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%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
# MAGE tutorial : the sample types # MAGE tutorial : the sample types
In this tutorial, we will learn how to generate a multiview dataset presenting : In this tutorial, we will learn how to generate a multiview dataset presenting :
* redundancy, * redundancy,
* complementarity and * complementarity and
* mutual error. * mutual error.
## Definitions ## Definitions
In this tutorial, will will denote a sample as In this tutorial, will will denote a sample as
* **Redundant** if all the views have enough information to classify it correctly without collaboration, * **Redundant** if all the views have enough information to classify it correctly without collaboration,
* **Complementary** if only some of the views have enough information to classify it correctly without collaboration it is useful the assess the ability to extract the relevant information among the views. * **Complementary** if only some of the views have enough information to classify it correctly without collaboration it is useful the assess the ability to extract the relevant information among the views.
* Part of the **Mutual Error** if none of the views has enough information to classify it correctly without collaboration. A mutliview classifier able to classify these examples is apt to get information from several features from different views and combine it to classify the examples. * Part of the **Mutual Error** if none of the views has enough information to classify it correctly without collaboration. A mutliview classifier able to classify these examples is apt to get information from several features from different views and combine it to classify the examples.
## Hands on experience : initialization ## Hands on experience : initialization
We will initialize the arguments as earlier : We will initialize the arguments as earlier :
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
from multiview_generator.gaussian_classes import MultiViewGaussianSubProblemsGenerator from multiview_generator.gaussian_classes import MultiViewGaussianSubProblemsGenerator
from tabulate import tabulate from tabulate import tabulate
import numpy as np import numpy as np
import os import os
random_state = np.random.RandomState(42) random_state = np.random.RandomState(42)
name = "tuto" name = "tuto"
n_views = 4 n_views = 4
n_classes = 3 n_classes = 3
error_matrix = [ error_matrix = [
[0.4, 0.4, 0.4, 0.4], [0.4, 0.4, 0.4, 0.4],
[0.55, 0.4, 0.4, 0.4], [0.55, 0.4, 0.4, 0.4],
[0.4, 0.5, 0.52, 0.55] [0.4, 0.5, 0.52, 0.55]
] ]
n_samples = 2000 n_samples = 2000
n_features = 3 n_features = 3
class_weights = [0.333, 0.333, 0.333,] class_weights = [0.333, 0.333, 0.333,]
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
To control the three previously introduced characteristics, we have to provide three floats : To control the three previously introduced characteristics, we have to provide three floats :
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
complementarity = 0.3 complementarity = 0.3
redundancy = 0.2 redundancy = 0.2
mutual_error = 0.1 mutual_error = 0.1
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
Now we can generate the dataset with the given configuration. Now we can generate the dataset with the given configuration.
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
generator = MultiViewGaussianSubProblemsGenerator(name=name, n_views=n_views, generator = MultiViewGaussianSubProblemsGenerator(name=name, n_views=n_views,
n_classes=n_classes, n_classes=n_classes,
n_samples=n_samples, n_samples=n_samples,
n_features=n_features, n_features=n_features,
class_weights=class_weights, class_weights=class_weights,
error_matrix=error_matrix, error_matrix=error_matrix,
random_state=random_state, random_state=random_state,
redundancy=redundancy, redundancy=redundancy,
complementarity=complementarity, complementarity=complementarity,
mutual_error=mutual_error) mutual_error=mutual_error)
dataset, y = generator.generate_multi_view_dataset() dataset, y = generator.generate_multi_view_dataset()
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
Here, the generator distinguishes four types of examples, the thrre previously introduced and the ones that were used to fill the dataset. Here, the generator distinguishes four types of examples, the thrre previously introduced and the ones that were used to fill the dataset.
## Dataset analysis using [SuMMIT](https://gitlab.lis-lab.fr/baptiste.bauvin/summit) ## Dataset analysis using [SuMMIT](https://gitlab.lis-lab.fr/baptiste.bauvin/summit)
In order to differentiate them, we use `generator.sample_ids`. In this attribute, we can find an array with the ids of all the generated exmaples, characterizing their type : In order to differentiate them, we use `generator.sample_ids`. In this attribute, we can find an array with the ids of all the generated exmaples, characterizing their type :
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
generator.sample_ids[:10] generator.sample_ids[:10]
``` ```
%% Output %% Output
['0_l_0_m-0_0.37-1_0.04-2_0.27-3_0.81', ['0_l_0_m-0_0.37-1_0.04-2_0.27-3_0.81',
'1_l_0_m-0_0.48-1_1.28-2_0.28-3_0.55', '1_l_0_m-0_0.48-1_1.28-2_0.28-3_0.55',
'2_l_0_m-0_0.96-1_0.32-2_0.08-3_0.56', '2_l_0_m-0_0.96-1_0.32-2_0.08-3_0.56',
'3_l_0_m-0_2.49-1_0.18-2_0.97-3_0.35', '3_l_0_m-0_2.49-1_0.18-2_0.97-3_0.35',
'4_l_0_m-0_0.11-1_0.92-2_0.21-3_0.4', '4_l_0_m-0_0.11-1_0.92-2_0.21-3_0.4',
'5_l_0_m-0_0.84-1_0.43-2_0.48-3_1.17', '5_l_0_m-0_0.84-1_0.43-2_0.48-3_1.17',
'6_l_0_m-0_0.84-1_1.41-2_0.13-3_0.46', '6_l_0_m-0_0.84-1_1.41-2_0.13-3_0.46',
'7_l_0_m-0_0.14-1_0.64-2_0.62-3_0.4', '7_l_0_m-0_0.14-1_0.64-2_0.62-3_0.4',
'8_l_0_m-0_0.04-1_0.31-2_0.63-3_0.21', '8_l_0_m-0_0.04-1_0.31-2_0.63-3_0.21',
'9_l_0_m-0_0.86-1_1.18-2_0.09-3_0.35'] '9_l_0_m-0_0.86-1_1.18-2_0.09-3_0.35']
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
Here, we printed the 10 first ones, and we have : Here, we printed the 10 first ones, and we have :
* the redundant samples tagged `_r-`, * the redundant samples tagged `_r-`,
* the mutual error ones tagged `_m-`, * the mutual error ones tagged `_m-`,
* the complementary ones tagged `_c-` and * the complementary ones tagged `_c-` and
<!-- * the filling ones tagged `example_`. --> <!-- * the filling ones tagged `example_`. -->
To get a visualization on these properties, we will use SuMMIT with decision trees on each view. To get a visualization on these properties, we will use [SuMMIT](https://gitlab.lis-lab.fr/baptiste.bauvin/summit) with decision trees on each view.
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
from summit.execute import execute from summit.execute import execute
generator.to_hdf5_mc('supplementary_material') generator.to_hdf5_mc('supplementary_material')
execute(config_path=os.path.join('supplementary_material','config_summit.yml')) execute(config_path=os.path.join('supplementary_material','config_summit.yml'))
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
To extract the result, we need a small script that will fetch the right folder : To extract the result, we need a small script that will fetch the right folder :
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
import os import os
from datetime import datetime from datetime import datetime
from IPython.display import display from IPython.display import display
from IPython.display import IFrame from IPython.display import IFrame
def fetch_latest_dir(experiment_directories, latest_date=datetime(1560,12,25,12,12)): def fetch_latest_dir(experiment_directories, latest_date=datetime(1560,12,25,12,12)):
for experiment_directory in experiment_directories: for experiment_directory in experiment_directories:
experiment_time = experiment_directory.split("-")[0].split("_")[1:] experiment_time = experiment_directory.split("-")[0].split("_")[1:]
experiment_time += experiment_directory.split('-')[1].split("_")[:2] experiment_time += experiment_directory.split('-')[1].split("_")[:2]
experiment_time = map(int, experiment_time) experiment_time = map(int, experiment_time)
dt = datetime(*experiment_time) dt = datetime(*experiment_time)
if dt > latest_date: if dt > latest_date:
latest_date=dt latest_date=dt
latest_experiment_dir = experiment_directory latest_experiment_dir = experiment_directory
return latest_experiment_dir return latest_experiment_dir
experiment_directory = fetch_latest_dir(os.listdir(os.path.join('supplementary_material', 'tuto'))) experiment_directory = fetch_latest_dir(os.listdir(os.path.join('supplementary_material', 'tuto')))
error_fig_path = os.path.join('supplementary_material','tuto', experiment_directory, "error_analysis_2D.html") error_fig_path = os.path.join('supplementary_material','tuto', experiment_directory, "error_analysis_2D.html")
IFrame(src=error_fig_path, width=900, height=500) IFrame(src=os.path.join('supplementary_material',"error_analysis_2D.html") , width=900, height=500)
``` ```
%% Output %% Output
<IPython.lib.display.IFrame at 0x7f149d3a6f98> <IPython.lib.display.IFrame at 0x7ff88f74e4a8>
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
This graph represents the failure of each classifier on each sample. So a black rectangle on row i, column j means that classifier j always failed to classify example i. This graph represents the failure of each classifier on each sample. So a black rectangle on row i, column j means that classifier j always failed to classify example i.
So, by [zooming in](link_to_gif), we can focus on several samples and we see that the type of samples are well defined as the mutual error ones are systematically misclassified by the decision trees, the redundant ones are well-classified and the complementary ones are classified only by a portion of the views. So, by [zooming in](https://baptiste.bauvin.pages.lis-lab.fr/summit/_images/zoom_plotly.gif), we can focus on several samples and we see that the type of samples are well defined as the mutual error ones are systematically misclassified by the decision trees, the redundant ones are well-classified and the complementary ones are classified only by a portion of the views.
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docs/source/conf.py
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...@@ -12,11 +12,18 @@ ...@@ -12,11 +12,18 @@
# #
import os import os
import sys import sys
sys.path.insert(0, os.path.abspath('../../multiview_generator')) repo_path = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
# print(repo_path)
# print(os.path.join(repo_path, "multiview_generator", "base"))
# quit()
sys.path.insert(0, os.path.abspath('.'))
sys.path.insert(0, os.path.join(repo_path, "multiview_generator'"))
sys.path.insert(0, repo_path)
# -- Project information ----------------------------------------------------- # -- Project information -----------------------------------------------------
project = 'Mulitivew Generator' project = 'MAGE'
copyright = '2020, Baptiste Bauvin' copyright = '2020, Baptiste Bauvin'
author = 'Baptiste Bauvin' author = 'Baptiste Bauvin'
...@@ -31,6 +38,7 @@ release = '0.0' ...@@ -31,6 +38,7 @@ release = '0.0'
# ones. # ones.
extensions = ['sphinx.ext.autodoc', extensions = ['sphinx.ext.autodoc',
'sphinx.ext.extlinks', 'sphinx.ext.extlinks',
'sphinx_rtd_theme',
# 'sphinx.ext.doctest', # 'sphinx.ext.doctest',
# 'sphinx.ext.intersphinx', # 'sphinx.ext.intersphinx',
# 'sphinx.ext.todo', # 'sphinx.ext.todo',
...@@ -42,11 +50,24 @@ extensions = ['sphinx.ext.autodoc', ...@@ -42,11 +50,24 @@ extensions = ['sphinx.ext.autodoc',
# 'sphinx.ext.viewcode', # 'sphinx.ext.viewcode',
# 'sphinx.ext.githubpages', # 'sphinx.ext.githubpages',
'sphinx.ext.napoleon', 'sphinx.ext.napoleon',
"autoapi.extension",
'nbsphinx', 'nbsphinx',
"nbsphinx_link" "nbsphinx_link"
# 'm2r' # 'm2r'
] ]
autoapi_type = 'python'
autoapi_dirs = [os.path.join(repo_path, "multiview_generator",""),]
autoapi_options = ["members", "show-module-summary", 'undoc-members']
autoapi_ignore = ["*tests*"]
autoapi_keep_files = False
autoapi_add_toctree_entry = False
add_module_names = False
autoapi_template_dir = os.path.join(repo_path, "docs", "source", "templates_autoapi")
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates', 'templates_autoapi']
source_suffix = ['.rst', '.md', '.ipynb', ".nblink"] source_suffix = ['.rst', '.md', '.ipynb', ".nblink"]
# Add any paths that contain templates here, relative to this directory. # Add any paths that contain templates here, relative to this directory.
...@@ -63,12 +84,12 @@ exclude_patterns = ['_build', '**.ipynb_checkpoints'] ...@@ -63,12 +84,12 @@ exclude_patterns = ['_build', '**.ipynb_checkpoints']
# The theme to use for HTML and HTML Help pages. See the documentation for # The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes. # a list of builtin themes.
# #
html_theme = 'nature' html_theme = 'sphinx_rtd_theme'
# Add any paths that contain custom static files (such as style sheets) here, # 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, # relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css". # so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static'] html_static_path = ['_static',]
rst_prolog = """ rst_prolog = """
.. role:: python(code) .. role:: python(code)
...@@ -77,18 +98,18 @@ rst_prolog = """ ...@@ -77,18 +98,18 @@ rst_prolog = """
.. role :: yaml(code) .. role :: yaml(code)
:language: yaml :language: yaml
.. |gene| replace:: SMuDGE .. |gene| replace:: MAGE
.. |gene_f| replace:: Supervised MUltimodal Dataset Generation Engine .. |gene_f| replace:: Multi-view Artificial Generation Engine
.. |HPO| replace:: hyper-parameters optimization .. |HPO| replace:: hyper-parameters optimization
""" """
extlinks = {'base_source': ( extlinks = {'base_source': (
'https://gitlab.lis-lab.fr/baptiste.bauvin/smudge/-/tree/master/', 'https://gitlab.lis-lab.fr/dev/multiview_generator',
"base_source"), "base_source"),
'base_doc': ( 'base_doc': (
'http://baptiste.bauvin.pages.lis-lab.fr/smudge/', 'base_doc'), 'https://dev.pages.lis-lab.fr/multiview_generator/', 'base_doc'),
'summit':('https://gitlab.lis-lab.fr/baptiste.bauvin/summit', 'summit')} 'summit':('https://gitlab.lis-lab.fr/baptiste.bauvin/summit', 'summit')}
html_js_files = [ html_js_files = [
......
docs/source/documentation.rst
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|gene| documentation |gene| documentation
==================== ====================
.. automodule:: multiple_sub_problems .. toctree::
:maxdepth: 2
.. autoclass:: MultiViewSubProblemsGenerator autoapi/multiview_generator/base/index
:members: autoapi/multiview_generator/gaussian_classes/index
autoapi/multiview_generator/sub_problems/index
autoapi/multiview_generator/utils/index
\ No newline at end of file
docs/source/index.rst
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...@@ -3,10 +3,10 @@ ...@@ -3,10 +3,10 @@
You can adapt this file completely to your liking, but it should at least You can adapt this file completely to your liking, but it should at least
contain the root `toctree` directive. contain the root `toctree` directive.
Welcome to multiview_generator's documentation! Welcome to |gene|'s documentation
=============================================== ===============================================
To install MAGE, clone the gitlab repository and run To install |gene|, clone the gitlab repository and run
.. code-block:: .. code-block::
...@@ -24,8 +24,8 @@ To install MAGE, clone the gitlab repository and run ...@@ -24,8 +24,8 @@ To install MAGE, clone the gitlab repository and run
include_tuto3 include_tuto3
documentation documentation
Read me Read Me
========= =======
.. include:: readme_link.rst .. include:: readme_link.rst
......
multiview_generator/gaussian_classes.py
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import numpy as np import numpy as np
import itertools import itertools
import math import math
...@@ -22,7 +21,6 @@ class MultiViewGaussianSubProblemsGenerator(MultiViewSubProblemsGenerator): ...@@ -22,7 +21,6 @@ class MultiViewGaussianSubProblemsGenerator(MultiViewSubProblemsGenerator):
sub_problem_generators="StumpsGenerator", random_vertices=False, sub_problem_generators="StumpsGenerator", random_vertices=False,
min_rndm_val=-1, max_rndm_val=1, **kwargs): min_rndm_val=-1, max_rndm_val=1, **kwargs):
""" """
:param random_state: int or np.random.RandomState object to fix the :param random_state: int or np.random.RandomState object to fix the
random seed random seed
:param n_samples: int representing the number of samples in the dataset :param n_samples: int representing the number of samples in the dataset
...@@ -74,18 +72,15 @@ class MultiViewGaussianSubProblemsGenerator(MultiViewSubProblemsGenerator): ...@@ -74,18 +72,15 @@ class MultiViewGaussianSubProblemsGenerator(MultiViewSubProblemsGenerator):
def generate_multi_view_dataset(self, ): def generate_multi_view_dataset(self, ):
""" """
This is the main method. It will generate a multiview dataset according This is the main method. It will generate a multiview dataset according to the configuration.
to the configuration.
To do so, To do so,
* it generates the labels of the multiview dataset, * it generates the labels of the multiview dataset,
* then it assigns all the subsets of samples (redundant, ...) * then it assigns all the subsets of samples (redundant, ...)
* finally, for each view it generates a monoview dataset according * finally, for each view it generates a monoview dataset according to the configuration
to the configuration
:return: view_data a list containing the views np.ndarrays and y, the :return: view_data a list containing the views np.ndarrays and y, the label array.
label array.
""" """
# Generate the labels # Generate the labels
......
multiview_generator/sub_problems.py
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...@@ -59,7 +59,7 @@ class StumpsGenerator(BaseSubProblem): ...@@ -59,7 +59,7 @@ class StumpsGenerator(BaseSubProblem):
uniform noise features : all the remaining ones uniform noise features : all the remaining ones
:return: data a np.ndarray of dimension n_classes, n_samples_per_class, :return: data a np.ndarray of dimension n_classes, n_samples_per_class, \
n_features containing the samples' descriptions, sorted by class n_features containing the samples' descriptions, sorted by class
""" """
self.n_relevant_features = math.ceil(math.log2(self.n_classes)) self.n_relevant_features = math.ceil(math.log2(self.n_classes))
...@@ -223,16 +223,14 @@ class TreesGenerator(BaseSubProblem): # pragma: no cover ...@@ -223,16 +223,14 @@ class TreesGenerator(BaseSubProblem): # pragma: no cover
class RingsGenerator(BaseSubProblem): class RingsGenerator(BaseSubProblem):
def gen_data(self): def gen_data(self):
""" r"""Generates the samples according to gaussian distributions with scales
Generates the samples according to gaussian distributions with scales
computed with the given error and class separation. The generator first computed with the given error and class separation. The generator first
computes a radius according to the gaussian distribution, then computes a radius according to the gaussian distribution, then
generates n_features-1 random angles to build the polar coordinates of generates n_features-1 random angles to build the polar coordinates of
the samples. The dataset returned is the cartesian version of this the samples. The dataset returned is the cartesian version of this
"polar" dataset. "polar" dataset.
:return: data a np.ndarray of dimension n_classes, n_samples_per_class, :return: data a np.ndarray of dimension n_classes, n_samples_per_class, n_features containing the samples' descriptions, sorted by class
n_features containing the samples' descriptions, sorted by class
""" """
if self.n_features<2: if self.n_features<2:
raise ValueError("n_features for view {} must be at least 2, (now: {})".format(1, self.n_features)) raise ValueError("n_features for view {} must be at least 2, (now: {})".format(1, self.n_features))
......
setup.py
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...@@ -180,9 +180,11 @@ def setup_package(): ...@@ -180,9 +180,11 @@ def setup_package():
extras_require = { extras_require = {
'dev': ['pytest', 'pytest-cov'], 'dev': ['pytest', 'pytest-cov'],
'doc': ['sphinx>=1.8', 'numpydoc', 'sphinx_gallery', 'matplotlib', "jupyter", 'doc': ['sphinx>=1.8', 'numpydoc', 'sphinx_gallery', 'matplotlib', "jupyter",
'pandoc', 'nbshpinx', 'nbsphinx_link']} 'pandoc', 'nbshpinx', 'nbsphinx_link', 'sphinx_rtd_theme']}
include_package_data = True include_package_data = True
command_options = {'build_sphinx': {'build_dir':('setup.py', './docs/build/')}}
setup(name=name, setup(name=name,
version=version, version=version,
description=description, description=description,
...@@ -198,7 +200,8 @@ def setup_package(): ...@@ -198,7 +200,8 @@ def setup_package():
install_requires=install_requires, install_requires=install_requires,
python_requires=python_requires, python_requires=python_requires,
extras_require=extras_require, extras_require=extras_require,
include_package_data=include_package_data) include_package_data=include_package_data,
command_options=command_options)
if __name__ == "__main__": if __name__ == "__main__":
......
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