diff --git a/README.rst b/README.rst
index 7a2c3a1529d990445bc81f96edd50ccfe58fbe12..8b123cec4e92bebf74fe7546bb6abeceec05807e 100644
--- a/README.rst
+++ b/README.rst
@@ -12,7 +12,7 @@ Documentation
The **documentation** including installation instructions, API documentation
and examples is
-`available online <http://dev.pages.lis-lab.fr/multimodal>`_.
+`available online <http://dev.pages.lis-lab.fr/scikit-multimodallearn>`_.
Installation
@@ -31,7 +31,7 @@ Installation using pip
~~~~~~~~~~~~~~~~~~~~~~
**scikit-multimodallearn** is
-`available on PyPI <https://pypi.org/project/multiconfusion/>`_
+`available on PyPI <https://pypi.org/project/scikit-multimodallearn/>`_
and can be installed using **pip**::
pip install scikit-multimodallearn
@@ -105,6 +105,22 @@ 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
~~~~~~~~~~
@@ -113,7 +129,6 @@ References
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>`_,
diff --git a/doc/conf.py b/doc/conf.py
index a45f0cf2f943497097cbe6fc091262596b280486..f1717cc721342e1812b136b25512af7590f68ae8 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -3,12 +3,14 @@
from datetime import date
import os
import sys
-sys.path.insert(0, os.path.abspath('../metriclearning'))
+sys.path.insert(0, os.path.abspath('../multimodal'))
sys.path.insert(0, os.path.abspath('../..'))
+sys.path.insert(0, os.path.abspath('../../multimodal'))
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
+
+import multimodal
# 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
@@ -28,6 +30,9 @@ extensions = [
'sphinx.ext.doctest',
'sphinx.ext.imgmath',
'numpydoc',
+ 'sphinx.ext.napoleon',
+ 'sphinx_gallery.gen_gallery'
+ # 'm2r',
# 'sphinx_gallery.gen_gallery'
]
@@ -44,18 +49,18 @@ source_encoding = 'utf-8'
master_doc = 'index'
# General information about the project.
-project = 'metriclearning'
+project = 'scikit-multimodallearn'
author = 'Dominique Benielli'
-copyright = '2017-{}, LIS UMR 7020'.format(date.today().year)
+copyright = '2020-{}, 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__
+version = multimodal.__version__
# The full version, including alpha/beta/rc tags.
-release = metriclearning.__version__
+release = multimodal.__version__
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
@@ -69,7 +74,7 @@ release = metriclearning.__version__
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
-exclude_patterns = []
+exclude_patterns = ['_build']
# The reST default role (used for this markup: `text`) to use for all
# documents.
@@ -243,7 +248,7 @@ man_pages = [
# 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.',
+ 'Multimodal Multi-View Learning with kernels and boosting algorithms.',
'Miscellaneous')]
# Documents to append as an appendix to all manuals.
@@ -267,11 +272,11 @@ numpydoc_show_class_members = False
sphinx_gallery_conf = {
'doc_module': (project,),
- 'backreferences_dir': 'backreferences',
+ 'backreferences_dir': 'tutorial/backreferences',
# path to your examples scripts
'examples_dirs': '../examples',
# path where to save gallery generated examples
- 'gallery_dirs': 'auto_examples'}
+ 'gallery_dirs': 'tutorial/auto_examples'}
# Generate the plots for the gallery
plot_gallery = 'True'
diff --git a/doc/docmumbo/install_devel.rst b/doc/docmumbo/install_devel.rst
deleted file mode 100644
index 189ff2a1521a0954bae8e2cbb40bae67be79c7bc..0000000000000000000000000000000000000000
--- a/doc/docmumbo/install_devel.rst
+++ /dev/null
@@ -1,70 +0,0 @@
-Installation and development
-============================
-
-Dependencies
-------------
-
-**multimodalboost** works with **Python 3.5 or later**.
-
-**multimodalboost** depends on **scikit-learn** (version >= 0.19).
-
-Optionally, **matplotlib** is required when running the examples.
-
-Installation
-------------
-
-**multimodalboost** is
-`available on PyPI <https://pypi.org/project/multimodalboost/>`_
-and can be installed using **pip**::
-
- 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/multimodalboost.git
- cd multimodalboost
- python setup.py install
-
-or alternatively use **pip**::
-
- pip install git+https://gitlab.lis-lab.fr/dev/multimodalboost.git
-
-Development
------------
-
-The development of multimodalboost follows the guidelines provided by the
-scikit-learn community.
-
-Refer to the `Developer's Guide <http://scikit-learn.org/stable/developers>`_
-of the scikit-learn project for more details.
-
-Source code
------------
-
-You can get the **source code** from the **Git** repository of the project::
-
- git clone git@gitlab.lis-lab.fr:dev/multimodalboost.git
-
-
-Testing
--------
-
-**pytest** and **pytest-cov** are required to run the **test suite** with::
-
- cd multimodalboost
- 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**.
-
-Generating the documentation
-----------------------------
-
-The generation of the documentation requires **sphinx**, **sphinx-gallery**,
-**numpydoc** and **matplotlib** and can be run with::
-
- python setup.py build_sphinx
-
-The resulting files are stored in the directory **build/sphinx/html**.
diff --git a/doc/index.rst b/doc/index.rst
index a8fc5521206fe86b1c6bd1929762b08cf727a24e..6e5825639d13fb3884055b1dc02338107056d949 100644
--- a/doc/index.rst
+++ b/doc/index.rst
@@ -1,7 +1,4 @@
-.. 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 scikit-multimodallearn's documentation!
==================================================
@@ -15,8 +12,11 @@ package for machine learning in Python.
Documentation
-------------
+:Release: |version|
+:Date: |today|
+
.. toctree::
- :maxdepth: 2
+ :maxdepth: 3
:caption: Contents:
@@ -27,21 +27,6 @@ Documentation
-Documentation
--------------
-
-:Release: |version|
-:Date: |today|
-
-.. toctree::
- :maxdepth: 1
-
- install_devel
- api
- auto_examples/index
- credits
-
-
Indices and tables
------------------
diff --git a/doc/reference/api.rst b/doc/reference/api.rst
index 815df9e47b1b8db0d2991cdac90322fe30f99d3d..e68064331b202159580220038d1e22dda86f78ef 100644
--- a/doc/reference/api.rst
+++ b/doc/reference/api.rst
@@ -1,9 +1,19 @@
API Documentation
=================
+datasets
+--------
+
+.. automodule:: multimodal.datasets.data_sample
+ :members:
+ :inherited-members:
+
+
+Boosting
+--------
multimodal.boosting.mumbo
--------------------------
++++++++++++++++++++++++++
.. automodule:: multimodal.boosting.mumbo
:members:
@@ -11,7 +21,7 @@ multimodal.boosting.mumbo
multimodal.boosting.cumbo
--------------------------
++++++++++++++++++++++++++
.. automodule:: multimodal.boosting.cumbo
:members:
@@ -19,8 +29,35 @@ multimodal.boosting.cumbo
multimodal.boosting.boost
--------------------------
++++++++++++++++++++++++++
.. automodule:: multimodal.boosting.boost
:members:
:inherited-members:
+
+
+Kernels
+-------
+
+multimodal.kernels.mvml
++++++++++++++++++++++++
+
+.. automodule:: multimodal.kernels.mvml
+ :members:
+ :inherited-members:
+
+
+multimodal.kernels.lpMKL
+++++++++++++++++++++++++
+
+.. automodule:: multimodal.kernels.lpMKL
+ :members:
+ :inherited-members:
+
+
+multimodal.kernels.mkernel
+++++++++++++++++++++++++++
+
+.. automodule:: multimodal.kernels.mkernel
+ :members:
+ :inherited-members:
diff --git a/examples/README.txt b/examples/README.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f749137f56fe6950ead3682bcee3a44915567dea
--- /dev/null
+++ b/examples/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/cumbo_plot_2_views_2_classes.py b/examples/cumbo_plot_2_views_2_classes.py
new file mode 100644
index 0000000000000000000000000000000000000000..28760f5fdc8319f73014f13e111887b9591717df
--- /dev/null
+++ b/examples/cumbo_plot_2_views_2_classes.py
@@ -0,0 +1,127 @@
+# -*- coding: utf-8 -*-
+"""
+==========================
+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.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 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 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_3_views_3_classes.py b/examples/cumbo_plot_3_views_3_classes.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a03a103d15cea1025ba950587f847e407781984
--- /dev/null
+++ b/examples/cumbo_plot_3_views_3_classes.py
@@ -0,0 +1,121 @@
+# -*- coding: utf-8 -*-
+"""
+==========================
+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.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_plot_2_views_2_classes.py b/examples/mumbo_plot_2_views_2_classes.py
new file mode 100644
index 0000000000000000000000000000000000000000..a2f864eb092a749e645c24697cb56bd80a1aabca
--- /dev/null
+++ b/examples/mumbo_plot_2_views_2_classes.py
@@ -0,0 +1,127 @@
+# -*- coding: utf-8 -*-
+"""
+==========================
+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_3_views_3_classes.py b/examples/mumbo_plot_3_views_3_classes.py
new file mode 100644
index 0000000000000000000000000000000000000000..624a7910a7847541084b34432fd1e6f2edc5a59a
--- /dev/null
+++ b/examples/mumbo_plot_3_views_3_classes.py
@@ -0,0 +1,121 @@
+# -*- coding: utf-8 -*-
+"""
+==========================
+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/multimodal/boosting/cumbo.py b/multimodal/boosting/cumbo.py
index 55a07f917a96674eed42f36fe7c37d676d7175ce..6753e71b30e88d48683dfe5dc50e1f4a816282a6 100644
--- a/multimodal/boosting/cumbo.py
+++ b/multimodal/boosting/cumbo.py
@@ -5,32 +5,6 @@ This module contains a **Mu**\ lti\ **C**\ onfusion **M**\ Matrix **B**\ osting
estimator for classification implemented in the ``MuCumboClassifier`` class.
"""
-# 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 multiconfusion. If not, see <http://www.gnu.org/licenses/>.
-#
-# Author: Dominique Benielli- Laboratoire d'Informatique et Systèmes - UMR 7020
-
-# The implementation of the MuCumboClassifier in this module used the code of
-# sklearn.ensemble.AdaBoostClassifier as a model and tried to use the same
-# structure, notations and behavior where possible.
-
import numpy as np
from sklearn.base import ClassifierMixin
from sklearn.ensemble import BaseEnsemble
diff --git a/multimodal/datasets/data_sample.py b/multimodal/datasets/data_sample.py
index a50c56736f6397e763f061f3a792bfb720ff05c5..037bcbc7642c910271cd57513628e952a652700d 100644
--- a/multimodal/datasets/data_sample.py
+++ b/multimodal/datasets/data_sample.py
@@ -6,18 +6,29 @@ nbL and nbEx numbers,
MultiModalArray class inherit from numpy ndarray and contains a 2d data ndarray
with the shape (n_samples, n_view_i * n_features_i)
-0 1 2 3
-======== ==== ==== ====
-xxxxxxxx xxxx xxxx xxxx
-xxxxxxxx xxxx xxxx xxxx
-xxxxxxxx xxxx xxxx xxxx
-xxxxxxxx xxxx xxxx xxxx
-xxxxxxxx xxxx xxxx xxxx
-xxxxxxxx xxxx xxxx xxxx
-xxxxxxxx xxxx xxxx xxxx
-xxxxxxxx xxxx xxxx xxxx
-xxxxxxxx xxxx xxxx xxxx
-======== ==== ==== ====
+.. tabularcolumns:: |l|l|l|l|
+
++----------+------+------+------+
+| 0 | 1 | 2 | 3 |
++==========+======+======+======+
+| xxxxxxxx | xxxx | xxxx | xxxx |
++----------+------+------+------+
+| xxxxxxxx | xxxx | xxxx | xxxx |
++----------+------+------+------+
+| xxxxxxxx | xxxx | xxxx | xxxx |
++----------+------+------+------+
+| xxxxxxxx | xxxx | xxxx | xxxx |
++----------+------+------+------+
+| xxxxxxxx | xxxx | xxxx | xxxx |
++----------+------+------+------+
+| xxxxxxxx | xxxx | xxxx | xxxx |
++----------+------+------+------+
+| xxxxxxxx | xxxx | xxxx | xxxx |
++----------+------+------+------+
+| xxxxxxxx | xxxx | xxxx | xxxx |
++----------+------+------+------+
+| xxxxxxxx | xxxx | xxxx | xxxx |
++----------+------+------+------+
MultiModalSparseArray inherit from scipy sparce matrix with the shape (n_samples, n_view_i * n_features_i)
diff --git a/multimodal/kernels/mvml.py b/multimodal/kernels/mvml.py
index 6ab1d62a3867b6fc740af20f39aadee810c9b38f..6d585d0a7d3002cd3c8eae018ad47deb5dd28219 100644
--- a/multimodal/kernels/mvml.py
+++ b/multimodal/kernels/mvml.py
@@ -417,7 +417,7 @@ class MVML(MKernel, BaseEstimator, ClassifierMixin):
Parameters
----------
- test_kernels : `Metriclearn_array` of test kernels
+ test_kernels : `` of test kernels
g : learning solution that is learned in learn_mvml