Merged private algos

.gitignore

-TODO
-*.pyc
-.idea/**
-ipynb/.ipynb_checkpoints/**
-docs/source/monomulti/.ipynb_checkpoints/**
-results/*
-data/*
-Data/*
-multiview_platform/MonoMultiviewClassifiers/Results/*
-multiview_platform/tests/temp_tests/**
-multiview-machine-learning-omis.iml
-multiview_platform.egg-inf*
-build*
-dist*
-multiview_platform/.idea/*
-.gitignore
-multiview_platform/examples/results/example_1/*
-multiview_platform/examples/results/example_2_1_1/*
-multiview_platform/examples/results/example_2_1_2/*
-multiview_platform/examples/results/example_2_2_1/*
-multiview_platform/examples/results/example_2_3/*
-multiview_platform/examples/results/example_3/*
-multiview_platform/examples/results/example_4/*
-multiview_platform/examples/results/example_5/*

.gitlab-ci.yml

@@ -6,6 +6,10 @@ tests:
     script:
         - pip3 install -e . --no-deps
         - pytest-3
+    coverage: '/^TOTAL.+?(\d+\%)$/'
+    artifacts:
+        paths:
+            - htmlcov/
 
 # generate the documentation
 doc:
@@ -18,8 +22,8 @@ doc:
     script:
         - export LC_ALL=$(locale -a | grep en_US)
         - export LANG=$(locale -a | grep en_US)
-        - pip3 install -e . --no-deps
-        - sphinx-apidoc -o docs/source multiview_platform
+        - pip3 install -e .[doc]
+        - sphinx-apidoc -o docs/source summit
         - cd docs/source
         - sphinx-build -b html . ../build
         - cd ../..
@@ -27,6 +31,7 @@ doc:
     artifacts:
         paths:
             - public
+            - htmlcov
 
 # project public
 pages:
@@ -38,12 +43,17 @@ pages:
     script:
         - export LC_ALL=$(locale -a | grep en_US)
         - export LANG=$(locale -a | grep en_US)
-        - pip3 install -e . --no-deps
-        - sphinx-apidoc -o docs/source multiview_platform
+        - pip3 install -e .[doc]
+        - pytest-3
+        - sphinx-apidoc -o docs/source summit
         - cd docs/source
         - sphinx-build -b html . ../build
         - cd ../..
         - cp -r ./docs/build public
+        - mkdir public/coverage
+        - mv htmlcov/index.html public/coverage/
+        - mv htmlcov/style.css public/coverage/
     artifacts:
        paths:
             - public
+

README.md

-[![License: GPL v3](https://img.shields.io/badge/License-GPL%20v3-blue.svg)](http://www.gnu.org/licenses/gpl-3.0)
-[![Build Status](https://gitlab.lis-lab.fr/baptiste.bauvin/summit/badges/develop/pipeline.svg)](https://gitlab.lis-lab.fr/baptiste.bauvin/summit/badges/develop/pipeline.svg)
-# Supervised MultiModal Integration Tool's Readme
-
-This project aims to be an easy-to-use solution to run a prior benchmark on a dataset and evaluate mono- & multi-view algorithms capacity to classify it correctly.
-
-## Getting Started
-
-### Prerequisites (will be automatically installed)
-
-To be able to use this project, you'll need :
-
-* [Python 3.6](https://docs.python.org/3/) 
-
-And the following python modules :
-
-* [numpy](http://www.numpy.org/), [scipy](https://scipy.org/),
-* [matplotlib](http://matplotlib.org/) - Used to plot results,
-* [sklearn](http://scikit-learn.org/stable/) - Used for the monoview classifiers,
-* [joblib](https://pypi.python.org/pypi/joblib) - Used to compute on multiple threads,
-* [h5py](https://www.h5py.org) - Used to generate HDF5 datasets on hard drive and use them to spare RAM,
-* [pickle](https://docs.python.org/3/library/pickle.html) - Used to store some results,
-* [pandas](https://pandas.pydata.org/) - Used to manipulate data efficiently,
-* [six](https://pypi.org/project/six/) - 
-* [m2r](https://pypi.org/project/m2r/) - Used to generate documentation from the readme,
-* [docutils](https://pypi.org/project/docutils/) - Used to generate documentation,
-* [pyyaml](https://pypi.org/project/PyYAML/) - Used to read the config files,
-* [plotly](https://plot.ly/) - Used to generate interactive HTML visuals,
-* [tabulate](https://pypi.org/project/tabulate/) - Used to generated the confusion matrix.
-
-
-### Installing
-
-Once you cloned the project from the [gitlab repository](https://gitlab.lis-lab.fr/baptiste.bauvin/summit/), you just have to use :  
-
-```
-cd path/to/summit/
-pip install -e .
-```
-In the `summit` directory to install SuMMIT and its dependencies.
-
-### Running on simulated data
-
-In order to run it you'll need to try on **simulated** data with the command
-```python 
-from multiview_platform.execute import execute
-execute("example 1")
-```
-This will run the first example. 
-
-For more information about the examples, see the [documentation](http://baptiste.bauvin.pages.lis-lab.fr/summit/).
-Results will be stored in the results directory of the installation path : 
-`path/to/summit/multiview_platform/examples/results`.
-
-The documentation proposes a detailed interpretation of the results through [6 tutorials](http://baptiste.bauvin.pages.lis-lab.fr/summit/). 
-
-### Discovering the arguments
-
-All the arguments of the platform are stored in a YAML config file. Some config files are given as examples. 
-The file stored in `summit/config_files/config.yml` is documented and it is highly recommended
-to read it carefully before playing around with the parameters.   
-
-You can create your own configuration file. In order to run the platform with it, run : 
-```python
-from multiview_platform.execute import execute
-execute(config_path="/absolute/path/to/your/config/file")
-```
-
-For further information about classifier-specific arguments, see the [documentation](http://baptiste.bauvin.pages.lis-lab.fr/summit/). 
- 
-
-### Dataset compatibility
-
-In order to start a benchmark on your own dataset, you need to format it so SuMMIT can use it. To do so, a [python script](https://gitlab.lis-lab.fr/baptiste.bauvin/summit/-/blob/master/format_dataset.py) is provided.
-
-For more information, see [Example 6](http://baptiste.bauvin.pages.lis-lab.fr/summit/tutorials/example4.html)
-
-### Running on your dataset 
-
-Once you have formatted your dataset, to run SuMMIT on it you need to modify the config file as  
-```yaml
-name: ["your_file_name"]
-*
-pathf: "path/to/your/dataset"
-```
-This will run a full benchmark on your dataset using all available views and labels.
- 
-It is highly recommended to follow the documentation's [tutorials](http://baptiste.bauvin.pages.lis-lab.fr/summit/tutorials/index.html) to learn the use of each parameter. 
- 
-
-## Author
-
-* **Baptiste BAUVIN**
-
-### Contributors
-
-* **Dominique BENIELLI**
-* **Alexis PROD'HOMME**

README.rst

+.. |pipeline| image:: https://gitlab.lis-lab.fr/baptiste.bauvin/summit/badges/master/pipeline.svg
+    :alt: Pipeline status
+
+.. |license| image:: https://img.shields.io/badge/License-GPL%20v3-blue.svg
+    :target: http://www.gnu.org/licenses/gpl-3.0
+    :alt: License: GPL v3
+
+.. |coverage| image:: https://gitlab.lis-lab.fr/baptiste.bauvin/summit/badges/master/coverage.svg
+    :target: http://baptiste.bauvin.pages.lis-lab.fr/summit/coverage/index.html
+    :alt: Coverage
+
+|pipeline| |license| |coverage|
+
+
+
+
+Supervised MultiModal Integration Tool's Readme
+===============================================
+
+This project aims to be an easy-to-use solution to run a prior benchmark on a dataset and evaluate mono- & multi-view algorithms capacity to classify it correctly.
+
+Getting Started
+---------------
+
+SuMMIT has been designed and uses continuous integration for Linux platforms (ubuntu 18.04), but we try to keep it as compatible as possible with Mac and Windows.
+
++----------+-------------------+
+| Platform | Last positive test|
++==========+===================+
+|   Linux  |  |pipeline|       |
++----------+-------------------+
+| Mac      | 1st of May, 2020  |
++----------+-------------------+
+| Windows  | 1st of May, 2020  |
++----------+-------------------+
+
+
+Prerequisites
+<<<<<<<<<<<<<
+
+To be able to use this project, you'll need :
+
+* `Python 3 <https://docs.python.org/3/>`_
+
+And the following python modules will be automatically installed  :
+
+* `numpy <http://www.numpy.org/>`_, `scipy <https://scipy.org/>`_,
+* `matplotlib <http://matplotlib.org/>`_ - Used to plot results,
+* `sklearn <http://scikit-learn.org/stable/>`_ - Used for the monoview classifiers,
+* `joblib <https://pypi.python.org/pypi/joblib>`_ - Used to compute on multiple threads,
+* `h5py <https://www.h5py.org>`_ - Used to generate HDF5 datasets on hard drive and use them to spare RAM,
+* `pickle <https://docs.python.org/3/library/pickle.html>`_ - Used to store some results,
+* `pandas <https://pandas.pydata.org/>`_ - Used to manipulate data efficiently,
+* `six <https://pypi.org/project/six/>`_ -
+* `m2r <https://pypi.org/project/m2r/>`_ - Used to generate documentation from the readme,
+* `docutils <https://pypi.org/project/docutils/>`_ - Used to generate documentation,
+* `pyyaml <https://pypi.org/project/PyYAML/>`_ - Used to read the config files,
+* `plotly <https://plot.ly/>`_ - Used to generate interactive HTML visuals,
+* `tabulate <https://pypi.org/project/tabulate/>`_ - Used to generated the confusion matrix.
+
+
+Installing
+<<<<<<<<<<
+
+Once you cloned the project from the `gitlab repository <https://gitlab.lis-lab.fr/baptiste.bauvin/summit/>`_, you just have to use :
+
+.. code:: bash
+
+    cd path/to/summit/
+    pip install -e .
+
+
+In the `summit` directory to install SuMMIT and its dependencies.
+
+Running the tests
+<<<<<<<<<<<<<<<<<
+
+To run the test suite of SuMMIT, run :
+
+.. code:: bash
+
+    cd path/to/summit
+    pip install -e .[dev]
+    pytest
+
+The coverage report is automatically generated and stored in the ``htmlcov/`` directory
+
+Building the documentation
+<<<<<<<<<<<<<<<<<<<<<<<<<<
+
+To locally build the `documentation <http://baptiste.bauvin.pages.lis-lab.fr/summit/index.html>`_ run :
+
+.. code:: bash
+
+    cd path/to/summit
+    pip install -e .[doc]
+    python setup.py build_sphinx
+
+The built html files will be stored in ``path/to/summit/build/sphinx/html``
+
+Running on simulated data
+<<<<<<<<<<<<<<<<<<<<<<<<<
+
+For your first go with SuMMIT, you can run it on simulated data with
+
+.. code:: bash
+
+    python
+    >>> from summit.execute import execute
+    >>> execute("example 1")
+
+This will run the benchmark of `documentation's Example 1 <http://baptiste.bauvin.pages.lis-lab.fr/summit/tutorials/example1.html>`_.
+
+For more information about the examples, see the `documentation <http://baptiste.bauvin.pages.lis-lab.fr/summit/index.html>`_.
+Results will, by default, be stored in the results directory of the installation path :
+``path/to/summit/multiview_platform/examples/results``.
+
+The documentation proposes a detailed interpretation of the results and arguments of SuMMIT through `6 tutorials <http://baptiste.bauvin.pages.lis-lab.fr/summit/>`_.
+
+Dataset compatibility
+<<<<<<<<<<<<<<<<<<<<<
+
+In order to start a benchmark on your own dataset, you need to format it so SuMMIT can use it. To do so, a `python script <https://gitlab.lis-lab.fr/baptiste.bauvin/summit/-/blob/master/format_dataset.py>`_ is provided.
+
+For more information, see `Example 5 <http://baptiste.bauvin.pages.lis-lab.fr/summit/tutorials/example5.html>`_
+
+Running on your dataset
++++++++++++++++++++++++
+
+Once you have formatted your dataset, to run SuMMIT on it you need to modify the config file as
+
+.. code:: yaml
+
+    name: ["your_file_name"]
+    pathf: "path/to/your/dataset"
+
+
+It is however highly recommended to follow the documentation's `tutorials <http://baptiste.bauvin.pages.lis-lab.fr/summit/tutorials/index.html>`_ to learn the use of each parameter.
+ 
+
+Authors
+-------
+
+* **Baptiste BAUVIN**
+* **Dominique BENIELLI**
+* **Alexis PROD'HOMME**
+

config_files/config_private_algos.yml

 # The base configuration of the benchmark
 log: True
-name: ["digits"]
+name: ["mnist_0_9_train"]
 label: "_"
 file_type: ".hdf5"
-views:
-pathf: "/home/baptiste/Documents/Datasets/Digits/"
+views: ["NIMST_data", ]
+pathf: "/home/baptiste/Documents/Datasets/MNist/"
 nice: 0
-random_state: 42
+random_state: 43
 nb_cores: 1
-full: True
+full: False
 debug: True
 add_noise: False
 noise_std: 0.0
@@ -17,15 +17,18 @@ track_tracebacks: False
 
 # All the classification-realted configuration options
 multiclass_method: "oneVersusOne"
-split: 0.75
+split: 0.96
 nb_folds: 5
-nb_class:
+nb_class: 2
 classes:
-type: ["multiview",]
-algos_monoview: ["decision_tree", "random_forest"]
+type: ["monoview",]
+algos_monoview: ["hm_gb_cbound","cb_boost"]
 algos_multiview: ["mumbo","mvml"]
-stats_iter: 2
-metrics: ["accuracy_score", "f1_score"]
-metric_princ: "accuracy_score"
-hps_type: "randomized_search-equiv"
-hps_iter: 2
\ No newline at end of file
+stats_iter: 1
+metrics:
+  zero_one_loss: {}
+  f1_score: {}
+metric_princ: "zero_one_loss"
+hps_type: "None"
+hps_args:
+  n_iter: 2
\ No newline at end of file

docs/build/.buildinfo

 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 2563b85d273675f134744be21dec5485
+config: 50408771402db3b72187e770c685f635
 tags: 645f666f9bcd5a90fca523b33c5a78b7