separately, none of the single
+views allows for a good classification of the data.
+
+Nevertheless, the MuMBo algorithm take adavantage of the complementarity of
+the views to rightly classify the points.
+
+
+.. code-block:: default
+
+
+ import numpy as np
+ from multimodal.boosting.mumbo import MumboClassifier
+ from matplotlib import pyplot as plt
+
+
+ def generate_data(n_samples, lim):
+ """Generate random data in a rectangle"""
+ lim = np.array(lim)
+ n_features = lim.shape[0]
+ data = np.random.random((n_samples, n_features))
+ data = (lim[:, 1]-lim[:, 0]) * data + lim[:, 0]
+ return data
+
+
+ seed = 12
+ np.random.seed(seed)
+
+ n_samples = 300
+
+ view_0 = np.concatenate((generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 2.], [0., 1.]])))
+
+ view_1 = np.concatenate((generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]])))
+
+ view_2 = np.concatenate((generate_data(n_samples, [[0., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[1., 2.], [0., 1.]])))
+
+ X = np.concatenate((view_0, view_1, view_2), axis=1)
+
+ y = np.zeros(3*n_samples, dtype=np.int64)
+ y[n_samples:2*n_samples] = 1
+ y[2*n_samples:] = 2
+
+ views_ind = np.array([0, 2, 4, 6])
+
+ n_estimators = 4
+ clf = MumboClassifier(n_estimators=n_estimators)
+ clf.fit(X, y, views_ind)
+
+ print('\nAfter 4 iterations, the MuMBo classifier reaches exact '
+ 'classification for the\nlearning samples:')
+ for ind, score in enumerate(clf.staged_score(X, y)):
+ print(' - iteration {}, score: {}'.format(ind + 1, score))
+
+ print('\nThe resulting MuMBo classifier uses four sub-classifiers that are '
+ 'wheighted\nusing the following weights:\n'
+ ' estimator weights: {}'.format(clf.estimator_weights_))
+
+ print('\nThe first sub-classifier uses the data of view 0 to compute '
+ 'its classification\nresults, the second and third sub-classifiers use '
+ 'the data of view 1, while the\nfourth one uses the data of '
+ 'view 2:\n'
+ ' best views: {}'. format(clf.best_views_))
+
+ print('\nThe first figure displays the data, splitting the representation '
+ 'between the\nthree views.')
+
+ styles = ('.b', '.r', '.g')
+ fig = plt.figure(figsize=(12., 11.))
+ fig.suptitle('Representation of the data', size=16)
+ for ind_view in range(3):
+ ax = plt.subplot(3, 1, ind_view + 1)
+ ax.set_title('View {}'.format(ind_view))
+ ind_feature = ind_view * 2
+ for ind_class in range(3):
+ ind_samples = (y == ind_class)
+ ax.plot(X[ind_samples, ind_feature],
+ X[ind_samples, ind_feature + 1],
+ styles[ind_class],
+ label='Class {}'.format(ind_class))
+ ax.legend(loc='upper left', framealpha=0.9)
+
+ print('\nThe second figure displays the classification results for the '
+ 'sub-classifiers\non the learning sample data.\n')
+
+ fig = plt.figure(figsize=(14., 11.))
+ fig.suptitle('Classification results on the learning data for the '
+ 'sub-classifiers', size=16)
+ for ind_estimator in range(n_estimators):
+ best_view = clf.best_views_[ind_estimator]
+ y_pred = clf.estimators_[ind_estimator].predict(
+ X[:, 2*best_view:2*best_view+2])
+ background_color = (1.0, 1.0, 0.9)
+ for ind_view in range(3):
+ ax = plt.subplot(3, 4, ind_estimator + 4*ind_view + 1)
+ if ind_view == best_view:
+ ax.set_facecolor(background_color)
+ ax.set_title(
+ 'Sub-classifier {} - View {}'.format(ind_estimator, ind_view))
+ ind_feature = ind_view * 2
+ for ind_class in range(3):
+ ind_samples = (y_pred == ind_class)
+ ax.plot(X[ind_samples, ind_feature],
+ X[ind_samples, ind_feature + 1],
+ styles[ind_class],
+ label='Class {}'.format(ind_class))
+ ax.legend(title='Predicted class:', loc='upper left', framealpha=0.9)
+
+ plt.show()
+
+
+.. rst-class:: sphx-glr-timing
+
+ **Total running time of the script:** ( 0 minutes 0.000 seconds)
+
+
+.. _sphx_glr_download_tutorial_auto_examples_mumbo_mumbo_plot_3_views_3_classes.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+ :class: sphx-glr-footer-example
+
+
+
+ .. container:: sphx-glr-download
+
+ :download:`Download Python source code: mumbo_plot_3_views_3_classes.py <mumbo_plot_3_views_3_classes.py>`
+
+
+
+ .. container:: sphx-glr-download
+
+ :download:`Download Jupyter notebook: mumbo_plot_3_views_3_classes.ipynb <mumbo_plot_3_views_3_classes.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+ `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/doc/tutorial/auto_examples/mumbo/mumbo_plot_3_views_3_classes_codeobj.pickle b/doc/tutorial/auto_examples/mumbo/mumbo_plot_3_views_3_classes_codeobj.pickle
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diff --git a/doc/tutorial/auto_examples/mumbo/plot_mumbo_2_views_2_classes.ipynb b/doc/tutorial/auto_examples/mumbo/plot_mumbo_2_views_2_classes.ipynb
new file mode 100644
index 0000000..bfbb2d3
--- /dev/null
+++ b/doc/tutorial/auto_examples/mumbo/plot_mumbo_2_views_2_classes.ipynb
@@ -0,0 +1,54 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n================================\nMumbo 2 views, 2 classes example\n================================\n\nIn this toy example, we generate data from two classes, split between two\ntwo-dimensional views.\n\nFor each view, the data are generated so that half of the points of each class\nare well separated in the plane, while the other half of the points are not\nseparated and placed in the same area. We also insure that the points that are\nnot separated in one view are well separated in the other view.\n\nThus, in the figure representing the data, the points represented by crosses\n(x) are well separated in view 0 while they are not separated in view 1, while\nthe points represented by dots (.) are well separated in view 1 while they are\nnot separated in view 0. In this figure, the blue symbols represent points\nof class 0, while red symbols represent points of class 1.\n\nThe MuMBo algorithm take adavantage of the complementarity of the two views to\nrightly classify the points.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\nfrom multimodal.boosting.mumbo import MumboClassifier\nfrom matplotlib import pyplot as plt\n\n\ndef generate_data(n_samples, lim):\n \"\"\"Generate random data in a rectangle\"\"\"\n lim = np.array(lim)\n n_features = lim.shape[0]\n data = np.random.random((n_samples, n_features))\n data = (lim[:, 1]-lim[:, 0]) * data + lim[:, 0]\n return data\n\n\nseed = 12\nnp.random.seed(seed)\n\nn_samples = 100\n\nview_0 = np.concatenate((generate_data(n_samples, [[0., 1.], [0., 1.]]),\n generate_data(n_samples, [[1., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [1., 2.]])))\n\nview_1 = np.concatenate((generate_data(n_samples, [[1., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [1., 2.]]),\n generate_data(n_samples, [[0., 1.], [0., 1.]])))\n\nX = np.concatenate((view_0, view_1), axis=1)\n\ny = np.zeros(4*n_samples, dtype=np.int64)\ny[2*n_samples:] = 1\n\nviews_ind = np.array([0, 2, 4])\n\nn_estimators = 3\nclf = MumboClassifier(n_estimators=n_estimators)\nclf.fit(X, y, views_ind)\n\nprint('\\nAfter 3 iterations, the MuMBo classifier reaches exact '\n 'classification for the\\nlearning samples:')\nfor ind, score in enumerate(clf.staged_score(X, y)):\n print(' - iteration {}, score: {}'.format(ind + 1, score))\n\n\nprint('\\nThe resulting MuMBo classifier uses three sub-classifiers that are '\n 'wheighted\\nusing the following weights:\\n'\n ' estimator weights: {}'.format(clf.estimator_weights_))\n\nprint('\\nThe two first sub-classifiers use the data of view 0 to compute '\n 'their\\nclassification results, while the third one uses the data of '\n 'view 1:\\n'\n ' best views: {}'. format(clf.best_views_))\n\nprint('\\nThe first figure displays the data, splitting the representation '\n 'between the\\ntwo views.')\n\nfig = plt.figure(figsize=(10., 8.))\nfig.suptitle('Representation of the data', size=16)\nfor ind_view in range(2):\n ax = plt.subplot(2, 1, ind_view + 1)\n ax.set_title('View {}'.format(ind_view))\n ind_feature = ind_view * 2\n styles = ('.b', 'xb', '.r', 'xr')\n labels = ('non-separated', 'separated')\n for ind in range(4):\n ind_class = ind // 2\n label = labels[(ind + ind_view) % 2]\n ax.plot(X[n_samples*ind:n_samples*(ind+1), ind_feature],\n X[n_samples*ind:n_samples*(ind+1), ind_feature + 1],\n styles[ind],\n label='Class {} ({})'.format(ind_class, label))\n ax.legend()\n\nprint('\\nThe second figure displays the classification results for the '\n 'sub-classifiers\\non the learning sample data.\\n')\n\nstyles = ('.b', '.r')\nfig = plt.figure(figsize=(12., 7.))\nfig.suptitle('Classification results on the learning data for the '\n 'sub-classifiers', size=16)\nfor ind_estimator in range(n_estimators):\n best_view = clf.best_views_[ind_estimator]\n y_pred = clf.estimators_[ind_estimator].predict(\n X[:, 2*best_view:2*best_view+2])\n background_color = (1.0, 1.0, 0.9)\n for ind_view in range(2):\n ax = plt.subplot(2, 3, ind_estimator + 3*ind_view + 1)\n if ind_view == best_view:\n ax.set_facecolor(background_color)\n ax.set_title(\n 'Sub-classifier {} - View {}'.format(ind_estimator, ind_view))\n ind_feature = ind_view * 2\n for ind_class in range(2):\n ind_samples = (y_pred == ind_class)\n ax.plot(X[ind_samples, ind_feature],\n X[ind_samples, ind_feature + 1],\n styles[ind_class],\n label='Class {}'.format(ind_class))\n ax.legend(title='Predicted class:')\n\nplt.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/doc/docmumbo/auto_examples/plot_2_views_2_classes.py b/doc/tutorial/auto_examples/mumbo/plot_mumbo_2_views_2_classes.py
similarity index 81%
rename from doc/docmumbo/auto_examples/plot_2_views_2_classes.py
rename to doc/tutorial/auto_examples/mumbo/plot_mumbo_2_views_2_classes.py
index 6d1c788..91f0dc7 100644
--- a/doc/docmumbo/auto_examples/plot_2_views_2_classes.py
+++ b/doc/tutorial/auto_examples/mumbo/plot_mumbo_2_views_2_classes.py
@@ -1,8 +1,8 @@
# -*- coding: utf-8 -*-
"""
-==========================
-2 views, 2 classes example
-==========================
+================================
+Mumbo 2 views, 2 classes example
+================================
In this toy example, we generate data from two classes, split between two
two-dimensional views.
@@ -22,30 +22,8 @@ The MuMBo algorithm take adavantage of the complementarity of the two views to
rightly classify the points.
"""
-# Université d'Aix Marseille (AMU) -
-# Centre National de la Recherche Scientifique (CNRS) -
-# Université de Toulon (UTLN).
-# Copyright © 2017-2018 AMU, CNRS, UTLN
-#
-# This file is part of multimodalboost.
-#
-# multimodalboost is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as published by
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# multimodalboost is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with multimodalboost. If not, see <http://www.gnu.org/licenses/>.
-#
-# Author: Florent JAILLET - Laboratoire d'Informatique et Systèmes - UMR 7020
-
import numpy as np
-from multimodalboost.mumbo import MumboClassifier
+from multimodal.boosting.mumbo import MumboClassifier
from matplotlib import pyplot as plt
diff --git a/doc/tutorial/auto_examples/mumbo/plot_mumbo_2_views_2_classes.py.md5 b/doc/tutorial/auto_examples/mumbo/plot_mumbo_2_views_2_classes.py.md5
new file mode 100644
index 0000000..a896dd0
--- /dev/null
+++ b/doc/tutorial/auto_examples/mumbo/plot_mumbo_2_views_2_classes.py.md5
@@ -0,0 +1 @@
+3da5e295c169f88e209f82c289afdd0b
\ No newline at end of file
diff --git a/doc/docmumbo/auto_examples/plot_2_views_2_classes.rst b/doc/tutorial/auto_examples/mumbo/plot_mumbo_2_views_2_classes.rst
similarity index 75%
rename from doc/docmumbo/auto_examples/plot_2_views_2_classes.rst
rename to doc/tutorial/auto_examples/mumbo/plot_mumbo_2_views_2_classes.rst
index b578f41..1904945 100644
--- a/doc/docmumbo/auto_examples/plot_2_views_2_classes.rst
+++ b/doc/tutorial/auto_examples/mumbo/plot_mumbo_2_views_2_classes.rst
@@ -1,15 +1,15 @@
.. note::
:class: sphx-glr-download-link-note
- Click :ref:`here <sphx_glr_download_auto_examples_plot_2_views_2_classes.py>` to download the full example code
+ Click :ref:`here <sphx_glr_download_tutorial_auto_examples_mumbo_plot_mumbo_2_views_2_classes.py>` to download the full example code
.. rst-class:: sphx-glr-example-title
-.. _sphx_glr_auto_examples_plot_2_views_2_classes.py:
+.. _sphx_glr_tutorial_auto_examples_mumbo_plot_mumbo_2_views_2_classes.py:
-==========================
-2 views, 2 classes example
-==========================
+================================
+Mumbo 2 views, 2 classes example
+================================
In this toy example, we generate data from two classes, split between two
two-dimensional views.
@@ -35,12 +35,12 @@ rightly classify the points.
*
- .. image:: /auto_examples/images/sphx_glr_plot_2_views_2_classes_001.png
+ .. image:: /tutorial/auto_examples/mumbo/images/sphx_glr_plot_mumbo_2_views_2_classes_001.png
:class: sphx-glr-multi-img
*
- .. image:: /auto_examples/images/sphx_glr_plot_2_views_2_classes_002.png
+ .. image:: /tutorial/auto_examples/mumbo/images/sphx_glr_plot_mumbo_2_views_2_classes_002.png
:class: sphx-glr-multi-img
@@ -71,8 +71,6 @@ rightly classify the points.
The second figure displays the classification results for the sub-classifiers
on the learning sample data.
- /home/dominique/projets/ANR-Lives/multimodal/multimodalboost/examples/plot_2_views_2_classes.py:149: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
- plt.show()
@@ -85,30 +83,8 @@ rightly classify the points.
.. code-block:: default
- # Université d'Aix Marseille (AMU) -
- # Centre National de la Recherche Scientifique (CNRS) -
- # Université de Toulon (UTLN).
- # Copyright © 2017-2018 AMU, CNRS, UTLN
- #
- # This file is part of multimodalboost.
- #
- # multimodalboost is free software: you can redistribute it and/or modify
- # it under the terms of the GNU Lesser General Public License as published by
- # the Free Software Foundation, either version 3 of the License, or
- # (at your option) any later version.
- #
- # multimodalboost is distributed in the hope that it will be useful,
- # but WITHOUT ANY WARRANTY; without even the implied warranty of
- # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- # GNU Lesser General Public License for more details.
- #
- # You should have received a copy of the GNU Lesser General Public License
- # along with multimodalboost. If not, see <http://www.gnu.org/licenses/>.
- #
- # Author: Florent JAILLET - Laboratoire d'Informatique et Systèmes - UMR 7020
-
import numpy as np
- from multimodalboost.mumbo import MumboClassifier
+ from multimodal.boosting.mumbo import MumboClassifier
from matplotlib import pyplot as plt
@@ -214,10 +190,10 @@ rightly classify the points.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 0.733 seconds)
+ **Total running time of the script:** ( 0 minutes 0.706 seconds)
-.. _sphx_glr_download_auto_examples_plot_2_views_2_classes.py:
+.. _sphx_glr_download_tutorial_auto_examples_mumbo_plot_mumbo_2_views_2_classes.py:
.. only :: html
@@ -229,13 +205,13 @@ rightly classify the points.
.. container:: sphx-glr-download
- :download:`Download Python source code: plot_2_views_2_classes.py <plot_2_views_2_classes.py>`
+ :download:`Download Python source code: plot_mumbo_2_views_2_classes.py <plot_mumbo_2_views_2_classes.py>`
.. container:: sphx-glr-download
- :download:`Download Jupyter notebook: plot_2_views_2_classes.ipynb <plot_2_views_2_classes.ipynb>`
+ :download:`Download Jupyter notebook: plot_mumbo_2_views_2_classes.ipynb <plot_mumbo_2_views_2_classes.ipynb>`
.. only:: html
diff --git a/doc/tutorial/auto_examples/mumbo/plot_mumbo_2_views_2_classes_codeobj.pickle b/doc/tutorial/auto_examples/mumbo/plot_mumbo_2_views_2_classes_codeobj.pickle
new file mode 100644
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diff --git a/doc/tutorial/auto_examples/mumbo/plot_mumbo_3_views_3_classes.ipynb b/doc/tutorial/auto_examples/mumbo/plot_mumbo_3_views_3_classes.ipynb
new file mode 100644
index 0000000..371d83e
--- /dev/null
+++ b/doc/tutorial/auto_examples/mumbo/plot_mumbo_3_views_3_classes.ipynb
@@ -0,0 +1,54 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n================================\nMumbo 3 views, 3 classes example\n================================\n\nIn this toy example, we generate data from three classes, split between three\ntwo-dimensional views.\n\nFor each view, the data are generated so that the points for two classes are\nwell seperated, while the points for the third class are not seperated with\nthe two other classes. That means that, taken separately, none of the single\nviews allows for a good classification of the data.\n\nNevertheless, the MuMBo algorithm take adavantage of the complementarity of\nthe views to rightly classify the points.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\nfrom multimodal.boosting.mumbo import MumboClassifier\nfrom matplotlib import pyplot as plt\n\n\ndef generate_data(n_samples, lim):\n \"\"\"Generate random data in a rectangle\"\"\"\n lim = np.array(lim)\n n_features = lim.shape[0]\n data = np.random.random((n_samples, n_features))\n data = (lim[:, 1]-lim[:, 0]) * data + lim[:, 0]\n return data\n\n\nseed = 12\nnp.random.seed(seed)\n\nn_samples = 300\n\nview_0 = np.concatenate((generate_data(n_samples, [[0., 1.], [0., 1.]]),\n generate_data(n_samples, [[1., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 2.], [0., 1.]])))\n\nview_1 = np.concatenate((generate_data(n_samples, [[1., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [0., 1.]])))\n\nview_2 = np.concatenate((generate_data(n_samples, [[0., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [0., 1.]]),\n generate_data(n_samples, [[1., 2.], [0., 1.]])))\n\nX = np.concatenate((view_0, view_1, view_2), axis=1)\n\ny = np.zeros(3*n_samples, dtype=np.int64)\ny[n_samples:2*n_samples] = 1\ny[2*n_samples:] = 2\n\nviews_ind = np.array([0, 2, 4, 6])\n\nn_estimators = 4\nclf = MumboClassifier(n_estimators=n_estimators)\nclf.fit(X, y, views_ind)\n\nprint('\\nAfter 4 iterations, the MuMBo classifier reaches exact '\n 'classification for the\\nlearning samples:')\nfor ind, score in enumerate(clf.staged_score(X, y)):\n print(' - iteration {}, score: {}'.format(ind + 1, score))\n\nprint('\\nThe resulting MuMBo classifier uses four sub-classifiers that are '\n 'wheighted\\nusing the following weights:\\n'\n ' estimator weights: {}'.format(clf.estimator_weights_))\n\nprint('\\nThe first sub-classifier uses the data of view 0 to compute '\n 'its classification\\nresults, the second and third sub-classifiers use '\n 'the data of view 1, while the\\nfourth one uses the data of '\n 'view 2:\\n'\n ' best views: {}'. format(clf.best_views_))\n\nprint('\\nThe first figure displays the data, splitting the representation '\n 'between the\\nthree views.')\n\nstyles = ('.b', '.r', '.g')\nfig = plt.figure(figsize=(12., 11.))\nfig.suptitle('Representation of the data', size=16)\nfor ind_view in range(3):\n ax = plt.subplot(3, 1, ind_view + 1)\n ax.set_title('View {}'.format(ind_view))\n ind_feature = ind_view * 2\n for ind_class in range(3):\n ind_samples = (y == ind_class)\n ax.plot(X[ind_samples, ind_feature],\n X[ind_samples, ind_feature + 1],\n styles[ind_class],\n label='Class {}'.format(ind_class))\n ax.legend(loc='upper left', framealpha=0.9)\n\nprint('\\nThe second figure displays the classification results for the '\n 'sub-classifiers\\non the learning sample data.\\n')\n\nfig = plt.figure(figsize=(14., 11.))\nfig.suptitle('Classification results on the learning data for the '\n 'sub-classifiers', size=16)\nfor ind_estimator in range(n_estimators):\n best_view = clf.best_views_[ind_estimator]\n y_pred = clf.estimators_[ind_estimator].predict(\n X[:, 2*best_view:2*best_view+2])\n background_color = (1.0, 1.0, 0.9)\n for ind_view in range(3):\n ax = plt.subplot(3, 4, ind_estimator + 4*ind_view + 1)\n if ind_view == best_view:\n ax.set_facecolor(background_color)\n ax.set_title(\n 'Sub-classifier {} - View {}'.format(ind_estimator, ind_view))\n ind_feature = ind_view * 2\n for ind_class in range(3):\n ind_samples = (y_pred == ind_class)\n ax.plot(X[ind_samples, ind_feature],\n X[ind_samples, ind_feature + 1],\n styles[ind_class],\n label='Class {}'.format(ind_class))\n ax.legend(title='Predicted class:', loc='upper left', framealpha=0.9)\n\nplt.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/doc/docmumbo/auto_examples/plot_3_views_3_classes.py b/doc/tutorial/auto_examples/mumbo/plot_mumbo_3_views_3_classes.py
similarity index 80%
rename from doc/docmumbo/auto_examples/plot_3_views_3_classes.py
rename to doc/tutorial/auto_examples/mumbo/plot_mumbo_3_views_3_classes.py
index 7de610b..62cd311 100644
--- a/doc/docmumbo/auto_examples/plot_3_views_3_classes.py
+++ b/doc/tutorial/auto_examples/mumbo/plot_mumbo_3_views_3_classes.py
@@ -1,8 +1,8 @@
# -*- coding: utf-8 -*-
"""
-==========================
-3 views, 3 classes example
-==========================
+================================
+Mumbo 3 views, 3 classes example
+================================
In this toy example, we generate data from three classes, split between three
two-dimensional views.
@@ -16,30 +16,8 @@ Nevertheless, the MuMBo algorithm take adavantage of the complementarity of
the views to rightly classify the points.
"""
-# Université d'Aix Marseille (AMU) -
-# Centre National de la Recherche Scientifique (CNRS) -
-# Université de Toulon (UTLN).
-# Copyright © 2017-2018 AMU, CNRS, UTLN
-#
-# This file is part of multimodalboost.
-#
-# multimodalboost is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as published by
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# multimodalboost is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with multimodalboost. If not, see <http://www.gnu.org/licenses/>.
-#
-# Author: Florent JAILLET - Laboratoire d'Informatique et Systèmes - UMR 7020
-
import numpy as np
-from multimodalboost.mumbo import MumboClassifier
+from multimodal.boosting.mumbo import MumboClassifier
from matplotlib import pyplot as plt
diff --git a/doc/tutorial/auto_examples/mumbo/plot_mumbo_3_views_3_classes.py.md5 b/doc/tutorial/auto_examples/mumbo/plot_mumbo_3_views_3_classes.py.md5
new file mode 100644
index 0000000..4cf20db
--- /dev/null
+++ b/doc/tutorial/auto_examples/mumbo/plot_mumbo_3_views_3_classes.py.md5
@@ -0,0 +1 @@
+ad27973e4efa5744deba52fe840963e6
\ No newline at end of file
diff --git a/doc/docmumbo/auto_examples/plot_3_views_3_classes.rst b/doc/tutorial/auto_examples/mumbo/plot_mumbo_3_views_3_classes.rst
similarity index 75%
rename from doc/docmumbo/auto_examples/plot_3_views_3_classes.rst
rename to doc/tutorial/auto_examples/mumbo/plot_mumbo_3_views_3_classes.rst
index 08d7745..0f55099 100644
--- a/doc/docmumbo/auto_examples/plot_3_views_3_classes.rst
+++ b/doc/tutorial/auto_examples/mumbo/plot_mumbo_3_views_3_classes.rst
@@ -1,15 +1,15 @@
.. note::
:class: sphx-glr-download-link-note
- Click :ref:`here <sphx_glr_download_auto_examples_plot_3_views_3_classes.py>` to download the full example code
+ Click :ref:`here <sphx_glr_download_tutorial_auto_examples_mumbo_plot_mumbo_3_views_3_classes.py>` to download the full example code
.. rst-class:: sphx-glr-example-title
-.. _sphx_glr_auto_examples_plot_3_views_3_classes.py:
+.. _sphx_glr_tutorial_auto_examples_mumbo_plot_mumbo_3_views_3_classes.py:
-==========================
-3 views, 3 classes example
-==========================
+================================
+Mumbo 3 views, 3 classes example
+================================
In this toy example, we generate data from three classes, split between three
two-dimensional views.
@@ -29,12 +29,12 @@ the views to rightly classify the points.
*
- .. image:: /auto_examples/images/sphx_glr_plot_3_views_3_classes_001.png
+ .. image:: /tutorial/auto_examples/mumbo/images/sphx_glr_plot_mumbo_3_views_3_classes_001.png
:class: sphx-glr-multi-img
*
- .. image:: /auto_examples/images/sphx_glr_plot_3_views_3_classes_002.png
+ .. image:: /tutorial/auto_examples/mumbo/images/sphx_glr_plot_mumbo_3_views_3_classes_002.png
:class: sphx-glr-multi-img
@@ -67,8 +67,6 @@ the views to rightly classify the points.
The second figure displays the classification results for the sub-classifiers
on the learning sample data.
- /home/dominique/projets/ANR-Lives/multimodal/multimodalboost/examples/plot_3_views_3_classes.py:143: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
- plt.show()
@@ -81,30 +79,8 @@ the views to rightly classify the points.
.. code-block:: default
- # Université d'Aix Marseille (AMU) -
- # Centre National de la Recherche Scientifique (CNRS) -
- # Université de Toulon (UTLN).
- # Copyright © 2017-2018 AMU, CNRS, UTLN
- #
- # This file is part of multimodalboost.
- #
- # multimodalboost is free software: you can redistribute it and/or modify
- # it under the terms of the GNU Lesser General Public License as published by
- # the Free Software Foundation, either version 3 of the License, or
- # (at your option) any later version.
- #
- # multimodalboost is distributed in the hope that it will be useful,
- # but WITHOUT ANY WARRANTY; without even the implied warranty of
- # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- # GNU Lesser General Public License for more details.
- #
- # You should have received a copy of the GNU Lesser General Public License
- # along with multimodalboost. If not, see <http://www.gnu.org/licenses/>.
- #
- # Author: Florent JAILLET - Laboratoire d'Informatique et Systèmes - UMR 7020
-
import numpy as np
- from multimodalboost.mumbo import MumboClassifier
+ from multimodal.boosting.mumbo import MumboClassifier
from matplotlib import pyplot as plt
@@ -210,10 +186,10 @@ the views to rightly classify the points.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 1.293 seconds)
+ **Total running time of the script:** ( 0 minutes 1.321 seconds)
-.. _sphx_glr_download_auto_examples_plot_3_views_3_classes.py:
+.. _sphx_glr_download_tutorial_auto_examples_mumbo_plot_mumbo_3_views_3_classes.py:
.. only :: html
@@ -225,13 +201,13 @@ the views to rightly classify the points.
.. container:: sphx-glr-download
- :download:`Download Python source code: plot_3_views_3_classes.py <plot_3_views_3_classes.py>`
+ :download:`Download Python source code: plot_mumbo_3_views_3_classes.py <plot_mumbo_3_views_3_classes.py>`
.. container:: sphx-glr-download
- :download:`Download Jupyter notebook: plot_3_views_3_classes.ipynb <plot_3_views_3_classes.ipynb>`
+ :download:`Download Jupyter notebook: plot_mumbo_3_views_3_classes.ipynb <plot_mumbo_3_views_3_classes.ipynb>`
.. only:: html
diff --git a/doc/tutorial/auto_examples/mumbo/plot_mumbo_3_views_3_classes_codeobj.pickle b/doc/tutorial/auto_examples/mumbo/plot_mumbo_3_views_3_classes_codeobj.pickle
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diff --git a/doc/tutorial/auto_examples/mumbo/sg_execution_times.rst b/doc/tutorial/auto_examples/mumbo/sg_execution_times.rst
new file mode 100644
index 0000000..008474c
--- /dev/null
+++ b/doc/tutorial/auto_examples/mumbo/sg_execution_times.rst
@@ -0,0 +1,14 @@
+
+:orphan:
+
+.. _sphx_glr_tutorial_auto_examples_mumbo_sg_execution_times:
+
+Computation times
+=================
+**00:02.027** total execution time for **tutorial_auto_examples_mumbo** files:
+
++--------------------------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorial_auto_examples_mumbo_plot_mumbo_3_views_3_classes.py` (``plot_mumbo_3_views_3_classes.py``) | 00:01.321 | 0.0 MB |
++--------------------------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorial_auto_examples_mumbo_plot_mumbo_2_views_2_classes.py` (``plot_mumbo_2_views_2_classes.py``) | 00:00.706 | 0.0 MB |
++--------------------------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/doc/tutorial/auto_examples/mumbo_plot_2_views_2_classes.ipynb b/doc/tutorial/auto_examples/mumbo_plot_2_views_2_classes.ipynb
new file mode 100644
index 0000000..bfbb2d3
--- /dev/null
+++ b/doc/tutorial/auto_examples/mumbo_plot_2_views_2_classes.ipynb
@@ -0,0 +1,54 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n================================\nMumbo 2 views, 2 classes example\n================================\n\nIn this toy example, we generate data from two classes, split between two\ntwo-dimensional views.\n\nFor each view, the data are generated so that half of the points of each class\nare well separated in the plane, while the other half of the points are not\nseparated and placed in the same area. We also insure that the points that are\nnot separated in one view are well separated in the other view.\n\nThus, in the figure representing the data, the points represented by crosses\n(x) are well separated in view 0 while they are not separated in view 1, while\nthe points represented by dots (.) are well separated in view 1 while they are\nnot separated in view 0. In this figure, the blue symbols represent points\nof class 0, while red symbols represent points of class 1.\n\nThe MuMBo algorithm take adavantage of the complementarity of the two views to\nrightly classify the points.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\nfrom multimodal.boosting.mumbo import MumboClassifier\nfrom matplotlib import pyplot as plt\n\n\ndef generate_data(n_samples, lim):\n \"\"\"Generate random data in a rectangle\"\"\"\n lim = np.array(lim)\n n_features = lim.shape[0]\n data = np.random.random((n_samples, n_features))\n data = (lim[:, 1]-lim[:, 0]) * data + lim[:, 0]\n return data\n\n\nseed = 12\nnp.random.seed(seed)\n\nn_samples = 100\n\nview_0 = np.concatenate((generate_data(n_samples, [[0., 1.], [0., 1.]]),\n generate_data(n_samples, [[1., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [1., 2.]])))\n\nview_1 = np.concatenate((generate_data(n_samples, [[1., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [1., 2.]]),\n generate_data(n_samples, [[0., 1.], [0., 1.]])))\n\nX = np.concatenate((view_0, view_1), axis=1)\n\ny = np.zeros(4*n_samples, dtype=np.int64)\ny[2*n_samples:] = 1\n\nviews_ind = np.array([0, 2, 4])\n\nn_estimators = 3\nclf = MumboClassifier(n_estimators=n_estimators)\nclf.fit(X, y, views_ind)\n\nprint('\\nAfter 3 iterations, the MuMBo classifier reaches exact '\n 'classification for the\\nlearning samples:')\nfor ind, score in enumerate(clf.staged_score(X, y)):\n print(' - iteration {}, score: {}'.format(ind + 1, score))\n\n\nprint('\\nThe resulting MuMBo classifier uses three sub-classifiers that are '\n 'wheighted\\nusing the following weights:\\n'\n ' estimator weights: {}'.format(clf.estimator_weights_))\n\nprint('\\nThe two first sub-classifiers use the data of view 0 to compute '\n 'their\\nclassification results, while the third one uses the data of '\n 'view 1:\\n'\n ' best views: {}'. format(clf.best_views_))\n\nprint('\\nThe first figure displays the data, splitting the representation '\n 'between the\\ntwo views.')\n\nfig = plt.figure(figsize=(10., 8.))\nfig.suptitle('Representation of the data', size=16)\nfor ind_view in range(2):\n ax = plt.subplot(2, 1, ind_view + 1)\n ax.set_title('View {}'.format(ind_view))\n ind_feature = ind_view * 2\n styles = ('.b', 'xb', '.r', 'xr')\n labels = ('non-separated', 'separated')\n for ind in range(4):\n ind_class = ind // 2\n label = labels[(ind + ind_view) % 2]\n ax.plot(X[n_samples*ind:n_samples*(ind+1), ind_feature],\n X[n_samples*ind:n_samples*(ind+1), ind_feature + 1],\n styles[ind],\n label='Class {} ({})'.format(ind_class, label))\n ax.legend()\n\nprint('\\nThe second figure displays the classification results for the '\n 'sub-classifiers\\non the learning sample data.\\n')\n\nstyles = ('.b', '.r')\nfig = plt.figure(figsize=(12., 7.))\nfig.suptitle('Classification results on the learning data for the '\n 'sub-classifiers', size=16)\nfor ind_estimator in range(n_estimators):\n best_view = clf.best_views_[ind_estimator]\n y_pred = clf.estimators_[ind_estimator].predict(\n X[:, 2*best_view:2*best_view+2])\n background_color = (1.0, 1.0, 0.9)\n for ind_view in range(2):\n ax = plt.subplot(2, 3, ind_estimator + 3*ind_view + 1)\n if ind_view == best_view:\n ax.set_facecolor(background_color)\n ax.set_title(\n 'Sub-classifier {} - View {}'.format(ind_estimator, ind_view))\n ind_feature = ind_view * 2\n for ind_class in range(2):\n ind_samples = (y_pred == ind_class)\n ax.plot(X[ind_samples, ind_feature],\n X[ind_samples, ind_feature + 1],\n styles[ind_class],\n label='Class {}'.format(ind_class))\n ax.legend(title='Predicted class:')\n\nplt.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/doc/tutorial/auto_examples/mumbo_plot_2_views_2_classes.py b/doc/tutorial/auto_examples/mumbo_plot_2_views_2_classes.py
new file mode 100644
index 0000000..91f0dc7
--- /dev/null
+++ b/doc/tutorial/auto_examples/mumbo_plot_2_views_2_classes.py
@@ -0,0 +1,127 @@
+# -*- coding: utf-8 -*-
+"""
+================================
+Mumbo 2 views, 2 classes example
+================================
+
+In this toy example, we generate data from two classes, split between two
+two-dimensional views.
+
+For each view, the data are generated so that half of the points of each class
+are well separated in the plane, while the other half of the points are not
+separated and placed in the same area. We also insure that the points that are
+not separated in one view are well separated in the other view.
+
+Thus, in the figure representing the data, the points represented by crosses
+(x) are well separated in view 0 while they are not separated in view 1, while
+the points represented by dots (.) are well separated in view 1 while they are
+not separated in view 0. In this figure, the blue symbols represent points
+of class 0, while red symbols represent points of class 1.
+
+The MuMBo algorithm take adavantage of the complementarity of the two views to
+rightly classify the points.
+"""
+
+import numpy as np
+from multimodal.boosting.mumbo import MumboClassifier
+from matplotlib import pyplot as plt
+
+
+def generate_data(n_samples, lim):
+ """Generate random data in a rectangle"""
+ lim = np.array(lim)
+ n_features = lim.shape[0]
+ data = np.random.random((n_samples, n_features))
+ data = (lim[:, 1]-lim[:, 0]) * data + lim[:, 0]
+ return data
+
+
+seed = 12
+np.random.seed(seed)
+
+n_samples = 100
+
+view_0 = np.concatenate((generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [1., 2.]])))
+
+view_1 = np.concatenate((generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [1., 2.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]])))
+
+X = np.concatenate((view_0, view_1), axis=1)
+
+y = np.zeros(4*n_samples, dtype=np.int64)
+y[2*n_samples:] = 1
+
+views_ind = np.array([0, 2, 4])
+
+n_estimators = 3
+clf = MumboClassifier(n_estimators=n_estimators)
+clf.fit(X, y, views_ind)
+
+print('\nAfter 3 iterations, the MuMBo classifier reaches exact '
+ 'classification for the\nlearning samples:')
+for ind, score in enumerate(clf.staged_score(X, y)):
+ print(' - iteration {}, score: {}'.format(ind + 1, score))
+
+
+print('\nThe resulting MuMBo classifier uses three sub-classifiers that are '
+ 'wheighted\nusing the following weights:\n'
+ ' estimator weights: {}'.format(clf.estimator_weights_))
+
+print('\nThe two first sub-classifiers use the data of view 0 to compute '
+ 'their\nclassification results, while the third one uses the data of '
+ 'view 1:\n'
+ ' best views: {}'. format(clf.best_views_))
+
+print('\nThe first figure displays the data, splitting the representation '
+ 'between the\ntwo views.')
+
+fig = plt.figure(figsize=(10., 8.))
+fig.suptitle('Representation of the data', size=16)
+for ind_view in range(2):
+ ax = plt.subplot(2, 1, ind_view + 1)
+ ax.set_title('View {}'.format(ind_view))
+ ind_feature = ind_view * 2
+ styles = ('.b', 'xb', '.r', 'xr')
+ labels = ('non-separated', 'separated')
+ for ind in range(4):
+ ind_class = ind // 2
+ label = labels[(ind + ind_view) % 2]
+ ax.plot(X[n_samples*ind:n_samples*(ind+1), ind_feature],
+ X[n_samples*ind:n_samples*(ind+1), ind_feature + 1],
+ styles[ind],
+ label='Class {} ({})'.format(ind_class, label))
+ ax.legend()
+
+print('\nThe second figure displays the classification results for the '
+ 'sub-classifiers\non the learning sample data.\n')
+
+styles = ('.b', '.r')
+fig = plt.figure(figsize=(12., 7.))
+fig.suptitle('Classification results on the learning data for the '
+ 'sub-classifiers', size=16)
+for ind_estimator in range(n_estimators):
+ best_view = clf.best_views_[ind_estimator]
+ y_pred = clf.estimators_[ind_estimator].predict(
+ X[:, 2*best_view:2*best_view+2])
+ background_color = (1.0, 1.0, 0.9)
+ for ind_view in range(2):
+ ax = plt.subplot(2, 3, ind_estimator + 3*ind_view + 1)
+ if ind_view == best_view:
+ ax.set_facecolor(background_color)
+ ax.set_title(
+ 'Sub-classifier {} - View {}'.format(ind_estimator, ind_view))
+ ind_feature = ind_view * 2
+ for ind_class in range(2):
+ ind_samples = (y_pred == ind_class)
+ ax.plot(X[ind_samples, ind_feature],
+ X[ind_samples, ind_feature + 1],
+ styles[ind_class],
+ label='Class {}'.format(ind_class))
+ ax.legend(title='Predicted class:')
+
+plt.show()
diff --git a/doc/tutorial/auto_examples/mumbo_plot_2_views_2_classes.rst b/doc/tutorial/auto_examples/mumbo_plot_2_views_2_classes.rst
new file mode 100644
index 0000000..91c4903
--- /dev/null
+++ b/doc/tutorial/auto_examples/mumbo_plot_2_views_2_classes.rst
@@ -0,0 +1,170 @@
+.. note::
+ :class: sphx-glr-download-link-note
+
+ Click :ref:`here <sphx_glr_download_tutorial_auto_examples_mumbo_plot_2_views_2_classes.py>` to download the full example code
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorial_auto_examples_mumbo_plot_2_views_2_classes.py:
+
+
+================================
+Mumbo 2 views, 2 classes example
+================================
+
+In this toy example, we generate data from two classes, split between two
+two-dimensional views.
+
+For each view, the data are generated so that half of the points of each class
+are well separated in the plane, while the other half of the points are not
+separated and placed in the same area. We also insure that the points that are
+not separated in one view are well separated in the other view.
+
+Thus, in the figure representing the data, the points represented by crosses
+(x) are well separated in view 0 while they are not separated in view 1, while
+the points represented by dots (.) are well separated in view 1 while they are
+not separated in view 0. In this figure, the blue symbols represent points
+of class 0, while red symbols represent points of class 1.
+
+The MuMBo algorithm take adavantage of the complementarity of the two views to
+rightly classify the points.
+
+
+.. code-block:: default
+
+
+ import numpy as np
+ from multimodal.boosting.mumbo import MumboClassifier
+ from matplotlib import pyplot as plt
+
+
+ def generate_data(n_samples, lim):
+ """Generate random data in a rectangle"""
+ lim = np.array(lim)
+ n_features = lim.shape[0]
+ data = np.random.random((n_samples, n_features))
+ data = (lim[:, 1]-lim[:, 0]) * data + lim[:, 0]
+ return data
+
+
+ seed = 12
+ np.random.seed(seed)
+
+ n_samples = 100
+
+ view_0 = np.concatenate((generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [1., 2.]])))
+
+ view_1 = np.concatenate((generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [1., 2.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]])))
+
+ X = np.concatenate((view_0, view_1), axis=1)
+
+ y = np.zeros(4*n_samples, dtype=np.int64)
+ y[2*n_samples:] = 1
+
+ views_ind = np.array([0, 2, 4])
+
+ n_estimators = 3
+ clf = MumboClassifier(n_estimators=n_estimators)
+ clf.fit(X, y, views_ind)
+
+ print('\nAfter 3 iterations, the MuMBo classifier reaches exact '
+ 'classification for the\nlearning samples:')
+ for ind, score in enumerate(clf.staged_score(X, y)):
+ print(' - iteration {}, score: {}'.format(ind + 1, score))
+
+
+ print('\nThe resulting MuMBo classifier uses three sub-classifiers that are '
+ 'wheighted\nusing the following weights:\n'
+ ' estimator weights: {}'.format(clf.estimator_weights_))
+
+ print('\nThe two first sub-classifiers use the data of view 0 to compute '
+ 'their\nclassification results, while the third one uses the data of '
+ 'view 1:\n'
+ ' best views: {}'. format(clf.best_views_))
+
+ print('\nThe first figure displays the data, splitting the representation '
+ 'between the\ntwo views.')
+
+ fig = plt.figure(figsize=(10., 8.))
+ fig.suptitle('Representation of the data', size=16)
+ for ind_view in range(2):
+ ax = plt.subplot(2, 1, ind_view + 1)
+ ax.set_title('View {}'.format(ind_view))
+ ind_feature = ind_view * 2
+ styles = ('.b', 'xb', '.r', 'xr')
+ labels = ('non-separated', 'separated')
+ for ind in range(4):
+ ind_class = ind // 2
+ label = labels[(ind + ind_view) % 2]
+ ax.plot(X[n_samples*ind:n_samples*(ind+1), ind_feature],
+ X[n_samples*ind:n_samples*(ind+1), ind_feature + 1],
+ styles[ind],
+ label='Class {} ({})'.format(ind_class, label))
+ ax.legend()
+
+ print('\nThe second figure displays the classification results for the '
+ 'sub-classifiers\non the learning sample data.\n')
+
+ styles = ('.b', '.r')
+ fig = plt.figure(figsize=(12., 7.))
+ fig.suptitle('Classification results on the learning data for the '
+ 'sub-classifiers', size=16)
+ for ind_estimator in range(n_estimators):
+ best_view = clf.best_views_[ind_estimator]
+ y_pred = clf.estimators_[ind_estimator].predict(
+ X[:, 2*best_view:2*best_view+2])
+ background_color = (1.0, 1.0, 0.9)
+ for ind_view in range(2):
+ ax = plt.subplot(2, 3, ind_estimator + 3*ind_view + 1)
+ if ind_view == best_view:
+ ax.set_facecolor(background_color)
+ ax.set_title(
+ 'Sub-classifier {} - View {}'.format(ind_estimator, ind_view))
+ ind_feature = ind_view * 2
+ for ind_class in range(2):
+ ind_samples = (y_pred == ind_class)
+ ax.plot(X[ind_samples, ind_feature],
+ X[ind_samples, ind_feature + 1],
+ styles[ind_class],
+ label='Class {}'.format(ind_class))
+ ax.legend(title='Predicted class:')
+
+ plt.show()
+
+
+.. rst-class:: sphx-glr-timing
+
+ **Total running time of the script:** ( 0 minutes 0.000 seconds)
+
+
+.. _sphx_glr_download_tutorial_auto_examples_mumbo_plot_2_views_2_classes.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+ :class: sphx-glr-footer-example
+
+
+
+ .. container:: sphx-glr-download
+
+ :download:`Download Python source code: mumbo_plot_2_views_2_classes.py <mumbo_plot_2_views_2_classes.py>`
+
+
+
+ .. container:: sphx-glr-download
+
+ :download:`Download Jupyter notebook: mumbo_plot_2_views_2_classes.ipynb <mumbo_plot_2_views_2_classes.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+ `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/doc/tutorial/auto_examples/mumbo_plot_2_views_2_classes_codeobj.pickle b/doc/tutorial/auto_examples/mumbo_plot_2_views_2_classes_codeobj.pickle
new file mode 100644
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diff --git a/doc/tutorial/auto_examples/mumbo_plot_3_views_3_classes.ipynb b/doc/tutorial/auto_examples/mumbo_plot_3_views_3_classes.ipynb
new file mode 100644
index 0000000..371d83e
--- /dev/null
+++ b/doc/tutorial/auto_examples/mumbo_plot_3_views_3_classes.ipynb
@@ -0,0 +1,54 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n================================\nMumbo 3 views, 3 classes example\n================================\n\nIn this toy example, we generate data from three classes, split between three\ntwo-dimensional views.\n\nFor each view, the data are generated so that the points for two classes are\nwell seperated, while the points for the third class are not seperated with\nthe two other classes. That means that, taken separately, none of the single\nviews allows for a good classification of the data.\n\nNevertheless, the MuMBo algorithm take adavantage of the complementarity of\nthe views to rightly classify the points.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\nfrom multimodal.boosting.mumbo import MumboClassifier\nfrom matplotlib import pyplot as plt\n\n\ndef generate_data(n_samples, lim):\n \"\"\"Generate random data in a rectangle\"\"\"\n lim = np.array(lim)\n n_features = lim.shape[0]\n data = np.random.random((n_samples, n_features))\n data = (lim[:, 1]-lim[:, 0]) * data + lim[:, 0]\n return data\n\n\nseed = 12\nnp.random.seed(seed)\n\nn_samples = 300\n\nview_0 = np.concatenate((generate_data(n_samples, [[0., 1.], [0., 1.]]),\n generate_data(n_samples, [[1., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 2.], [0., 1.]])))\n\nview_1 = np.concatenate((generate_data(n_samples, [[1., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [0., 1.]])))\n\nview_2 = np.concatenate((generate_data(n_samples, [[0., 2.], [0., 1.]]),\n generate_data(n_samples, [[0., 1.], [0., 1.]]),\n generate_data(n_samples, [[1., 2.], [0., 1.]])))\n\nX = np.concatenate((view_0, view_1, view_2), axis=1)\n\ny = np.zeros(3*n_samples, dtype=np.int64)\ny[n_samples:2*n_samples] = 1\ny[2*n_samples:] = 2\n\nviews_ind = np.array([0, 2, 4, 6])\n\nn_estimators = 4\nclf = MumboClassifier(n_estimators=n_estimators)\nclf.fit(X, y, views_ind)\n\nprint('\\nAfter 4 iterations, the MuMBo classifier reaches exact '\n 'classification for the\\nlearning samples:')\nfor ind, score in enumerate(clf.staged_score(X, y)):\n print(' - iteration {}, score: {}'.format(ind + 1, score))\n\nprint('\\nThe resulting MuMBo classifier uses four sub-classifiers that are '\n 'wheighted\\nusing the following weights:\\n'\n ' estimator weights: {}'.format(clf.estimator_weights_))\n\nprint('\\nThe first sub-classifier uses the data of view 0 to compute '\n 'its classification\\nresults, the second and third sub-classifiers use '\n 'the data of view 1, while the\\nfourth one uses the data of '\n 'view 2:\\n'\n ' best views: {}'. format(clf.best_views_))\n\nprint('\\nThe first figure displays the data, splitting the representation '\n 'between the\\nthree views.')\n\nstyles = ('.b', '.r', '.g')\nfig = plt.figure(figsize=(12., 11.))\nfig.suptitle('Representation of the data', size=16)\nfor ind_view in range(3):\n ax = plt.subplot(3, 1, ind_view + 1)\n ax.set_title('View {}'.format(ind_view))\n ind_feature = ind_view * 2\n for ind_class in range(3):\n ind_samples = (y == ind_class)\n ax.plot(X[ind_samples, ind_feature],\n X[ind_samples, ind_feature + 1],\n styles[ind_class],\n label='Class {}'.format(ind_class))\n ax.legend(loc='upper left', framealpha=0.9)\n\nprint('\\nThe second figure displays the classification results for the '\n 'sub-classifiers\\non the learning sample data.\\n')\n\nfig = plt.figure(figsize=(14., 11.))\nfig.suptitle('Classification results on the learning data for the '\n 'sub-classifiers', size=16)\nfor ind_estimator in range(n_estimators):\n best_view = clf.best_views_[ind_estimator]\n y_pred = clf.estimators_[ind_estimator].predict(\n X[:, 2*best_view:2*best_view+2])\n background_color = (1.0, 1.0, 0.9)\n for ind_view in range(3):\n ax = plt.subplot(3, 4, ind_estimator + 4*ind_view + 1)\n if ind_view == best_view:\n ax.set_facecolor(background_color)\n ax.set_title(\n 'Sub-classifier {} - View {}'.format(ind_estimator, ind_view))\n ind_feature = ind_view * 2\n for ind_class in range(3):\n ind_samples = (y_pred == ind_class)\n ax.plot(X[ind_samples, ind_feature],\n X[ind_samples, ind_feature + 1],\n styles[ind_class],\n label='Class {}'.format(ind_class))\n ax.legend(title='Predicted class:', loc='upper left', framealpha=0.9)\n\nplt.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/doc/tutorial/auto_examples/mumbo_plot_3_views_3_classes.py b/doc/tutorial/auto_examples/mumbo_plot_3_views_3_classes.py
new file mode 100644
index 0000000..62cd311
--- /dev/null
+++ b/doc/tutorial/auto_examples/mumbo_plot_3_views_3_classes.py
@@ -0,0 +1,121 @@
+# -*- coding: utf-8 -*-
+"""
+================================
+Mumbo 3 views, 3 classes example
+================================
+
+In this toy example, we generate data from three classes, split between three
+two-dimensional views.
+
+For each view, the data are generated so that the points for two classes are
+well seperated, while the points for the third class are not seperated with
+the two other classes. That means that, taken separately, none of the single
+views allows for a good classification of the data.
+
+Nevertheless, the MuMBo algorithm take adavantage of the complementarity of
+the views to rightly classify the points.
+"""
+
+import numpy as np
+from multimodal.boosting.mumbo import MumboClassifier
+from matplotlib import pyplot as plt
+
+
+def generate_data(n_samples, lim):
+ """Generate random data in a rectangle"""
+ lim = np.array(lim)
+ n_features = lim.shape[0]
+ data = np.random.random((n_samples, n_features))
+ data = (lim[:, 1]-lim[:, 0]) * data + lim[:, 0]
+ return data
+
+
+seed = 12
+np.random.seed(seed)
+
+n_samples = 300
+
+view_0 = np.concatenate((generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 2.], [0., 1.]])))
+
+view_1 = np.concatenate((generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]])))
+
+view_2 = np.concatenate((generate_data(n_samples, [[0., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[1., 2.], [0., 1.]])))
+
+X = np.concatenate((view_0, view_1, view_2), axis=1)
+
+y = np.zeros(3*n_samples, dtype=np.int64)
+y[n_samples:2*n_samples] = 1
+y[2*n_samples:] = 2
+
+views_ind = np.array([0, 2, 4, 6])
+
+n_estimators = 4
+clf = MumboClassifier(n_estimators=n_estimators)
+clf.fit(X, y, views_ind)
+
+print('\nAfter 4 iterations, the MuMBo classifier reaches exact '
+ 'classification for the\nlearning samples:')
+for ind, score in enumerate(clf.staged_score(X, y)):
+ print(' - iteration {}, score: {}'.format(ind + 1, score))
+
+print('\nThe resulting MuMBo classifier uses four sub-classifiers that are '
+ 'wheighted\nusing the following weights:\n'
+ ' estimator weights: {}'.format(clf.estimator_weights_))
+
+print('\nThe first sub-classifier uses the data of view 0 to compute '
+ 'its classification\nresults, the second and third sub-classifiers use '
+ 'the data of view 1, while the\nfourth one uses the data of '
+ 'view 2:\n'
+ ' best views: {}'. format(clf.best_views_))
+
+print('\nThe first figure displays the data, splitting the representation '
+ 'between the\nthree views.')
+
+styles = ('.b', '.r', '.g')
+fig = plt.figure(figsize=(12., 11.))
+fig.suptitle('Representation of the data', size=16)
+for ind_view in range(3):
+ ax = plt.subplot(3, 1, ind_view + 1)
+ ax.set_title('View {}'.format(ind_view))
+ ind_feature = ind_view * 2
+ for ind_class in range(3):
+ ind_samples = (y == ind_class)
+ ax.plot(X[ind_samples, ind_feature],
+ X[ind_samples, ind_feature + 1],
+ styles[ind_class],
+ label='Class {}'.format(ind_class))
+ ax.legend(loc='upper left', framealpha=0.9)
+
+print('\nThe second figure displays the classification results for the '
+ 'sub-classifiers\non the learning sample data.\n')
+
+fig = plt.figure(figsize=(14., 11.))
+fig.suptitle('Classification results on the learning data for the '
+ 'sub-classifiers', size=16)
+for ind_estimator in range(n_estimators):
+ best_view = clf.best_views_[ind_estimator]
+ y_pred = clf.estimators_[ind_estimator].predict(
+ X[:, 2*best_view:2*best_view+2])
+ background_color = (1.0, 1.0, 0.9)
+ for ind_view in range(3):
+ ax = plt.subplot(3, 4, ind_estimator + 4*ind_view + 1)
+ if ind_view == best_view:
+ ax.set_facecolor(background_color)
+ ax.set_title(
+ 'Sub-classifier {} - View {}'.format(ind_estimator, ind_view))
+ ind_feature = ind_view * 2
+ for ind_class in range(3):
+ ind_samples = (y_pred == ind_class)
+ ax.plot(X[ind_samples, ind_feature],
+ X[ind_samples, ind_feature + 1],
+ styles[ind_class],
+ label='Class {}'.format(ind_class))
+ ax.legend(title='Predicted class:', loc='upper left', framealpha=0.9)
+
+plt.show()
diff --git a/doc/tutorial/auto_examples/mumbo_plot_3_views_3_classes.rst b/doc/tutorial/auto_examples/mumbo_plot_3_views_3_classes.rst
new file mode 100644
index 0000000..9f11b4e
--- /dev/null
+++ b/doc/tutorial/auto_examples/mumbo_plot_3_views_3_classes.rst
@@ -0,0 +1,164 @@
+.. note::
+ :class: sphx-glr-download-link-note
+
+ Click :ref:`here <sphx_glr_download_tutorial_auto_examples_mumbo_plot_3_views_3_classes.py>` to download the full example code
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorial_auto_examples_mumbo_plot_3_views_3_classes.py:
+
+
+================================
+Mumbo 3 views, 3 classes example
+================================
+
+In this toy example, we generate data from three classes, split between three
+two-dimensional views.
+
+For each view, the data are generated so that the points for two classes are
+well seperated, while the points for the third class are not seperated with
+the two other classes. That means that, taken separately, none of the single
+views allows for a good classification of the data.
+
+Nevertheless, the MuMBo algorithm take adavantage of the complementarity of
+the views to rightly classify the points.
+
+
+.. code-block:: default
+
+
+ import numpy as np
+ from multimodal.boosting.mumbo import MumboClassifier
+ from matplotlib import pyplot as plt
+
+
+ def generate_data(n_samples, lim):
+ """Generate random data in a rectangle"""
+ lim = np.array(lim)
+ n_features = lim.shape[0]
+ data = np.random.random((n_samples, n_features))
+ data = (lim[:, 1]-lim[:, 0]) * data + lim[:, 0]
+ return data
+
+
+ seed = 12
+ np.random.seed(seed)
+
+ n_samples = 300
+
+ view_0 = np.concatenate((generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 2.], [0., 1.]])))
+
+ view_1 = np.concatenate((generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]])))
+
+ view_2 = np.concatenate((generate_data(n_samples, [[0., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[1., 2.], [0., 1.]])))
+
+ X = np.concatenate((view_0, view_1, view_2), axis=1)
+
+ y = np.zeros(3*n_samples, dtype=np.int64)
+ y[n_samples:2*n_samples] = 1
+ y[2*n_samples:] = 2
+
+ views_ind = np.array([0, 2, 4, 6])
+
+ n_estimators = 4
+ clf = MumboClassifier(n_estimators=n_estimators)
+ clf.fit(X, y, views_ind)
+
+ print('\nAfter 4 iterations, the MuMBo classifier reaches exact '
+ 'classification for the\nlearning samples:')
+ for ind, score in enumerate(clf.staged_score(X, y)):
+ print(' - iteration {}, score: {}'.format(ind + 1, score))
+
+ print('\nThe resulting MuMBo classifier uses four sub-classifiers that are '
+ 'wheighted\nusing the following weights:\n'
+ ' estimator weights: {}'.format(clf.estimator_weights_))
+
+ print('\nThe first sub-classifier uses the data of view 0 to compute '
+ 'its classification\nresults, the second and third sub-classifiers use '
+ 'the data of view 1, while the\nfourth one uses the data of '
+ 'view 2:\n'
+ ' best views: {}'. format(clf.best_views_))
+
+ print('\nThe first figure displays the data, splitting the representation '
+ 'between the\nthree views.')
+
+ styles = ('.b', '.r', '.g')
+ fig = plt.figure(figsize=(12., 11.))
+ fig.suptitle('Representation of the data', size=16)
+ for ind_view in range(3):
+ ax = plt.subplot(3, 1, ind_view + 1)
+ ax.set_title('View {}'.format(ind_view))
+ ind_feature = ind_view * 2
+ for ind_class in range(3):
+ ind_samples = (y == ind_class)
+ ax.plot(X[ind_samples, ind_feature],
+ X[ind_samples, ind_feature + 1],
+ styles[ind_class],
+ label='Class {}'.format(ind_class))
+ ax.legend(loc='upper left', framealpha=0.9)
+
+ print('\nThe second figure displays the classification results for the '
+ 'sub-classifiers\non the learning sample data.\n')
+
+ fig = plt.figure(figsize=(14., 11.))
+ fig.suptitle('Classification results on the learning data for the '
+ 'sub-classifiers', size=16)
+ for ind_estimator in range(n_estimators):
+ best_view = clf.best_views_[ind_estimator]
+ y_pred = clf.estimators_[ind_estimator].predict(
+ X[:, 2*best_view:2*best_view+2])
+ background_color = (1.0, 1.0, 0.9)
+ for ind_view in range(3):
+ ax = plt.subplot(3, 4, ind_estimator + 4*ind_view + 1)
+ if ind_view == best_view:
+ ax.set_facecolor(background_color)
+ ax.set_title(
+ 'Sub-classifier {} - View {}'.format(ind_estimator, ind_view))
+ ind_feature = ind_view * 2
+ for ind_class in range(3):
+ ind_samples = (y_pred == ind_class)
+ ax.plot(X[ind_samples, ind_feature],
+ X[ind_samples, ind_feature + 1],
+ styles[ind_class],
+ label='Class {}'.format(ind_class))
+ ax.legend(title='Predicted class:', loc='upper left', framealpha=0.9)
+
+ plt.show()
+
+
+.. rst-class:: sphx-glr-timing
+
+ **Total running time of the script:** ( 0 minutes 0.000 seconds)
+
+
+.. _sphx_glr_download_tutorial_auto_examples_mumbo_plot_3_views_3_classes.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+ :class: sphx-glr-footer-example
+
+
+
+ .. container:: sphx-glr-download
+
+ :download:`Download Python source code: mumbo_plot_3_views_3_classes.py <mumbo_plot_3_views_3_classes.py>`
+
+
+
+ .. container:: sphx-glr-download
+
+ :download:`Download Jupyter notebook: mumbo_plot_3_views_3_classes.ipynb <mumbo_plot_3_views_3_classes.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+ `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/doc/tutorial/auto_examples/mumbo_plot_3_views_3_classes_codeobj.pickle b/doc/tutorial/auto_examples/mumbo_plot_3_views_3_classes_codeobj.pickle
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diff --git a/doc/tutorial/auto_examples/mvml/images/sphx_glr_plot_mvml__001.png b/doc/tutorial/auto_examples/mvml/images/sphx_glr_plot_mvml__001.png
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diff --git a/doc/tutorial/auto_examples/mvml/images/thumb/sphx_glr_mvml_plot__thumb.png b/doc/tutorial/auto_examples/mvml/images/thumb/sphx_glr_mvml_plot__thumb.png
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diff --git a/doc/tutorial/auto_examples/mvml/mvml_plot_.ipynb b/doc/tutorial/auto_examples/mvml/mvml_plot_.ipynb
new file mode 100644
index 0000000..830427c
--- /dev/null
+++ b/doc/tutorial/auto_examples/mvml/mvml_plot_.ipynb
@@ -0,0 +1,54 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n# MVML\n\nDemonstration on how MVML (in file mvml.py) is intended to be used with very simple simulated dataset\n\nDemonstration uses scikit-learn for retrieving datasets and for calculating rbf kernel function, see\nhttp://scikit-learn.org/stable/\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import datasets\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.metrics.pairwise import rbf_kernel\nfrom multimodal.kernels.mvml import MVML\nfrom multimodal.datasets.data_sample import DataSample\nfrom multimodal.tests.datasets.get_dataset_path import get_dataset_path\n\n\nnp.random.seed(4)\n\n# =========== create a simple dataset ============\n\nn_tot = 200\nhalf = int(n_tot/2)\nn_tr = 120\n\n# create a bit more data than needed so that we can take \"half\" amount of samples for each class\nX0, y0 = datasets.make_moons(n_samples=n_tot+2, noise=0.3, shuffle=False)\nX1, y1 = datasets.make_circles(n_samples=n_tot+2, noise=0.1, shuffle=False)\n\n# make multi-view correspondence (select equal number of samples for both classes and order the data same way\n# in both views)\n\nyinds0 = np.append(np.where(y0 == 0)[0][0:half], np.where(y0 == 1)[0][0:half])\nyinds1 = np.append(np.where(y1 == 0)[0][0:half], np.where(y1 == 1)[0][0:half])\n\nX0 = X0[yinds0, :]\nX1 = X1[yinds1, :]\nY = np.append(np.zeros(half)-1, np.ones(half)) # labels -1 and 1\n\n# show data\n# =========== create a simple dataset ============\n\nn_tot = 200\nhalf = int(n_tot/2)\nn_tr = 120\n\n# create a bit more data than needed so that we can take \"half\" amount of samples for each class\nX0, y0 = datasets.make_moons(n_samples=n_tot+2, noise=0.3, shuffle=False)\nX1, y1 = datasets.make_circles(n_samples=n_tot+2, noise=0.1, shuffle=False)\n\n# make multi-view correspondence (select equal number of samples for both classes and order the data same way\n# in both views)\n\nyinds0 = np.append(np.where(y0 == 0)[0][0:half], np.where(y0 == 1)[0][0:half])\nyinds1 = np.append(np.where(y1 == 0)[0][0:half], np.where(y1 == 1)[0][0:half])\n\nX0 = X0[yinds0, :]\nX1 = X1[yinds1, :]\nY = np.append(np.zeros(half)-1, np.ones(half)) # labels -1 and 1\n\n# show data\nplt.figure(figsize=(10., 8.))\nplt.subplot(121)\nplt.scatter(X0[:, 0], X0[:, 1], c=Y)\nplt.title(\"all data, view 1\")\nplt.subplot(122)\nplt.scatter(X1[:, 0], X1[:, 1], c=Y)\nplt.title(\"all data, view 2\")\nplt.show()\n\n# shuffle\norder = np.random.permutation(n_tot)\nX0 = X0[order, :]\nX1 = X1[order, :]\nY = Y[order]\n\n# make kernel dictionaries\nkernel_dict = {}\ntest_kernel_dict = {}\nkernel_dict[0] = rbf_kernel(X0[0:n_tr, :])\nkernel_dict[1] = rbf_kernel(X1[0:n_tr, :])\ntest_kernel_dict[0] = rbf_kernel(X0[n_tr:n_tot, :], X0[0:n_tr, :])\ntest_kernel_dict[1] = rbf_kernel(X1[n_tr:n_tot, :], X1[0:n_tr, :])\n\n# input_x = get_dataset_path(\"input_x_dic.pkl\")\n# f = open(input_x, \"wb\")\n# pickle.dump(input_x, f)\n#\n#\n# d= DataSample(kernel_dict)\n# a = d.data\n#\n# =========== use MVML in classifying the data ============\n#\n# demo on how the code is intended to be used; parameters are not cross-validated, just picked some\n# # with approximation\n# # default: learn A, don't learn w (learn_A=1, learn_w=0)\nmvml = MVML(lmbda=0.1, eta=1, nystrom_param=0.2, kernel='precomputed')\nmvml.fit(kernel_dict, Y[0:n_tr])\n\n\n#\n\npred1 = np.sign(mvml.predict(test_kernel_dict) ) # take sign for classification result\n#\n# without approximation\nmvml2 = MVML(lmbda=0.1, eta=1, nystrom_param=1, kernel='precomputed') # without approximation\nmvml2.fit(kernel_dict, Y[0:n_tr])\npred2 = np.sign(mvml2.predict(test_kernel_dict))\n#\n# use MVML_Cov, don't learn w\nmvml3 = MVML(lmbda=0.1, eta=1,learn_A=3, nystrom_param=1, kernel='precomputed')\nmvml3.fit(kernel_dict, Y[0:n_tr])\npred3 = np.sign(mvml.predict(test_kernel_dict))\n#\n# use MVML_I, don't learn w\nmvml3 = MVML(lmbda=0.1, eta=1,learn_A=4, nystrom_param=1, kernel='precomputed')\n\npred4 = np.sign(mvml.predict(test_kernel_dict))\n#\n#\n# # =========== show results ============\n#\n# # accuracies\nacc1 = accuracy_score(Y[n_tr:n_tot], pred1)\nacc2 = accuracy_score(Y[n_tr:n_tot], pred2)\nacc3 = accuracy_score(Y[n_tr:n_tot], pred3)\nacc4 = accuracy_score(Y[n_tr:n_tot], pred4)\n#\n# # display obtained accuracies\n#\nprint(\"MVML: \", acc1)\nprint(\"MVMLsparse: \", acc2)\nprint(\"MVML_Cov: \", acc3)\nprint(\"MVML_I: \", acc4)\n#\n#\n# # plot data and some classification results\n#\nplt.figure(2, figsize=(10., 8.))\nplt.subplot(341)\nplt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=Y[n_tr:n_tot])\nplt.title(\"orig. view 1\")\nplt.subplot(342)\nplt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=Y[n_tr:n_tot])\nplt.title(\"orig. view 2\")\n#\npred1[np.where(pred1[:, 0] != Y[n_tr:n_tot])] = 0\npred1 = pred1.reshape((pred1.shape[0]))\nplt.subplot(343)\nplt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred1)\nplt.title(\"MVML view 1\")\nplt.subplot(344)\nplt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred1)\nplt.title(\"MVML view 2\")\n#\npred2[np.where(pred2[:, 0] != Y[n_tr:n_tot])] = 0\npred2 = pred2.reshape((pred2.shape[0]))\nplt.subplot(345)\nplt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred2)\nplt.title(\"MVMLsparse view 1\")\nplt.subplot(346)\nplt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred2)\nplt.title(\"MVMLsparse view 2\")\n#\npred3[np.where(pred3[:, 0] != Y[n_tr:n_tot])] = 0\npred3 = pred3.reshape((pred3.shape[0]))\n#\nplt.subplot(347)\nplt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred3)\nplt.title(\"MVML_Cov view 1\")\nplt.subplot(348)\nplt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred3)\nplt.title(\"MVML_Cov view 2\")\n#\npred4[np.where(pred4[:, 0] != Y[n_tr:n_tot])] = 0\npred4 = pred4.reshape((pred4.shape[0]))\nplt.subplot(349)\nplt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred4)\nplt.title(\"MVML_I view 1\")\nplt.subplot(3,4,10)\nplt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred4)\nplt.title(\"MVML_I view 2\")\n#\nplt.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/doc/docmetricexamples/demo.py b/doc/tutorial/auto_examples/mvml/mvml_plot_.py
similarity index 76%
rename from doc/docmetricexamples/demo.py
rename to doc/tutorial/auto_examples/mvml/mvml_plot_.py
index 108ac8c..1abf1ea 100644
--- a/doc/docmetricexamples/demo.py
+++ b/doc/tutorial/auto_examples/mvml/mvml_plot_.py
@@ -1,19 +1,23 @@
+# -*- coding: utf-8 -*-
+"""
+====
+MVML
+====
+Demonstration on how MVML (in file mvml.py) is intended to be used with very simple simulated dataset
+
+Demonstration uses scikit-learn for retrieving datasets and for calculating rbf kernel function, see
+http://scikit-learn.org/stable/
+"""
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.metrics import accuracy_score
from sklearn.metrics.pairwise import rbf_kernel
-from metriclearning.mvml import MVML
-from metriclearning.datasets.data_sample import DataSample
-from metriclearning.tests.datasets.get_dataset_path import get_dataset_path
-import pickle
-"""
-Demonstration on how MVML (in file mvml.py) is intended to be used with very simple simulated dataset
+from multimodal.kernels.mvml import MVML
+from multimodal.datasets.data_sample import DataSample
+from multimodal.tests.datasets.get_dataset_path import get_dataset_path
-Demonstration uses scikit-learn for retrieving datasets and for calculating rbf kernel function, see
-http://scikit-learn.org/stable/
-"""
np.random.seed(4)
@@ -59,14 +63,14 @@ X1 = X1[yinds1, :]
Y = np.append(np.zeros(half)-1, np.ones(half)) # labels -1 and 1
# show data
-plt.figure(1)
+plt.figure(figsize=(10., 8.))
plt.subplot(121)
plt.scatter(X0[:, 0], X0[:, 1], c=Y)
plt.title("all data, view 1")
plt.subplot(122)
plt.scatter(X1[:, 0], X1[:, 1], c=Y)
plt.title("all data, view 2")
-# plt.show()
+plt.show()
# shuffle
order = np.random.permutation(n_tot)
@@ -85,60 +89,66 @@ test_kernel_dict[1] = rbf_kernel(X1[n_tr:n_tot, :], X1[0:n_tr, :])
# input_x = get_dataset_path("input_x_dic.pkl")
# f = open(input_x, "wb")
# pickle.dump(input_x, f)
-
-
-d= DataSample(kernel_dict)
-a = d.data
-
+#
+#
+# d= DataSample(kernel_dict)
+# a = d.data
+#
# =========== use MVML in classifying the data ============
-
+#
# demo on how the code is intended to be used; parameters are not cross-validated, just picked some
-# mvml = MVML(kernel_dict, Y[0:n_tr], [0.1, 1], nystrom_param=0.2)
-mvml = MVML( [0.1, 1], nystrom_param=0.2)
-A1, g1, w1 = mvml.fit(a, Y[0:n_tr])
-# with approximation
-# mvml = MVML(kernel_dict, Y[0:n_tr], [0.1, 1], nystrom_param=1) # without approximation
-
-A1, g1, w1 = mvml.learn_mvml() # default: learn A, don't learn w (learn_A=1, learn_w=0)
-pred1 = np.sign(mvml.predict_mvml(test_kernel_dict, g1, w1)) # take sign for classification result
-
-A2, g2, w2 = mvml.learn_mvml(learn_A=2, learn_w=1) # learn sparse A and learn w
-pred2 = np.sign(mvml.predict_mvml(test_kernel_dict, g2, w2))
-# print(w2)
-
-A3, g3, w3 = mvml.learn_mvml(learn_A=3) # use MVML_Cov, don't learn w
-pred3 = np.sign(mvml.predict_mvml(test_kernel_dict, g3, w3))
-
-A4, g4, w4 = mvml.learn_mvml(learn_A=4) # use MVML_I, don't learn w
-pred4 = np.sign(mvml.predict_mvml(test_kernel_dict, g4, w4))
-
-
-# =========== show results ============
-
-# accuracies
+# # with approximation
+# # default: learn A, don't learn w (learn_A=1, learn_w=0)
+mvml = MVML(lmbda=0.1, eta=1, nystrom_param=0.2, kernel='precomputed')
+mvml.fit(kernel_dict, Y[0:n_tr])
+
+
+#
+
+pred1 = np.sign(mvml.predict(test_kernel_dict) ) # take sign for classification result
+#
+# without approximation
+mvml2 = MVML(lmbda=0.1, eta=1, nystrom_param=1, kernel='precomputed') # without approximation
+mvml2.fit(kernel_dict, Y[0:n_tr])
+pred2 = np.sign(mvml2.predict(test_kernel_dict))
+#
+# use MVML_Cov, don't learn w
+mvml3 = MVML(lmbda=0.1, eta=1,learn_A=3, nystrom_param=1, kernel='precomputed')
+mvml3.fit(kernel_dict, Y[0:n_tr])
+pred3 = np.sign(mvml.predict(test_kernel_dict))
+#
+# use MVML_I, don't learn w
+mvml3 = MVML(lmbda=0.1, eta=1,learn_A=4, nystrom_param=1, kernel='precomputed')
+
+pred4 = np.sign(mvml.predict(test_kernel_dict))
+#
+#
+# # =========== show results ============
+#
+# # accuracies
acc1 = accuracy_score(Y[n_tr:n_tot], pred1)
acc2 = accuracy_score(Y[n_tr:n_tot], pred2)
acc3 = accuracy_score(Y[n_tr:n_tot], pred3)
acc4 = accuracy_score(Y[n_tr:n_tot], pred4)
-
-# display obtained accuracies
-
+#
+# # display obtained accuracies
+#
print("MVML: ", acc1)
print("MVMLsparse: ", acc2)
print("MVML_Cov: ", acc3)
print("MVML_I: ", acc4)
-
-
-# plot data and some classification results
-
-plt.figure(2)
+#
+#
+# # plot data and some classification results
+#
+plt.figure(2, figsize=(10., 8.))
plt.subplot(341)
plt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=Y[n_tr:n_tot])
plt.title("orig. view 1")
plt.subplot(342)
plt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=Y[n_tr:n_tot])
plt.title("orig. view 2")
-
+#
pred1[np.where(pred1[:, 0] != Y[n_tr:n_tot])] = 0
pred1 = pred1.reshape((pred1.shape[0]))
plt.subplot(343)
@@ -147,7 +157,7 @@ plt.title("MVML view 1")
plt.subplot(344)
plt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred1)
plt.title("MVML view 2")
-
+#
pred2[np.where(pred2[:, 0] != Y[n_tr:n_tot])] = 0
pred2 = pred2.reshape((pred2.shape[0]))
plt.subplot(345)
@@ -156,17 +166,17 @@ plt.title("MVMLsparse view 1")
plt.subplot(346)
plt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred2)
plt.title("MVMLsparse view 2")
-
+#
pred3[np.where(pred3[:, 0] != Y[n_tr:n_tot])] = 0
pred3 = pred3.reshape((pred3.shape[0]))
-
+#
plt.subplot(347)
plt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred3)
plt.title("MVML_Cov view 1")
plt.subplot(348)
plt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred3)
plt.title("MVML_Cov view 2")
-
+#
pred4[np.where(pred4[:, 0] != Y[n_tr:n_tot])] = 0
pred4 = pred4.reshape((pred4.shape[0]))
plt.subplot(349)
@@ -175,9 +185,5 @@ plt.title("MVML_I view 1")
plt.subplot(3,4,10)
plt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred4)
plt.title("MVML_I view 2")
-
-# plt.figure(3)
-# plt.spy(A2)
-# plt.title("sparse learned A")
-
+#
plt.show()
diff --git a/doc/tutorial/auto_examples/mvml/mvml_plot_.rst b/doc/tutorial/auto_examples/mvml/mvml_plot_.rst
new file mode 100644
index 0000000..490cadf
--- /dev/null
+++ b/doc/tutorial/auto_examples/mvml/mvml_plot_.rst
@@ -0,0 +1,232 @@
+.. note::
+ :class: sphx-glr-download-link-note
+
+ Click :ref:`here <sphx_glr_download_tutorial_auto_examples_mvml_mvml_plot_.py>` to download the full example code
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorial_auto_examples_mvml_mvml_plot_.py:
+
+
+====
+MVML
+====
+Demonstration on how MVML (in file mvml.py) is intended to be used with very simple simulated dataset
+
+Demonstration uses scikit-learn for retrieving datasets and for calculating rbf kernel function, see
+http://scikit-learn.org/stable/
+
+
+.. code-block:: default
+
+
+ import numpy as np
+ import matplotlib.pyplot as plt
+ from sklearn import datasets
+ from sklearn.metrics import accuracy_score
+ from sklearn.metrics.pairwise import rbf_kernel
+ from multimodal.kernels.mvml import MVML
+ from multimodal.datasets.data_sample import DataSample
+ from multimodal.tests.datasets.get_dataset_path import get_dataset_path
+
+
+ np.random.seed(4)
+
+ # =========== create a simple dataset ============
+
+ n_tot = 200
+ half = int(n_tot/2)
+ n_tr = 120
+
+ # create a bit more data than needed so that we can take "half" amount of samples for each class
+ X0, y0 = datasets.make_moons(n_samples=n_tot+2, noise=0.3, shuffle=False)
+ X1, y1 = datasets.make_circles(n_samples=n_tot+2, noise=0.1, shuffle=False)
+
+ # make multi-view correspondence (select equal number of samples for both classes and order the data same way
+ # in both views)
+
+ yinds0 = np.append(np.where(y0 == 0)[0][0:half], np.where(y0 == 1)[0][0:half])
+ yinds1 = np.append(np.where(y1 == 0)[0][0:half], np.where(y1 == 1)[0][0:half])
+
+ X0 = X0[yinds0, :]
+ X1 = X1[yinds1, :]
+ Y = np.append(np.zeros(half)-1, np.ones(half)) # labels -1 and 1
+
+ # show data
+ # =========== create a simple dataset ============
+
+ n_tot = 200
+ half = int(n_tot/2)
+ n_tr = 120
+
+ # create a bit more data than needed so that we can take "half" amount of samples for each class
+ X0, y0 = datasets.make_moons(n_samples=n_tot+2, noise=0.3, shuffle=False)
+ X1, y1 = datasets.make_circles(n_samples=n_tot+2, noise=0.1, shuffle=False)
+
+ # make multi-view correspondence (select equal number of samples for both classes and order the data same way
+ # in both views)
+
+ yinds0 = np.append(np.where(y0 == 0)[0][0:half], np.where(y0 == 1)[0][0:half])
+ yinds1 = np.append(np.where(y1 == 0)[0][0:half], np.where(y1 == 1)[0][0:half])
+
+ X0 = X0[yinds0, :]
+ X1 = X1[yinds1, :]
+ Y = np.append(np.zeros(half)-1, np.ones(half)) # labels -1 and 1
+
+ # show data
+ plt.figure(figsize=(10., 8.))
+ plt.subplot(121)
+ plt.scatter(X0[:, 0], X0[:, 1], c=Y)
+ plt.title("all data, view 1")
+ plt.subplot(122)
+ plt.scatter(X1[:, 0], X1[:, 1], c=Y)
+ plt.title("all data, view 2")
+ plt.show()
+
+ # shuffle
+ order = np.random.permutation(n_tot)
+ X0 = X0[order, :]
+ X1 = X1[order, :]
+ Y = Y[order]
+
+ # make kernel dictionaries
+ kernel_dict = {}
+ test_kernel_dict = {}
+ kernel_dict[0] = rbf_kernel(X0[0:n_tr, :])
+ kernel_dict[1] = rbf_kernel(X1[0:n_tr, :])
+ test_kernel_dict[0] = rbf_kernel(X0[n_tr:n_tot, :], X0[0:n_tr, :])
+ test_kernel_dict[1] = rbf_kernel(X1[n_tr:n_tot, :], X1[0:n_tr, :])
+
+ # input_x = get_dataset_path("input_x_dic.pkl")
+ # f = open(input_x, "wb")
+ # pickle.dump(input_x, f)
+ #
+ #
+ # d= DataSample(kernel_dict)
+ # a = d.data
+ #
+ # =========== use MVML in classifying the data ============
+ #
+ # demo on how the code is intended to be used; parameters are not cross-validated, just picked some
+ # # with approximation
+ # # default: learn A, don't learn w (learn_A=1, learn_w=0)
+ mvml = MVML(lmbda=0.1, eta=1, nystrom_param=0.2, kernel='precomputed')
+ mvml.fit(kernel_dict, Y[0:n_tr])
+
+
+ #
+
+ pred1 = np.sign(mvml.predict(test_kernel_dict) ) # take sign for classification result
+ #
+ # without approximation
+ mvml2 = MVML(lmbda=0.1, eta=1, nystrom_param=1, kernel='precomputed') # without approximation
+ mvml2.fit(kernel_dict, Y[0:n_tr])
+ pred2 = np.sign(mvml2.predict(test_kernel_dict))
+ #
+ # use MVML_Cov, don't learn w
+ mvml3 = MVML(lmbda=0.1, eta=1,learn_A=3, nystrom_param=1, kernel='precomputed')
+ mvml3.fit(kernel_dict, Y[0:n_tr])
+ pred3 = np.sign(mvml.predict(test_kernel_dict))
+ #
+ # use MVML_I, don't learn w
+ mvml3 = MVML(lmbda=0.1, eta=1,learn_A=4, nystrom_param=1, kernel='precomputed')
+
+ pred4 = np.sign(mvml.predict(test_kernel_dict))
+ #
+ #
+ # # =========== show results ============
+ #
+ # # accuracies
+ acc1 = accuracy_score(Y[n_tr:n_tot], pred1)
+ acc2 = accuracy_score(Y[n_tr:n_tot], pred2)
+ acc3 = accuracy_score(Y[n_tr:n_tot], pred3)
+ acc4 = accuracy_score(Y[n_tr:n_tot], pred4)
+ #
+ # # display obtained accuracies
+ #
+ print("MVML: ", acc1)
+ print("MVMLsparse: ", acc2)
+ print("MVML_Cov: ", acc3)
+ print("MVML_I: ", acc4)
+ #
+ #
+ # # plot data and some classification results
+ #
+ plt.figure(2, figsize=(10., 8.))
+ plt.subplot(341)
+ plt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=Y[n_tr:n_tot])
+ plt.title("orig. view 1")
+ plt.subplot(342)
+ plt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=Y[n_tr:n_tot])
+ plt.title("orig. view 2")
+ #
+ pred1[np.where(pred1[:, 0] != Y[n_tr:n_tot])] = 0
+ pred1 = pred1.reshape((pred1.shape[0]))
+ plt.subplot(343)
+ plt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred1)
+ plt.title("MVML view 1")
+ plt.subplot(344)
+ plt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred1)
+ plt.title("MVML view 2")
+ #
+ pred2[np.where(pred2[:, 0] != Y[n_tr:n_tot])] = 0
+ pred2 = pred2.reshape((pred2.shape[0]))
+ plt.subplot(345)
+ plt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred2)
+ plt.title("MVMLsparse view 1")
+ plt.subplot(346)
+ plt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred2)
+ plt.title("MVMLsparse view 2")
+ #
+ pred3[np.where(pred3[:, 0] != Y[n_tr:n_tot])] = 0
+ pred3 = pred3.reshape((pred3.shape[0]))
+ #
+ plt.subplot(347)
+ plt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred3)
+ plt.title("MVML_Cov view 1")
+ plt.subplot(348)
+ plt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred3)
+ plt.title("MVML_Cov view 2")
+ #
+ pred4[np.where(pred4[:, 0] != Y[n_tr:n_tot])] = 0
+ pred4 = pred4.reshape((pred4.shape[0]))
+ plt.subplot(349)
+ plt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred4)
+ plt.title("MVML_I view 1")
+ plt.subplot(3,4,10)
+ plt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred4)
+ plt.title("MVML_I view 2")
+ #
+ plt.show()
+
+
+.. rst-class:: sphx-glr-timing
+
+ **Total running time of the script:** ( 0 minutes 0.000 seconds)
+
+
+.. _sphx_glr_download_tutorial_auto_examples_mvml_mvml_plot_.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+ :class: sphx-glr-footer-example
+
+
+
+ .. container:: sphx-glr-download
+
+ :download:`Download Python source code: mvml_plot_.py <mvml_plot_.py>`
+
+
+
+ .. container:: sphx-glr-download
+
+ :download:`Download Jupyter notebook: mvml_plot_.ipynb <mvml_plot_.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+ `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/doc/tutorial/auto_examples/mvml/mvml_plot__codeobj.pickle b/doc/tutorial/auto_examples/mvml/mvml_plot__codeobj.pickle
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diff --git a/doc/tutorial/auto_examples/mvml/plot_mvml_.ipynb b/doc/tutorial/auto_examples/mvml/plot_mvml_.ipynb
new file mode 100644
index 0000000..211b617
--- /dev/null
+++ b/doc/tutorial/auto_examples/mvml/plot_mvml_.ipynb
@@ -0,0 +1,72 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n# MVML\n\nDemonstration on how MVML (in file mvml.py) is intended to be used with very simple simulated dataset\n\nDemonstration uses scikit-learn for retrieving datasets and for calculating rbf kernel function, see\nhttp://scikit-learn.org/stable/\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import datasets\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.metrics.pairwise import rbf_kernel\nfrom multimodal.kernels.mvml import MVML\nfrom multimodal.datasets.data_sample import DataSample\nfrom multimodal.tests.datasets.get_dataset_path import get_dataset_path\n\n\nnp.random.seed(4)\n\n# =========== create a simple dataset ============\n\nn_tot = 200\nhalf = int(n_tot/2)\nn_tr = 120\n\n# create a bit more data than needed so that we can take \"half\" amount of samples for each class\nX0, y0 = datasets.make_moons(n_samples=n_tot+2, noise=0.3, shuffle=False)\nX1, y1 = datasets.make_circles(n_samples=n_tot+2, noise=0.1, shuffle=False)\n\n# make multi-view correspondence (select equal number of samples for both classes and order the data same way\n# in both views)\n\nyinds0 = np.append(np.where(y0 == 0)[0][0:half], np.where(y0 == 1)[0][0:half])\nyinds1 = np.append(np.where(y1 == 0)[0][0:half], np.where(y1 == 1)[0][0:half])\n\nX0 = X0[yinds0, :]\nX1 = X1[yinds1, :]\nY = np.append(np.zeros(half)-1, np.ones(half)) # labels -1 and 1\n\n# show data\n# =========== create a simple dataset ============\n\nn_tot = 200\nhalf = int(n_tot/2)\nn_tr = 120\n\n# create a bit more data than needed so that we can take \"half\" amount of samples for each class\nX0, y0 = datasets.make_moons(n_samples=n_tot+2, noise=0.3, shuffle=False)\nX1, y1 = datasets.make_circles(n_samples=n_tot+2, noise=0.1, shuffle=False)\n\n# make multi-view correspondence (select equal number of samples for both classes and order the data same way\n# in both views)\n\nyinds0 = np.append(np.where(y0 == 0)[0][0:half], np.where(y0 == 1)[0][0:half])\nyinds1 = np.append(np.where(y1 == 0)[0][0:half], np.where(y1 == 1)[0][0:half])\n\nX0 = X0[yinds0, :]\nX1 = X1[yinds1, :]\nY = np.append(np.zeros(half)-1, np.ones(half)) # labels -1 and 1\n\n# show data\nplt.figure(figsize=(10., 8.))\nplt.subplot(121)\nplt.scatter(X0[:, 0], X0[:, 1], c=Y)\nplt.title(\"all data, view 1\")\nplt.subplot(122)\nplt.scatter(X1[:, 0], X1[:, 1], c=Y)\nplt.title(\"all data, view 2\")\nplt.show()\n\n# shuffle\norder = np.random.permutation(n_tot)\nX0 = X0[order, :]\nX1 = X1[order, :]\nY = Y[order]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "make kernel dictionaries\n################################\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "kernel_dict = {}\ntest_kernel_dict = {}\nkernel_dict[0] = rbf_kernel(X0[0:n_tr, :])\nkernel_dict[1] = rbf_kernel(X1[0:n_tr, :])\ntest_kernel_dict[0] = rbf_kernel(X0[n_tr:n_tot, :], X0[0:n_tr, :])\ntest_kernel_dict[1] = rbf_kernel(X1[n_tr:n_tot, :], X1[0:n_tr, :])\nx_dict = {}\nx_dict[0] = X0[0:n_tr, :]\nx_dict[1] = X1[0:n_tr, :]\ntest_x_dict = {}\ntest_x_dict[0] = X0[n_tr:n_tot, :]\ntest_x_dict[1] = X1[n_tr:n_tot, :]\n# d= DataSample(kernel_dict)\n# a = d.data\n#\n# =========== use MVML in classifying the data ============\n# kernel precomputed\n# demo on how the code is intended to be used; parameters are not cross-validated, just picked some\n# # with approximation\n# # default: learn A, don't learn w (learn_A=1, learn_w=0)\nmvml = MVML(lmbda=0.1, eta=1, nystrom_param=0.2, kernel='precomputed')\nmvml.fit(kernel_dict, Y[0:n_tr])\n\n\n#\n\npred1 = np.sign(mvml.predict(test_kernel_dict) ) # take sign for classification result\n#\n# without approximation\nmvml2 = MVML(lmbda=0.1, eta=1, nystrom_param=1, kernel='precomputed') # without approximation\nmvml2.fit(kernel_dict, Y[0:n_tr])\npred2 = np.sign(mvml2.predict(test_kernel_dict))\n#\n# use MVML_Cov, don't learn w\nmvml3 = MVML(lmbda=0.1, eta=1,learn_A=3, nystrom_param=1, kernel='precomputed')\nmvml3.fit(kernel_dict, Y[0:n_tr])\npred3 = np.sign(mvml3.predict(test_kernel_dict))\n#\n# use MVML_I, don't learn w\nmvml4 = MVML(lmbda=0.1, eta=1,learn_A=4, nystrom_param=1, kernel='precomputed')\nmvml4.fit(kernel_dict, Y[0:n_tr])\npred4 = np.sign(mvml4.predict(test_kernel_dict))\n#\n# use kernel rbf equivalent to case 1\nmvml5 = MVML(lmbda=0.1, eta=1, nystrom_param=0.2, kernel='rbf')\nmvml5.fit(x_dict, Y[0:n_tr])\npred5 = np.sign(mvml5.predict(test_x_dict))\n#\n#\n# # =========== show results ============\n#\n# # accuracies\nacc1 = accuracy_score(Y[n_tr:n_tot], pred1)\nacc2 = accuracy_score(Y[n_tr:n_tot], pred2)\nacc3 = accuracy_score(Y[n_tr:n_tot], pred3)\nacc4 = accuracy_score(Y[n_tr:n_tot], pred4)\nacc5 = accuracy_score(Y[n_tr:n_tot], pred5)\n#\n# # display obtained accuracies\n#\nprint(\"MVML: \", acc1)\nprint(\"MVMLsparse: \", acc2)\nprint(\"MVML_Cov: \", acc3)\nprint(\"MVML_I: \", acc4)\nprint(\"MVML_rbf: \", acc5)\n#\n#\n# # plot data and some classification results\n#\nplt.figure(2, figsize=(10., 8.))\nplt.subplot(341)\nplt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=Y[n_tr:n_tot])\nplt.title(\"orig. view 1\")\nplt.subplot(342)\nplt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=Y[n_tr:n_tot])\nplt.title(\"orig. view 2\")\n#\npred1[np.where(pred1[:, 0] != Y[n_tr:n_tot])] = 0\npred1 = pred1.reshape((pred1.shape[0]))\nplt.subplot(343)\nplt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred1)\nplt.title(\"MVML view 1\")\nplt.subplot(344)\nplt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred1)\nplt.title(\"MVML view 2\")\n#\npred2[np.where(pred2[:, 0] != Y[n_tr:n_tot])] = 0\npred2 = pred2.reshape((pred2.shape[0]))\nplt.subplot(345)\nplt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred2)\nplt.title(\"MVMLsparse view 1\")\nplt.subplot(346)\nplt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred2)\nplt.title(\"MVMLsparse view 2\")\n#\npred3[np.where(pred3[:, 0] != Y[n_tr:n_tot])] = 0\npred3 = pred3.reshape((pred3.shape[0]))\n#\nplt.subplot(347)\nplt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred3)\nplt.title(\"MVML_Cov view 1\")\nplt.subplot(348)\nplt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred3)\nplt.title(\"MVML_Cov view 2\")\n#\npred4[np.where(pred4[:, 0] != Y[n_tr:n_tot])] = 0\npred4 = pred4.reshape((pred4.shape[0]))\nplt.subplot(349)\nplt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred4)\nplt.title(\"MVML_I view 1\")\nplt.subplot(3,4,10)\nplt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred4)\nplt.title(\"MVML_I view 2\")\n#\npred5[np.where(pred5[:, 0] != Y[n_tr:n_tot])] = 0\npred5 = pred5.reshape((pred5.shape[0]))\nplt.subplot(3,4,11)\nplt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred5)\nplt.title(\"MVML_rbf_kernel view 1\")\nplt.subplot(3,4,12)\nplt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred5)\nplt.title(\"MVML_rbf_kernel view 2\")\n#\nplt.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/doc/tutorial/auto_examples/mvml/plot_mvml_.py b/doc/tutorial/auto_examples/mvml/plot_mvml_.py
new file mode 100644
index 0000000..a449a7c
--- /dev/null
+++ b/doc/tutorial/auto_examples/mvml/plot_mvml_.py
@@ -0,0 +1,206 @@
+# -*- coding: utf-8 -*-
+"""
+====
+MVML
+====
+Demonstration on how MVML (in file mvml.py) is intended to be used with very simple simulated dataset
+
+Demonstration uses scikit-learn for retrieving datasets and for calculating rbf kernel function, see
+http://scikit-learn.org/stable/
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn import datasets
+from sklearn.metrics import accuracy_score
+from sklearn.metrics.pairwise import rbf_kernel
+from multimodal.kernels.mvml import MVML
+from multimodal.datasets.data_sample import DataSample
+from multimodal.tests.datasets.get_dataset_path import get_dataset_path
+
+
+np.random.seed(4)
+
+# =========== create a simple dataset ============
+
+n_tot = 200
+half = int(n_tot/2)
+n_tr = 120
+
+# create a bit more data than needed so that we can take "half" amount of samples for each class
+X0, y0 = datasets.make_moons(n_samples=n_tot+2, noise=0.3, shuffle=False)
+X1, y1 = datasets.make_circles(n_samples=n_tot+2, noise=0.1, shuffle=False)
+
+# make multi-view correspondence (select equal number of samples for both classes and order the data same way
+# in both views)
+
+yinds0 = np.append(np.where(y0 == 0)[0][0:half], np.where(y0 == 1)[0][0:half])
+yinds1 = np.append(np.where(y1 == 0)[0][0:half], np.where(y1 == 1)[0][0:half])
+
+X0 = X0[yinds0, :]
+X1 = X1[yinds1, :]
+Y = np.append(np.zeros(half)-1, np.ones(half)) # labels -1 and 1
+
+# show data
+# =========== create a simple dataset ============
+
+n_tot = 200
+half = int(n_tot/2)
+n_tr = 120
+
+# create a bit more data than needed so that we can take "half" amount of samples for each class
+X0, y0 = datasets.make_moons(n_samples=n_tot+2, noise=0.3, shuffle=False)
+X1, y1 = datasets.make_circles(n_samples=n_tot+2, noise=0.1, shuffle=False)
+
+# make multi-view correspondence (select equal number of samples for both classes and order the data same way
+# in both views)
+
+yinds0 = np.append(np.where(y0 == 0)[0][0:half], np.where(y0 == 1)[0][0:half])
+yinds1 = np.append(np.where(y1 == 0)[0][0:half], np.where(y1 == 1)[0][0:half])
+
+X0 = X0[yinds0, :]
+X1 = X1[yinds1, :]
+Y = np.append(np.zeros(half)-1, np.ones(half)) # labels -1 and 1
+
+# show data
+plt.figure(figsize=(10., 8.))
+plt.subplot(121)
+plt.scatter(X0[:, 0], X0[:, 1], c=Y)
+plt.title("all data, view 1")
+plt.subplot(122)
+plt.scatter(X1[:, 0], X1[:, 1], c=Y)
+plt.title("all data, view 2")
+plt.show()
+
+# shuffle
+order = np.random.permutation(n_tot)
+X0 = X0[order, :]
+X1 = X1[order, :]
+Y = Y[order]
+##################################
+# make kernel dictionaries
+#################################
+kernel_dict = {}
+test_kernel_dict = {}
+kernel_dict[0] = rbf_kernel(X0[0:n_tr, :])
+kernel_dict[1] = rbf_kernel(X1[0:n_tr, :])
+test_kernel_dict[0] = rbf_kernel(X0[n_tr:n_tot, :], X0[0:n_tr, :])
+test_kernel_dict[1] = rbf_kernel(X1[n_tr:n_tot, :], X1[0:n_tr, :])
+x_dict = {}
+x_dict[0] = X0[0:n_tr, :]
+x_dict[1] = X1[0:n_tr, :]
+test_x_dict = {}
+test_x_dict[0] = X0[n_tr:n_tot, :]
+test_x_dict[1] = X1[n_tr:n_tot, :]
+# d= DataSample(kernel_dict)
+# a = d.data
+#
+# =========== use MVML in classifying the data ============
+# kernel precomputed
+# demo on how the code is intended to be used; parameters are not cross-validated, just picked some
+# # with approximation
+# # default: learn A, don't learn w (learn_A=1, learn_w=0)
+mvml = MVML(lmbda=0.1, eta=1, nystrom_param=0.2, kernel='precomputed')
+mvml.fit(kernel_dict, Y[0:n_tr])
+
+
+#
+
+pred1 = np.sign(mvml.predict(test_kernel_dict) ) # take sign for classification result
+#
+# without approximation
+mvml2 = MVML(lmbda=0.1, eta=1, nystrom_param=1, kernel='precomputed') # without approximation
+mvml2.fit(kernel_dict, Y[0:n_tr])
+pred2 = np.sign(mvml2.predict(test_kernel_dict))
+#
+# use MVML_Cov, don't learn w
+mvml3 = MVML(lmbda=0.1, eta=1,learn_A=3, nystrom_param=1, kernel='precomputed')
+mvml3.fit(kernel_dict, Y[0:n_tr])
+pred3 = np.sign(mvml3.predict(test_kernel_dict))
+#
+# use MVML_I, don't learn w
+mvml4 = MVML(lmbda=0.1, eta=1,learn_A=4, nystrom_param=1, kernel='precomputed')
+mvml4.fit(kernel_dict, Y[0:n_tr])
+pred4 = np.sign(mvml4.predict(test_kernel_dict))
+#
+# use kernel rbf equivalent to case 1
+mvml5 = MVML(lmbda=0.1, eta=1, nystrom_param=0.2, kernel='rbf')
+mvml5.fit(x_dict, Y[0:n_tr])
+pred5 = np.sign(mvml5.predict(test_x_dict))
+#
+#
+# # =========== show results ============
+#
+# # accuracies
+acc1 = accuracy_score(Y[n_tr:n_tot], pred1)
+acc2 = accuracy_score(Y[n_tr:n_tot], pred2)
+acc3 = accuracy_score(Y[n_tr:n_tot], pred3)
+acc4 = accuracy_score(Y[n_tr:n_tot], pred4)
+acc5 = accuracy_score(Y[n_tr:n_tot], pred5)
+#
+# # display obtained accuracies
+#
+print("MVML: ", acc1)
+print("MVMLsparse: ", acc2)
+print("MVML_Cov: ", acc3)
+print("MVML_I: ", acc4)
+print("MVML_rbf: ", acc5)
+#
+#
+# # plot data and some classification results
+#
+plt.figure(2, figsize=(10., 8.))
+plt.subplot(341)
+plt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=Y[n_tr:n_tot])
+plt.title("orig. view 1")
+plt.subplot(342)
+plt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=Y[n_tr:n_tot])
+plt.title("orig. view 2")
+#
+pred1[np.where(pred1[:, 0] != Y[n_tr:n_tot])] = 0
+pred1 = pred1.reshape((pred1.shape[0]))
+plt.subplot(343)
+plt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred1)
+plt.title("MVML view 1")
+plt.subplot(344)
+plt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred1)
+plt.title("MVML view 2")
+#
+pred2[np.where(pred2[:, 0] != Y[n_tr:n_tot])] = 0
+pred2 = pred2.reshape((pred2.shape[0]))
+plt.subplot(345)
+plt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred2)
+plt.title("MVMLsparse view 1")
+plt.subplot(346)
+plt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred2)
+plt.title("MVMLsparse view 2")
+#
+pred3[np.where(pred3[:, 0] != Y[n_tr:n_tot])] = 0
+pred3 = pred3.reshape((pred3.shape[0]))
+#
+plt.subplot(347)
+plt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred3)
+plt.title("MVML_Cov view 1")
+plt.subplot(348)
+plt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred3)
+plt.title("MVML_Cov view 2")
+#
+pred4[np.where(pred4[:, 0] != Y[n_tr:n_tot])] = 0
+pred4 = pred4.reshape((pred4.shape[0]))
+plt.subplot(349)
+plt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred4)
+plt.title("MVML_I view 1")
+plt.subplot(3,4,10)
+plt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred4)
+plt.title("MVML_I view 2")
+#
+pred5[np.where(pred5[:, 0] != Y[n_tr:n_tot])] = 0
+pred5 = pred5.reshape((pred5.shape[0]))
+plt.subplot(3,4,11)
+plt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred5)
+plt.title("MVML_rbf_kernel view 1")
+plt.subplot(3,4,12)
+plt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred5)
+plt.title("MVML_rbf_kernel view 2")
+#
+plt.show()
diff --git a/doc/tutorial/auto_examples/mvml/plot_mvml_.py.md5 b/doc/tutorial/auto_examples/mvml/plot_mvml_.py.md5
new file mode 100644
index 0000000..4ee8d03
--- /dev/null
+++ b/doc/tutorial/auto_examples/mvml/plot_mvml_.py.md5
@@ -0,0 +1 @@
+870a37ab95c98874716ccd8b360cbc36
\ No newline at end of file
diff --git a/doc/tutorial/auto_examples/mvml/plot_mvml_.rst b/doc/tutorial/auto_examples/mvml/plot_mvml_.rst
new file mode 100644
index 0000000..cce0d8f
--- /dev/null
+++ b/doc/tutorial/auto_examples/mvml/plot_mvml_.rst
@@ -0,0 +1,299 @@
+.. note::
+ :class: sphx-glr-download-link-note
+
+ Click :ref:`here <sphx_glr_download_tutorial_auto_examples_mvml_plot_mvml_.py>` to download the full example code
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorial_auto_examples_mvml_plot_mvml_.py:
+
+
+====
+MVML
+====
+Demonstration on how MVML (in file mvml.py) is intended to be used with very simple simulated dataset
+
+Demonstration uses scikit-learn for retrieving datasets and for calculating rbf kernel function, see
+http://scikit-learn.org/stable/
+
+
+.. code-block:: default
+
+
+ import numpy as np
+ import matplotlib.pyplot as plt
+ from sklearn import datasets
+ from sklearn.metrics import accuracy_score
+ from sklearn.metrics.pairwise import rbf_kernel
+ from multimodal.kernels.mvml import MVML
+ from multimodal.datasets.data_sample import DataSample
+ from multimodal.tests.datasets.get_dataset_path import get_dataset_path
+
+
+ np.random.seed(4)
+
+ # =========== create a simple dataset ============
+
+ n_tot = 200
+ half = int(n_tot/2)
+ n_tr = 120
+
+ # create a bit more data than needed so that we can take "half" amount of samples for each class
+ X0, y0 = datasets.make_moons(n_samples=n_tot+2, noise=0.3, shuffle=False)
+ X1, y1 = datasets.make_circles(n_samples=n_tot+2, noise=0.1, shuffle=False)
+
+ # make multi-view correspondence (select equal number of samples for both classes and order the data same way
+ # in both views)
+
+ yinds0 = np.append(np.where(y0 == 0)[0][0:half], np.where(y0 == 1)[0][0:half])
+ yinds1 = np.append(np.where(y1 == 0)[0][0:half], np.where(y1 == 1)[0][0:half])
+
+ X0 = X0[yinds0, :]
+ X1 = X1[yinds1, :]
+ Y = np.append(np.zeros(half)-1, np.ones(half)) # labels -1 and 1
+
+ # show data
+ # =========== create a simple dataset ============
+
+ n_tot = 200
+ half = int(n_tot/2)
+ n_tr = 120
+
+ # create a bit more data than needed so that we can take "half" amount of samples for each class
+ X0, y0 = datasets.make_moons(n_samples=n_tot+2, noise=0.3, shuffle=False)
+ X1, y1 = datasets.make_circles(n_samples=n_tot+2, noise=0.1, shuffle=False)
+
+ # make multi-view correspondence (select equal number of samples for both classes and order the data same way
+ # in both views)
+
+ yinds0 = np.append(np.where(y0 == 0)[0][0:half], np.where(y0 == 1)[0][0:half])
+ yinds1 = np.append(np.where(y1 == 0)[0][0:half], np.where(y1 == 1)[0][0:half])
+
+ X0 = X0[yinds0, :]
+ X1 = X1[yinds1, :]
+ Y = np.append(np.zeros(half)-1, np.ones(half)) # labels -1 and 1
+
+ # show data
+ plt.figure(figsize=(10., 8.))
+ plt.subplot(121)
+ plt.scatter(X0[:, 0], X0[:, 1], c=Y)
+ plt.title("all data, view 1")
+ plt.subplot(122)
+ plt.scatter(X1[:, 0], X1[:, 1], c=Y)
+ plt.title("all data, view 2")
+ plt.show()
+
+ # shuffle
+ order = np.random.permutation(n_tot)
+ X0 = X0[order, :]
+ X1 = X1[order, :]
+ Y = Y[order]
+
+
+
+.. image:: /tutorial/auto_examples/mvml/images/sphx_glr_plot_mvml__001.png
+ :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+ /home/dominique/projets/ANR-Lives/scikit-multimodallearn/examples/mvml/plot_mvml_.py:73: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
+ plt.show()
+
+
+
+
+make kernel dictionaries
+################################
+
+
+.. code-block:: default
+
+ kernel_dict = {}
+ test_kernel_dict = {}
+ kernel_dict[0] = rbf_kernel(X0[0:n_tr, :])
+ kernel_dict[1] = rbf_kernel(X1[0:n_tr, :])
+ test_kernel_dict[0] = rbf_kernel(X0[n_tr:n_tot, :], X0[0:n_tr, :])
+ test_kernel_dict[1] = rbf_kernel(X1[n_tr:n_tot, :], X1[0:n_tr, :])
+ x_dict = {}
+ x_dict[0] = X0[0:n_tr, :]
+ x_dict[1] = X1[0:n_tr, :]
+ test_x_dict = {}
+ test_x_dict[0] = X0[n_tr:n_tot, :]
+ test_x_dict[1] = X1[n_tr:n_tot, :]
+ # d= DataSample(kernel_dict)
+ # a = d.data
+ #
+ # =========== use MVML in classifying the data ============
+ # kernel precomputed
+ # demo on how the code is intended to be used; parameters are not cross-validated, just picked some
+ # # with approximation
+ # # default: learn A, don't learn w (learn_A=1, learn_w=0)
+ mvml = MVML(lmbda=0.1, eta=1, nystrom_param=0.2, kernel='precomputed')
+ mvml.fit(kernel_dict, Y[0:n_tr])
+
+
+ #
+
+ pred1 = np.sign(mvml.predict(test_kernel_dict) ) # take sign for classification result
+ #
+ # without approximation
+ mvml2 = MVML(lmbda=0.1, eta=1, nystrom_param=1, kernel='precomputed') # without approximation
+ mvml2.fit(kernel_dict, Y[0:n_tr])
+ pred2 = np.sign(mvml2.predict(test_kernel_dict))
+ #
+ # use MVML_Cov, don't learn w
+ mvml3 = MVML(lmbda=0.1, eta=1,learn_A=3, nystrom_param=1, kernel='precomputed')
+ mvml3.fit(kernel_dict, Y[0:n_tr])
+ pred3 = np.sign(mvml3.predict(test_kernel_dict))
+ #
+ # use MVML_I, don't learn w
+ mvml4 = MVML(lmbda=0.1, eta=1,learn_A=4, nystrom_param=1, kernel='precomputed')
+ mvml4.fit(kernel_dict, Y[0:n_tr])
+ pred4 = np.sign(mvml4.predict(test_kernel_dict))
+ #
+ # use kernel rbf equivalent to case 1
+ mvml5 = MVML(lmbda=0.1, eta=1, nystrom_param=0.2, kernel='rbf')
+ mvml5.fit(x_dict, Y[0:n_tr])
+ pred5 = np.sign(mvml5.predict(test_x_dict))
+ #
+ #
+ # # =========== show results ============
+ #
+ # # accuracies
+ acc1 = accuracy_score(Y[n_tr:n_tot], pred1)
+ acc2 = accuracy_score(Y[n_tr:n_tot], pred2)
+ acc3 = accuracy_score(Y[n_tr:n_tot], pred3)
+ acc4 = accuracy_score(Y[n_tr:n_tot], pred4)
+ acc5 = accuracy_score(Y[n_tr:n_tot], pred5)
+ #
+ # # display obtained accuracies
+ #
+ print("MVML: ", acc1)
+ print("MVMLsparse: ", acc2)
+ print("MVML_Cov: ", acc3)
+ print("MVML_I: ", acc4)
+ print("MVML_rbf: ", acc5)
+ #
+ #
+ # # plot data and some classification results
+ #
+ plt.figure(2, figsize=(10., 8.))
+ plt.subplot(341)
+ plt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=Y[n_tr:n_tot])
+ plt.title("orig. view 1")
+ plt.subplot(342)
+ plt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=Y[n_tr:n_tot])
+ plt.title("orig. view 2")
+ #
+ pred1[np.where(pred1[:, 0] != Y[n_tr:n_tot])] = 0
+ pred1 = pred1.reshape((pred1.shape[0]))
+ plt.subplot(343)
+ plt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred1)
+ plt.title("MVML view 1")
+ plt.subplot(344)
+ plt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred1)
+ plt.title("MVML view 2")
+ #
+ pred2[np.where(pred2[:, 0] != Y[n_tr:n_tot])] = 0
+ pred2 = pred2.reshape((pred2.shape[0]))
+ plt.subplot(345)
+ plt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred2)
+ plt.title("MVMLsparse view 1")
+ plt.subplot(346)
+ plt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred2)
+ plt.title("MVMLsparse view 2")
+ #
+ pred3[np.where(pred3[:, 0] != Y[n_tr:n_tot])] = 0
+ pred3 = pred3.reshape((pred3.shape[0]))
+ #
+ plt.subplot(347)
+ plt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred3)
+ plt.title("MVML_Cov view 1")
+ plt.subplot(348)
+ plt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred3)
+ plt.title("MVML_Cov view 2")
+ #
+ pred4[np.where(pred4[:, 0] != Y[n_tr:n_tot])] = 0
+ pred4 = pred4.reshape((pred4.shape[0]))
+ plt.subplot(349)
+ plt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred4)
+ plt.title("MVML_I view 1")
+ plt.subplot(3,4,10)
+ plt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred4)
+ plt.title("MVML_I view 2")
+ #
+ pred5[np.where(pred5[:, 0] != Y[n_tr:n_tot])] = 0
+ pred5 = pred5.reshape((pred5.shape[0]))
+ plt.subplot(3,4,11)
+ plt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred5)
+ plt.title("MVML_rbf_kernel view 1")
+ plt.subplot(3,4,12)
+ plt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred5)
+ plt.title("MVML_rbf_kernel view 2")
+ #
+ plt.show()
+
+
+
+.. image:: /tutorial/auto_examples/mvml/images/sphx_glr_plot_mvml__002.png
+ :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+ WARNING:root:warning appears during fit process{'precond_A': 1, 'precond_A_1': 1}
+ WARNING:root:warning appears during fit process{'precond_A': 1, 'precond_A_1': 4}
+ WARNING:root:warning appears during fit process{'precond_A': 1}
+ WARNING:root:warning appears during fit process{'precond_A_1': 1}
+ WARNING:root:warning appears during fit process{'precond_A': 1, 'precond_A_1': 1}
+ MVML: 0.7875
+ MVMLsparse: 0.8375
+ MVML_Cov: 0.85
+ MVML_I: 0.8625
+ MVML_rbf: 0.7875
+ /home/dominique/projets/ANR-Lives/scikit-multimodallearn/examples/mvml/plot_mvml_.py:206: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
+ plt.show()
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+ **Total running time of the script:** ( 0 minutes 5.285 seconds)
+
+
+.. _sphx_glr_download_tutorial_auto_examples_mvml_plot_mvml_.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+ :class: sphx-glr-footer-example
+
+
+
+ .. container:: sphx-glr-download
+
+ :download:`Download Python source code: plot_mvml_.py <plot_mvml_.py>`
+
+
+
+ .. container:: sphx-glr-download
+
+ :download:`Download Jupyter notebook: plot_mvml_.ipynb <plot_mvml_.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+ `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/doc/tutorial/auto_examples/mvml/plot_mvml__codeobj.pickle b/doc/tutorial/auto_examples/mvml/plot_mvml__codeobj.pickle
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diff --git a/doc/tutorial/auto_examples/mvml/sg_execution_times.rst b/doc/tutorial/auto_examples/mvml/sg_execution_times.rst
new file mode 100644
index 0000000..bca9ff8
--- /dev/null
+++ b/doc/tutorial/auto_examples/mvml/sg_execution_times.rst
@@ -0,0 +1,12 @@
+
+:orphan:
+
+.. _sphx_glr_tutorial_auto_examples_mvml_sg_execution_times:
+
+Computation times
+=================
+**00:05.285** total execution time for **tutorial_auto_examples_mvml** files:
+
++-------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorial_auto_examples_mvml_plot_mvml_.py` (``plot_mvml_.py``) | 00:05.285 | 0.0 MB |
++-------------------------------------------------------------------------------+-----------+--------+
diff --git a/doc/tutorial/auto_examples/mvml_plot_.ipynb b/doc/tutorial/auto_examples/mvml_plot_.ipynb
new file mode 100644
index 0000000..830427c
--- /dev/null
+++ b/doc/tutorial/auto_examples/mvml_plot_.ipynb
@@ -0,0 +1,54 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n# MVML\n\nDemonstration on how MVML (in file mvml.py) is intended to be used with very simple simulated dataset\n\nDemonstration uses scikit-learn for retrieving datasets and for calculating rbf kernel function, see\nhttp://scikit-learn.org/stable/\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import datasets\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.metrics.pairwise import rbf_kernel\nfrom multimodal.kernels.mvml import MVML\nfrom multimodal.datasets.data_sample import DataSample\nfrom multimodal.tests.datasets.get_dataset_path import get_dataset_path\n\n\nnp.random.seed(4)\n\n# =========== create a simple dataset ============\n\nn_tot = 200\nhalf = int(n_tot/2)\nn_tr = 120\n\n# create a bit more data than needed so that we can take \"half\" amount of samples for each class\nX0, y0 = datasets.make_moons(n_samples=n_tot+2, noise=0.3, shuffle=False)\nX1, y1 = datasets.make_circles(n_samples=n_tot+2, noise=0.1, shuffle=False)\n\n# make multi-view correspondence (select equal number of samples for both classes and order the data same way\n# in both views)\n\nyinds0 = np.append(np.where(y0 == 0)[0][0:half], np.where(y0 == 1)[0][0:half])\nyinds1 = np.append(np.where(y1 == 0)[0][0:half], np.where(y1 == 1)[0][0:half])\n\nX0 = X0[yinds0, :]\nX1 = X1[yinds1, :]\nY = np.append(np.zeros(half)-1, np.ones(half)) # labels -1 and 1\n\n# show data\n# =========== create a simple dataset ============\n\nn_tot = 200\nhalf = int(n_tot/2)\nn_tr = 120\n\n# create a bit more data than needed so that we can take \"half\" amount of samples for each class\nX0, y0 = datasets.make_moons(n_samples=n_tot+2, noise=0.3, shuffle=False)\nX1, y1 = datasets.make_circles(n_samples=n_tot+2, noise=0.1, shuffle=False)\n\n# make multi-view correspondence (select equal number of samples for both classes and order the data same way\n# in both views)\n\nyinds0 = np.append(np.where(y0 == 0)[0][0:half], np.where(y0 == 1)[0][0:half])\nyinds1 = np.append(np.where(y1 == 0)[0][0:half], np.where(y1 == 1)[0][0:half])\n\nX0 = X0[yinds0, :]\nX1 = X1[yinds1, :]\nY = np.append(np.zeros(half)-1, np.ones(half)) # labels -1 and 1\n\n# show data\nplt.figure(figsize=(10., 8.))\nplt.subplot(121)\nplt.scatter(X0[:, 0], X0[:, 1], c=Y)\nplt.title(\"all data, view 1\")\nplt.subplot(122)\nplt.scatter(X1[:, 0], X1[:, 1], c=Y)\nplt.title(\"all data, view 2\")\nplt.show()\n\n# shuffle\norder = np.random.permutation(n_tot)\nX0 = X0[order, :]\nX1 = X1[order, :]\nY = Y[order]\n\n# make kernel dictionaries\nkernel_dict = {}\ntest_kernel_dict = {}\nkernel_dict[0] = rbf_kernel(X0[0:n_tr, :])\nkernel_dict[1] = rbf_kernel(X1[0:n_tr, :])\ntest_kernel_dict[0] = rbf_kernel(X0[n_tr:n_tot, :], X0[0:n_tr, :])\ntest_kernel_dict[1] = rbf_kernel(X1[n_tr:n_tot, :], X1[0:n_tr, :])\n\n# input_x = get_dataset_path(\"input_x_dic.pkl\")\n# f = open(input_x, \"wb\")\n# pickle.dump(input_x, f)\n#\n#\n# d= DataSample(kernel_dict)\n# a = d.data\n#\n# =========== use MVML in classifying the data ============\n#\n# demo on how the code is intended to be used; parameters are not cross-validated, just picked some\n# # with approximation\n# # default: learn A, don't learn w (learn_A=1, learn_w=0)\nmvml = MVML(lmbda=0.1, eta=1, nystrom_param=0.2, kernel='precomputed')\nmvml.fit(kernel_dict, Y[0:n_tr])\n\n\n#\n\npred1 = np.sign(mvml.predict(test_kernel_dict) ) # take sign for classification result\n#\n# without approximation\nmvml2 = MVML(lmbda=0.1, eta=1, nystrom_param=1, kernel='precomputed') # without approximation\nmvml2.fit(kernel_dict, Y[0:n_tr])\npred2 = np.sign(mvml2.predict(test_kernel_dict))\n#\n# use MVML_Cov, don't learn w\nmvml3 = MVML(lmbda=0.1, eta=1,learn_A=3, nystrom_param=1, kernel='precomputed')\nmvml3.fit(kernel_dict, Y[0:n_tr])\npred3 = np.sign(mvml.predict(test_kernel_dict))\n#\n# use MVML_I, don't learn w\nmvml3 = MVML(lmbda=0.1, eta=1,learn_A=4, nystrom_param=1, kernel='precomputed')\n\npred4 = np.sign(mvml.predict(test_kernel_dict))\n#\n#\n# # =========== show results ============\n#\n# # accuracies\nacc1 = accuracy_score(Y[n_tr:n_tot], pred1)\nacc2 = accuracy_score(Y[n_tr:n_tot], pred2)\nacc3 = accuracy_score(Y[n_tr:n_tot], pred3)\nacc4 = accuracy_score(Y[n_tr:n_tot], pred4)\n#\n# # display obtained accuracies\n#\nprint(\"MVML: \", acc1)\nprint(\"MVMLsparse: \", acc2)\nprint(\"MVML_Cov: \", acc3)\nprint(\"MVML_I: \", acc4)\n#\n#\n# # plot data and some classification results\n#\nplt.figure(2, figsize=(10., 8.))\nplt.subplot(341)\nplt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=Y[n_tr:n_tot])\nplt.title(\"orig. view 1\")\nplt.subplot(342)\nplt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=Y[n_tr:n_tot])\nplt.title(\"orig. view 2\")\n#\npred1[np.where(pred1[:, 0] != Y[n_tr:n_tot])] = 0\npred1 = pred1.reshape((pred1.shape[0]))\nplt.subplot(343)\nplt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred1)\nplt.title(\"MVML view 1\")\nplt.subplot(344)\nplt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred1)\nplt.title(\"MVML view 2\")\n#\npred2[np.where(pred2[:, 0] != Y[n_tr:n_tot])] = 0\npred2 = pred2.reshape((pred2.shape[0]))\nplt.subplot(345)\nplt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred2)\nplt.title(\"MVMLsparse view 1\")\nplt.subplot(346)\nplt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred2)\nplt.title(\"MVMLsparse view 2\")\n#\npred3[np.where(pred3[:, 0] != Y[n_tr:n_tot])] = 0\npred3 = pred3.reshape((pred3.shape[0]))\n#\nplt.subplot(347)\nplt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred3)\nplt.title(\"MVML_Cov view 1\")\nplt.subplot(348)\nplt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred3)\nplt.title(\"MVML_Cov view 2\")\n#\npred4[np.where(pred4[:, 0] != Y[n_tr:n_tot])] = 0\npred4 = pred4.reshape((pred4.shape[0]))\nplt.subplot(349)\nplt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred4)\nplt.title(\"MVML_I view 1\")\nplt.subplot(3,4,10)\nplt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred4)\nplt.title(\"MVML_I view 2\")\n#\nplt.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/doc/tutorial/auto_examples/mvml_plot_.py b/doc/tutorial/auto_examples/mvml_plot_.py
new file mode 100644
index 0000000..1abf1ea
--- /dev/null
+++ b/doc/tutorial/auto_examples/mvml_plot_.py
@@ -0,0 +1,189 @@
+# -*- coding: utf-8 -*-
+"""
+====
+MVML
+====
+Demonstration on how MVML (in file mvml.py) is intended to be used with very simple simulated dataset
+
+Demonstration uses scikit-learn for retrieving datasets and for calculating rbf kernel function, see
+http://scikit-learn.org/stable/
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn import datasets
+from sklearn.metrics import accuracy_score
+from sklearn.metrics.pairwise import rbf_kernel
+from multimodal.kernels.mvml import MVML
+from multimodal.datasets.data_sample import DataSample
+from multimodal.tests.datasets.get_dataset_path import get_dataset_path
+
+
+np.random.seed(4)
+
+# =========== create a simple dataset ============
+
+n_tot = 200
+half = int(n_tot/2)
+n_tr = 120
+
+# create a bit more data than needed so that we can take "half" amount of samples for each class
+X0, y0 = datasets.make_moons(n_samples=n_tot+2, noise=0.3, shuffle=False)
+X1, y1 = datasets.make_circles(n_samples=n_tot+2, noise=0.1, shuffle=False)
+
+# make multi-view correspondence (select equal number of samples for both classes and order the data same way
+# in both views)
+
+yinds0 = np.append(np.where(y0 == 0)[0][0:half], np.where(y0 == 1)[0][0:half])
+yinds1 = np.append(np.where(y1 == 0)[0][0:half], np.where(y1 == 1)[0][0:half])
+
+X0 = X0[yinds0, :]
+X1 = X1[yinds1, :]
+Y = np.append(np.zeros(half)-1, np.ones(half)) # labels -1 and 1
+
+# show data
+# =========== create a simple dataset ============
+
+n_tot = 200
+half = int(n_tot/2)
+n_tr = 120
+
+# create a bit more data than needed so that we can take "half" amount of samples for each class
+X0, y0 = datasets.make_moons(n_samples=n_tot+2, noise=0.3, shuffle=False)
+X1, y1 = datasets.make_circles(n_samples=n_tot+2, noise=0.1, shuffle=False)
+
+# make multi-view correspondence (select equal number of samples for both classes and order the data same way
+# in both views)
+
+yinds0 = np.append(np.where(y0 == 0)[0][0:half], np.where(y0 == 1)[0][0:half])
+yinds1 = np.append(np.where(y1 == 0)[0][0:half], np.where(y1 == 1)[0][0:half])
+
+X0 = X0[yinds0, :]
+X1 = X1[yinds1, :]
+Y = np.append(np.zeros(half)-1, np.ones(half)) # labels -1 and 1
+
+# show data
+plt.figure(figsize=(10., 8.))
+plt.subplot(121)
+plt.scatter(X0[:, 0], X0[:, 1], c=Y)
+plt.title("all data, view 1")
+plt.subplot(122)
+plt.scatter(X1[:, 0], X1[:, 1], c=Y)
+plt.title("all data, view 2")
+plt.show()
+
+# shuffle
+order = np.random.permutation(n_tot)
+X0 = X0[order, :]
+X1 = X1[order, :]
+Y = Y[order]
+
+# make kernel dictionaries
+kernel_dict = {}
+test_kernel_dict = {}
+kernel_dict[0] = rbf_kernel(X0[0:n_tr, :])
+kernel_dict[1] = rbf_kernel(X1[0:n_tr, :])
+test_kernel_dict[0] = rbf_kernel(X0[n_tr:n_tot, :], X0[0:n_tr, :])
+test_kernel_dict[1] = rbf_kernel(X1[n_tr:n_tot, :], X1[0:n_tr, :])
+
+# input_x = get_dataset_path("input_x_dic.pkl")
+# f = open(input_x, "wb")
+# pickle.dump(input_x, f)
+#
+#
+# d= DataSample(kernel_dict)
+# a = d.data
+#
+# =========== use MVML in classifying the data ============
+#
+# demo on how the code is intended to be used; parameters are not cross-validated, just picked some
+# # with approximation
+# # default: learn A, don't learn w (learn_A=1, learn_w=0)
+mvml = MVML(lmbda=0.1, eta=1, nystrom_param=0.2, kernel='precomputed')
+mvml.fit(kernel_dict, Y[0:n_tr])
+
+
+#
+
+pred1 = np.sign(mvml.predict(test_kernel_dict) ) # take sign for classification result
+#
+# without approximation
+mvml2 = MVML(lmbda=0.1, eta=1, nystrom_param=1, kernel='precomputed') # without approximation
+mvml2.fit(kernel_dict, Y[0:n_tr])
+pred2 = np.sign(mvml2.predict(test_kernel_dict))
+#
+# use MVML_Cov, don't learn w
+mvml3 = MVML(lmbda=0.1, eta=1,learn_A=3, nystrom_param=1, kernel='precomputed')
+mvml3.fit(kernel_dict, Y[0:n_tr])
+pred3 = np.sign(mvml.predict(test_kernel_dict))
+#
+# use MVML_I, don't learn w
+mvml3 = MVML(lmbda=0.1, eta=1,learn_A=4, nystrom_param=1, kernel='precomputed')
+
+pred4 = np.sign(mvml.predict(test_kernel_dict))
+#
+#
+# # =========== show results ============
+#
+# # accuracies
+acc1 = accuracy_score(Y[n_tr:n_tot], pred1)
+acc2 = accuracy_score(Y[n_tr:n_tot], pred2)
+acc3 = accuracy_score(Y[n_tr:n_tot], pred3)
+acc4 = accuracy_score(Y[n_tr:n_tot], pred4)
+#
+# # display obtained accuracies
+#
+print("MVML: ", acc1)
+print("MVMLsparse: ", acc2)
+print("MVML_Cov: ", acc3)
+print("MVML_I: ", acc4)
+#
+#
+# # plot data and some classification results
+#
+plt.figure(2, figsize=(10., 8.))
+plt.subplot(341)
+plt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=Y[n_tr:n_tot])
+plt.title("orig. view 1")
+plt.subplot(342)
+plt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=Y[n_tr:n_tot])
+plt.title("orig. view 2")
+#
+pred1[np.where(pred1[:, 0] != Y[n_tr:n_tot])] = 0
+pred1 = pred1.reshape((pred1.shape[0]))
+plt.subplot(343)
+plt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred1)
+plt.title("MVML view 1")
+plt.subplot(344)
+plt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred1)
+plt.title("MVML view 2")
+#
+pred2[np.where(pred2[:, 0] != Y[n_tr:n_tot])] = 0
+pred2 = pred2.reshape((pred2.shape[0]))
+plt.subplot(345)
+plt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred2)
+plt.title("MVMLsparse view 1")
+plt.subplot(346)
+plt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred2)
+plt.title("MVMLsparse view 2")
+#
+pred3[np.where(pred3[:, 0] != Y[n_tr:n_tot])] = 0
+pred3 = pred3.reshape((pred3.shape[0]))
+#
+plt.subplot(347)
+plt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred3)
+plt.title("MVML_Cov view 1")
+plt.subplot(348)
+plt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred3)
+plt.title("MVML_Cov view 2")
+#
+pred4[np.where(pred4[:, 0] != Y[n_tr:n_tot])] = 0
+pred4 = pred4.reshape((pred4.shape[0]))
+plt.subplot(349)
+plt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred4)
+plt.title("MVML_I view 1")
+plt.subplot(3,4,10)
+plt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred4)
+plt.title("MVML_I view 2")
+#
+plt.show()
diff --git a/doc/tutorial/auto_examples/mvml_plot_.rst b/doc/tutorial/auto_examples/mvml_plot_.rst
new file mode 100644
index 0000000..7d5e65d
--- /dev/null
+++ b/doc/tutorial/auto_examples/mvml_plot_.rst
@@ -0,0 +1,232 @@
+.. note::
+ :class: sphx-glr-download-link-note
+
+ Click :ref:`here <sphx_glr_download_tutorial_auto_examples_mvml_plot_.py>` to download the full example code
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorial_auto_examples_mvml_plot_.py:
+
+
+====
+MVML
+====
+Demonstration on how MVML (in file mvml.py) is intended to be used with very simple simulated dataset
+
+Demonstration uses scikit-learn for retrieving datasets and for calculating rbf kernel function, see
+http://scikit-learn.org/stable/
+
+
+.. code-block:: default
+
+
+ import numpy as np
+ import matplotlib.pyplot as plt
+ from sklearn import datasets
+ from sklearn.metrics import accuracy_score
+ from sklearn.metrics.pairwise import rbf_kernel
+ from multimodal.kernels.mvml import MVML
+ from multimodal.datasets.data_sample import DataSample
+ from multimodal.tests.datasets.get_dataset_path import get_dataset_path
+
+
+ np.random.seed(4)
+
+ # =========== create a simple dataset ============
+
+ n_tot = 200
+ half = int(n_tot/2)
+ n_tr = 120
+
+ # create a bit more data than needed so that we can take "half" amount of samples for each class
+ X0, y0 = datasets.make_moons(n_samples=n_tot+2, noise=0.3, shuffle=False)
+ X1, y1 = datasets.make_circles(n_samples=n_tot+2, noise=0.1, shuffle=False)
+
+ # make multi-view correspondence (select equal number of samples for both classes and order the data same way
+ # in both views)
+
+ yinds0 = np.append(np.where(y0 == 0)[0][0:half], np.where(y0 == 1)[0][0:half])
+ yinds1 = np.append(np.where(y1 == 0)[0][0:half], np.where(y1 == 1)[0][0:half])
+
+ X0 = X0[yinds0, :]
+ X1 = X1[yinds1, :]
+ Y = np.append(np.zeros(half)-1, np.ones(half)) # labels -1 and 1
+
+ # show data
+ # =========== create a simple dataset ============
+
+ n_tot = 200
+ half = int(n_tot/2)
+ n_tr = 120
+
+ # create a bit more data than needed so that we can take "half" amount of samples for each class
+ X0, y0 = datasets.make_moons(n_samples=n_tot+2, noise=0.3, shuffle=False)
+ X1, y1 = datasets.make_circles(n_samples=n_tot+2, noise=0.1, shuffle=False)
+
+ # make multi-view correspondence (select equal number of samples for both classes and order the data same way
+ # in both views)
+
+ yinds0 = np.append(np.where(y0 == 0)[0][0:half], np.where(y0 == 1)[0][0:half])
+ yinds1 = np.append(np.where(y1 == 0)[0][0:half], np.where(y1 == 1)[0][0:half])
+
+ X0 = X0[yinds0, :]
+ X1 = X1[yinds1, :]
+ Y = np.append(np.zeros(half)-1, np.ones(half)) # labels -1 and 1
+
+ # show data
+ plt.figure(figsize=(10., 8.))
+ plt.subplot(121)
+ plt.scatter(X0[:, 0], X0[:, 1], c=Y)
+ plt.title("all data, view 1")
+ plt.subplot(122)
+ plt.scatter(X1[:, 0], X1[:, 1], c=Y)
+ plt.title("all data, view 2")
+ plt.show()
+
+ # shuffle
+ order = np.random.permutation(n_tot)
+ X0 = X0[order, :]
+ X1 = X1[order, :]
+ Y = Y[order]
+
+ # make kernel dictionaries
+ kernel_dict = {}
+ test_kernel_dict = {}
+ kernel_dict[0] = rbf_kernel(X0[0:n_tr, :])
+ kernel_dict[1] = rbf_kernel(X1[0:n_tr, :])
+ test_kernel_dict[0] = rbf_kernel(X0[n_tr:n_tot, :], X0[0:n_tr, :])
+ test_kernel_dict[1] = rbf_kernel(X1[n_tr:n_tot, :], X1[0:n_tr, :])
+
+ # input_x = get_dataset_path("input_x_dic.pkl")
+ # f = open(input_x, "wb")
+ # pickle.dump(input_x, f)
+ #
+ #
+ # d= DataSample(kernel_dict)
+ # a = d.data
+ #
+ # =========== use MVML in classifying the data ============
+ #
+ # demo on how the code is intended to be used; parameters are not cross-validated, just picked some
+ # # with approximation
+ # # default: learn A, don't learn w (learn_A=1, learn_w=0)
+ mvml = MVML(lmbda=0.1, eta=1, nystrom_param=0.2, kernel='precomputed')
+ mvml.fit(kernel_dict, Y[0:n_tr])
+
+
+ #
+
+ pred1 = np.sign(mvml.predict(test_kernel_dict) ) # take sign for classification result
+ #
+ # without approximation
+ mvml2 = MVML(lmbda=0.1, eta=1, nystrom_param=1, kernel='precomputed') # without approximation
+ mvml2.fit(kernel_dict, Y[0:n_tr])
+ pred2 = np.sign(mvml2.predict(test_kernel_dict))
+ #
+ # use MVML_Cov, don't learn w
+ mvml3 = MVML(lmbda=0.1, eta=1,learn_A=3, nystrom_param=1, kernel='precomputed')
+ mvml3.fit(kernel_dict, Y[0:n_tr])
+ pred3 = np.sign(mvml.predict(test_kernel_dict))
+ #
+ # use MVML_I, don't learn w
+ mvml3 = MVML(lmbda=0.1, eta=1,learn_A=4, nystrom_param=1, kernel='precomputed')
+
+ pred4 = np.sign(mvml.predict(test_kernel_dict))
+ #
+ #
+ # # =========== show results ============
+ #
+ # # accuracies
+ acc1 = accuracy_score(Y[n_tr:n_tot], pred1)
+ acc2 = accuracy_score(Y[n_tr:n_tot], pred2)
+ acc3 = accuracy_score(Y[n_tr:n_tot], pred3)
+ acc4 = accuracy_score(Y[n_tr:n_tot], pred4)
+ #
+ # # display obtained accuracies
+ #
+ print("MVML: ", acc1)
+ print("MVMLsparse: ", acc2)
+ print("MVML_Cov: ", acc3)
+ print("MVML_I: ", acc4)
+ #
+ #
+ # # plot data and some classification results
+ #
+ plt.figure(2, figsize=(10., 8.))
+ plt.subplot(341)
+ plt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=Y[n_tr:n_tot])
+ plt.title("orig. view 1")
+ plt.subplot(342)
+ plt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=Y[n_tr:n_tot])
+ plt.title("orig. view 2")
+ #
+ pred1[np.where(pred1[:, 0] != Y[n_tr:n_tot])] = 0
+ pred1 = pred1.reshape((pred1.shape[0]))
+ plt.subplot(343)
+ plt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred1)
+ plt.title("MVML view 1")
+ plt.subplot(344)
+ plt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred1)
+ plt.title("MVML view 2")
+ #
+ pred2[np.where(pred2[:, 0] != Y[n_tr:n_tot])] = 0
+ pred2 = pred2.reshape((pred2.shape[0]))
+ plt.subplot(345)
+ plt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred2)
+ plt.title("MVMLsparse view 1")
+ plt.subplot(346)
+ plt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred2)
+ plt.title("MVMLsparse view 2")
+ #
+ pred3[np.where(pred3[:, 0] != Y[n_tr:n_tot])] = 0
+ pred3 = pred3.reshape((pred3.shape[0]))
+ #
+ plt.subplot(347)
+ plt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred3)
+ plt.title("MVML_Cov view 1")
+ plt.subplot(348)
+ plt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred3)
+ plt.title("MVML_Cov view 2")
+ #
+ pred4[np.where(pred4[:, 0] != Y[n_tr:n_tot])] = 0
+ pred4 = pred4.reshape((pred4.shape[0]))
+ plt.subplot(349)
+ plt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred4)
+ plt.title("MVML_I view 1")
+ plt.subplot(3,4,10)
+ plt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred4)
+ plt.title("MVML_I view 2")
+ #
+ plt.show()
+
+
+.. rst-class:: sphx-glr-timing
+
+ **Total running time of the script:** ( 0 minutes 0.000 seconds)
+
+
+.. _sphx_glr_download_tutorial_auto_examples_mvml_plot_.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+ :class: sphx-glr-footer-example
+
+
+
+ .. container:: sphx-glr-download
+
+ :download:`Download Python source code: mvml_plot_.py <mvml_plot_.py>`
+
+
+
+ .. container:: sphx-glr-download
+
+ :download:`Download Jupyter notebook: mvml_plot_.ipynb <mvml_plot_.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+ `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/doc/tutorial/auto_examples/mvml_plot__codeobj.pickle b/doc/tutorial/auto_examples/mvml_plot__codeobj.pickle
new file mode 100644
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diff --git a/doc/tutorial/auto_examples/sg_execution_times.rst b/doc/tutorial/auto_examples/sg_execution_times.rst
new file mode 100644
index 0000000..299a794
--- /dev/null
+++ b/doc/tutorial/auto_examples/sg_execution_times.rst
@@ -0,0 +1,18 @@
+
+:orphan:
+
+.. _sphx_glr_tutorial_auto_examples_sg_execution_times:
+
+Computation times
+=================
+**00:02.336** total execution time for **tutorial_auto_examples** files:
+
++-----------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_auto_examples_mumbo_plot_3_views_3_classes.py` (``mumbo_plot_3_views_3_classes.py``) | 00:01.313 | 0.0 MB |
++-----------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_auto_examples_mumbo_plot_2_views_2_classes.py` (``mumbo_plot_2_views_2_classes.py``) | 00:01.023 | 0.0 MB |
++-----------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_auto_examples_cumbo_plot_3_views_3_classes.py` (``cumbo_plot_3_views_3_classes.py``) | 00:01.313 | 0.0 MB |
++-----------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_auto_examples_cumbo_plot_2_views_2_classes.py` (``cumbo_plot_2_views_2_classes.py``) | 00:01.023 | 0.0 MB |
++-----------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/doc/docmumbo/backreferences/multimodalboost.mumbo.MumboClassifier.decision_function.examples b/doc/tutorial/backreferences/metriclearning.datasets.base.examples
similarity index 100%
rename from doc/docmumbo/backreferences/multimodalboost.mumbo.MumboClassifier.decision_function.examples
rename to doc/tutorial/backreferences/metriclearning.datasets.base.examples
diff --git a/doc/docmumbo/backreferences/multimodalboost.mumbo.MumboClassifier.examples b/doc/tutorial/backreferences/metriclearning.datasets.base.load_dict.examples
similarity index 100%
rename from doc/docmumbo/backreferences/multimodalboost.mumbo.MumboClassifier.examples
rename to doc/tutorial/backreferences/metriclearning.datasets.base.load_dict.examples
diff --git a/doc/docmumbo/backreferences/multimodalboost.mumbo.MumboClassifier.fit.examples b/doc/tutorial/backreferences/metriclearning.datasets.base.load_npz_X_y.examples
similarity index 100%
rename from doc/docmumbo/backreferences/multimodalboost.mumbo.MumboClassifier.fit.examples
rename to doc/tutorial/backreferences/metriclearning.datasets.base.load_npz_X_y.examples
diff --git a/doc/docmumbo/backreferences/multimodalboost.mumbo.MumboClassifier.get_params.examples b/doc/tutorial/backreferences/metriclearning.datasets.base.save_dict.examples
similarity index 100%
rename from doc/docmumbo/backreferences/multimodalboost.mumbo.MumboClassifier.get_params.examples
rename to doc/tutorial/backreferences/metriclearning.datasets.base.save_dict.examples
diff --git a/doc/docmumbo/backreferences/multimodalboost.mumbo.MumboClassifier.predict.examples b/doc/tutorial/backreferences/metriclearning.datasets.base.save_npz_X_y.examples
similarity index 100%
rename from doc/docmumbo/backreferences/multimodalboost.mumbo.MumboClassifier.predict.examples
rename to doc/tutorial/backreferences/metriclearning.datasets.base.save_npz_X_y.examples
diff --git a/doc/docmumbo/backreferences/multimodalboost.mumbo.MumboClassifier.score.examples b/doc/tutorial/backreferences/metriclearning.datasets.data_sample.DataSample.data.examples
similarity index 100%
rename from doc/docmumbo/backreferences/multimodalboost.mumbo.MumboClassifier.score.examples
rename to doc/tutorial/backreferences/metriclearning.datasets.data_sample.DataSample.data.examples
diff --git a/doc/docmumbo/backreferences/multimodalboost.mumbo.MumboClassifier.set_params.examples b/doc/tutorial/backreferences/metriclearning.datasets.data_sample.DataSample.examples
similarity index 100%
rename from doc/docmumbo/backreferences/multimodalboost.mumbo.MumboClassifier.set_params.examples
rename to doc/tutorial/backreferences/metriclearning.datasets.data_sample.DataSample.examples
diff --git a/doc/docmumbo/backreferences/multimodalboost.mumbo.MumboClassifier.staged_decision_function.examples b/doc/tutorial/backreferences/metriclearning.datasets.data_sample.Metriclearn_array.add_view.examples
similarity index 100%
rename from doc/docmumbo/backreferences/multimodalboost.mumbo.MumboClassifier.staged_decision_function.examples
rename to doc/tutorial/backreferences/metriclearning.datasets.data_sample.Metriclearn_array.add_view.examples
diff --git a/doc/docmumbo/backreferences/multimodalboost.mumbo.MumboClassifier.staged_predict.examples b/doc/tutorial/backreferences/metriclearning.datasets.data_sample.Metriclearn_array.examples
similarity index 100%
rename from doc/docmumbo/backreferences/multimodalboost.mumbo.MumboClassifier.staged_predict.examples
rename to doc/tutorial/backreferences/metriclearning.datasets.data_sample.Metriclearn_array.examples
diff --git a/doc/docmumbo/backreferences/multimodalboost.mumbo.MumboClassifier.staged_score.examples b/doc/tutorial/backreferences/metriclearning.datasets.data_sample.Metriclearn_array.get_col.examples
similarity index 100%
rename from doc/docmumbo/backreferences/multimodalboost.mumbo.MumboClassifier.staged_score.examples
rename to doc/tutorial/backreferences/metriclearning.datasets.data_sample.Metriclearn_array.get_col.examples
diff --git a/doc/docmumbo/backreferences/multimodalboost.mumbo.examples b/doc/tutorial/backreferences/metriclearning.datasets.data_sample.Metriclearn_array.get_raw.examples
similarity index 100%
rename from doc/docmumbo/backreferences/multimodalboost.mumbo.examples
rename to doc/tutorial/backreferences/metriclearning.datasets.data_sample.Metriclearn_array.get_raw.examples
diff --git a/doc/tutorial/backreferences/metriclearning.datasets.data_sample.Metriclearn_array.get_view.examples b/doc/tutorial/backreferences/metriclearning.datasets.data_sample.Metriclearn_array.get_view.examples
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diff --git a/doc/tutorial/backreferences/multimodal.boosting.cumbo.MuCumboClassifier.staged_decision_function.examples b/doc/tutorial/backreferences/multimodal.boosting.cumbo.MuCumboClassifier.staged_decision_function.examples
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diff --git a/doc/tutorial/backreferences/multimodal.boosting.cumbo.MuCumboClassifier.staged_predict.examples b/doc/tutorial/backreferences/multimodal.boosting.cumbo.MuCumboClassifier.staged_predict.examples
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diff --git a/doc/tutorial/backreferences/multimodal.datasets.data_sample.MultiModalArray.T.examples b/doc/tutorial/backreferences/multimodal.datasets.data_sample.MultiModalArray.T.examples
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diff --git a/doc/tutorial/backreferences/multimodal.datasets.data_sample.MultiModalArray.all.examples b/doc/tutorial/backreferences/multimodal.datasets.data_sample.MultiModalArray.all.examples
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diff --git a/doc/tutorial/backreferences/multimodal.datasets.data_sample.MultiModalArray.argmax.examples b/doc/tutorial/backreferences/multimodal.datasets.data_sample.MultiModalArray.argmax.examples
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diff --git a/doc/docmumbo/credits.rst b/doc/tutorial/credits.rst
similarity index 53%
rename from doc/docmumbo/credits.rst
rename to doc/tutorial/credits.rst
index a4ff1ec..999b01e 100644
--- a/doc/docmumbo/credits.rst
+++ b/doc/tutorial/credits.rst
@@ -1,11 +1,11 @@
Credits
=======
-**multimodalboost** is developped by the
+**scikit-multimodallearnt** is developped by the
`development team <https://developpement.lis-lab.fr/>`_ of the
-`LIS <http://www.lis-lab.fr/>`_.
+`LIS <http://www.lis-lab.fr/>`_. supported by `ANR Lives <https://lives.lif.univ-mrs.fr/>`_
-If you use **multimodalboost** in a scientific publication, please cite the
+If you use **scikit-multimodallearn** in a scientific publication, please cite the
following paper::
@InProceedings{Koco:2011:BAM,
@@ -27,9 +27,33 @@ following paper::
supervised learning},
}
+
+ @InProceedings{Huu:2019:BAMCC,
+ author={Huusari, Riika, Kadri Hachem and Capponi, C{\'e}cile},
+ editor={},
+ title={Multi-view Metric Learning in Vector-valued Kernel Spaces},
+ booktitle={arXiv:1803.07821v1},
+ year={2018},
+ location={Athens, Greece},
+ publisher={},
+ address={},
+ pages={209--228},
+ numpages = {12}
+ isbn={978-3-642-23783-6}
+ url={https://link.springer.com/chapter/10.1007/978-3-642-23783-6_14},
+ keywords={boosting, classification, multiview learning,
+ merric learning, vector-valued, kernel spaces},
+ }
+
References
----------
+* Sokol Koço, Cécile Capponi,
+ `"Learning from Imbalanced Datasets with cross-view cooperation"`
+ Linking and mining heterogeneous an multi-view data, Unsupervised and
+ semi-supervised learning Series Editor M. Emre Celeri, pp 161-182, Springer
+
+
* Sokol Koço, Cécile Capponi,
`"A boosting approach to multiview classification with cooperation"
<https://link.springer.com/chapter/10.1007/978-3-642-23783-6_14>`_,
@@ -41,6 +65,11 @@ References
learning methods" <http://www.theses.fr/en/2013AIXM4101>`_,
PhD Thesis, Aix-Marseille Université, 2013.
+* Riikka Huusari, Hachem Kadri and Cécile Capponi,
+ "Multi-View Metric Learning in Vector-Valued Kernel Spaces"
+ in International Conference on Artificial Intelligence and Statistics (AISTATS) 2018
+
+
Copyright
---------
@@ -53,7 +82,5 @@ Copyright © 2017-2018 AMU, CNRS, UTLN
License
-------
-**multimodalboost** is free software: you can redistribute it and/or modify
-it under the terms of the **GNU Lesser General Public License** as published by
-the Free Software Foundation, either **version 3** of the License, or
-(at your option) any later version.
+**scikit-multimodallearn** is free software: you can redistribute it and/or modify
+it under the terms of the **New BSD License**
diff --git a/doc/doccumbo/install_devel.rst b/doc/tutorial/install_devel.rst
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rename from doc/doccumbo/install_devel.rst
rename to doc/tutorial/install_devel.rst
index eb63b90..215d921 100644
--- a/doc/doccumbo/install_devel.rst
+++ b/doc/tutorial/install_devel.rst
@@ -4,37 +4,37 @@ Installation and development
Dependencies
------------
-**multiconfusion** works with **Python 3.5 or later**.
+**scikit-multimodallearn** works with **Python 3.5 or later**.
-**multiconfusion** depends on **scikit-learn** (version >= 0.19).
+**scikit-multimodallearn** depends on **scikit-learn** (version >= 0.19) and **cvxopt**.
Optionally, **matplotlib** is required when running the examples.
Installation
------------
-**multiconfusion** is
-`available on PyPI <https://pypi.org/project/multimodalboost/>`_
+**scikit-multimodallearn* is
+`available on PyPI <https://pypi.org/project/scikit-multimodallearn/>`_
and can be installed using **pip**::
- pip install multiconfusion
+ pip install multimodalboost
If you prefer to install directly from the **source code**, clone the **Git**
repository of the project and run the **setup.py** file with the following
commands::
- git clone git@gitlab.lis-lab.fr:dev/multiconfusion.git
+ git clone git@gitlab.lis-lab.fr:dev/scikit-multimodallearn.git
cd multimodalboost
python setup.py install
or alternatively use **pip**::
- pip install git+https://gitlab.lis-lab.fr/dev/multiconfusion.git
+ pip install git+https://gitlab.lis-lab.fr/dev/scikit-multimodallearn.git
Development
-----------
-The development of multimodalboost follows the guidelines provided by the
+The development of scikit-multimodallearn follows the guidelines provided by the
scikit-learn community.
Refer to the `Developer's Guide <http://scikit-learn.org/stable/developers>`_
@@ -45,7 +45,7 @@ Source code
You can get the **source code** from the **Git** repository of the project::
- git clone git@gitlab.lis-lab.fr:dev/multiconfusion.git
+ git clone git@gitlab.lis-lab.fr:dev/scikit-multimodallearn.git
Testing
@@ -53,7 +53,7 @@ Testing
**pytest** and **pytest-cov** are required to run the **test suite** with::
- pytest-3
+ pytest
A code coverage report is displayed in the terminal when running the tests.
An HTML version of the report is also stored in the directory **htmlcov**.
@@ -66,10 +66,4 @@ The generation of the documentation requires **sphinx**, **sphinx-gallery**,
python setup.py build_sphinx
-ou::
-
- cd doc
- sphinx-build -b html . ./build
-
-
The resulting files are stored in the directory **build/sphinx/html**.
diff --git a/examples/README.txt b/examples/README.txt
index f749137..192d2d5 100644
--- a/examples/README.txt
+++ b/examples/README.txt
@@ -1,11 +1,7 @@
.. _examples:
-Examples
-========
+Multimodal Examples
+===================
-MuMBo Examples
---------------
-The following toy examples illustrate how the MuMBo algorithm exploits
-cooperation between views for classification.
diff --git a/examples/cumbo/README.txt b/examples/cumbo/README.txt
new file mode 100644
index 0000000..41a7732
--- /dev/null
+++ b/examples/cumbo/README.txt
@@ -0,0 +1,11 @@
+.. _examples:
+
+Examples
+========
+
+MuCuMBo Examples
+----------------
+
+The following toy examples illustrate how the MuCuMBo algorithm exploits
+cooperation between views for classification.
+
diff --git a/examples/cumbo/plot_cumbo_2_views_2_classes.py b/examples/cumbo/plot_cumbo_2_views_2_classes.py
new file mode 100644
index 0000000..c669f69
--- /dev/null
+++ b/examples/cumbo/plot_cumbo_2_views_2_classes.py
@@ -0,0 +1,127 @@
+# -*- coding: utf-8 -*-
+"""
+==================================
+MuCombo 2 views, 2 classes example
+==================================
+
+In this toy example, we generate data from two classes, split between two
+two-dimensional views.
+
+For each view, the data are generated so that half of the points of each class
+are well separated in the plane, while the other half of the points are not
+separated and placed in the same area. We also insure that the points that are
+not separated in one view are well separated in the other view.
+
+Thus, in the figure representing the data, the points represented by crosses
+(x) are well separated in view 0 while they are not separated in view 1, while
+the points represented by dots (.) are well separated in view 1 while they are
+not separated in view 0. In this figure, the blue symbols represent points
+of class 0, while red symbols represent points of class 1.
+
+The MuCuMBo algorithm take adavantage of the complementarity of the two views to
+rightly classify the points.
+"""
+
+import numpy as np
+from multimodal.boosting.cumbo import MuCumboClassifier
+from matplotlib import pyplot as plt
+
+
+def generate_data(n_samples, lim):
+ """Generate random data in a rectangle"""
+ lim = np.array(lim)
+ n_features = lim.shape[0]
+ data = np.random.random((n_samples, n_features))
+ data = (lim[:, 1]-lim[:, 0]) * data + lim[:, 0]
+ return data
+
+
+seed = 12
+np.random.seed(seed)
+
+n_samples = 100
+
+view_0 = np.concatenate((generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [1., 2.]])))
+
+view_1 = np.concatenate((generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [1., 2.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]])))
+
+X = np.concatenate((view_0, view_1), axis=1)
+
+y = np.zeros(4*n_samples, dtype=np.int64)
+y[2*n_samples:] = 1
+
+views_ind = np.array([0, 2, 4])
+
+n_estimators = 3
+clf = MuCumboClassifier(n_estimators=n_estimators)
+clf.fit(X, y, views_ind)
+
+print('\nAfter 3 iterations, the MuCuMBo classifier reaches exact '
+ 'classification for the\nlearning samples:')
+for ind, score in enumerate(clf.staged_score(X, y)):
+ print(' - iteration {}, score: {}'.format(ind + 1, score))
+
+
+print('\nThe resulting MuCuMBo classifier uses three sub-classifiers that are '
+ 'wheighted\nusing the following weights:\n'
+ ' estimator weights: {}'.format(clf.estimator_weights_alpha_))
+
+# print('\nThe two first sub-classifiers use the data of view 0 to compute '
+# 'their\nclassification results, while the third one uses the data of '
+# 'view 1:\n'
+# ' best views: {}'. format(clf.best_views_))
+
+print('\nThe first figure displays the data, splitting the representation '
+ 'between the\ntwo views.')
+
+fig = plt.figure(figsize=(10., 8.))
+fig.suptitle('Representation of the data', size=16)
+for ind_view in range(2):
+ ax = plt.subplot(2, 1, ind_view + 1)
+ ax.set_title('View {}'.format(ind_view))
+ ind_feature = ind_view * 2
+ styles = ('.b', 'xb', '.r', 'xr')
+ labels = ('non-separated', 'separated')
+ for ind in range(4):
+ ind_class = ind // 2
+ label = labels[(ind + ind_view) % 2]
+ ax.plot(X[n_samples*ind:n_samples*(ind+1), ind_feature],
+ X[n_samples*ind:n_samples*(ind+1), ind_feature + 1],
+ styles[ind],
+ label='Class {} ({})'.format(ind_class, label))
+ ax.legend()
+
+print('\nThe second figure displays the classification results for the '
+ 'sub-classifiers\non the learning sample data.\n')
+
+styles = ('.b', '.r')
+# fig = plt.figure(figsize=(12., 7.))
+# fig.suptitle('Classification results on the learning data for the '
+# 'sub-classifiers', size=16)
+# for ind_estimator in range(n_estimators):
+# best_view = clf.best_views_[ind_estimator]
+# y_pred = clf.estimators_[ind_estimator].predict(
+# X[:, 2*best_view:2*best_view+2])
+# background_color = (1.0, 1.0, 0.9)
+# for ind_view in range(2):
+# ax = plt.subplot(2, 3, ind_estimator + 3*ind_view + 1)
+# if ind_view == best_view:
+# ax.set_facecolor(background_color)
+# ax.set_title(
+# 'Sub-classifier {} - View {}'.format(ind_estimator, ind_view))
+# ind_feature = ind_view * 2
+# for ind_class in range(2):
+# ind_samples = (y_pred == ind_class)
+# ax.plot(X[ind_samples, ind_feature],
+# X[ind_samples, ind_feature + 1],
+# styles[ind_class],
+# label='Class {}'.format(ind_class))
+# ax.legend(title='Predicted class:')
+
+plt.show()
diff --git a/examples/cumbo/plot_cumbo_3_views_3_classes.py b/examples/cumbo/plot_cumbo_3_views_3_classes.py
new file mode 100644
index 0000000..058b2dc
--- /dev/null
+++ b/examples/cumbo/plot_cumbo_3_views_3_classes.py
@@ -0,0 +1,121 @@
+# -*- coding: utf-8 -*-
+"""
+==================================
+MuCumbo 3 views, 3 classes example
+==================================
+
+In this toy example, we generate data from three classes, split between three
+two-dimensional views.
+
+For each view, the data are generated so that the points for two classes are
+well seperated, while the points for the third class are not seperated with
+the two other classes. That means that, taken separately, none of the single
+views allows for a good classification of the data.
+
+Nevertheless, the MuCuMbo algorithm take adavantage of the complementarity of
+the views to rightly classify the points.
+"""
+
+import numpy as np
+from multimodal.boosting.cumbo import MuCumboClassifier
+from matplotlib import pyplot as plt
+
+
+def generate_data(n_samples, lim):
+ """Generate random data in a rectangle"""
+ lim = np.array(lim)
+ n_features = lim.shape[0]
+ data = np.random.random((n_samples, n_features))
+ data = (lim[:, 1]-lim[:, 0]) * data + lim[:, 0]
+ return data
+
+
+seed = 12
+np.random.seed(seed)
+
+n_samples = 300
+
+view_0 = np.concatenate((generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 2.], [0., 1.]])))
+
+view_1 = np.concatenate((generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]])))
+
+view_2 = np.concatenate((generate_data(n_samples, [[0., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[1., 2.], [0., 1.]])))
+
+X = np.concatenate((view_0, view_1, view_2), axis=1)
+
+y = np.zeros(3*n_samples, dtype=np.int64)
+y[n_samples:2*n_samples] = 1
+y[2*n_samples:] = 2
+
+views_ind = np.array([0, 2, 4, 6])
+
+n_estimators = 4
+clf = MuCumboClassifier(n_estimators=n_estimators)
+clf.fit(X, y, views_ind)
+
+print('\nAfter 4 iterations, the MuCuMBo classifier reaches exact '
+ 'classification for the\nlearning samples:')
+for ind, score in enumerate(clf.staged_score(X, y)):
+ print(' - iteration {}, score: {}'.format(ind + 1, score))
+
+print('\nThe resulting MuCuMBo classifier uses four sub-classifiers that are '
+ 'wheighted\nusing the following weights:\n'
+ ' estimator weights alpha: {}'.format(clf.estimator_weights_alpha_))
+
+# print('\nThe first sub-classifier uses the data of view 0 to compute '
+# 'its classification\nresults, the second and third sub-classifiers use '
+# 'the data of view 1, while the\nfourth one uses the data of '
+# 'view 2:\n'
+# ' best views: {}'. format(clf.best_views_))
+
+print('\nThe first figure displays the data, splitting the representation '
+ 'between the\nthree views.')
+
+styles = ('.b', '.r', '.g')
+fig = plt.figure(figsize=(12., 11.))
+fig.suptitle('Representation of the data', size=16)
+for ind_view in range(3):
+ ax = plt.subplot(3, 1, ind_view + 1)
+ ax.set_title('View {}'.format(ind_view))
+ ind_feature = ind_view * 2
+ for ind_class in range(3):
+ ind_samples = (y == ind_class)
+ ax.plot(X[ind_samples, ind_feature],
+ X[ind_samples, ind_feature + 1],
+ styles[ind_class],
+ label='Class {}'.format(ind_class))
+ ax.legend(loc='upper left', framealpha=0.9)
+
+print('\nThe second figure displays the classification results for the '
+ 'sub-classifiers\non the learning sample data.\n')
+
+# fig = plt.figure(figsize=(14., 11.))
+# fig.suptitle('Classification results on the learning data for the '
+# 'sub-classifiers', size=16)
+# for ind_estimator in range(n_estimators):
+# best_view = clf.best_views_[ind_estimator]
+# y_pred = clf.estimators_[ind_estimator].predict(
+# X[:, 2*best_view:2*best_view+2])
+# background_color = (1.0, 1.0, 0.9)
+# for ind_view in range(3):
+# ax = plt.subplot(3, 4, ind_estimator + 4*ind_view + 1)
+# if ind_view == best_view:
+# ax.set_facecolor(background_color)
+# ax.set_title(
+# 'Sub-classifier {} - View {}'.format(ind_estimator, ind_view))
+# ind_feature = ind_view * 2
+# for ind_class in range(3):
+# ind_samples = (y_pred == ind_class)
+# ax.plot(X[ind_samples, ind_feature],
+# X[ind_samples, ind_feature + 1],
+# styles[ind_class],
+# label='Class {}'.format(ind_class))
+# ax.legend(title='Predicted class:', loc='upper left', framealpha=0.9)
+
+plt.show()
diff --git a/examples/mumbo/README.txt b/examples/mumbo/README.txt
new file mode 100644
index 0000000..f749137
--- /dev/null
+++ b/examples/mumbo/README.txt
@@ -0,0 +1,11 @@
+.. _examples:
+
+Examples
+========
+
+MuMBo Examples
+--------------
+
+The following toy examples illustrate how the MuMBo algorithm exploits
+cooperation between views for classification.
+
diff --git a/examples/mumbo/plot_mumbo_2_views_2_classes.py b/examples/mumbo/plot_mumbo_2_views_2_classes.py
new file mode 100644
index 0000000..91f0dc7
--- /dev/null
+++ b/examples/mumbo/plot_mumbo_2_views_2_classes.py
@@ -0,0 +1,127 @@
+# -*- coding: utf-8 -*-
+"""
+================================
+Mumbo 2 views, 2 classes example
+================================
+
+In this toy example, we generate data from two classes, split between two
+two-dimensional views.
+
+For each view, the data are generated so that half of the points of each class
+are well separated in the plane, while the other half of the points are not
+separated and placed in the same area. We also insure that the points that are
+not separated in one view are well separated in the other view.
+
+Thus, in the figure representing the data, the points represented by crosses
+(x) are well separated in view 0 while they are not separated in view 1, while
+the points represented by dots (.) are well separated in view 1 while they are
+not separated in view 0. In this figure, the blue symbols represent points
+of class 0, while red symbols represent points of class 1.
+
+The MuMBo algorithm take adavantage of the complementarity of the two views to
+rightly classify the points.
+"""
+
+import numpy as np
+from multimodal.boosting.mumbo import MumboClassifier
+from matplotlib import pyplot as plt
+
+
+def generate_data(n_samples, lim):
+ """Generate random data in a rectangle"""
+ lim = np.array(lim)
+ n_features = lim.shape[0]
+ data = np.random.random((n_samples, n_features))
+ data = (lim[:, 1]-lim[:, 0]) * data + lim[:, 0]
+ return data
+
+
+seed = 12
+np.random.seed(seed)
+
+n_samples = 100
+
+view_0 = np.concatenate((generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [1., 2.]])))
+
+view_1 = np.concatenate((generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [1., 2.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]])))
+
+X = np.concatenate((view_0, view_1), axis=1)
+
+y = np.zeros(4*n_samples, dtype=np.int64)
+y[2*n_samples:] = 1
+
+views_ind = np.array([0, 2, 4])
+
+n_estimators = 3
+clf = MumboClassifier(n_estimators=n_estimators)
+clf.fit(X, y, views_ind)
+
+print('\nAfter 3 iterations, the MuMBo classifier reaches exact '
+ 'classification for the\nlearning samples:')
+for ind, score in enumerate(clf.staged_score(X, y)):
+ print(' - iteration {}, score: {}'.format(ind + 1, score))
+
+
+print('\nThe resulting MuMBo classifier uses three sub-classifiers that are '
+ 'wheighted\nusing the following weights:\n'
+ ' estimator weights: {}'.format(clf.estimator_weights_))
+
+print('\nThe two first sub-classifiers use the data of view 0 to compute '
+ 'their\nclassification results, while the third one uses the data of '
+ 'view 1:\n'
+ ' best views: {}'. format(clf.best_views_))
+
+print('\nThe first figure displays the data, splitting the representation '
+ 'between the\ntwo views.')
+
+fig = plt.figure(figsize=(10., 8.))
+fig.suptitle('Representation of the data', size=16)
+for ind_view in range(2):
+ ax = plt.subplot(2, 1, ind_view + 1)
+ ax.set_title('View {}'.format(ind_view))
+ ind_feature = ind_view * 2
+ styles = ('.b', 'xb', '.r', 'xr')
+ labels = ('non-separated', 'separated')
+ for ind in range(4):
+ ind_class = ind // 2
+ label = labels[(ind + ind_view) % 2]
+ ax.plot(X[n_samples*ind:n_samples*(ind+1), ind_feature],
+ X[n_samples*ind:n_samples*(ind+1), ind_feature + 1],
+ styles[ind],
+ label='Class {} ({})'.format(ind_class, label))
+ ax.legend()
+
+print('\nThe second figure displays the classification results for the '
+ 'sub-classifiers\non the learning sample data.\n')
+
+styles = ('.b', '.r')
+fig = plt.figure(figsize=(12., 7.))
+fig.suptitle('Classification results on the learning data for the '
+ 'sub-classifiers', size=16)
+for ind_estimator in range(n_estimators):
+ best_view = clf.best_views_[ind_estimator]
+ y_pred = clf.estimators_[ind_estimator].predict(
+ X[:, 2*best_view:2*best_view+2])
+ background_color = (1.0, 1.0, 0.9)
+ for ind_view in range(2):
+ ax = plt.subplot(2, 3, ind_estimator + 3*ind_view + 1)
+ if ind_view == best_view:
+ ax.set_facecolor(background_color)
+ ax.set_title(
+ 'Sub-classifier {} - View {}'.format(ind_estimator, ind_view))
+ ind_feature = ind_view * 2
+ for ind_class in range(2):
+ ind_samples = (y_pred == ind_class)
+ ax.plot(X[ind_samples, ind_feature],
+ X[ind_samples, ind_feature + 1],
+ styles[ind_class],
+ label='Class {}'.format(ind_class))
+ ax.legend(title='Predicted class:')
+
+plt.show()
diff --git a/examples/mumbo/plot_mumbo_3_views_3_classes.py b/examples/mumbo/plot_mumbo_3_views_3_classes.py
new file mode 100644
index 0000000..62cd311
--- /dev/null
+++ b/examples/mumbo/plot_mumbo_3_views_3_classes.py
@@ -0,0 +1,121 @@
+# -*- coding: utf-8 -*-
+"""
+================================
+Mumbo 3 views, 3 classes example
+================================
+
+In this toy example, we generate data from three classes, split between three
+two-dimensional views.
+
+For each view, the data are generated so that the points for two classes are
+well seperated, while the points for the third class are not seperated with
+the two other classes. That means that, taken separately, none of the single
+views allows for a good classification of the data.
+
+Nevertheless, the MuMBo algorithm take adavantage of the complementarity of
+the views to rightly classify the points.
+"""
+
+import numpy as np
+from multimodal.boosting.mumbo import MumboClassifier
+from matplotlib import pyplot as plt
+
+
+def generate_data(n_samples, lim):
+ """Generate random data in a rectangle"""
+ lim = np.array(lim)
+ n_features = lim.shape[0]
+ data = np.random.random((n_samples, n_features))
+ data = (lim[:, 1]-lim[:, 0]) * data + lim[:, 0]
+ return data
+
+
+seed = 12
+np.random.seed(seed)
+
+n_samples = 300
+
+view_0 = np.concatenate((generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 2.], [0., 1.]])))
+
+view_1 = np.concatenate((generate_data(n_samples, [[1., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]])))
+
+view_2 = np.concatenate((generate_data(n_samples, [[0., 2.], [0., 1.]]),
+ generate_data(n_samples, [[0., 1.], [0., 1.]]),
+ generate_data(n_samples, [[1., 2.], [0., 1.]])))
+
+X = np.concatenate((view_0, view_1, view_2), axis=1)
+
+y = np.zeros(3*n_samples, dtype=np.int64)
+y[n_samples:2*n_samples] = 1
+y[2*n_samples:] = 2
+
+views_ind = np.array([0, 2, 4, 6])
+
+n_estimators = 4
+clf = MumboClassifier(n_estimators=n_estimators)
+clf.fit(X, y, views_ind)
+
+print('\nAfter 4 iterations, the MuMBo classifier reaches exact '
+ 'classification for the\nlearning samples:')
+for ind, score in enumerate(clf.staged_score(X, y)):
+ print(' - iteration {}, score: {}'.format(ind + 1, score))
+
+print('\nThe resulting MuMBo classifier uses four sub-classifiers that are '
+ 'wheighted\nusing the following weights:\n'
+ ' estimator weights: {}'.format(clf.estimator_weights_))
+
+print('\nThe first sub-classifier uses the data of view 0 to compute '
+ 'its classification\nresults, the second and third sub-classifiers use '
+ 'the data of view 1, while the\nfourth one uses the data of '
+ 'view 2:\n'
+ ' best views: {}'. format(clf.best_views_))
+
+print('\nThe first figure displays the data, splitting the representation '
+ 'between the\nthree views.')
+
+styles = ('.b', '.r', '.g')
+fig = plt.figure(figsize=(12., 11.))
+fig.suptitle('Representation of the data', size=16)
+for ind_view in range(3):
+ ax = plt.subplot(3, 1, ind_view + 1)
+ ax.set_title('View {}'.format(ind_view))
+ ind_feature = ind_view * 2
+ for ind_class in range(3):
+ ind_samples = (y == ind_class)
+ ax.plot(X[ind_samples, ind_feature],
+ X[ind_samples, ind_feature + 1],
+ styles[ind_class],
+ label='Class {}'.format(ind_class))
+ ax.legend(loc='upper left', framealpha=0.9)
+
+print('\nThe second figure displays the classification results for the '
+ 'sub-classifiers\non the learning sample data.\n')
+
+fig = plt.figure(figsize=(14., 11.))
+fig.suptitle('Classification results on the learning data for the '
+ 'sub-classifiers', size=16)
+for ind_estimator in range(n_estimators):
+ best_view = clf.best_views_[ind_estimator]
+ y_pred = clf.estimators_[ind_estimator].predict(
+ X[:, 2*best_view:2*best_view+2])
+ background_color = (1.0, 1.0, 0.9)
+ for ind_view in range(3):
+ ax = plt.subplot(3, 4, ind_estimator + 4*ind_view + 1)
+ if ind_view == best_view:
+ ax.set_facecolor(background_color)
+ ax.set_title(
+ 'Sub-classifier {} - View {}'.format(ind_estimator, ind_view))
+ ind_feature = ind_view * 2
+ for ind_class in range(3):
+ ind_samples = (y_pred == ind_class)
+ ax.plot(X[ind_samples, ind_feature],
+ X[ind_samples, ind_feature + 1],
+ styles[ind_class],
+ label='Class {}'.format(ind_class))
+ ax.legend(title='Predicted class:', loc='upper left', framealpha=0.9)
+
+plt.show()
diff --git a/examples/mvml/README.txt b/examples/mvml/README.txt
new file mode 100644
index 0000000..b310f9f
--- /dev/null
+++ b/examples/mvml/README.txt
@@ -0,0 +1,10 @@
+.. _examples:
+
+Examples
+========
+
+MVML
+----
+
+The following toy examples illustrate how the MVML algorithm
+
diff --git a/examples/mvml/plot_mvml_.py b/examples/mvml/plot_mvml_.py
new file mode 100644
index 0000000..a449a7c
--- /dev/null
+++ b/examples/mvml/plot_mvml_.py
@@ -0,0 +1,206 @@
+# -*- coding: utf-8 -*-
+"""
+====
+MVML
+====
+Demonstration on how MVML (in file mvml.py) is intended to be used with very simple simulated dataset
+
+Demonstration uses scikit-learn for retrieving datasets and for calculating rbf kernel function, see
+http://scikit-learn.org/stable/
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn import datasets
+from sklearn.metrics import accuracy_score
+from sklearn.metrics.pairwise import rbf_kernel
+from multimodal.kernels.mvml import MVML
+from multimodal.datasets.data_sample import DataSample
+from multimodal.tests.datasets.get_dataset_path import get_dataset_path
+
+
+np.random.seed(4)
+
+# =========== create a simple dataset ============
+
+n_tot = 200
+half = int(n_tot/2)
+n_tr = 120
+
+# create a bit more data than needed so that we can take "half" amount of samples for each class
+X0, y0 = datasets.make_moons(n_samples=n_tot+2, noise=0.3, shuffle=False)
+X1, y1 = datasets.make_circles(n_samples=n_tot+2, noise=0.1, shuffle=False)
+
+# make multi-view correspondence (select equal number of samples for both classes and order the data same way
+# in both views)
+
+yinds0 = np.append(np.where(y0 == 0)[0][0:half], np.where(y0 == 1)[0][0:half])
+yinds1 = np.append(np.where(y1 == 0)[0][0:half], np.where(y1 == 1)[0][0:half])
+
+X0 = X0[yinds0, :]
+X1 = X1[yinds1, :]
+Y = np.append(np.zeros(half)-1, np.ones(half)) # labels -1 and 1
+
+# show data
+# =========== create a simple dataset ============
+
+n_tot = 200
+half = int(n_tot/2)
+n_tr = 120
+
+# create a bit more data than needed so that we can take "half" amount of samples for each class
+X0, y0 = datasets.make_moons(n_samples=n_tot+2, noise=0.3, shuffle=False)
+X1, y1 = datasets.make_circles(n_samples=n_tot+2, noise=0.1, shuffle=False)
+
+# make multi-view correspondence (select equal number of samples for both classes and order the data same way
+# in both views)
+
+yinds0 = np.append(np.where(y0 == 0)[0][0:half], np.where(y0 == 1)[0][0:half])
+yinds1 = np.append(np.where(y1 == 0)[0][0:half], np.where(y1 == 1)[0][0:half])
+
+X0 = X0[yinds0, :]
+X1 = X1[yinds1, :]
+Y = np.append(np.zeros(half)-1, np.ones(half)) # labels -1 and 1
+
+# show data
+plt.figure(figsize=(10., 8.))
+plt.subplot(121)
+plt.scatter(X0[:, 0], X0[:, 1], c=Y)
+plt.title("all data, view 1")
+plt.subplot(122)
+plt.scatter(X1[:, 0], X1[:, 1], c=Y)
+plt.title("all data, view 2")
+plt.show()
+
+# shuffle
+order = np.random.permutation(n_tot)
+X0 = X0[order, :]
+X1 = X1[order, :]
+Y = Y[order]
+##################################
+# make kernel dictionaries
+#################################
+kernel_dict = {}
+test_kernel_dict = {}
+kernel_dict[0] = rbf_kernel(X0[0:n_tr, :])
+kernel_dict[1] = rbf_kernel(X1[0:n_tr, :])
+test_kernel_dict[0] = rbf_kernel(X0[n_tr:n_tot, :], X0[0:n_tr, :])
+test_kernel_dict[1] = rbf_kernel(X1[n_tr:n_tot, :], X1[0:n_tr, :])
+x_dict = {}
+x_dict[0] = X0[0:n_tr, :]
+x_dict[1] = X1[0:n_tr, :]
+test_x_dict = {}
+test_x_dict[0] = X0[n_tr:n_tot, :]
+test_x_dict[1] = X1[n_tr:n_tot, :]
+# d= DataSample(kernel_dict)
+# a = d.data
+#
+# =========== use MVML in classifying the data ============
+# kernel precomputed
+# demo on how the code is intended to be used; parameters are not cross-validated, just picked some
+# # with approximation
+# # default: learn A, don't learn w (learn_A=1, learn_w=0)
+mvml = MVML(lmbda=0.1, eta=1, nystrom_param=0.2, kernel='precomputed')
+mvml.fit(kernel_dict, Y[0:n_tr])
+
+
+#
+
+pred1 = np.sign(mvml.predict(test_kernel_dict) ) # take sign for classification result
+#
+# without approximation
+mvml2 = MVML(lmbda=0.1, eta=1, nystrom_param=1, kernel='precomputed') # without approximation
+mvml2.fit(kernel_dict, Y[0:n_tr])
+pred2 = np.sign(mvml2.predict(test_kernel_dict))
+#
+# use MVML_Cov, don't learn w
+mvml3 = MVML(lmbda=0.1, eta=1,learn_A=3, nystrom_param=1, kernel='precomputed')
+mvml3.fit(kernel_dict, Y[0:n_tr])
+pred3 = np.sign(mvml3.predict(test_kernel_dict))
+#
+# use MVML_I, don't learn w
+mvml4 = MVML(lmbda=0.1, eta=1,learn_A=4, nystrom_param=1, kernel='precomputed')
+mvml4.fit(kernel_dict, Y[0:n_tr])
+pred4 = np.sign(mvml4.predict(test_kernel_dict))
+#
+# use kernel rbf equivalent to case 1
+mvml5 = MVML(lmbda=0.1, eta=1, nystrom_param=0.2, kernel='rbf')
+mvml5.fit(x_dict, Y[0:n_tr])
+pred5 = np.sign(mvml5.predict(test_x_dict))
+#
+#
+# # =========== show results ============
+#
+# # accuracies
+acc1 = accuracy_score(Y[n_tr:n_tot], pred1)
+acc2 = accuracy_score(Y[n_tr:n_tot], pred2)
+acc3 = accuracy_score(Y[n_tr:n_tot], pred3)
+acc4 = accuracy_score(Y[n_tr:n_tot], pred4)
+acc5 = accuracy_score(Y[n_tr:n_tot], pred5)
+#
+# # display obtained accuracies
+#
+print("MVML: ", acc1)
+print("MVMLsparse: ", acc2)
+print("MVML_Cov: ", acc3)
+print("MVML_I: ", acc4)
+print("MVML_rbf: ", acc5)
+#
+#
+# # plot data and some classification results
+#
+plt.figure(2, figsize=(10., 8.))
+plt.subplot(341)
+plt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=Y[n_tr:n_tot])
+plt.title("orig. view 1")
+plt.subplot(342)
+plt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=Y[n_tr:n_tot])
+plt.title("orig. view 2")
+#
+pred1[np.where(pred1[:, 0] != Y[n_tr:n_tot])] = 0
+pred1 = pred1.reshape((pred1.shape[0]))
+plt.subplot(343)
+plt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred1)
+plt.title("MVML view 1")
+plt.subplot(344)
+plt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred1)
+plt.title("MVML view 2")
+#
+pred2[np.where(pred2[:, 0] != Y[n_tr:n_tot])] = 0
+pred2 = pred2.reshape((pred2.shape[0]))
+plt.subplot(345)
+plt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred2)
+plt.title("MVMLsparse view 1")
+plt.subplot(346)
+plt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred2)
+plt.title("MVMLsparse view 2")
+#
+pred3[np.where(pred3[:, 0] != Y[n_tr:n_tot])] = 0
+pred3 = pred3.reshape((pred3.shape[0]))
+#
+plt.subplot(347)
+plt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred3)
+plt.title("MVML_Cov view 1")
+plt.subplot(348)
+plt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred3)
+plt.title("MVML_Cov view 2")
+#
+pred4[np.where(pred4[:, 0] != Y[n_tr:n_tot])] = 0
+pred4 = pred4.reshape((pred4.shape[0]))
+plt.subplot(349)
+plt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred4)
+plt.title("MVML_I view 1")
+plt.subplot(3,4,10)
+plt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred4)
+plt.title("MVML_I view 2")
+#
+pred5[np.where(pred5[:, 0] != Y[n_tr:n_tot])] = 0
+pred5 = pred5.reshape((pred5.shape[0]))
+plt.subplot(3,4,11)
+plt.scatter(X0[n_tr:n_tot, 0], X0[n_tr:n_tot, 1], c=pred5)
+plt.title("MVML_rbf_kernel view 1")
+plt.subplot(3,4,12)
+plt.scatter(X1[n_tr:n_tot, 0], X1[n_tr:n_tot, 1], c=pred5)
+plt.title("MVML_rbf_kernel view 2")
+#
+plt.show()
diff --git a/multimodal/boosting/cumbo.py b/multimodal/boosting/cumbo.py
index 6753e71..0d8d42c 100644
--- a/multimodal/boosting/cumbo.py
+++ b/multimodal/boosting/cumbo.py
@@ -74,10 +74,10 @@ class MuCumboClassifier(BaseEnsemble, ClassifierMixin, UBoosting):
Empirical loss for each iteration.
- best_views\_ : numpy.ndarray of integers, shape = (len(estimators\_),)
+ best\_views\_ : numpy.ndarray of integers, shape = (len(estimators\_),)
Indices of the best view for each estimator in the boosted ensemble.
- n_yi : numpy ndarray of int contains number of train sample for each classe shape (n_classes,)
+ n_yi\_ : numpy ndarray of int contains number of train sample for each classe shape (n_classes,)
Examples
--------
@@ -247,10 +247,10 @@ class MuCumboClassifier(BaseEnsemble, ClassifierMixin, UBoosting):
predicted_classes[np.arange(n_views), :]]
cost = np.exp(
label_score
- - label_score[:, np.arange(n_samples), y][:, :, np.newaxis]) / self.n_yi[np.newaxis, np.newaxis, :]
+ - label_score[:, np.arange(n_samples), y][:, :, np.newaxis]) / self.n_yi_[np.newaxis, np.newaxis, :]
score_function_dif = np.exp(
label_score
- - label_score[:, np.arange(n_samples), y][:, :, np.newaxis]) / self.n_yi[np.newaxis, np.newaxis, :]
+ - label_score[:, np.arange(n_samples), y][:, :, np.newaxis]) / self.n_yi_[np.newaxis, np.newaxis, :]
cost[:, np.arange(n_samples), y] -= np.sum(cost, axis=2)
return (cost, label_score, score_function_dif)
@@ -304,7 +304,7 @@ class MuCumboClassifier(BaseEnsemble, ClassifierMixin, UBoosting):
solvers.options['show_progress'] = False
n_view = self.n_views_
m = self.n_classes_
- coef = 1.0/np.tile(self.n_yi, self.n_views_).squeeze() * score_function_dif_Tminus1
+ coef = 1.0/np.tile(self.n_yi_, self.n_views_).squeeze() * score_function_dif_Tminus1
zeta_v = np.repeat(alphas, self.n_classes_) * indicate_vue * delta_vue
zeta_v_yi = np.repeat(alphas, self.n_classes_) * indicate_vue_yi * delta_vue
zeta = zeta_v + zeta_v_yi
@@ -325,8 +325,8 @@ class MuCumboClassifier(BaseEnsemble, ClassifierMixin, UBoosting):
try:
solver = solvers.cp(F, A=A, b=b, G=G, h=h, dim={'l':2*n_view*m})['x']
except ValueError or ArithmeticError or OverflowError as e:
- norm = np.sum(1.0/self.n_yi)
- yi_norm = self.n_yi * (norm )
+ norm = np.sum(1.0/self.n_yi_)
+ yi_norm = self.n_yi_ * (norm )
solver = matrix(1.0/np.tile(yi_norm, n_view).squeeze(), (n_view * m, 1))
print("Value Error on the evaluation on beta coefficient %s "% e)
return solver
@@ -349,7 +349,11 @@ class MuCumboClassifier(BaseEnsemble, ClassifierMixin, UBoosting):
Parameters
----------
- X : {array-like, sparse matrix}, shape = (n_samples, n_features)
+ X : dict dictionary with all views
+ or
+ `MultiModalData` , `MultiModalArray`, `MultiModalSparseArray`
+ or
+ {array-like, sparse matrix}, shape = (n_samples, n_features)
Training multi-view input samples.
Sparse matrix can be CSC, CSR, COO, DOK, or LIL.
COO, DOK and LIL are converted to CSR.
@@ -387,8 +391,7 @@ class MuCumboClassifier(BaseEnsemble, ClassifierMixin, UBoosting):
ValueError where `X` and `view_ind` are not compatibles
"""
- warnings.filterwarnings("ignore")
- self.X_ = self._global_X_transform(X, views_ind=views_ind)
+ warnings.filterwarnings("ignore", category=RuntimeWarning)
if (self.base_estimator is None or
isinstance(self.base_estimator, (BaseDecisionTree,
BaseForest))):
@@ -398,8 +401,7 @@ class MuCumboClassifier(BaseEnsemble, ClassifierMixin, UBoosting):
dtype = None
accept_sparse = ['csr', 'csc']
-
-
+ self.X_ = self._global_X_transform(X, views_ind=views_ind)
views_ind_, n_views = self.X_._validate_views_ind(self.X_.views_ind,
self.X_.shape[1])
check_X_y(self.X_, y)
@@ -410,7 +412,7 @@ class MuCumboClassifier(BaseEnsemble, ClassifierMixin, UBoosting):
self.classes_, y = np.unique(y, return_inverse=True)
self.n_classes_ = len(self.classes_)
self.n_views_ = n_views
- self.n_features_ = X.shape[1]
+ self.n_features_ = self.X_.shape[1]
if self.n_classes_ == 1:
# This case would lead to division by 0 when computing the cost
# matrix so it needs special handling (but it is an obvious case as
@@ -431,7 +433,7 @@ class MuCumboClassifier(BaseEnsemble, ClassifierMixin, UBoosting):
random_state = check_random_state(self.random_state)
(cost, label_score, label_score_global,
predicted_classes, score_function_dif, betas, n_yi) = self._init_var(n_views, y)
- self.n_yi = n_yi
+ self.n_yi_ = n_yi
for current_iteration in range(self.n_iterations_):
# list de h pris a l'etape t
dist = self._compute_dist(cost, y)
@@ -647,7 +649,6 @@ class MuCumboClassifier(BaseEnsemble, ClassifierMixin, UBoosting):
score : float
Mean accuracy of self.predict(X) wrt. y.
"""
- X = self._validate_X_predict(X)
return super(MuCumboClassifier, self).score(X, y)
def staged_score(self, X, y):
diff --git a/multimodal/boosting/mumbo.py b/multimodal/boosting/mumbo.py
index e9eabb6..3ddee63 100644
--- a/multimodal/boosting/mumbo.py
+++ b/multimodal/boosting/mumbo.py
@@ -275,7 +275,11 @@ class MumboClassifier(BaseEnsemble, ClassifierMixin, UBoosting):
Parameters
----------
- X : {array-like, sparse matrix}, shape = (n_samples, n_features)
+ X : dict dictionary with all views
+ or
+ `MultiModalData` , `MultiModalArray`, `MultiModalSparseArray`
+ or
+ {array-like, sparse matrix}, shape = (n_samples, n_features)
Training multi-view input samples.
Sparse matrix can be CSC, CSR, COO, DOK, or LIL.
COO, DOK and LIL are converted to CSR.
diff --git a/multimodal/datasets/base.py b/multimodal/datasets/base.py
index 3d58c39..560db71 100644
--- a/multimodal/datasets/base.py
+++ b/multimodal/datasets/base.py
@@ -25,7 +25,7 @@ def load_dict(filename_):
return ret_di
-def _create_pickle_files(self, adr, dsample):
+def _create_pickle_files(adr, dsample):
f = open(adr + ".sample.pkl", "wb")
pickle.dump(dsample, f)
f.close()
diff --git a/multimodal/datasets/data_sample.py b/multimodal/datasets/data_sample.py
index 037bcbc..7543874 100644
--- a/multimodal/datasets/data_sample.py
+++ b/multimodal/datasets/data_sample.py
@@ -219,7 +219,6 @@ class MultiModalSparseArray(sp.csr_matrix, sp.csc_matrix, MultiModalSparseInfo,
>>> data = load_dict(get_dataset_path(file))
"""
-
def __init__(self, *arg, **kwargs ):
"""Constructor of Metriclearn_array"""
if sp.issparse(arg[0]):
diff --git a/multimodal/kernels/lpMKL.py b/multimodal/kernels/lpMKL.py
index 9de928a..cb051c0 100644
--- a/multimodal/kernels/lpMKL.py
+++ b/multimodal/kernels/lpMKL.py
@@ -243,17 +243,14 @@ class MKL(BaseEstimator, ClassifierMixin, MKernel):
Parameters
----------
- X : - Metriclearn_array {array-like, sparse matrix}, shape = (n_samples, n_features)
- Training multi-view input samples. can be also Kernel where attibute 'kernel'
- is set to precompute "precomputed"
-
- - Dictionary of {array like} with shape = (n_samples, n_features) for multi-view
- for each view.
-
- - Array of {array like} with shape = (n_samples, n_features) for multi-view
- for each view.
-
- - {array like} with (n_samples, nviews * n_features) with 'views_ind' diferent to 'None'
+ X : dict dictionary with all views {array like} with shape = (n_samples, n_features) for multi-view
+ for each view.
+ or
+ `MultiModalData` , `MultiModalArray`
+ or
+ {array-like,}, shape = (n_samples, n_features)
+ Training multi-view input samples. can be also Kernel where attibute 'kernel'
+ is set to precompute "precomputed"
views_ind : array-like (default=[0, n_features//2, n_features])
Paramater specifying how to extract the data views from X:
@@ -320,5 +317,19 @@ class MKL(BaseEstimator, ClassifierMixin, MKernel):
return np.dot(kernel, C)
+ def score(self, X, y):
+ """Return the mean accuracy on the given test data and labels.
+
+ Parameters
+ ----------
+ X : {array-like} of shape = (n_samples, n_features)
+ y : array-like, shape = (n_samples,)
+ True labels for X.
+ Returns
+ -------
+ score : float
+ Mean accuracy of self.predict(X) wrt. y.
+ """
+ return super(MKL, self).score(X, y)
diff --git a/multimodal/kernels/mvml.py b/multimodal/kernels/mvml.py
index 6d585d0..3fb46b4 100644
--- a/multimodal/kernels/mvml.py
+++ b/multimodal/kernels/mvml.py
@@ -89,6 +89,7 @@ class MVML(MKernel, BaseEstimator, ClassifierMixin):
y_ : array-like, shape = (n_samples,)
Target values (class labels).
+
"""
# r_cond = 10-30
@@ -550,3 +551,19 @@ class MVML(MKernel, BaseEstimator, ClassifierMixin):
v * m:(v + 1) * m]
return A_new
+
+ def score(self, X, y):
+ """Return the mean accuracy on the given test data and labels.
+
+ Parameters
+ ----------
+ X : {array-like} of shape = (n_samples, n_features)
+ y : array-like, shape = (n_samples,)
+ True labels for X.
+
+ Returns
+ -------
+ score : float
+ Mean accuracy of self.predict(X) wrt. y.
+ """
+ return super(MVML, self).score(X, y)
\ No newline at end of file
diff --git a/multimodal/tests/datasets/input_x_dic.pkl b/multimodal/tests/datasets/input_x_dic.pkl
index ac879caaa3bd4b29030decb48b42d522ad341595..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 100644
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diff --git a/multimodal/tests/test_cumbo.py b/multimodal/tests/test_cumbo.py
index 153bc94..7841d32 100644
--- a/multimodal/tests/test_cumbo.py
+++ b/multimodal/tests/test_cumbo.py
@@ -14,7 +14,7 @@ from sklearn.ensemble import RandomForestClassifier
from sklearn.cluster import KMeans
from sklearn.tree import DecisionTreeClassifier
from sklearn import datasets
-
+from sklearn.utils.estimator_checks import check_estimator
from multimodal.boosting.cumbo import MuCumboClassifier
from multimodal.tests.data.get_dataset_path import get_dataset_path
from multimodal.datasets.data_sample import MultiModalArray
@@ -208,7 +208,7 @@ class TestMuCumboClassifier(unittest.TestCase):
clf = MuCumboClassifier()
clf.n_views_ = 3
clf.n_classes_ = 3
- clf.n_yi = np.array([1, 1, 2])
+ clf.n_yi_ = np.array([1, 1, 2])
A, b, G, h, l = clf._prepare_beta_solver()
y_i = np.array([0, 1, 2, 0])
predicted_classes = np.array([[0, 0, 1, 1], [0, 1, 0, 2], [2, 2, 0, 0]])
@@ -234,7 +234,7 @@ class TestMuCumboClassifier(unittest.TestCase):
clf = MuCumboClassifier()
clf.n_views_ = 3
clf.n_classes_ = 3
- clf.n_yi = np.array([1, 1, 2])
+ clf.n_yi_ = np.array([1, 1, 2])
cost_Tminus1 =np.array([[[-7.45744585e+01, 3.67879439e-01, 7.42065790e+01],
[ 4.78511743e-06, 3.87742081e-02, -3.87789932e-02],
[ 2.47875218e-03, -2.48182428e-03, 3.07210618e-06],
@@ -356,7 +356,7 @@ class TestMuCumboClassifier(unittest.TestCase):
clf = MuCumboClassifier()
clf.n_views_ = 3
clf.n_classes_ = 3
- clf.n_yi = np.array([1, 1, 2])
+ clf.n_yi_ = np.array([1, 1, 2])
betas = clf._compute_betas(alphas, y, score_function_Tminus1, predicted_classes)
cost, label_score, score_function_dif = clf._compute_cost(label_score, predicted_classes, y, alphas,
betas, use_coop_coef=True)
@@ -862,9 +862,9 @@ class TestMuCumboClassifier(unittest.TestCase):
# clf.fit(X, y, views_ind)
# assert_equal(clf.score(X, y), 1.)
#
- #
- # def test_classifier():
- # return check_estimator(MuCumboClassifier)
+
+ def test_classifier(self):
+ return check_estimator(MuCumboClassifier)
#
#
# def test_iris():
diff --git a/setup.py b/setup.py
index bff0c3e..5ec883a 100644
--- a/setup.py
+++ b/setup.py
@@ -20,7 +20,8 @@ def setup_package():
'Documentation': 'http://{}.pages.lis-lab.fr/{}'.format(group, name),
'Source': url,
'Tracker': '{}/issues'.format(url)}
- author = 'Dominique Benielli'
+ author = 'Dominique Benielli and Sokol Koço and Florent Jaillet and Riikka Huusari ' \
+ 'and Cécile Capponi and Hachem Kadri'
author_email = 'contact.dev@lis-lab.fr'
license = 'newBSD'
classifiers = [
--
GitLab